From 9af3b518b1edfa1b2c220455d858b6ea7a43cd39 Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Sat, 12 Oct 2019 16:20:26 +0100 Subject: [PATCH 01/35] Created using Colaboratory --- teste.ipynb | 881 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 881 insertions(+) create mode 100644 teste.ipynb diff --git a/teste.ipynb b/teste.ipynb new file mode 100644 index 000000000..ef70079c2 --- /dev/null +++ b/teste.ipynb @@ -0,0 +1,881 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "teste.ipynb", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hq3vClcPl_3t", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Step 1 - Business Understanding" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-04h9-VJmqOG", + "colab_type": "text" + }, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cxE3wjiNmKq3", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Step 2 - Data Understanding" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "29NsG4GzmWrJ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "## Analise exploratoria de dados" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "qT7MWYo3noyg", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b2xKC2C0LPQF", + "colab_type": "text" + }, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EzryGoeFLUXg", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "e1d07f95-ba36-4fd8-b914-b364d09212d6" + }, + "source": [ + "print(\"Helo\")" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Helo\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yxjf7ChMLkQ1", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 164 + }, + "outputId": "b1c5bb6d-92eb-4dcb-f37f-1c187f9380be" + }, + "source": [ + "HelloWorld.__doc__" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mHelloWorld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'HelloWorld' is not defined" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "7SRj3W1pNIBU", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import keyword" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "BaZPsvehNK0F", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "af53edf6-3322-4097-a6fd-e47746e420b5" + }, + "source": [ + "keyword.iskeyword('flag')" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "False" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3Xy4-b36NOkg", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "d5599e60-cb16-4afc-e37b-0071fc0d8d3c" + }, + "source": [ + "keyword.iskeyword('global')" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "x_iLid2YNTwV", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 164 + }, + "outputId": "93d48189-40d9-4af0-def9-247a5b800224" + }, + "source": [ + "numpy.set_printoptions(precision= 2, suppress=True)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_printoptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprecision\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuppress\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'numpy' is not defined" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "_MvbXtlMfWMK", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ChZWly_rfZB4", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 164 + }, + "outputId": "3ae0879a-cc1d-4373-cb19-2bfec1d26bf0" + }, + "source": [ + "numpy.set_printoptions(precision= 2, suppress=True)" + 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2, suppress=True)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "gUAjYC42fcvX", + "colab_type": "code", + "colab": {} + }, + "source": [ + "np.random.seed(seed= 20111974)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ius2_r8NfgZs", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 198 + }, + "outputId": "1436b45f-3c27-49e8-ada6-ad8a595baf75" + }, + "source": [ + "mu= 0\n", + "sigma= 1\n", + "numpy.random.normal(size= 10)" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0msigma\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'numpy' is not defined" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4jnOBIAtfjiV", + "colab_type": "code", + "colab": {} + }, + "source": [ + "mu=0" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "eaqISYLyflC6", + "colab_type": "code", + "colab": {} + }, + "source": [ + "sigma=1" + ], + 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}, + { + "cell_type": "code", + "metadata": { + "id": "g5JjSEZdizKP", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "ca5b24d0-8f4f-4846-b672-34342f37aa9f" + }, + "source": [ + "f'Distribuição N({np.mean(v_Array)}, {np.std(v_Array)})'" + ], + "execution_count": 39, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Distribuição N(0.002039323041103302, 0.9960906293570095)'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 39 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tCmOXy-4i1Vs", + "colab_type": "code", + "colab": {} + }, + "source": [ + "v_Array= np.random.normal(mu, sigma, size= 1000000) # Array 1D de size= 1000000" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "IXpnc4Wvi6oq", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": 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"output_type": "execute_result", + "data": { + "text/plain": [ + "'Distribuição N(0.0002892972723094128, 1.0001202837422036)'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 43 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6SZ2Bcp7jbV9", + "colab_type": "code", + "colab": {} + }, + "source": [ + "v_Array= np.random.normal(mu, sigma, size= 100000000) # Array 1D de size= 100000000" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "IyMXCPc4jgf-", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "2642c77e-493b-4bcf-87c3-4470a66ed5cd" + }, + "source": [ + "f'Distribuição N({np.mean(v_Array)}, {np.std(v_Array)})'" + ], + "execution_count": 45, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Distribuição N(0.00011967896623555603, 0.999944390106086)'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 45 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LGkOmAPXjiEJ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From d0805acbd02d0afdb12feb2996f067dfc51c6ec2 Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Wed, 16 Oct 2019 11:03:23 +0100 Subject: [PATCH 02/35] Delete teste.ipynb --- teste.ipynb | 881 ---------------------------------------------------- 1 file changed, 881 deletions(-) delete mode 100644 teste.ipynb diff --git a/teste.ipynb b/teste.ipynb deleted file mode 100644 index ef70079c2..000000000 --- a/teste.ipynb +++ /dev/null @@ -1,881 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "teste.ipynb", - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - } - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "hq3vClcPl_3t", - "colab_type": "code", - "colab": {} - }, - "source": [ - "# Step 1 - Business Understanding" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-04h9-VJmqOG", - "colab_type": "text" - }, - "source": [ - "" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "cxE3wjiNmKq3", - "colab_type": "code", - "colab": {} - }, - "source": [ - "# Step 2 - Data Understanding" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "29NsG4GzmWrJ", - "colab_type": "code", - "colab": {} - }, - "source": [ - "## Analise exploratoria de dados" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "qT7MWYo3noyg", - "colab_type": "code", - "colab": {} - }, - "source": [ - "import numpy as np" - ], - 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"id": "tCmOXy-4i1Vs", - "colab_type": "code", - "colab": {} - }, - "source": [ - "v_Array= np.random.normal(mu, sigma, size= 1000000) # Array 1D de size= 1000000" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "IXpnc4Wvi6oq", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "outputId": "9b4eb599-5057-48df-e934-81b87ec8fe2c" - }, - "source": [ - "f'Distribuição N({np.mean(v_Array)}, {np.std(v_Array)})'" - ], - "execution_count": 41, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'Distribuição N(-1.1062145143945444e-06, 0.999473966169304)'" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 41 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "w5OxcHL5i8fN", - "colab_type": "code", - "colab": {} - }, - "source": [ - "v_Array= np.random.normal(mu, sigma, size= 10000000) # Array 1D de size= 10000000" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "5RS-8T1tjYbF", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "outputId": "3444feae-15eb-4217-a1d6-4520a4efb2e3" - }, - "source": [ - "f'Distribuição N({np.mean(v_Array)}, {np.std(v_Array)})'" - ], - "execution_count": 43, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'Distribuição N(0.0002892972723094128, 1.0001202837422036)'" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 43 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "6SZ2Bcp7jbV9", - "colab_type": "code", - "colab": {} - }, - "source": [ - "v_Array= np.random.normal(mu, sigma, size= 100000000) # Array 1D de size= 100000000" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "IyMXCPc4jgf-", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "outputId": "2642c77e-493b-4bcf-87c3-4470a66ed5cd" - }, - "source": [ - "f'Distribuição N({np.mean(v_Array)}, {np.std(v_Array)})'" - ], - "execution_count": 45, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'Distribuição N(0.00011967896623555603, 0.999944390106086)'" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 45 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "LGkOmAPXjiEJ", - "colab_type": "code", - "colab": {} - }, - "source": [ - "" - ], - "execution_count": 0, - "outputs": [] - } - ] -} \ No newline at end of file From f1aeb20cbf80e14178440a3db6b1635f7269022c Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Wed, 16 Oct 2019 11:08:17 +0100 Subject: [PATCH 03/35] Created using Colaboratory --- Testes_Iniciais_Ensaios.ipynb | 91 +++++++++++++++++++++++++++++++++++ 1 file changed, 91 insertions(+) create mode 100644 Testes_Iniciais_Ensaios.ipynb diff --git a/Testes_Iniciais_Ensaios.ipynb b/Testes_Iniciais_Ensaios.ipynb new file mode 100644 index 000000000..9feb8fa31 --- /dev/null +++ b/Testes_Iniciais_Ensaios.ipynb @@ -0,0 +1,91 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Testes Iniciais - Ensaios.ipynb", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3WIRjagw-l5e", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "6b819eaa-0f6a-4f03-a41b-c426c6def3e2" + }, + "source": [ + "# 12/10/2019 - NOTAS DA FORMAÇÃO DATA SCIENCE WITH PYTHON (DSWP)\n", + "# Comentários de uma linha em Python\n", + "'''\n", + "Aqui você pode colocar seus comentários.\n", + "Linha 1\n", + "Linha 2\n", + "...\n", + "Linha k\n", + "'''" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'\\nAqui você pode colocar seus comentários.\\nLinha 1\\nLinha 2\\n...\\nLinha k\\n'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yewM8pPzADTf", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "2a5f7e1b-8942-4416-8c5b-2398e56c0c36" + }, + "source": [ + "v_teste = 'Danielle'\n", + "print(v_teste)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Danielle\n" + ], + "name": "stdout" + } + ] + } + ] +} \ No newline at end of file From 88fdd95cfc08cc34babf5dde3968e1451b6e9419 Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Wed, 16 Oct 2019 11:14:18 +0100 Subject: [PATCH 04/35] Created using Colaboratory --- Testes_Iniciais_Ensaios.ipynb | 55 ++++++++++++++++++++++++++++++----- 1 file changed, 47 insertions(+), 8 deletions(-) diff --git a/Testes_Iniciais_Ensaios.ipynb b/Testes_Iniciais_Ensaios.ipynb index 9feb8fa31..eec714e05 100644 --- a/Testes_Iniciais_Ensaios.ipynb +++ b/Testes_Iniciais_Ensaios.ipynb @@ -23,6 +23,16 @@ "\"Open" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "2Ua1xLbZDwny", + "colab_type": "text" + }, + "source": [ + "## Comentarios" + ] + }, { "cell_type": "code", "metadata": { @@ -32,11 +42,13 @@ "base_uri": "https://localhost:8080/", "height": 34 }, - "outputId": "6b819eaa-0f6a-4f03-a41b-c426c6def3e2" + "outputId": "651a6b42-f6a3-462b-e237-ce6b34cd1c6e" }, "source": [ "# 12/10/2019 - NOTAS DA FORMAÇÃO DATA SCIENCE WITH PYTHON (DSWP)\n", "# Comentários de uma linha em Python\n", + "\n", + "# NOTA: os comentarios de multiplas linhas ficam com cor diferente ?\n", "'''\n", "Aqui você pode colocar seus comentários.\n", "Linha 1\n", @@ -45,7 +57,7 @@ "Linha k\n", "'''" ], - "execution_count": 6, + "execution_count": 14, "outputs": [ { "output_type": "execute_result", @@ -57,7 +69,7 @@ "metadata": { "tags": [] }, - "execution_count": 6 + "execution_count": 14 } ] }, @@ -68,24 +80,51 @@ "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", - "height": 34 + "height": 51 }, - "outputId": "2a5f7e1b-8942-4416-8c5b-2398e56c0c36" + "outputId": "0f05f7f0-b01c-48ba-d7a4-87567411c669" }, "source": [ "v_teste = 'Danielle'\n", - "print(v_teste)" + "print(v_teste)\n", + "print('linha 1'\n", + " 'linha 2'\n", + " 'linha 3')" ], - "execution_count": 12, + "execution_count": 17, "outputs": [ { "output_type": "stream", "text": [ - "Danielle\n" + "Danielle\n", + "linha 1linha 2linha 3\n" ], "name": "stdout" } ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r4x0MC59C64g", + "colab_type": "text" + }, + "source": [ + "## Criando Funcões em Pyhton" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "i6eacaPEDAL9", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] } ] } \ No newline at end of file From 826d49468e80baeca6c082f96d4f92b0a6c118c2 Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Fri, 18 Oct 2019 10:11:36 +0100 Subject: [PATCH 05/35] Created using Colaboratory --- Teste_Ensaios_Array.ipynb | 179 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 179 insertions(+) create mode 100644 Teste_Ensaios_Array.ipynb diff --git a/Teste_Ensaios_Array.ipynb b/Teste_Ensaios_Array.ipynb new file mode 100644 index 000000000..eb503a24b --- /dev/null +++ b/Teste_Ensaios_Array.ipynb @@ -0,0 +1,179 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Teste - Ensaios Array.ipynb", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pnbTkXnSED9Q", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "rK3qEzgZDq64", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# FORMA 1: METODO RANDOM : NORMAL (UTILIZADO TAMBEM PARA DISTRIBUICAO NORMAL)\n", + "\n", + "#Numero randomico entre 0 e 1 (criou-se um array por conta do SIZE)\n", + "v_Array_1= np.random.normal(0, 1, size= 10) # Array 1D de size= 10\n", + "v_Array_1" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Ok5EEOwlEG6N", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Isso ja nao é array (embora urilize o RANDOM.NORMAL)\n", + "v_naoArray_1 = np.random.normal(0,1)\n", + "type(v_naoArray_1)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "PSHfrTCHHpqJ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# FORMA 1: METODO RANDOM : RANDOM\n", + "v_Array_1 = np.random.random(10)\n", + "v_Array_1" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "B7TQWNY4Ig8H", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# FORMA 1: METODO RANDOM : RANDINT\n", + "np.random.randint(1, 7, size= 100)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "SzsSiQpME_4Q", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "7274ba1b-3196-4597-b6c8-dad9a142f6d1" + }, + "source": [ + "# FORMA 2: METODO ARRAY : DEVEMOS ESPECIFICAR OS VALORES (?)\n", + "\n", + "v_Array_2 = np.array([2,4,6,8,10,'dani'])\n", + "v_Array_2" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['2', '4', '6', '8', '10', 'dani'], dtype=' Date: Sat, 19 Oct 2019 10:28:43 +0100 Subject: [PATCH 06/35] Created using Colaboratory --- Testes_Iniciais_Ensaios.ipynb | 4351 ++++++++++++++++++++++++++++++++- 1 file changed, 4343 insertions(+), 8 deletions(-) diff --git a/Testes_Iniciais_Ensaios.ipynb b/Testes_Iniciais_Ensaios.ipynb index eec714e05..5f5a205ca 100644 --- a/Testes_Iniciais_Ensaios.ipynb +++ b/Testes_Iniciais_Ensaios.ipynb @@ -5,6 +5,23 @@ "colab": { "name": "Testes Iniciais - Ensaios.ipynb", "provenance": [], + "collapsed_sections": [ + "2Ua1xLbZDwny", + "r4x0MC59C64g", + "TbyM06TtFfLF", + "jXNDh0daKx41", + "Manc8hLoSiTN", + "LDBhoLTbTZv0", + "VE9uNSTJ41ZH", + "2hQxjJ2b7T1o", + "CMaUjKT7AHOY", + "h5T3r9biCQYP", + "wLiKR32yFlNT", + "V_ip7E2rNLy3", + "bUubha1SQv1O", + "G4rHISvTTXp2", + "xyd_W697VxI-" + ], "include_colab_link": true }, "kernelspec": { @@ -38,11 +55,11 @@ "metadata": { "id": "3WIRjagw-l5e", "colab_type": "code", + "outputId": "651a6b42-f6a3-462b-e237-ce6b34cd1c6e", "colab": { "base_uri": "https://localhost:8080/", "height": 34 - }, - "outputId": "651a6b42-f6a3-462b-e237-ce6b34cd1c6e" + } }, "source": [ "# 12/10/2019 - NOTAS DA FORMAÇÃO DATA SCIENCE WITH PYTHON (DSWP)\n", @@ -57,7 +74,7 @@ "Linha k\n", "'''" ], - "execution_count": 14, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -78,11 +95,11 @@ "metadata": { "id": "yewM8pPzADTf", "colab_type": "code", + "outputId": "0f05f7f0-b01c-48ba-d7a4-87567411c669", "colab": { "base_uri": "https://localhost:8080/", "height": 51 - }, - "outputId": "0f05f7f0-b01c-48ba-d7a4-87567411c669" + } }, "source": [ "v_teste = 'Danielle'\n", @@ -91,7 +108,7 @@ " 'linha 2'\n", " 'linha 3')" ], - "execution_count": 17, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -110,7 +127,7 @@ "colab_type": "text" }, "source": [ - "## Criando Funcões em Pyhton" + " ## Criando Funcões em Pyhton + DocString" ] }, { @@ -118,10 +135,4328 @@ "metadata": { "id": "i6eacaPEDAL9", "colab_type": "code", + "outputId": "43b58bcc-8227-477d-cc5b-1bf73cee5b74", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "def PrimeiraFuncao():\n", + " '''\n", + " Documentacao de codigo.\n", + " Chamamos de \"DocString\" da funcao\n", + " '''\n", + " print('Hello World')\n", + " \n", + "PrimeiraFuncao()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Hello World\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ohSmNzoyEfog", + "colab_type": "code", + "outputId": "c2188a43-2f97-4b03-927d-9d63e9426ca0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "PrimeiraFuncao.__doc__" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'\\n Documentacao de codigo.\\n Chamamos de \"DocString\" da funcao\\n '" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 23 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TbyM06TtFfLF", + "colab_type": "text" + }, + "source": [ + "## Variaveis" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lURhRZVKFhnV", + "colab_type": "code", + "outputId": "8e8d319a-4d16-4773-8ce7-59f32f92b8b9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 252 + } + }, + "source": [ + "s_Nome = 'string com o nome'\n", + "i_Idade = 40\n", + "f_Valor = 4.50 # float representado por PONTO como divisor das casas decimais\n", + "l_Pessoa = ['Diego', 'Danielle', 123, 4.5] # lista []\n", + "l_Pes_End= ['Diego', ['Rua 4', 'Male', 38]] # lista dentro de lista\n", + "t_Fruta = ('Morango','Uva','Laranja') # Tupla ()\n", + "\n", + "print(s_Nome) \n", + "print(i_Idade)\n", + "print(f_Valor)\n", + "print(l_Pessoa)\n", + "\n", + "print(l_Pes_End[1][1]) # lista dentro de lista > impressao de um indice especifico\n", + "print(t_Fruta[0])\n", + "print(t_Fruta[-1])\n", + "\n", + "l_ListaTeste = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n", + "# NOTA: O primeiro indice é o ZERO. Aqui, queremos o indice 1 (incluido) ate o indice 4 (excluido)\n", + "print(l_ListaTeste[1:4]) \n", + "# NOTA: Contando de tras pra frente (o ultimo elemento é o UM)\n", + "print(l_ListaTeste[-4:-1])\n", + "\n", + "# NOTA: Dicionario CHAVE : VALOR, CHAVE : VALOR\n", + "d_Tradutor = {} # Dicionario {}\n", + "d_Tradutor = {'Nome' : 'Danielle', 'Estado Civil' : 'Casada', 'Idade' : 40}\n", + "# Podemos adicionar os elementos de um dicionario ao longo do programa\n", + "d_Tradutor['Cidade'] = 'Lisboa'\n", + "d_Tradutor[3.1416] = 'PI'\n", + "d_Tradutor" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "string com o nome\n", + "40\n", + "4.5\n", + "['Diego', 'Danielle', 123, 4.5]\n", + "Male\n", + "Morango\n", + "Laranja\n", + "[20, 30, 40]\n", + "[70, 80, 90]\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{3.1416: 'PI',\n", + " 'Cidade': 'Lisboa',\n", + " 'Estado Civil': 'Casada',\n", + " 'Idade': 40,\n", + " 'Nome': 'Danielle'}" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 1 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jXNDh0daKx41", + "colab_type": "text" + }, + "source": [ + "## TUPLAS sao imutaveis (nao podem sem alteradas)\n", + "\n", + "Tupla é uma Lista imutável. O que diferencia a Estrutura de Dados Lista da Estrutura de Dados Tupla é que a primeira pode ter elementos adicionados a qualquer momento, enquanto que a segunda estrutura, após definida, não permite a adição ou remoção de elementos.\n", + "\n", + "Mas, como contorno, podemos converter a TUPLA em LISTA, alterar o valor do indice, e converter novamente para TUPLA.\n", + "\n", + "\n", + "Os elementos de uma lista são delimitados por colchetes, enquanto que os elementos de uma tupla por parêntesis.\n", + "\n", + "> Os elementos de uma Tupla devem ser separados por vírgulas e podem (ou não) ser delimitados por parêntesis." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_bU_DwvyUDQO", + "colab_type": "text" + }, + "source": [ + "**TUPLE()**\n", + "\n", + "\n", + "**LIST []**\n", + "\n", + "**DIC{}**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zzco_PkJH4yO", + "colab_type": "code", + "outputId": "b9ec620c-f59e-4705-bb73-b2d9f7918dce", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# Criando uma TUPLA\n", + "x = (\"apple\", \"banana\", \"cherry\")\n", + "\n", + "# Convertendo a TUPLA em LISTA\n", + "y = list(x)\n", + "\n", + "# alterando o indice UM da LISTA\n", + "y[1] = \"kiwi\"\n", + "\n", + "# convertendo a LISTA em TUPLA\n", + "x = tuple(y)\n", + "\n", + "print(x)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "('apple', 'kiwi', 'cherry')\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "IhojqgFdPjQv", + "colab_type": "code", + "outputId": "63bef887-947d-4f33-9e3a-ff8d39e7bce5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + } + }, + "source": [ + "t_TuplaTeste = 1,2,3\n", + "print(t_TuplaTeste)\n", + "type(t_TuplaTeste)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(1, 2, 3)\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "tuple" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 74 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Manc8hLoSiTN", + "colab_type": "text" + }, + "source": [ + "## Palavras Reservadas" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "eYDm7hajR_6T", + "colab_type": "code", + "outputId": "0060cb4b-fe15-44a9-e220-1d32dd341704", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "import keyword\n", + "keyword.iskeyword('lambda')\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 89 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LDBhoLTbTZv0", + "colab_type": "text" + }, + "source": [ + "## Deletar variaveis (?)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Cqlev4EoTd4R", + "colab_type": "code", "colab": {} }, "source": [ - "" + "l_Nome = ['Danielle', 'Diego', 'Neide']\n", + "del l_Nome" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VE9uNSTJ41ZH", + "colab_type": "text" + }, + "source": [ + "# Operações Aritméticas" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LeRwoNYL45U7", + "colab_type": "code", + "outputId": "36918102-8b4a-42b7-d8df-9a8ee459cbb3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 118 + } + }, + "source": [ + "A = 10\n", + "B = 3\n", + "print('Divisao =', (A/B)) # divisao\n", + "print('Quociente =', A // B) # apenas o quociente\n", + "print('Resto =', A % B) # apenas o resto\n", + "print('Potencia =', A ** B) # potencia\n", + "\n", + "# Radiciação: todo número elevado ao seu inverso é a sua raiz, isto é, basta elevarmos N a (1/2) que iremos obter a raiz quadrada de N.\n", + "print('Raiz Quadrada =', 9 ** (1/2))\n", + "print('Raiz Cubica = ', 9 ** (1/3))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Divisao = 3.3333333333333335\n", + "Quociente = 3\n", + "Resto = 1\n", + "Potencia = 1000\n", + "Raiz Quadrada = 3.0\n", + "Raiz Cubica = 2.080083823051904\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2hQxjJ2b7T1o", + "colab_type": "text" + }, + "source": [ + "#Função PRINT()" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "28DBw72o7YX2", + "colab_type": "code", + "outputId": "99651f5b-f1f5-44c8-c246-8113e10c06c1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "print('Texto qualquer cujo separador foi alterado','Segunda parte do texto',sep='@')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Texto qualquer cujo separador foi alterado@Segunda parte do texto\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CLFRctIQ7gJH", + "colab_type": "code", + "outputId": "98201e0f-8a05-4532-e5fa-5c6e86b736c3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67 + } + }, + "source": [ + "print('Novo texto','com enter entre o que deve','ser concatenado',sep='\\n')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Novo texto\n", + "com enter entre o que deve\n", + "ser concatenado\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "fUF4jJ_l7_nG", + "colab_type": "code", + "outputId": "5852d651-e3e6-4c2d-aa28-7e74e8eed330", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + } + }, + "source": [ + "print('Passos para estudar:','Fazer o mais importante primeiro','Concentrar','Silenciar', sep = '\\n * ')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Passos para estudar:\n", + " * Fazer o mais importante primeiro\n", + " * Concentrar\n", + " * Silenciar\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "IFf-o_Hv9lFB", + "colab_type": "code", + "outputId": "c69319e3-c7fa-4b44-cb6d-a79af29c89a3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "i_A = 2.3435\n", + "i_B = 10.5555\n", + "print('O primeiro numero é {:.2f} e o segundo numero é {:.3f}'.format(i_A, i_B))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "O primeiro numero é 2.34 e o segundo numero é 10.556\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CMaUjKT7AHOY", + "colab_type": "text" + }, + "source": [ + "#F-Strings" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "B54NkNG3AKau", + "colab_type": "code", + "outputId": "8e22f6c8-6a92-401f-cb4f-a60bbec31a99", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "s_Nome = 'Danielle'\n", + "i_Idade = 40\n", + "f'Meu nome é {s_Nome} e eu tenho {i_Idade} anos.'" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Meu nome é Danielle e eu tenho 40 anos.'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 56 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6cU5V4rVAv0S", + "colab_type": "code", + "outputId": "a0606c82-c1cf-4e66-e3bb-1bfdb9325b14", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "def Converte2Upper(s_Input):\n", + " return s_Input.upper()\n", + "\n", + "s_Nome = 'Danielle'\n", + "f'Meu nome é {Converte2Upper(s_Nome)}'" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Meu nome é DANIELLE'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 60 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "h5T3r9biCQYP", + "colab_type": "text" + }, + "source": [ + "#ROUND(), FLOOR(), CEIL(), TRUNC()" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cxZ1uJnhCR4c", + "colab_type": "code", + "outputId": "d810f438-c5b3-4676-8d94-90e70829a8f8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + } + }, + "source": [ + "import math as mt\n", + "A = 10\n", + "B = 3\n", + "\n", + "print(A/B)\n", + "\n", + "f'{round(A/B, 4)}'\n", + "f'{round(5.59095,4)}' # arredondamento\n", + "print('FLOOR > Maior valor inteiro menos que 5.59095 =', mt.floor(5.59095))\n", + "print('CEIL > Proximo valor inteiro maior ou igual a 5.59095 =', mt.ceil(5.59095))\n", + "print('TRUNC > Parte inteira de 5.59095 =', mt.trunc(5.59095) )\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "3.3333333333333335\n", + "FLOOR > Maior valor inteiro menos que 5.59095 = 5\n", + "CEIL > Proximo valor inteiro maior ou igual a 5.59095 = 6\n", + "TRUNC > Parte inteira de 5.59095 = 5\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8IprRmWMEdnS", + "colab_type": "code", + "outputId": "fcc2d7ad-d472-4d48-c8cf-e89ba86619fa", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "C = 13/3 # 4.333333333333333\n", + "f'O valor de C é {C:{8}.{4}}' # 8 espacos antes (?) com 4 decimais" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'O valor de C é 4.333'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 102 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wLiKR32yFlNT", + "colab_type": "text" + }, + "source": [ + "## Operadores de Comparacao" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GP-9MsXwE8B0", + "colab_type": "code", + "outputId": "4b57d9f0-d1ff-4778-a90d-1f34fc520da6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "3 == 4" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "False" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 103 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sdy36t88FqT8", + "colab_type": "code", + "outputId": "eea59e4a-f989-4e03-da38-13262e56201d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "3 != 4" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 104 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3FSUgQvNFr6F", + "colab_type": "code", + "outputId": "6c4eede0-ed76-4e70-a7d5-302e939bb731", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 129 + } + }, + "source": [ + "3 <> 4" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "error", + "ename": "SyntaxError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m 3 <> 4\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HtzHZEaCFtKI", + "colab_type": "code", + "outputId": "62081f6e-e805-41c1-c121-427a91a2206b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "import numpy as np\n", + "a_X = np.array([])\n", + "a_X = ([1,2,3,4,5,6,7,8,9,10])\n", + "a_X\n", + "#type(a_X) #array ou lista???" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 206 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "J9Yf4CsXGrpy", + "colab_type": "code", + "outputId": "e0932434-ae54-4978-c73e-f07a11ea594c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "a_X == 3" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "False" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 114 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bP41Lp29GuQl", + "colab_type": "code", + "outputId": "d1eef643-0ba9-4677-f0dc-285f0b7b4319", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "a_X == np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n", + "#type(np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ True, True, True, True, True, True, True, True, True,\n", + " True])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 215 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2bXq7ubzG4U5", + "colab_type": "code", + "outputId": "8a2a150e-1a9b-4d38-e46c-714b0446d082", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 521 + } + }, + "source": [ + "a_X * 3 # repete 3 vezes o conteudo! (isso é uma lista) \n", + "#type(a_X)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[1,\n", + " 2,\n", + " 3,\n", + " 4,\n", + " 5,\n", + " 6,\n", + " 7,\n", + " 8,\n", + " 9,\n", + " 10,\n", + " 1,\n", + " 2,\n", + " 3,\n", + " 4,\n", + " 5,\n", + " 6,\n", + " 7,\n", + " 8,\n", + " 9,\n", + " 10,\n", + " 1,\n", + " 2,\n", + " 3,\n", + " 4,\n", + " 5,\n", + " 6,\n", + " 7,\n", + " 8,\n", + " 9,\n", + " 10]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 200 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8VIPJO53OaV4", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V_ip7E2rNLy3", + "colab_type": "text" + }, + "source": [ + "## Random" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WNetIb1cHMVS", + "colab_type": "code", + "outputId": "1c620d51-86f3-4a1c-aac3-682ce306d4d4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "import numpy as np\n", + "#help(np.random.randint)\n", + "\n", + "# Return random integers from `low` (inclusive) to `high` (exclusive).\n", + "# randint(low, high=None, size=None, dtype='l')\n", + "i_Num = np.random.randint(2,4,size=1, dtype='l')\n", + "f'Que numero é {i_Num} e o tipo é {type(i_Num)}' #criou um array aleatorio por causa do parametro SIZE" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"Que numero é [2] e o tipo é \"" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 187 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YcIlzUu_MunU", + "colab_type": "code", + "outputId": "c7e3bd44-6a8e-4c5f-b59d-224bf8cde0ca", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "i_Num = np.random.randint(25,100)\n", + "f'Que numero é {i_Num} e o tipo é {type(i_Num)}' " + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"Que numero é 92 e o tipo é \"" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 188 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "794ORdetNYCa", + "colab_type": "code", + "outputId": "c2ec3c80-712b-4757-fa40-d67addcff494", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "\n", + "#Criar numeros aleatorios de matrizes (2 linhas e 4 colunas)\n", + " #Menor valor sera 10 (inclusive) e maior valor possivel sera 20 (excluido)\n", + "a_Matriz = np.random.randint(low=10, high=20, size=(2,4)) \n", + "\n", + "f'{a_Matriz} e seu tipo é {type(a_Matriz)}'\n", + "a_Matriz" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[17, 13, 10, 17],\n", + " [18, 15, 12, 12]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 217 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Q4my0iRYNx0B", + "colab_type": "code", + "outputId": "738e4b3c-688a-4a4c-db56-3bbf227eaa87", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "a_Matriz == 3" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[False, False, False, False],\n", + " [False, False, False, False]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 195 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XicD9PxpN6un", + "colab_type": "code", + "outputId": "06609601-b8c3-4957-b655-a28a6bcaf5c5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "a_Matriz * 3" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[42, 42, 39, 36],\n", + " [36, 39, 42, 42]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 197 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bUubha1SQv1O", + "colab_type": "text" + }, + "source": [ + "##IF" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "iEovFqpBQxdD", + "colab_type": "code", + "outputId": "c1d408d2-b727-4ce7-fdc0-7e25181b90bf", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "def AvaliaIdade(i_Idade1, i_Idade2):\n", + " if (i_Idade1 > i_Idade2):\n", + " s_Mensagem = f'A idade {i_Idade1} é maior que {i_Idade2}'\n", + " elif (i_Idade1 == i_Idade2):\n", + " s_Mensagem = f'A idade {i_Idade1} é igual que {i_Idade2}'\n", + " else:\n", + " s_Mensagem = f'A idade {i_Idade1} é menor que {i_Idade2}'\n", + " print(s_Mensagem) \n", + " \n", + "AvaliaIdade(45,50) " + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "A idade 45 é menor que 50\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "W8Bb-X5bSN5S", + "colab_type": "code", + "outputId": "06f37e43-c631-4ad7-f336-239d2dedc1c3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "if 1: # UM equivale ao valor boleano TRUE \n", + " print('Teste')\n", + "else:\n", + " print('Nada')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Teste\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "NiCn9omQSQrS", + "colab_type": "code", + "outputId": "f9c95a8b-a135-40c8-83ef-2c37a47e9bab", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "if True: print('Dani'); print('elle')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Dani\n", + "elle\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G4rHISvTTXp2", + "colab_type": "text" + }, + "source": [ + "#WHILE" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "I1sDS08BTZ9f", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def FacaEnquanto(i_Inicio, i_Limite):\n", + " while i_Inicio <= i_Limite:\n", + " print(f'O numero é {i_Inicio}') \n", + " \n", + " if ((i_Inicio > 1) and (i_Inicio%2) == 0):\n", + " break\n", + " \n", + " i_Inicio += 1\n", + " else:\n", + " print('Acabou')\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "BVpxm82TUl7Q", + "colab_type": "code", + "outputId": "17f8550a-f7fe-4b8b-cb87-d9093d324c58", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "FacaEnquanto(1,3)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "O numero é 1\n", + "O numero é 2\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xyd_W697VxI-", + "colab_type": "text" + }, + "source": [ + "#LOOP" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Sxc2PyFKU03n", + "colab_type": "code", + "outputId": "83014844-bb36-488f-b141-04b97b65d714", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 101 + } + }, + "source": [ + "# Definir uma lista\n", + "l_Vogais = ['a','e','i','o','u']\n", + "\n", + "for i in l_Vogais:\n", + " print(f'A vogal é {i}')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "A vogal é a\n", + "A vogal é e\n", + "A vogal é i\n", + "A vogal é o\n", + "A vogal é u\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "p5QAPCmNWG87", + "colab_type": "code", + "outputId": "c9d9a780-f7c9-4cee-a829-bc92e5edfe95", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 151 + } + }, + "source": [ + "for i in 'Danielle':\n", + " print(i)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "D\n", + "a\n", + "n\n", + "i\n", + "e\n", + "l\n", + "l\n", + "e\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "oL0EK4GbXAl7", + "colab_type": "code", + "outputId": "3119ea8f-72c1-4546-e436-cf4f5fcdedbc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 202 + } + }, + "source": [ + "l_Pais = ['Brasil','Belgica','Portugal','Italia','Franca']\n", + "#print(range(len(l_Pais)))\n", + "for i in range(len(l_Pais)):\n", + " print(f'Indice {i} = {l_Pais[i]}')\n", + " \n", + "print()\n", + "for i in l_Pais:\n", + " print(i)\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Indice 0 = Brasil\n", + "Indice 1 = Belgica\n", + "Indice 2 = Portugal\n", + "Indice 3 = Italia\n", + "Indice 4 = Franca\n", + "\n", + "Brasil\n", + "Belgica\n", + "Portugal\n", + "Italia\n", + "Franca\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P4pdPuQtbRgo", + "colab_type": "text" + }, + "source": [ + "##NUMPY" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "UdEVYyDWbSwY", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "SQC4wPIPbYXR", + "colab_type": "code", + "colab": {} + }, + "source": [ + "help(np.set_printoptions)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "p24OdEIOdCrc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "help(np.random.normal)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "oSeWeNh0b2xH", + "colab_type": "code", + "outputId": "983273dc-a621-4bf9-8094-1f6fbb9f6c41", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "\n", + "np.set_printoptions(precision = 4, suppress = True)\n", + "# np.set_printoptions() # volta para valores default\n", + "\n", + "'''\n", + "Define seed por questões de reproducibilidade, ou seja, \n", + "garante que todos vamos gerar os mesmos números aleatórios\n", + "'''\n", + "np.random.seed(seed = 27031979)\n", + "\n", + "\n", + "# Exemplo que usei anteriormente\n", + "# i_Num = np.random.randint(2,4,size=1, dtype='l'); print(i_Num)\n", + "\n", + "mu = 0\n", + "sigma = 2\n", + "v_Array= np.random.normal(mu, sigma, size= 10) # Array 1D de size= 10\n", + "v_Array" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([-0.1447, -0.646 , -1.0161, -2.1605, 1.1235, 1.6243, 1.1402,\n", + " 2.0511, 0.1722, 1.5833])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 59 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ts7fkp5weKRG", + "colab_type": "code", + "colab": {} + }, + "source": [ + "help(np.mean) # media aritmetica\n", + "help(np.std) #desvio padrao" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "rOd0sbzFfm8i", + "colab_type": "code", + "outputId": "6e2bc082-c6dd-4cce-d3c7-d8121f10d1d8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "v_Media = np.array([2,4,6,8,10])\n", + "np.mean(v_Media) #media\n", + "np.std(v_Media) #desvio padrao" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "2.8284271247461903" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 76 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bpxp6zoei2uD", + "colab_type": "text" + }, + "source": [ + "**Laboratorio 1**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "UXfCytgNgobd", + "colab_type": "code", + "outputId": "f824a969-0202-4dae-b473-00f26cce2fde", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 406 + } + }, + "source": [ + "import numpy as np\n", + "np.set_printoptions(precision = 2, suppress = True)\n", + "\n", + "#Define seed (para reproducibilidade)\n", + "np.random.seed(seed = 27031979)\n", + "\n", + "# Define a media e desvio padrao\n", + "mu = 0\n", + "sigma = 1\n", + "for i in [0,100, 1000, 10000, 100000, 1000000]:\n", + " v_Array= np.random.normal(mu, sigma, size=i) \n", + " #print(v_Array)\n", + " print(f'Tamnho {i} \\n Distribuicao Normal N({np.mean(v_Array)}, {np.std(v_Array)})')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Tamnho 0 \n", + " Distribuicao Normal N(nan, nan)\n", + "Tamnho 100 \n", + " Distribuicao Normal N(0.04224826266585371, 1.0051525925711935)\n", + "Tamnho 1000 \n", + " Distribuicao Normal N(0.026602058853514608, 0.9802245432463518)\n", + "Tamnho 10000 \n", + " Distribuicao Normal N(-0.01037062644967241, 1.0042312204002735)\n", + "Tamnho 100000 \n", + " Distribuicao Normal N(0.0024246846574069265, 1.0003356931235659)\n", + "Tamnho 1000000 \n", + " Distribuicao Normal N(0.0014251888746623506, 0.9993750873131747)\n" + ], + "name": "stdout" + }, + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:140: RuntimeWarning: Degrees of freedom <= 0 for slice\n", + " keepdims=keepdims)\n", + "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:110: RuntimeWarning: invalid value encountered in true_divide\n", + " arrmean, rcount, out=arrmean, casting='unsafe', subok=False)\n", + "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:132: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vZ1uCBXukcwo", + "colab_type": "code", + "colab": {} + }, + "source": [ + "help(np.percentile)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "m6RdAjDEkdxt", + "colab_type": "code", + "outputId": "1dbcd76f-1a73-45c0-aa65-1d70681bc17a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "# Em estatística descritiva, os percentis são medidas que dividem a amostra \n", + "# (por ordem crescente dos dados) em 100 partes, cada uma com uma percentagem \n", + "# de dados aproximadamente igual\n", + "\n", + "v_X = np.arange(15)\n", + "print(v_X)\n", + "np.percentile(v_X, q=[5, 25, 50, 75, 95])\n", + "\n", + "# O valor 0.7 representa 5% dos dados da amostra\n", + "# O valor 7 represenra 50% dos dados da amostra\n", + "\n", + "# Um percentil indica que há x% de dados inferiores\n", + "# Corresponde à frequência cumulativa de N .k/100, onde N é o tamanho amostral.\n", + "# Corresponde a frequencia cumulativa de 15 (tamanho da amostra) por 5%" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14]\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 0.7, 3.5, 7. , 10.5, 13.3])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 144 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0XybsQN-luvM", + "colab_type": "code", + "outputId": "808ce5fe-eb69-4a99-fd42-d0704b97457c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# Calcular a qual percentil pertence um dado\n", + "n = 15 # quantidade de elementos\n", + "p = 75 # porcentagem\n", + "LP = (n-1) * p/100\n", + "print(LP)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "10.5\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zNMKgK4e68bT", + "colab_type": "text" + }, + "source": [ + "* Percentis: divide as partes por 100 \n", + "* Decis: divide as parte por 10\n", + "* Quartis: divide as partes por 25 (quarta parte de 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ATQRjKLBA_qs", + "colab_type": "text" + }, + "source": [ + "## Ordenar os valores do array" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Z0QR2dpzA8aF", + "colab_type": "code", + "outputId": "6789a506-b7a3-42f2-ac4a-f12278653a58", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "v_X= np.random.random(10)\n", + "v_X" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0.95, 0.8 , 0.3 , 0.62, 0.55, 0.62, 0.16, 0.6 , 0.82, 0.44])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 146 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DyHZ8t5XBW19", + "colab_type": "code", + "outputId": "8dd5d19d-b489-405f-bb9a-417ec16e9db6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "np.sort(v_X)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0.16, 0.3 , 0.44, 0.55, 0.6 , 0.62, 0.62, 0.8 , 0.82, 0.95])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 147 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eW8v2rzCJpnd", + "colab_type": "text" + }, + "source": [ + "#PANDAS" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EmER6P3WJtvs", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import pandas as pd\n", + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "nUy19CmALiP1", + "colab_type": "code", + "outputId": "33034f5b-1c39-4a52-e495-9b943dc3a75c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 101 + } + }, + "source": [ + "# gerar um array com 100 tentativas de valores entre 1 e 6\n", + "v_array = np.random.randint(1,7, size = 100) \n", + "v_array" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([2, 6, 6, 5, 2, 2, 4, 6, 6, 5, 1, 1, 1, 3, 6, 6, 6, 1, 3, 5, 2, 6,\n", + " 5, 4, 6, 1, 1, 5, 4, 1, 3, 6, 1, 1, 3, 4, 2, 6, 6, 2, 5, 3, 4, 4,\n", + " 5, 1, 4, 1, 1, 1, 5, 1, 2, 3, 2, 4, 4, 6, 3, 4, 2, 5, 2, 1, 4, 4,\n", + " 3, 4, 1, 2, 5, 2, 3, 4, 5, 1, 3, 2, 5, 6, 6, 1, 5, 6, 4, 4, 2, 5,\n", + " 6, 6, 1, 3, 4, 6, 3, 4, 4, 4, 5, 6])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "eD8HYdidLsQK", + "colab_type": "code", + "outputId": "d96188a3-b57a-4479-c1bc-b5d1a263f3b3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 134 + } + }, + "source": [ + "# Metodo do PANDAS que mostra a quantidade de ocorrencia por elemento\n", + "pd.value_counts(v_array)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "6 20\n", + "4 20\n", + "1 19\n", + "5 15\n", + "2 14\n", + "3 12\n", + "dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "jLP7tQ-VMXBF", + "colab_type": "code", + "outputId": "e43cc314-b106-4405-e922-c54c4bb0ec96", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 168 + } + }, + "source": [ + "# Aumentar o numero de tentativas e verificar se a LEI DOS GRANDES NUMEROS é refletida aqui\n", + "\n", + "np.random.seed(seed = 27031979)\n", + "\n", + "v_Array_Tentativas = np.array([30,100,500,10000,50000,1000000,50000000,100000000,500000000])\n", + "\n", + "for i in v_Array_Tentativas:\n", + " v_Array_Dados = np.random.randint(1, 7, size = i)\n", + " print(f'Tentativas {i} => Media {np.mean(v_Array_Dados)}')\n", + " " + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Tentativas 30 => Media 3.3333333333333335\n", + "Tentativas 100 => Media 3.39\n", + "Tentativas 500 => Media 3.436\n", + "Tentativas 10000 => Media 3.4928\n", + "Tentativas 50000 => Media 3.5003\n", + "Tentativas 1000000 => Media 3.495305\n", + "Tentativas 50000000 => Media 3.50014224\n", + "Tentativas 100000000 => Media 3.50006209\n", + "Tentativas 500000000 => Media 3.499940162\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pzqw5ni4QNJu", + "colab_type": "text" + }, + "source": [ + "#Ensaio Array" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pnbTkXnSED9Q", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "rK3qEzgZDq64", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# FORMA 1: METODO RANDOM : NORMAL (UTILIZADO TAMBEM PARA DISTRIBUICAO NORMAL)\n", + "\n", + "#Numero randomico entre 0 e 1 (criou-se um array por conta do SIZE)\n", + "v_Array_1= np.random.normal(0, 1, size= 10) # Array 1D de size= 10\n", + "v_Array_1" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Ok5EEOwlEG6N", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Isso ja nao é array (embora urilize o RANDOM.NORMAL)\n", + "v_naoArray_1 = np.random.normal(0,1)\n", + "type(v_naoArray_1)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "PSHfrTCHHpqJ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# FORMA 1: METODO RANDOM : RANDOM\n", + "v_Array_1 = np.random.random(10) # de 0 a 1 ?\n", + "v_Array_1" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "B7TQWNY4Ig8H", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# FORMA 1: METODO RANDOM : RANDINT (inteiros)\n", + "v_Array_1 = np.random.randint(1, 7, size= 100)\n", + "v_Array_1" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "YX6pdiFmbnXx", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# FORMA 1 : METODO RANDOM : RANDN (distribuicao normal) \n", + "v_Array_5x7 = np.random.randn(5,7)\n", + "v_Array_5x7" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "SzsSiQpME_4Q", + "colab_type": "code", + "outputId": "7274ba1b-3196-4597-b6c8-dad9a142f6d1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# FORMA 2: METODO ARRAY : DEVEMOS ESPECIFICAR OS VALORES (?)\n", + "\n", + "v_Array_2 = np.array([2,4,6,8,10,'dani'])\n", + "v_Array_2" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['2', '4', '6', '8', '10', 'dani'], dtype=' 13))\n", + "v_Idx" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([14]),)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 21 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "drmi8AOeDHVS", + "colab_type": "code", + "outputId": "609ccf13-baaf-4176-e6c8-90b35c23fb16", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "v_Array_Where[v_Array_Where > 13]" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([14])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 22 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zZnUTkJmDFpt", + "colab_type": "code", + "outputId": "24d7a177-5789-4812-d814-975a34d3f156", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "np.where((np.arange(15) == 13))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([13]),)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 25 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Hiuh0lLWECkQ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "v_Array_A = np.arange(15)\n", + "v_Array_B = np.arange(0,15,step=2)\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "LqYnT9o8EJce", + "colab_type": "code", + "outputId": "7140295a-e939-43af-a2ea-56ee6ff2f012", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "np.intersect1d(v_Array_A, v_Array_B) #elementos em comun\n", + "v_Array_A" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 36 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "i9Qn2VLrEdHk", + "colab_type": "code", + "outputId": "5f11ea8d-3f7b-442b-e5cc-61ffc6d98fb6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "np.setdiff1d(v_Array_A, v_Array_B) #Array A menos os elementos de Array B\n", + "#v_Array_A" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 1, 3, 5, 7, 9, 11, 13])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 42 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "fOorpKbCEngi", + "colab_type": "code", + "outputId": "ae03344c-e716-405c-b113-04d9d0bcbf55", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# Quando os elementos de A estão em B\n", + "v_Array_A = np.arange(15)\n", + "v_Array_B = np.arange(0,30,step=2)\n", + "np.where(v_Array_A == v_Array_B) # os arrays devem ter o mesmo shape" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([0]),)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 48 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0erON4B0FDx3", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rPFxpds6Ft6L", + "colab_type": "text" + }, + "source": [ + "#Autovalor e Autovetor" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "561pNsIDFwn4", + "colab_type": "code", + "outputId": "7b716a61-e51d-4292-c5b7-37d4323a74e6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67 + } + }, + "source": [ + "np.random.seed(seed = 27031979)\n", + "v_Array_3x3_A = np.random.randint(1, 10, size=[3,3])\n", + "v_Array_3x3_A" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[9, 8, 1],\n", + " [3, 6, 3],\n", + " [9, 1, 5]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 61 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XEVPBP6MHZz6", + "colab_type": "code", + "outputId": "2ca3617e-4f61-475e-d84d-0f5193889a88", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67 + } + }, + "source": [ + "v_Array_3x3_B = np.random.randint(20, 30, size=[3,3])\n", + "v_Array_3x3_B" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[27, 20, 28],\n", + " [23, 26, 25],\n", + " [21, 28, 25]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 63 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "H5wNA9UwGbMD", + "colab_type": "code", + "colab": {} + }, + "source": [ + "#Compute the eigenvalues and right eigenvectors of a square array.\n", + "help(np.linalg.eig)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "4fO5MBA4HAVc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "v_AutoValor, v_AutoVector = np.linalg.eig([v_Array_3x3_A,v_Array_3x3_B]) # ACEITA MAIS DE UM ARRAY" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Pso_EjNkHJ51", + "colab_type": "code", + "outputId": "2892bb7f-a229-42aa-c68e-af89d8d05dd8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + } + }, + "source": [ + "v_AutoValor" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[15.10548021+0.j , 2.4472599 +3.61619235j,\n", + " 2.4472599 -3.61619235j],\n", + " [74.31715733+0.j , 1.84142133+1.3823059j ,\n", + " 1.84142133-1.3823059j ]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 68 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8nOFDkH3HKgO", + "colab_type": "code", + "outputId": "52a7c3cb-5926-4ae4-8cc9-bada06d427c3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + } + }, + "source": [ + "v_AutoVector" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[[-0.65577886+0.j , -0.2186708 +0.36809391j,\n", + " -0.2186708 -0.36809391j],\n", + " [-0.42225248+0.j , -0.08795125-0.40034742j,\n", + " -0.08795125+0.40034742j],\n", + " [-0.625825 +0.j , 0.80540454+0.j ,\n", + " 0.80540454-0.j ]],\n", + "\n", + " [[-0.58285262+0.j , -0.73756936+0.j ,\n", + " -0.73756936-0.j ],\n", + " [-0.57468917+0.j , 0.02973258+0.15098287j,\n", + " 0.02973258-0.15098287j],\n", + " [-0.57446948+0.j , 0.64148376-0.14425728j,\n", + " 0.64148376+0.14425728j]]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 69 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P6S-FUmKJLCi", + "colab_type": "text" + }, + "source": [ + "#NaN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b3J9v5_GJOQS", + "colab_type": "text" + }, + "source": [ + "Em computação, NaN (acrônimo em inglês para Not a Number) é um valor ou símbolo usado nas linguagens de programação para representar um valor numérico indefinido ou irrepresentável." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bKf3-RK8HMO-", + "colab_type": "code", + "colab": {} + }, + "source": [ + "help(np.nan)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "9PQFSY0qJaqp", + "colab_type": "code", + "outputId": "74fdaaf4-e459-4803-e01f-06a4a5cc2156", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "np.nan" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "nan" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 74 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "oMO194DmJkB4", + "colab_type": "code", + "outputId": "ad38b768-91a3-4b48-d4a6-2beffd6de7f4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 118 + } + }, + "source": [ + "#ATRIBUR, EM QUALQUER INDICE, O VALOR NAN\n", + "\n", + "np.random.seed(seed = 27031979)\n", + "\n", + "v_Array = np.random.random(25) # 25 numeros aleatorios # ISSO É UMA LISTA?\n", + "#print(f'Array {v_Array}')\n", + "#print(f'Dimensao {v_Array.ndim}')\n", + "#print(f'Shape {v_Array.shape}')\n", + " \n", + "v_Idx_Rand = np.random.randint(0, 25, size=5) # LISTA?\n", + "v_Idx_Rand = np.sort(v_Idx_Rand) # DUVIDA: nao ordenou pq sao INDICES e nao VALORES ? R: Ordenou quando passei a atribuir o resultado do SORT a uma variavel\n", + "print(f'Indices {v_Idx_Rand}')\n", + "#print(f'Dimensao {v_Idx_Rand.ndim}')\n", + "#print(f'Shape {v_Idx_Rand.shape}')\n", + "\n", + "for i in v_Idx_Rand:\n", + " v_Array[v_Idx_Rand] = np.nan\n", + " \n", + "v_Array = np.sort(v_Array) # DUVIDA: PORQUE NAO ORDENOU? R: Ordenou quando passei a atribuir o resultado do SORT a uma variavel\n", + "v_Array " + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Indices [13 18 21 23 23]\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0.02527809, 0.18315026, 0.24687826, 0.34412652, 0.35098484,\n", + " 0.37293865, 0.39364883, 0.40080898, 0.44942041, 0.50866884,\n", + " 0.54613419, 0.57396324, 0.5873962 , 0.67219587, 0.71682512,\n", + " 0.72288452, 0.80974869, 0.81004079, 0.82225737, 0.92419913,\n", + " 0.93235274, nan, nan, nan, nan])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 118 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9BNEkPiTLzHI", + "colab_type": "code", + "outputId": "8d8bf08b-2245-4437-a46a-49e724a71fbd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# contar quandos NaNs tem\n", + "np.isnan(v_Array).sum()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "4" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 101 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QqcU-tQjL-WW", + "colab_type": "code", + "outputId": "5b758a16-28d3-4118-b0e1-101f6924d186", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "#Quais is indices que tem o valor NAN\n", + "np.where(np.isnan(v_Array))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([21, 22, 23, 24]),)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 119 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Ax37SndvN9j0", + "colab_type": "code", + "outputId": "042edff5-07c2-4b9d-d14c-5a3e1c3aa5cb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 101 + } + }, + "source": [ + "#Excluir os NaNs\n", + "v_Array[np.isnan(v_Array)] # so os NANs\n", + "v_Array[~np.isnan(v_Array)] # sem os NANs" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0.02527809, 0.18315026, 0.24687826, 0.34412652, 0.35098484,\n", + " 0.37293865, 0.39364883, 0.40080898, 0.44942041, 0.50866884,\n", + " 0.54613419, 0.57396324, 0.5873962 , 0.67219587, 0.71682512,\n", + " 0.72288452, 0.80974869, 0.81004079, 0.82225737, 0.92419913,\n", + " 0.93235274])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 127 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SXynYbgpJWVA", + "colab_type": "text" + }, + "source": [ + "#Conversao" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pHAywmfQJXq0", + "colab_type": "code", + "outputId": "fed449c7-9bb6-4eed-a5ac-9ef5881beb68", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "#DE LISTA PARA ARRAY\n", + "import numpy as np\n", + "\n", + "v_lista_A = np.random.randint(0,10,5) #array\n", + "v_lista_B = [np.random.randint(0,10,5)] #lista\n", + "\n", + "type(v_lista_B)\n", + "\n", + "v_array_a = np.asarray(v_lista_A)\n", + "type(v_array_a)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HYhmbhpcKF5s", + "colab_type": "code", + "outputId": "05dc3b45-a96f-4650-a1be-5d43f22fa05b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "v_tupla = ([np.random.randint(0, 10, 3)],[np.random.randint(0, 10, 3)],[np.random.randint(0, 10, 3)])\n", + "v_tupla\n", + "\n", + "v_array_a = np.asarray(v_tupla)\n", + "type(v_array_a)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T2pUkGgqUdDB", + "colab_type": "text" + }, + "source": [ + "#HOJE" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "P_3QKtabUeJV", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "2fc9e4a8-5c0f-4e89-a7ae-e8398440073f" + }, + "source": [ + "import numpy as np\n", + "np.random.seed(seed = 20111974)\n", + "\n", + "# Simulando Retornos de ativos financeiros com a distribuição Normal(0,1):\n", + "v_Retornos= np.random.normal(0, 1, 100)\n", + "print(f'Média: {np.mean(v_Retornos)}')" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Média: -0.016996335492713833\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "OfAB4EMeVU6P", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + }, + "outputId": "143bc607-28e0-492a-e468-21dafabba3f1" + }, + "source": [ + "v_Percentis = np.percentile(v_Retornos, q=[1, 5, 25, 50, 55, 75, 99])\n", + "v_Percentis " + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([-2.0304241 , -1.49481318, -0.78361477, -0.12205087, 0.08182676,\n", + " 0.71947681, 2.06016123])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2uqq0-j8VpLn", + "colab_type": "code", + "colab": {} + }, + "source": [ + "\n", + "# LimSup = Q3 + 1.5 * IQR\n", + "# LimInd = Q1 - 1.5 * IQR\n", + "\n", + "v_LimSup = \n", + "v_LimINf = " ], "execution_count": 0, "outputs": [] From 6653da57601860bb02ed241eccd406e1dcfe093d Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Sat, 19 Oct 2019 10:30:14 +0100 Subject: [PATCH 07/35] Created using Colaboratory --- Testes_Iniciais_Ensaios.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/Testes_Iniciais_Ensaios.ipynb b/Testes_Iniciais_Ensaios.ipynb index 5f5a205ca..1e24d2480 100644 --- a/Testes_Iniciais_Ensaios.ipynb +++ b/Testes_Iniciais_Ensaios.ipynb @@ -4452,10 +4452,10 @@ }, "source": [ "\n", - "# LimSup = Q3 + 1.5 * IQR\n", - "# LimInd = Q1 - 1.5 * IQR\n", + "# LimSup = Q3 + 1.5 * IQR , onde Q3 75%\n", + "# LimInd = Q1 - 1.5 * IQR , onde Q1 25% \n", "\n", - "v_LimSup = \n", + "v_LimSup = v_Percentis[]\n", "v_LimINf = " ], "execution_count": 0, From f2f859e1c33dba811a2969124b200406d0f40a8d Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Sat, 19 Oct 2019 10:31:30 +0100 Subject: [PATCH 08/35] Created using Colaboratory --- Testes_Iniciais_Ensaios.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/Testes_Iniciais_Ensaios.ipynb b/Testes_Iniciais_Ensaios.ipynb index 1e24d2480..8ab059fcc 100644 --- a/Testes_Iniciais_Ensaios.ipynb +++ b/Testes_Iniciais_Ensaios.ipynb @@ -4453,10 +4453,10 @@ "source": [ "\n", "# LimSup = Q3 + 1.5 * IQR , onde Q3 75%\n", - "# LimInd = Q1 - 1.5 * IQR , onde Q1 25% \n", + "# LimInf = Q1 - 1.5 * IQR , onde Q1 25% \n", "\n", - "v_LimSup = v_Percentis[]\n", - "v_LimINf = " + "v_LimInf = (v_Percentis[5] + 1.5) * ()\n", + "v_LimSup = (v_Percentis[5] + 1.5) * ()\n" ], "execution_count": 0, "outputs": [] From 377047254d5bba7cf6dfba9bd6a60657431480da Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Sat, 19 Oct 2019 10:36:00 +0100 Subject: [PATCH 09/35] Created using Colaboratory --- Testes_Iniciais_Ensaios.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Testes_Iniciais_Ensaios.ipynb b/Testes_Iniciais_Ensaios.ipynb index 8ab059fcc..af3c0a91e 100644 --- a/Testes_Iniciais_Ensaios.ipynb +++ b/Testes_Iniciais_Ensaios.ipynb @@ -4455,7 +4455,7 @@ "# LimSup = Q3 + 1.5 * IQR , onde Q3 75%\n", "# LimInf = Q1 - 1.5 * IQR , onde Q1 25% \n", "\n", - "v_LimInf = (v_Percentis[5] + 1.5) * ()\n", + "v_LimInf = (v_Percentis[2] + 1.5) * ()\n", "v_LimSup = (v_Percentis[5] + 1.5) * ()\n" ], "execution_count": 0, From e0b1a02b7b9345eec4e173850e7d40e856bbf8d1 Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Sat, 19 Oct 2019 11:38:28 +0100 Subject: [PATCH 10/35] Created using Colaboratory --- Testes_Iniciais_Ensaios.ipynb | 296 +++++++++++++++++++++++++++++++++- 1 file changed, 290 insertions(+), 6 deletions(-) diff --git a/Testes_Iniciais_Ensaios.ipynb b/Testes_Iniciais_Ensaios.ipynb index af3c0a91e..7f9896f56 100644 --- a/Testes_Iniciais_Ensaios.ipynb +++ b/Testes_Iniciais_Ensaios.ipynb @@ -4386,11 +4386,11 @@ "metadata": { "id": "P_3QKtabUeJV", "colab_type": "code", + "outputId": "2fc9e4a8-5c0f-4e89-a7ae-e8398440073f", "colab": { "base_uri": "https://localhost:8080/", "height": 34 - }, - "outputId": "2fc9e4a8-5c0f-4e89-a7ae-e8398440073f" + } }, "source": [ "import numpy as np\n", @@ -4400,7 +4400,7 @@ "v_Retornos= np.random.normal(0, 1, 100)\n", "print(f'Média: {np.mean(v_Retornos)}')" ], - "execution_count": 1, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -4416,17 +4416,17 @@ "metadata": { "id": "OfAB4EMeVU6P", "colab_type": "code", + "outputId": "143bc607-28e0-492a-e468-21dafabba3f1", "colab": { "base_uri": "https://localhost:8080/", "height": 50 - }, - "outputId": "143bc607-28e0-492a-e468-21dafabba3f1" + } }, "source": [ "v_Percentis = np.percentile(v_Retornos, q=[1, 5, 25, 50, 55, 75, 99])\n", "v_Percentis " ], - "execution_count": 4, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -4454,12 +4454,296 @@ "\n", "# LimSup = Q3 + 1.5 * IQR , onde Q3 75%\n", "# LimInf = Q1 - 1.5 * IQR , onde Q1 25% \n", + "# IQR = Q3 - Q1\n", "\n", "v_LimInf = (v_Percentis[2] + 1.5) * ()\n", "v_LimSup = (v_Percentis[5] + 1.5) * ()\n" ], "execution_count": 0, "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-W6c0tnte5ty", + "colab_type": "text" + }, + "source": [ + "#Exercicio (REVER!)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YEgCI0OsfAZg", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 185 + }, + "outputId": "ad22f9c9-f308-4862-e192-0661d10ffdba" + }, + "source": [ + "# Preparando o exemplo:\n", + "import numpy as np\n", + "np.set_printoptions(precision=0)\n", + "np.random.seed(19741120)\n", + "m_X= np.array(np.random.normal(100, 10, size= 100))\n", + "m_X" + ], + "execution_count": 58, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 92., 89., 101., 89., 78., 92., 78., 97., 89., 103., 100.,\n", + " 87., 97., 100., 101., 98., 104., 91., 95., 93., 118., 88.,\n", + " 112., 105., 94., 91., 98., 110., 112., 123., 102., 107., 95.,\n", + " 109., 107., 122., 99., 113., 96., 114., 109., 99., 96., 109.,\n", + " 99., 85., 100., 109., 116., 114., 100., 95., 96., 100., 77.,\n", + " 110., 107., 92., 97., 84., 96., 97., 93., 94., 94., 97.,\n", + " 113., 113., 105., 108., 111., 99., 94., 98., 85., 102., 97.,\n", + " 94., 106., 105., 92., 104., 104., 99., 119., 92., 80., 106.,\n", + " 78., 106., 114., 112., 100., 94., 99., 123., 112., 101., 105.,\n", + " 95.])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 58 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wJWA0aSUfYWq", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "8c94b8cc-3069-44c8-b3b7-dff62214b9f1" + }, + "source": [ + "# Algumas estatísticas descritivas:\n", + "f'Média: {np.mean(m_X)}; Mediana: {np.median(m_X)}; STD: {np.std(m_X)}'" + ], + "execution_count": 37, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Média: 90.91190590171118; Mediana: 90.60982589102949; STD: 7.8465139716162495'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 37 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MI7GCS2NfcEE", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "a083b8e4-b4d7-4520-f840-7c3f5ae0b2fe" + }, + "source": [ + "# Lista aleatória de índices que vou alterar\n", + "np.random.seed(20111974)\n", + "l_Idx= np.random.randint(0, 10, 3)\n", + "np.sort(l_Idx)\n", + "l_Idx" + ], + "execution_count": 41, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([8, 8, 2])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 41 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vBY4nzhcffea", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "1c51be70-30fc-4eaf-dff9-e961a2365774" + }, + "source": [ + "m_X2= m_X.copy()\n", + "for i in l_Idx:\n", + " m_X2[i]= 2*m_X2[i]\n", + " \n", + "m_X2 " + ], + "execution_count": 52, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 92., 89., 202., 89., 78., 92., 78., 97., 356., 103.])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 52 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QCTsPu8_fpHi", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 272 + }, + "outputId": "3f73b9ba-f90b-4de8-ef4b-b90b25aca962" + }, + "source": [ + "# Import a biblioteca seaborn:\n", + "import seaborn as sns\n", + "sns.boxplot(y= m_X2)" + ], + "execution_count": 53, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 53 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mTRxk17jf0CA", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67 + }, + "outputId": "96878943-3c29-41c4-c4ab-82ae5c4ae272" + }, + "source": [ + "#Q1, Q3\n", + "#IQR = Q3-Q1\n", + "#Lim_Inf = Q1-1.5*IQR\n", + "#Lim_Sup = Q3+1.5*IQR\n", + "\n", + "v_Percentis = np.percentile(m_X2, q=[25, 50, 75])\n", + "print(v_Percentis[0])\n", + "print(v_Percentis[1])\n", + "\n", + "v_LimInf = v_Percentis[0] - 1.5 * (v_Percentis[0] - v_Percentis[2])\n", + "v_LimSup = v_Percentis[2] + 1.5 * (v_Percentis[0] - v_Percentis[2])\n", + "v_Mediana = v_Percentis[1]\n", + "\n", + "f'LimInf = {v_LimInf} e LimSup = {v_LimSup} e Mediana {v_Mediana} '\n" + ], + "execution_count": 54, + "outputs": [ + { + "output_type": "stream", + "text": [ + "88.64343370711052\n", + "92.3703261730598\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'LimInf = 108.14020505852992 e LimSup = 82.14450992330404 e Mediana 92.3703261730598 '" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 54 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "qAnmCYTnggkx", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "660896f0-b20c-4617-f2d8-8129d38df785" + }, + "source": [ + "#Se m_X2[i] < Lim_Inf então m_X2[i]= Lim_Inf\n", + "#Se m_X2[i] > Lim_Sup então m_X2[i]= Lim_Sup\n", + "\n", + "m_X2[m_X2 < v_LimInf] = v_LimInf\n", + "m_X2[m_X2 > v_LimSup] = v_LimSup\n", + "m_X2\n" + ], + "execution_count": 55, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([82., 82., 82., 82., 82., 82., 82., 82., 82., 82.])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 55 + } + ] } ] } \ No newline at end of file From f578a7e6246843000a38bd1ede9b38d2a94ccefd Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Sat, 19 Oct 2019 13:39:03 +0100 Subject: [PATCH 11/35] Created using Colaboratory --- Testes_Iniciais_Ensaios.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Testes_Iniciais_Ensaios.ipynb b/Testes_Iniciais_Ensaios.ipynb index 7f9896f56..dde41b777 100644 --- a/Testes_Iniciais_Ensaios.ipynb +++ b/Testes_Iniciais_Ensaios.ipynb @@ -4481,17 +4481,17 @@ "base_uri": "https://localhost:8080/", "height": 185 }, - "outputId": "ad22f9c9-f308-4862-e192-0661d10ffdba" + "outputId": "48403ab8-948e-4087-a1d1-cc18ededdc58" }, "source": [ "# Preparando o exemplo:\n", "import numpy as np\n", "np.set_printoptions(precision=0)\n", "np.random.seed(19741120)\n", - "m_X= np.array(np.random.normal(100, 10, size= 100))\n", + "m_X= np.array(np.random.normal(100, 10, size= 100)) #normal(loc=0.0, scale=1.0, size=None) #media, desvio padrao, tamanho\n", "m_X" ], - "execution_count": 58, + "execution_count": 62, "outputs": [ { "output_type": "execute_result", @@ -4512,7 +4512,7 @@ "metadata": { "tags": [] }, - "execution_count": 58 + "execution_count": 62 } ] }, From aeb85936d6c5d9413383bd4cd194235e067e3b0a Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Sun, 20 Oct 2019 17:51:16 +0100 Subject: [PATCH 12/35] Created using Colaboratory --- Testes_Iniciais_Ensaios.ipynb | 40 ++++++++++++++++++++++++++++++----- 1 file changed, 35 insertions(+), 5 deletions(-) diff --git a/Testes_Iniciais_Ensaios.ipynb b/Testes_Iniciais_Ensaios.ipynb index dde41b777..f3dd7eb2b 100644 --- a/Testes_Iniciais_Ensaios.ipynb +++ b/Testes_Iniciais_Ensaios.ipynb @@ -3537,18 +3537,18 @@ "metadata": { "id": "bFHJ6EP_ku_H", "colab_type": "code", - "outputId": "96342111-d784-413a-800a-d457da18e65e", + "outputId": "b151580e-b535-4284-e92a-1994d7444576", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ - "\n", - "v_Array1= np.arange(10).reshape(1,-1) ## ARRAY DE ARRAY ???\n", + "import numpy as np\n", + "v_Array1= np.arange(10).reshape(1,-1) ## ARRAY DE ARRAY ??? R: Array de 1X1\n", "v_Array1" ], - "execution_count": 0, + "execution_count": 9, "outputs": [ { "output_type": "execute_result", @@ -3560,7 +3560,37 @@ "metadata": { "tags": [] }, - "execution_count": 90 + "execution_count": 9 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "maTKNJMUEbp7", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "cc4c2100-84b4-4a1c-f811-5c80bfbf35fc" + }, + "source": [ + "v_Array1[0] #Output do indice 0: \"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\"" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 } ] }, From 4ad36c512f73974ef12b4b96a2b150749fd9c6b6 Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Tue, 22 Oct 2019 19:43:20 +0100 Subject: [PATCH 13/35] Created using Colaboratory --- Apoio_Python.ipynb | 5487 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 5487 insertions(+) create mode 100644 Apoio_Python.ipynb diff --git a/Apoio_Python.ipynb b/Apoio_Python.ipynb new file mode 100644 index 000000000..9f862ff28 --- /dev/null +++ b/Apoio_Python.ipynb @@ -0,0 +1,5487 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Apoio_Python.ipynb", + "provenance": [], + "collapsed_sections": [ + "2Ua1xLbZDwny", + "r4x0MC59C64g", + "TbyM06TtFfLF", + "jXNDh0daKx41", + "Manc8hLoSiTN", + "LDBhoLTbTZv0", + "VE9uNSTJ41ZH", + "2hQxjJ2b7T1o", + "CMaUjKT7AHOY", + "h5T3r9biCQYP", + "wLiKR32yFlNT", + "V_ip7E2rNLy3", + "bUubha1SQv1O", + "G4rHISvTTXp2", + "xyd_W697VxI-" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2Ua1xLbZDwny", + "colab_type": "text" + }, + "source": [ + "## Comentarios" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3WIRjagw-l5e", + "colab_type": "code", + "outputId": "651a6b42-f6a3-462b-e237-ce6b34cd1c6e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# 12/10/2019 - NOTAS DA FORMAÇÃO DATA SCIENCE WITH PYTHON (DSWP)\n", + "# Comentários de uma linha em Python\n", + "\n", + "# NOTA: os comentarios de multiplas linhas ficam com cor diferente ?\n", + "'''\n", + "Aqui você pode colocar seus comentários.\n", + "Linha 1\n", + "Linha 2\n", + "...\n", + "Linha k\n", + "'''" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'\\nAqui você pode colocar seus comentários.\\nLinha 1\\nLinha 2\\n...\\nLinha k\\n'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yewM8pPzADTf", + "colab_type": "code", + "outputId": "0f05f7f0-b01c-48ba-d7a4-87567411c669", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + } + }, + "source": [ + "v_teste = 'Danielle'\n", + "print(v_teste)\n", + "print('linha 1'\n", + " 'linha 2'\n", + " 'linha 3')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Danielle\n", + "linha 1linha 2linha 3\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r4x0MC59C64g", + "colab_type": "text" + }, + "source": [ + " ## Criando Funcões em Pyhton + DocString" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "i6eacaPEDAL9", + "colab_type": "code", + "outputId": "43b58bcc-8227-477d-cc5b-1bf73cee5b74", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "def PrimeiraFuncao():\n", + " '''\n", + " Documentacao de codigo.\n", + " Chamamos de \"DocString\" da funcao\n", + " '''\n", + " print('Hello World')\n", + " \n", + "PrimeiraFuncao()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Hello World\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ohSmNzoyEfog", + "colab_type": "code", + "outputId": "c2188a43-2f97-4b03-927d-9d63e9426ca0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "PrimeiraFuncao.__doc__" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'\\n Documentacao de codigo.\\n Chamamos de \"DocString\" da funcao\\n '" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 23 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TbyM06TtFfLF", + "colab_type": "text" + }, + "source": [ + "## Variaveis" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lURhRZVKFhnV", + "colab_type": "code", + "outputId": "8e8d319a-4d16-4773-8ce7-59f32f92b8b9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 252 + } + }, + "source": [ + "s_Nome = 'string com o nome'\n", + "i_Idade = 40\n", + "f_Valor = 4.50 # float representado por PONTO como divisor das casas decimais\n", + "l_Pessoa = ['Diego', 'Danielle', 123, 4.5] # lista []\n", + "l_Pes_End= ['Diego', ['Rua 4', 'Male', 38]] # lista dentro de lista\n", + "t_Fruta = ('Morango','Uva','Laranja') # Tupla ()\n", + "\n", + "print(s_Nome) \n", + "print(i_Idade)\n", + "print(f_Valor)\n", + "print(l_Pessoa)\n", + "\n", + "print(l_Pes_End[1][1]) # lista dentro de lista > impressao de um indice especifico\n", + "print(t_Fruta[0])\n", + "print(t_Fruta[-1])\n", + "\n", + "l_ListaTeste = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n", + "# NOTA: O primeiro indice é o ZERO. Aqui, queremos o indice 1 (incluido) ate o indice 4 (excluido)\n", + "print(l_ListaTeste[1:4]) \n", + "# NOTA: Contando de tras pra frente (o ultimo elemento é o UM)\n", + "print(l_ListaTeste[-4:-1])\n", + "\n", + "# NOTA: Dicionario CHAVE : VALOR, CHAVE : VALOR\n", + "d_Tradutor = {} # Dicionario {}\n", + "d_Tradutor = {'Nome' : 'Danielle', 'Estado Civil' : 'Casada', 'Idade' : 40}\n", + "# Podemos adicionar os elementos de um dicionario ao longo do programa\n", + "d_Tradutor['Cidade'] = 'Lisboa'\n", + "d_Tradutor[3.1416] = 'PI'\n", + "d_Tradutor" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "string com o nome\n", + "40\n", + "4.5\n", + "['Diego', 'Danielle', 123, 4.5]\n", + "Male\n", + "Morango\n", + "Laranja\n", + "[20, 30, 40]\n", + "[70, 80, 90]\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{3.1416: 'PI',\n", + " 'Cidade': 'Lisboa',\n", + " 'Estado Civil': 'Casada',\n", + " 'Idade': 40,\n", + " 'Nome': 'Danielle'}" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 1 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jXNDh0daKx41", + "colab_type": "text" + }, + "source": [ + "## TUPLAS sao imutaveis (nao podem sem alteradas)\n", + "\n", + "Tupla é uma Lista imutável. O que diferencia a Estrutura de Dados Lista da Estrutura de Dados Tupla é que a primeira pode ter elementos adicionados a qualquer momento, enquanto que a segunda estrutura, após definida, não permite a adição ou remoção de elementos.\n", + "\n", + "Mas, como contorno, podemos converter a TUPLA em LISTA, alterar o valor do indice, e converter novamente para TUPLA.\n", + "\n", + "\n", + "Os elementos de uma lista são delimitados por colchetes, enquanto que os elementos de uma tupla por parêntesis.\n", + "\n", + "> Os elementos de uma Tupla devem ser separados por vírgulas e podem (ou não) ser delimitados por parêntesis." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_bU_DwvyUDQO", + "colab_type": "text" + }, + "source": [ + "**TUPLE()**\n", + "\n", + "\n", + "**LIST []**\n", + "\n", + "**DIC{}**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zzco_PkJH4yO", + "colab_type": "code", + "outputId": "b9ec620c-f59e-4705-bb73-b2d9f7918dce", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# Criando uma TUPLA\n", + "x = (\"apple\", \"banana\", \"cherry\")\n", + "\n", + "# Convertendo a TUPLA em LISTA\n", + "y = list(x)\n", + "\n", + "# alterando o indice UM da LISTA\n", + "y[1] = \"kiwi\"\n", + "\n", + "# convertendo a LISTA em TUPLA\n", + "x = tuple(y)\n", + "\n", + "print(x)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "('apple', 'kiwi', 'cherry')\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "IhojqgFdPjQv", + "colab_type": "code", + "outputId": "63bef887-947d-4f33-9e3a-ff8d39e7bce5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + } + }, + "source": [ + "t_TuplaTeste = 1,2,3\n", + "print(t_TuplaTeste)\n", + "type(t_TuplaTeste)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(1, 2, 3)\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "tuple" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 74 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Manc8hLoSiTN", + "colab_type": "text" + }, + "source": [ + "## Palavras Reservadas" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "eYDm7hajR_6T", + "colab_type": "code", + "outputId": "0060cb4b-fe15-44a9-e220-1d32dd341704", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "import keyword\n", + "keyword.iskeyword('lambda')\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 89 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LDBhoLTbTZv0", + "colab_type": "text" + }, + "source": [ + "## Deletar variaveis (?)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Cqlev4EoTd4R", + "colab_type": "code", + "colab": {} + }, + "source": [ + "l_Nome = ['Danielle', 'Diego', 'Neide']\n", + "del l_Nome" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VE9uNSTJ41ZH", + "colab_type": "text" + }, + "source": [ + "# Operações Aritméticas" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LeRwoNYL45U7", + "colab_type": "code", + "outputId": "36918102-8b4a-42b7-d8df-9a8ee459cbb3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 118 + } + }, + "source": [ + "A = 10\n", + "B = 3\n", + "print('Divisao =', (A/B)) # divisao\n", + "print('Quociente =', A // B) # apenas o quociente\n", + "print('Resto =', A % B) # apenas o resto\n", + "print('Potencia =', A ** B) # potencia\n", + "\n", + "# Radiciação: todo número elevado ao seu inverso é a sua raiz, isto é, basta elevarmos N a (1/2) que iremos obter a raiz quadrada de N.\n", + "print('Raiz Quadrada =', 9 ** (1/2))\n", + "print('Raiz Cubica = ', 9 ** (1/3))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Divisao = 3.3333333333333335\n", + "Quociente = 3\n", + "Resto = 1\n", + "Potencia = 1000\n", + "Raiz Quadrada = 3.0\n", + "Raiz Cubica = 2.080083823051904\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2hQxjJ2b7T1o", + "colab_type": "text" + }, + "source": [ + "#Função PRINT()" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "28DBw72o7YX2", + "colab_type": "code", + "outputId": "99651f5b-f1f5-44c8-c246-8113e10c06c1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "print('Texto qualquer cujo separador foi alterado','Segunda parte do texto',sep='@')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Texto qualquer cujo separador foi alterado@Segunda parte do texto\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CLFRctIQ7gJH", + "colab_type": "code", + "outputId": "98201e0f-8a05-4532-e5fa-5c6e86b736c3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67 + } + }, + "source": [ + "print('Novo texto','com enter entre o que deve','ser concatenado',sep='\\n')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Novo texto\n", + "com enter entre o que deve\n", + "ser concatenado\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "fUF4jJ_l7_nG", + "colab_type": "code", + "outputId": "5852d651-e3e6-4c2d-aa28-7e74e8eed330", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + } + }, + "source": [ + "print('Passos para estudar:','Fazer o mais importante primeiro','Concentrar','Silenciar', sep = '\\n * ')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Passos para estudar:\n", + " * Fazer o mais importante primeiro\n", + " * Concentrar\n", + " * Silenciar\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "IFf-o_Hv9lFB", + "colab_type": "code", + "outputId": "c69319e3-c7fa-4b44-cb6d-a79af29c89a3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "i_A = 2.3435\n", + "i_B = 10.5555\n", + "print('O primeiro numero é {:.2f} e o segundo numero é {:.3f}'.format(i_A, i_B))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "O primeiro numero é 2.34 e o segundo numero é 10.556\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CMaUjKT7AHOY", + "colab_type": "text" + }, + "source": [ + "#F-Strings" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "B54NkNG3AKau", + "colab_type": "code", + "outputId": "8e22f6c8-6a92-401f-cb4f-a60bbec31a99", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "s_Nome = 'Danielle'\n", + "i_Idade = 40\n", + "f'Meu nome é {s_Nome} e eu tenho {i_Idade} anos.'" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Meu nome é Danielle e eu tenho 40 anos.'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 56 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6cU5V4rVAv0S", + "colab_type": "code", + "outputId": "a0606c82-c1cf-4e66-e3bb-1bfdb9325b14", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "def Converte2Upper(s_Input):\n", + " return s_Input.upper()\n", + "\n", + "s_Nome = 'Danielle'\n", + "f'Meu nome é {Converte2Upper(s_Nome)}'" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Meu nome é DANIELLE'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 60 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "h5T3r9biCQYP", + "colab_type": "text" + }, + "source": [ + "#ROUND(), FLOOR(), CEIL(), TRUNC()" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cxZ1uJnhCR4c", + "colab_type": "code", + "outputId": "d810f438-c5b3-4676-8d94-90e70829a8f8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + } + }, + "source": [ + "import math as mt\n", + "A = 10\n", + "B = 3\n", + "\n", + "print(A/B)\n", + "\n", + "f'{round(A/B, 4)}'\n", + "f'{round(5.59095,4)}' # arredondamento\n", + "print('FLOOR > Maior valor inteiro menos que 5.59095 =', mt.floor(5.59095))\n", + "print('CEIL > Proximo valor inteiro maior ou igual a 5.59095 =', mt.ceil(5.59095))\n", + "print('TRUNC > Parte inteira de 5.59095 =', mt.trunc(5.59095) )\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "3.3333333333333335\n", + "FLOOR > Maior valor inteiro menos que 5.59095 = 5\n", + "CEIL > Proximo valor inteiro maior ou igual a 5.59095 = 6\n", + "TRUNC > Parte inteira de 5.59095 = 5\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8IprRmWMEdnS", + "colab_type": "code", + "outputId": "fcc2d7ad-d472-4d48-c8cf-e89ba86619fa", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "C = 13/3 # 4.333333333333333\n", + "f'O valor de C é {C:{8}.{4}}' # 8 espacos antes (?) com 4 decimais" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'O valor de C é 4.333'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 102 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wLiKR32yFlNT", + "colab_type": "text" + }, + "source": [ + "## Operadores de Comparacao" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GP-9MsXwE8B0", + "colab_type": "code", + "outputId": "4b57d9f0-d1ff-4778-a90d-1f34fc520da6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "3 == 4" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "False" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 103 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sdy36t88FqT8", + "colab_type": "code", + "outputId": "eea59e4a-f989-4e03-da38-13262e56201d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "3 != 4" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 104 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3FSUgQvNFr6F", + "colab_type": "code", + "outputId": "6c4eede0-ed76-4e70-a7d5-302e939bb731", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 129 + } + }, + "source": [ + "3 <> 4" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "error", + "ename": "SyntaxError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m 3 <> 4\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HtzHZEaCFtKI", + "colab_type": "code", + "outputId": "62081f6e-e805-41c1-c121-427a91a2206b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "import numpy as np\n", + "a_X = np.array([])\n", + "a_X = ([1,2,3,4,5,6,7,8,9,10])\n", + "a_X\n", + "#type(a_X) #array ou lista???" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 206 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "J9Yf4CsXGrpy", + "colab_type": "code", + "outputId": "e0932434-ae54-4978-c73e-f07a11ea594c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "a_X == 3" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "False" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 114 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bP41Lp29GuQl", + "colab_type": "code", + "outputId": "d1eef643-0ba9-4677-f0dc-285f0b7b4319", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "a_X == np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n", + "#type(np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ True, True, True, True, True, True, True, True, True,\n", + " True])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 215 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2bXq7ubzG4U5", + "colab_type": "code", + "outputId": "8a2a150e-1a9b-4d38-e46c-714b0446d082", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 521 + } + }, + "source": [ + "a_X * 3 # repete 3 vezes o conteudo! (isso é uma lista) \n", + "#type(a_X)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[1,\n", + " 2,\n", + " 3,\n", + " 4,\n", + " 5,\n", + " 6,\n", + " 7,\n", + " 8,\n", + " 9,\n", + " 10,\n", + " 1,\n", + " 2,\n", + " 3,\n", + " 4,\n", + " 5,\n", + " 6,\n", + " 7,\n", + " 8,\n", + " 9,\n", + " 10,\n", + " 1,\n", + " 2,\n", + " 3,\n", + " 4,\n", + " 5,\n", + " 6,\n", + " 7,\n", + " 8,\n", + " 9,\n", + " 10]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 200 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8VIPJO53OaV4", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V_ip7E2rNLy3", + "colab_type": "text" + }, + "source": [ + "## Random" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WNetIb1cHMVS", + "colab_type": "code", + "outputId": "1c620d51-86f3-4a1c-aac3-682ce306d4d4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "import numpy as np\n", + "#help(np.random.randint)\n", + "\n", + "# Return random integers from `low` (inclusive) to `high` (exclusive).\n", + "# randint(low, high=None, size=None, dtype='l')\n", + "i_Num = np.random.randint(2,4,size=1, dtype='l')\n", + "f'Que numero é {i_Num} e o tipo é {type(i_Num)}' #criou um array aleatorio por causa do parametro SIZE" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"Que numero é [2] e o tipo é \"" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 187 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YcIlzUu_MunU", + "colab_type": "code", + "outputId": "c7e3bd44-6a8e-4c5f-b59d-224bf8cde0ca", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "i_Num = np.random.randint(25,100)\n", + "f'Que numero é {i_Num} e o tipo é {type(i_Num)}' " + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"Que numero é 92 e o tipo é \"" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 188 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "794ORdetNYCa", + "colab_type": "code", + "outputId": "c2ec3c80-712b-4757-fa40-d67addcff494", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "\n", + "#Criar numeros aleatorios de matrizes (2 linhas e 4 colunas)\n", + " #Menor valor sera 10 (inclusive) e maior valor possivel sera 20 (excluido)\n", + "a_Matriz = np.random.randint(low=10, high=20, size=(2,4)) \n", + "\n", + "f'{a_Matriz} e seu tipo é {type(a_Matriz)}'\n", + "a_Matriz" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[17, 13, 10, 17],\n", + " [18, 15, 12, 12]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 217 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Q4my0iRYNx0B", + "colab_type": "code", + "outputId": "738e4b3c-688a-4a4c-db56-3bbf227eaa87", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "a_Matriz == 3" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[False, False, False, False],\n", + " [False, False, False, False]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 195 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XicD9PxpN6un", + "colab_type": "code", + "outputId": "06609601-b8c3-4957-b655-a28a6bcaf5c5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "a_Matriz * 3" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[42, 42, 39, 36],\n", + " [36, 39, 42, 42]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 197 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bUubha1SQv1O", + "colab_type": "text" + }, + "source": [ + "##IF" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "iEovFqpBQxdD", + "colab_type": "code", + "outputId": "c1d408d2-b727-4ce7-fdc0-7e25181b90bf", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "def AvaliaIdade(i_Idade1, i_Idade2):\n", + " if (i_Idade1 > i_Idade2):\n", + " s_Mensagem = f'A idade {i_Idade1} é maior que {i_Idade2}'\n", + " elif (i_Idade1 == i_Idade2):\n", + " s_Mensagem = f'A idade {i_Idade1} é igual que {i_Idade2}'\n", + " else:\n", + " s_Mensagem = f'A idade {i_Idade1} é menor que {i_Idade2}'\n", + " print(s_Mensagem) \n", + " \n", + "AvaliaIdade(45,50) " + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "A idade 45 é menor que 50\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "W8Bb-X5bSN5S", + "colab_type": "code", + "outputId": "06f37e43-c631-4ad7-f336-239d2dedc1c3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "if 1: # UM equivale ao valor boleano TRUE \n", + " print('Teste')\n", + "else:\n", + " print('Nada')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Teste\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "NiCn9omQSQrS", + "colab_type": "code", + "outputId": "f9c95a8b-a135-40c8-83ef-2c37a47e9bab", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "if True: print('Dani'); print('elle')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Dani\n", + "elle\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G4rHISvTTXp2", + "colab_type": "text" + }, + "source": [ + "#WHILE" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "I1sDS08BTZ9f", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def FacaEnquanto(i_Inicio, i_Limite):\n", + " while i_Inicio <= i_Limite:\n", + " print(f'O numero é {i_Inicio}') \n", + " \n", + " if ((i_Inicio > 1) and (i_Inicio%2) == 0):\n", + " break\n", + " \n", + " i_Inicio += 1\n", + " else:\n", + " print('Acabou')\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "BVpxm82TUl7Q", + "colab_type": "code", + "outputId": "17f8550a-f7fe-4b8b-cb87-d9093d324c58", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "FacaEnquanto(1,3)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "O numero é 1\n", + "O numero é 2\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xyd_W697VxI-", + "colab_type": "text" + }, + "source": [ + "#LOOP" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Sxc2PyFKU03n", + "colab_type": "code", + "outputId": "83014844-bb36-488f-b141-04b97b65d714", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 101 + } + }, + "source": [ + "# Definir uma lista\n", + "l_Vogais = ['a','e','i','o','u']\n", + "\n", + "for i in l_Vogais:\n", + " print(f'A vogal é {i}')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "A vogal é a\n", + "A vogal é e\n", + "A vogal é i\n", + "A vogal é o\n", + "A vogal é u\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "p5QAPCmNWG87", + "colab_type": "code", + "outputId": "c9d9a780-f7c9-4cee-a829-bc92e5edfe95", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 151 + } + }, + "source": [ + "for i in 'Danielle':\n", + " print(i)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "D\n", + "a\n", + "n\n", + "i\n", + "e\n", + "l\n", + "l\n", + "e\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "oL0EK4GbXAl7", + "colab_type": "code", + "outputId": "3119ea8f-72c1-4546-e436-cf4f5fcdedbc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 202 + } + }, + "source": [ + "l_Pais = ['Brasil','Belgica','Portugal','Italia','Franca']\n", + "#print(range(len(l_Pais)))\n", + "for i in range(len(l_Pais)):\n", + " print(f'Indice {i} = {l_Pais[i]}')\n", + " \n", + "print()\n", + "for i in l_Pais:\n", + " print(i)\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Indice 0 = Brasil\n", + "Indice 1 = Belgica\n", + "Indice 2 = Portugal\n", + "Indice 3 = Italia\n", + "Indice 4 = Franca\n", + "\n", + "Brasil\n", + "Belgica\n", + "Portugal\n", + "Italia\n", + "Franca\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P4pdPuQtbRgo", + "colab_type": "text" + }, + "source": [ + "##NUMPY" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "UdEVYyDWbSwY", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "SQC4wPIPbYXR", + "colab_type": "code", + "colab": {} + }, + "source": [ + "help(np.set_printoptions)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "p24OdEIOdCrc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "help(np.random.normal)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "oSeWeNh0b2xH", + "colab_type": "code", + "outputId": "983273dc-a621-4bf9-8094-1f6fbb9f6c41", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "\n", + "np.set_printoptions(precision = 4, suppress = True)\n", + "# np.set_printoptions() # volta para valores default\n", + "\n", + "'''\n", + "Define seed por questões de reproducibilidade, ou seja, \n", + "garante que todos vamos gerar os mesmos números aleatórios\n", + "'''\n", + "np.random.seed(seed = 27031979)\n", + "\n", + "\n", + "# Exemplo que usei anteriormente\n", + "# i_Num = np.random.randint(2,4,size=1, dtype='l'); print(i_Num)\n", + "\n", + "mu = 0\n", + "sigma = 2\n", + "v_Array= np.random.normal(mu, sigma, size= 10) # Array 1D de size= 10\n", + "v_Array" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([-0.1447, -0.646 , -1.0161, -2.1605, 1.1235, 1.6243, 1.1402,\n", + " 2.0511, 0.1722, 1.5833])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 59 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ts7fkp5weKRG", + "colab_type": "code", + "colab": {} + }, + "source": [ + "help(np.mean) # media aritmetica\n", + "help(np.std) #desvio padrao" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "rOd0sbzFfm8i", + "colab_type": "code", + "outputId": "6e2bc082-c6dd-4cce-d3c7-d8121f10d1d8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "v_Media = np.array([2,4,6,8,10])\n", + "np.mean(v_Media) #media\n", + "np.std(v_Media) #desvio padrao" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "2.8284271247461903" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 76 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bpxp6zoei2uD", + "colab_type": "text" + }, + "source": [ + "**Laboratorio 1**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "UXfCytgNgobd", + "colab_type": "code", + "outputId": "f824a969-0202-4dae-b473-00f26cce2fde", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 406 + } + }, + "source": [ + "import numpy as np\n", + "np.set_printoptions(precision = 2, suppress = True)\n", + "\n", + "#Define seed (para reproducibilidade)\n", + "np.random.seed(seed = 27031979)\n", + "\n", + "# Define a media e desvio padrao\n", + "mu = 0\n", + "sigma = 1\n", + "for i in [0,100, 1000, 10000, 100000, 1000000]:\n", + " v_Array= np.random.normal(mu, sigma, size=i) \n", + " #print(v_Array)\n", + " print(f'Tamnho {i} \\n Distribuicao Normal N({np.mean(v_Array)}, {np.std(v_Array)})')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Tamnho 0 \n", + " Distribuicao Normal N(nan, nan)\n", + "Tamnho 100 \n", + " Distribuicao Normal N(0.04224826266585371, 1.0051525925711935)\n", + "Tamnho 1000 \n", + " Distribuicao Normal N(0.026602058853514608, 0.9802245432463518)\n", + "Tamnho 10000 \n", + " Distribuicao Normal N(-0.01037062644967241, 1.0042312204002735)\n", + "Tamnho 100000 \n", + " Distribuicao Normal N(0.0024246846574069265, 1.0003356931235659)\n", + "Tamnho 1000000 \n", + " Distribuicao Normal N(0.0014251888746623506, 0.9993750873131747)\n" + ], + "name": "stdout" + }, + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:140: RuntimeWarning: Degrees of freedom <= 0 for slice\n", + " keepdims=keepdims)\n", + "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:110: RuntimeWarning: invalid value encountered in true_divide\n", + " arrmean, rcount, out=arrmean, casting='unsafe', subok=False)\n", + "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:132: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vZ1uCBXukcwo", + "colab_type": "code", + "colab": {} + }, + "source": [ + "help(np.percentile)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "m6RdAjDEkdxt", + "colab_type": "code", + "outputId": "1dbcd76f-1a73-45c0-aa65-1d70681bc17a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "# Em estatística descritiva, os percentis são medidas que dividem a amostra \n", + "# (por ordem crescente dos dados) em 100 partes, cada uma com uma percentagem \n", + "# de dados aproximadamente igual\n", + "\n", + "v_X = np.arange(15)\n", + "print(v_X)\n", + "np.percentile(v_X, q=[5, 25, 50, 75, 95])\n", + "\n", + "# O valor 0.7 representa 5% dos dados da amostra\n", + "# O valor 7 represenra 50% dos dados da amostra\n", + "\n", + "# Um percentil indica que há x% de dados inferiores\n", + "# Corresponde à frequência cumulativa de N .k/100, onde N é o tamanho amostral.\n", + "# Corresponde a frequencia cumulativa de 15 (tamanho da amostra) por 5%" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14]\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 0.7, 3.5, 7. , 10.5, 13.3])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 144 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0XybsQN-luvM", + "colab_type": "code", + "outputId": "808ce5fe-eb69-4a99-fd42-d0704b97457c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# Calcular a qual percentil pertence um dado\n", + "n = 15 # quantidade de elementos\n", + "p = 75 # porcentagem\n", + "LP = (n-1) * p/100\n", + "print(LP)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "10.5\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zNMKgK4e68bT", + "colab_type": "text" + }, + "source": [ + "* Percentis: divide as partes por 100 \n", + "* Decis: divide as parte por 10\n", + "* Quartis: divide as partes por 25 (quarta parte de 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ATQRjKLBA_qs", + "colab_type": "text" + }, + "source": [ + "## Ordenar os valores do array" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Z0QR2dpzA8aF", + "colab_type": "code", + "outputId": "6789a506-b7a3-42f2-ac4a-f12278653a58", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "v_X= np.random.random(10)\n", + "v_X" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0.95, 0.8 , 0.3 , 0.62, 0.55, 0.62, 0.16, 0.6 , 0.82, 0.44])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 146 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DyHZ8t5XBW19", + "colab_type": "code", + "outputId": "8dd5d19d-b489-405f-bb9a-417ec16e9db6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "np.sort(v_X)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0.16, 0.3 , 0.44, 0.55, 0.6 , 0.62, 0.62, 0.8 , 0.82, 0.95])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 147 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eW8v2rzCJpnd", + "colab_type": "text" + }, + "source": [ + "#PANDAS" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EmER6P3WJtvs", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import pandas as pd\n", + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "nUy19CmALiP1", + "colab_type": "code", + "outputId": "33034f5b-1c39-4a52-e495-9b943dc3a75c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 101 + } + }, + "source": [ + "# gerar um array com 100 tentativas de valores entre 1 e 6\n", + "v_array = np.random.randint(1,7, size = 100) \n", + "v_array" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([2, 6, 6, 5, 2, 2, 4, 6, 6, 5, 1, 1, 1, 3, 6, 6, 6, 1, 3, 5, 2, 6,\n", + " 5, 4, 6, 1, 1, 5, 4, 1, 3, 6, 1, 1, 3, 4, 2, 6, 6, 2, 5, 3, 4, 4,\n", + " 5, 1, 4, 1, 1, 1, 5, 1, 2, 3, 2, 4, 4, 6, 3, 4, 2, 5, 2, 1, 4, 4,\n", + " 3, 4, 1, 2, 5, 2, 3, 4, 5, 1, 3, 2, 5, 6, 6, 1, 5, 6, 4, 4, 2, 5,\n", + " 6, 6, 1, 3, 4, 6, 3, 4, 4, 4, 5, 6])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "eD8HYdidLsQK", + "colab_type": "code", + "outputId": "d96188a3-b57a-4479-c1bc-b5d1a263f3b3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 134 + } + }, + "source": [ + "# Metodo do PANDAS que mostra a quantidade de ocorrencia por elemento\n", + "pd.value_counts(v_array)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "6 20\n", + "4 20\n", + "1 19\n", + "5 15\n", + "2 14\n", + "3 12\n", + "dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "jLP7tQ-VMXBF", + "colab_type": "code", + "outputId": "e43cc314-b106-4405-e922-c54c4bb0ec96", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 168 + } + }, + "source": [ + "# Aumentar o numero de tentativas e verificar se a LEI DOS GRANDES NUMEROS é refletida aqui\n", + "\n", + "np.random.seed(seed = 27031979)\n", + "\n", + "v_Array_Tentativas = np.array([30,100,500,10000,50000,1000000,50000000,100000000,500000000])\n", + "\n", + "for i in v_Array_Tentativas:\n", + " v_Array_Dados = np.random.randint(1, 7, size = i)\n", + " print(f'Tentativas {i} => Media {np.mean(v_Array_Dados)}')\n", + " " + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Tentativas 30 => Media 3.3333333333333335\n", + "Tentativas 100 => Media 3.39\n", + "Tentativas 500 => Media 3.436\n", + "Tentativas 10000 => Media 3.4928\n", + "Tentativas 50000 => Media 3.5003\n", + "Tentativas 1000000 => Media 3.495305\n", + "Tentativas 50000000 => Media 3.50014224\n", + "Tentativas 100000000 => Media 3.50006209\n", + "Tentativas 500000000 => Media 3.499940162\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pzqw5ni4QNJu", + "colab_type": "text" + }, + "source": [ + "#Ensaio Array" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pnbTkXnSED9Q", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "rK3qEzgZDq64", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# FORMA 1: METODO RANDOM : NORMAL (UTILIZADO TAMBEM PARA DISTRIBUICAO NORMAL)\n", + "\n", + "#Numero randomico entre 0 e 1 (criou-se um array por conta do SIZE)\n", + "v_Array_1= np.random.normal(0, 1, size= 10) # Array 1D de size= 10\n", + "v_Array_1" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Ok5EEOwlEG6N", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Isso ja nao é array (embora urilize o RANDOM.NORMAL)\n", + "v_naoArray_1 = np.random.normal(0,1)\n", + "type(v_naoArray_1)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "PSHfrTCHHpqJ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# FORMA 1: METODO RANDOM : RANDOM\n", + "v_Array_1 = np.random.random(10) # de 0 a 1 ?\n", + "v_Array_1" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "B7TQWNY4Ig8H", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# FORMA 1: METODO RANDOM : RANDINT (inteiros)\n", + "v_Array_1 = np.random.randint(1, 7, size= 100)\n", + "v_Array_1" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "YX6pdiFmbnXx", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# FORMA 1 : METODO RANDOM : RANDN (distribuicao normal) \n", + "v_Array_5x7 = np.random.randn(5,7)\n", + "v_Array_5x7" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "SzsSiQpME_4Q", + "colab_type": "code", + "outputId": "7274ba1b-3196-4597-b6c8-dad9a142f6d1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# FORMA 2: METODO ARRAY : DEVEMOS ESPECIFICAR OS VALORES (?)\n", + "\n", + "v_Array_2 = np.array([2,4,6,8,10,'dani'])\n", + "v_Array_2" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['2', '4', '6', '8', '10', 'dani'], dtype=' 2, v_A, 2*v_A)" + ], + "execution_count": 62, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([4, 4, 4, 4, 5])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 62 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "d0B9XQpFUFnj", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "aa241318-b2e8-4e40-cc67-b25a52922459" + }, + "source": [ + "#VALORES UNICOS DE DOIS ARRAYS\n", + "#Os elementos que tem APENAS EM A e APENAS EM B\n", + "v_A = np.array([2,2,2,4,5])\n", + "v_B = np.array([2,4,6,8,10])\n", + "\n", + "np.setxor1d(v_A, v_B)" + ], + "execution_count": 64, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 5, 6, 8, 10])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 64 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "l0_sSfdqUioL", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "16c97064-e7cc-4c34-8e70-64524b6823b0" + }, + "source": [ + "#UNIAO DOS CONJUNTOS\n", + "v_A = np.array([2,2,2,4,5])\n", + "v_B = np.array([2,4,6,8,10])\n", + "\n", + "np.union1d(v_A, v_B) #nao vem valores repetidos" + ], + "execution_count": 65, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 2, 4, 5, 6, 8, 10])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 65 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Xc8xQRqaREeD", + "colab_type": "text" + }, + "source": [ + "#Criar ndarrays 1D (N de NUMPY e D de Dimensao?)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MAHB4UEEOf-U", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np\n", + "np.set_printoptions(precision=2, suppress=True)\n", + "np.random.seed(seed = 27031979)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "GopCzuWnRTVc", + "colab_type": "code", + "outputId": "05cfd91f-035a-46d0-c042-2186f0c78a2d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "v_Array = np.array([10,76,0.67,'Daniele'])\n", + "v_Array.ndim # dimensao do array" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 29 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yNzYWwVBRd6F", + "colab_type": "code", + "outputId": "46b0fda3-d3bc-4ce8-b404-81a9bf41fea6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# Duas Dimensoes (linhas x colunas)\n", + "v_Array = np.random.randint(1,11, size=[2,3])\n", + "v_Array.ndim\n", + "\n", + "# Duas Dimensoes\n", + "v_Array = np.random.normal(0,10, size=[5,5])\n", + "v_Array\n", + "v_Array.ndim\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "2" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 41 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qzWsKZWdV_PD", + "colab_type": "text" + }, + "source": [ + "\n", + "#Operacoes com array (comparacoes e contas)\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "eaQq7AsxR113", + "colab_type": "code", + "outputId": "5db84159-5e71-4c81-c7bb-12e827304b4c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67 + } + }, + "source": [ + "v_Array_A = np.arange(0,50,step=1)\n", + "v_Array_A\n", + "v_Array_B = v_Array_A.copy()\n", + "v_Array_B" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", + " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n", + " 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 49 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "fuK6mA-CVGas", + "colab_type": "code", + "outputId": "cc196abe-9b5e-451b-c89d-0cb33a5ee3fe", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 118 + } + }, + "source": [ + "v_Array_A == v_Array_B" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ True, True, True, True, True, True, True, True, True,\n", + " True, True, True, True, True, True, True, True, True,\n", + " True, True, True, True, True, True, True, True, True,\n", + " True, True, True, True, True, True, True, True, True,\n", + " True, True, True, True, True, True, True, True, True,\n", + " True, True, True, True, True])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 51 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "he7NNaAEVWIW", + "colab_type": "code", + "outputId": "4d8d5997-68b2-4c9a-ba41-1d02d2290df0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 134 + } + }, + "source": [ + "print(v_Array_A + 2)\n", + "print()\n", + "print(v_Array_A * 10)\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[ 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25\n", + " 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49\n", + " 50 51]\n", + "\n", + "[ 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170\n", + " 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340 350\n", + " 360 370 380 390 400 410 420 430 440 450 460 470 480 490]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AkT3sbWcYsaj", + "colab_type": "text" + }, + "source": [ + "#Criar ndarrays 2D " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "69T83AmqYKef", + "colab_type": "code", + "outputId": "d55887b7-60e6-48a5-feea-227ceb1a8a0b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 101 + } + }, + "source": [ + "#Retorne uma amostra da distribuição normal.\n", + "#help(np.random.randn)\n", + "\n", + "# DUVIDA : COMO VOLTAR AO DEFAULT ?????\n", + "np.set_printoptions() #como volta ao default ???\n", + "np.random.seed(seed = 20111974)\n", + "v_Array_5x7 = np.random.randn(5,7)\n", + "v_Array_5x7" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 2.506, 1.114, 2.056, 0.565, 0.299, 1.049, -0.126],\n", + " [ 1.062, 1.138, 1.38 , -2.06 , 0.675, 0.727, -0.339],\n", + " [ 0.436, 0.591, -1.293, 1.177, -0.986, -1.79 , -1.089],\n", + " [-0.907, -1.023, -1.364, -0.294, 0.063, -1.142, -0.507],\n", + " [-0.835, -1.415, -0.216, -1.165, -0.608, -0.615, 1.077]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 83 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ziSX0PnhZHbd", + "colab_type": "code", + "outputId": "5923082d-6128-4e1d-867d-8a4e6074c349", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "print(f'Numero de Dimensao = {v_Array_5x7.ndim}')\n", + "print(f'Shape = {v_Array_5x7.shape}')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Numero de Dimensao = 2\n", + "Shape = (5, 7)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wv5Snh3cWFP3", + "colab_type": "text" + }, + "source": [ + "#Converte array 1D em 2D" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Wzk2yyijWEQT", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67 + }, + "outputId": "f5b867ac-ffc4-417d-f3b7-32d0dd5c7ec0" + }, + "source": [ + "v_A = np.array([1,2,3])\n", + "v_B = np.array(['A','B','C'])\n", + "\n", + "np.column_stack((v_A, v_B))\n" + ], + "execution_count": 71, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([['1', 'A'],\n", + " ['2', 'B'],\n", + " ['3', 'C']], dtype=' 13))\n", + "v_Idx" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([14]),)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 21 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "drmi8AOeDHVS", + "colab_type": "code", + "outputId": "609ccf13-baaf-4176-e6c8-90b35c23fb16", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "v_Array_Where[v_Array_Where > 13]" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([14])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 22 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zZnUTkJmDFpt", + "colab_type": "code", + "outputId": "24d7a177-5789-4812-d814-975a34d3f156", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "np.where((np.arange(15) == 13))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([13]),)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 25 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Hiuh0lLWECkQ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "v_Array_A = np.arange(15)\n", + "v_Array_B = np.arange(0,15,step=2)\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "LqYnT9o8EJce", + "colab_type": "code", + "outputId": "7140295a-e939-43af-a2ea-56ee6ff2f012", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "np.intersect1d(v_Array_A, v_Array_B) #elementos em comun\n", + "v_Array_A" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 36 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "i9Qn2VLrEdHk", + "colab_type": "code", + "outputId": "5f11ea8d-3f7b-442b-e5cc-61ffc6d98fb6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "np.setdiff1d(v_Array_A, v_Array_B) #Array A menos os elementos de Array B\n", + "#v_Array_A" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 1, 3, 5, 7, 9, 11, 13])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 42 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "fOorpKbCEngi", + "colab_type": "code", + "outputId": "ae03344c-e716-405c-b113-04d9d0bcbf55", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# Quando os elementos de A estão em B\n", + "v_Array_A = np.arange(15)\n", + "v_Array_B = np.arange(0,30,step=2)\n", + "np.where(v_Array_A == v_Array_B) # os arrays devem ter o mesmo shape" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([0]),)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 48 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rPFxpds6Ft6L", + "colab_type": "text" + }, + "source": [ + "#Autovalor e Autovetor" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "561pNsIDFwn4", + "colab_type": "code", + "outputId": "7b716a61-e51d-4292-c5b7-37d4323a74e6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67 + } + }, + "source": [ + "np.random.seed(seed = 27031979)\n", + "v_Array_3x3_A = np.random.randint(1, 10, size=[3,3])\n", + "v_Array_3x3_A" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[9, 8, 1],\n", + " [3, 6, 3],\n", + " [9, 1, 5]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 61 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XEVPBP6MHZz6", + "colab_type": "code", + "outputId": "2ca3617e-4f61-475e-d84d-0f5193889a88", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67 + } + }, + "source": [ + "v_Array_3x3_B = np.random.randint(20, 30, size=[3,3])\n", + "v_Array_3x3_B" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[27, 20, 28],\n", + " [23, 26, 25],\n", + " [21, 28, 25]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 63 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "H5wNA9UwGbMD", + "colab_type": "code", + "colab": {} + }, + "source": [ + "#Compute the eigenvalues and right eigenvectors of a square array.\n", + "help(np.linalg.eig)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "4fO5MBA4HAVc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "v_AutoValor, v_AutoVector = np.linalg.eig([v_Array_3x3_A,v_Array_3x3_B]) # ACEITA MAIS DE UM ARRAY" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Pso_EjNkHJ51", + "colab_type": "code", + "outputId": "2892bb7f-a229-42aa-c68e-af89d8d05dd8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + } + }, + "source": [ + "v_AutoValor" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[15.10548021+0.j , 2.4472599 +3.61619235j,\n", + " 2.4472599 -3.61619235j],\n", + " [74.31715733+0.j , 1.84142133+1.3823059j ,\n", + " 1.84142133-1.3823059j ]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 68 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8nOFDkH3HKgO", + "colab_type": "code", + "outputId": "52a7c3cb-5926-4ae4-8cc9-bada06d427c3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + } + }, + "source": [ + "v_AutoVector" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[[-0.65577886+0.j , -0.2186708 +0.36809391j,\n", + " -0.2186708 -0.36809391j],\n", + " [-0.42225248+0.j , -0.08795125-0.40034742j,\n", + " -0.08795125+0.40034742j],\n", + " [-0.625825 +0.j , 0.80540454+0.j ,\n", + " 0.80540454-0.j ]],\n", + "\n", + " [[-0.58285262+0.j , -0.73756936+0.j ,\n", + " -0.73756936-0.j ],\n", + " [-0.57468917+0.j , 0.02973258+0.15098287j,\n", + " 0.02973258-0.15098287j],\n", + " [-0.57446948+0.j , 0.64148376-0.14425728j,\n", + " 0.64148376+0.14425728j]]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 69 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P6S-FUmKJLCi", + "colab_type": "text" + }, + "source": [ + "#NaN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b3J9v5_GJOQS", + "colab_type": "text" + }, + "source": [ + "Em computação, NaN (acrônimo em inglês para Not a Number) é um valor ou símbolo usado nas linguagens de programação para representar um valor numérico indefinido ou irrepresentável." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bKf3-RK8HMO-", + "colab_type": "code", + "colab": {} + }, + "source": [ + "help(np.nan)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "9PQFSY0qJaqp", + "colab_type": "code", + "outputId": "74fdaaf4-e459-4803-e01f-06a4a5cc2156", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "np.nan" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "nan" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 74 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "oMO194DmJkB4", + "colab_type": "code", + "outputId": "ad38b768-91a3-4b48-d4a6-2beffd6de7f4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 118 + } + }, + "source": [ + "#ATRIBUR, EM QUALQUER INDICE, O VALOR NAN\n", + "\n", + "np.random.seed(seed = 27031979)\n", + "\n", + "v_Array = np.random.random(25) # 25 numeros aleatorios # ISSO É UMA LISTA?\n", + "#print(f'Array {v_Array}')\n", + "#print(f'Dimensao {v_Array.ndim}')\n", + "#print(f'Shape {v_Array.shape}')\n", + " \n", + "v_Idx_Rand = np.random.randint(0, 25, size=5) # LISTA?\n", + "v_Idx_Rand = np.sort(v_Idx_Rand) # DUVIDA: nao ordenou pq sao INDICES e nao VALORES ? R: Ordenou quando passei a atribuir o resultado do SORT a uma variavel\n", + "print(f'Indices {v_Idx_Rand}')\n", + "#print(f'Dimensao {v_Idx_Rand.ndim}')\n", + "#print(f'Shape {v_Idx_Rand.shape}')\n", + "\n", + "for i in v_Idx_Rand:\n", + " v_Array[v_Idx_Rand] = np.nan\n", + " \n", + "v_Array = np.sort(v_Array) # DUVIDA: PORQUE NAO ORDENOU? R: Ordenou quando passei a atribuir o resultado do SORT a uma variavel\n", + "v_Array " + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Indices [13 18 21 23 23]\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0.02527809, 0.18315026, 0.24687826, 0.34412652, 0.35098484,\n", + " 0.37293865, 0.39364883, 0.40080898, 0.44942041, 0.50866884,\n", + " 0.54613419, 0.57396324, 0.5873962 , 0.67219587, 0.71682512,\n", + " 0.72288452, 0.80974869, 0.81004079, 0.82225737, 0.92419913,\n", + " 0.93235274, nan, nan, nan, nan])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 118 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9BNEkPiTLzHI", + "colab_type": "code", + "outputId": "8d8bf08b-2245-4437-a46a-49e724a71fbd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# contar quandos NaNs tem\n", + "np.isnan(v_Array).sum()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "4" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 101 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QqcU-tQjL-WW", + "colab_type": "code", + "outputId": "5b758a16-28d3-4118-b0e1-101f6924d186", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "#Quais os indices que tem o valor NAN\n", + "np.where(np.isnan(v_Array))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([21, 22, 23, 24]),)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 119 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Ax37SndvN9j0", + "colab_type": "code", + "outputId": "042edff5-07c2-4b9d-d14c-5a3e1c3aa5cb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 101 + } + }, + "source": [ + "#Excluir os NaNs\n", + "v_Array[np.isnan(v_Array)] # so os NANs\n", + "v_Array[~np.isnan(v_Array)] # sem os NANs" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([0.02527809, 0.18315026, 0.24687826, 0.34412652, 0.35098484,\n", + " 0.37293865, 0.39364883, 0.40080898, 0.44942041, 0.50866884,\n", + " 0.54613419, 0.57396324, 0.5873962 , 0.67219587, 0.71682512,\n", + " 0.72288452, 0.80974869, 0.81004079, 0.82225737, 0.92419913,\n", + " 0.93235274])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 127 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SXynYbgpJWVA", + "colab_type": "text" + }, + "source": [ + "#Conversao" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pHAywmfQJXq0", + "colab_type": "code", + "outputId": "fed449c7-9bb6-4eed-a5ac-9ef5881beb68", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "#DE LISTA PARA ARRAY\n", + "import numpy as np\n", + "\n", + "v_lista_A = np.random.randint(0,10,5) #array\n", + "v_lista_B = [np.random.randint(0,10,5)] #lista\n", + "\n", + "type(v_lista_B)\n", + "\n", + "v_array_a = np.asarray(v_lista_A)\n", + "type(v_array_a)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HYhmbhpcKF5s", + "colab_type": "code", + "outputId": "05dc3b45-a96f-4650-a1be-5d43f22fa05b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "v_tupla = ([np.random.randint(0, 10, 3)],[np.random.randint(0, 10, 3)],[np.random.randint(0, 10, 3)])\n", + "v_tupla\n", + "\n", + "v_array_a = np.asarray(v_tupla)\n", + "type(v_array_a)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T2pUkGgqUdDB", + "colab_type": "text" + }, + "source": [ + "#HOJE" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "P_3QKtabUeJV", + "colab_type": "code", + "outputId": "2fc9e4a8-5c0f-4e89-a7ae-e8398440073f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "import numpy as np\n", + "np.random.seed(seed = 20111974)\n", + "\n", + "# Simulando Retornos de ativos financeiros com a distribuição Normal(0,1):\n", + "v_Retornos= np.random.normal(0, 1, 100)\n", + "print(f'Média: {np.mean(v_Retornos)}')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Média: -0.016996335492713833\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "OfAB4EMeVU6P", + "colab_type": "code", + "outputId": "143bc607-28e0-492a-e468-21dafabba3f1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + } + }, + "source": [ + "v_Percentis = np.percentile(v_Retornos, q=[1, 5, 25, 50, 55, 75, 99])\n", + "v_Percentis " + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([-2.0304241 , -1.49481318, -0.78361477, -0.12205087, 0.08182676,\n", + " 0.71947681, 2.06016123])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2uqq0-j8VpLn", + "colab_type": "code", + "colab": {} + }, + "source": [ + "\n", + "# LimSup = Q3 + 1.5 * IQR , onde Q3 75%\n", + "# LimInf = Q1 - 1.5 * IQR , onde Q1 25% \n", + "# IQR = Q3 - Q1\n", + "\n", + "v_LimInf = (v_Percentis[2] + 1.5) * ()\n", + "v_LimSup = (v_Percentis[5] + 1.5) * ()\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-W6c0tnte5ty", + "colab_type": "text" + }, + "source": [ + "#Exercicio (REVER!)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YEgCI0OsfAZg", + "colab_type": "code", + "outputId": "48403ab8-948e-4087-a1d1-cc18ededdc58", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 185 + } + }, + "source": [ + "# Preparando o exemplo:\n", + "import numpy as np\n", + "np.set_printoptions(precision=0)\n", + "np.random.seed(19741120)\n", + "m_X= np.array(np.random.normal(100, 10, size= 100)) #normal(loc=0.0, scale=1.0, size=None) #media, desvio padrao, tamanho\n", + "m_X" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 92., 89., 101., 89., 78., 92., 78., 97., 89., 103., 100.,\n", + " 87., 97., 100., 101., 98., 104., 91., 95., 93., 118., 88.,\n", + " 112., 105., 94., 91., 98., 110., 112., 123., 102., 107., 95.,\n", + " 109., 107., 122., 99., 113., 96., 114., 109., 99., 96., 109.,\n", + " 99., 85., 100., 109., 116., 114., 100., 95., 96., 100., 77.,\n", + " 110., 107., 92., 97., 84., 96., 97., 93., 94., 94., 97.,\n", + " 113., 113., 105., 108., 111., 99., 94., 98., 85., 102., 97.,\n", + " 94., 106., 105., 92., 104., 104., 99., 119., 92., 80., 106.,\n", + " 78., 106., 114., 112., 100., 94., 99., 123., 112., 101., 105.,\n", + " 95.])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 62 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wJWA0aSUfYWq", + "colab_type": "code", + "outputId": "8c94b8cc-3069-44c8-b3b7-dff62214b9f1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# Algumas estatísticas descritivas:\n", + "f'Média: {np.mean(m_X)}; Mediana: {np.median(m_X)}; STD: {np.std(m_X)}'" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Média: 90.91190590171118; Mediana: 90.60982589102949; STD: 7.8465139716162495'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 37 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MI7GCS2NfcEE", + "colab_type": "code", + "outputId": "a083b8e4-b4d7-4520-f840-7c3f5ae0b2fe", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "# Lista aleatória de índices que vou alterar\n", + "np.random.seed(20111974)\n", + "l_Idx= np.random.randint(0, 10, 3)\n", + "np.sort(l_Idx)\n", + "l_Idx" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([8, 8, 2])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 41 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vBY4nzhcffea", + "colab_type": "code", + "outputId": "1c51be70-30fc-4eaf-dff9-e961a2365774", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "m_X2= m_X.copy()\n", + "for i in l_Idx:\n", + " m_X2[i]= 2*m_X2[i]\n", + " \n", + "m_X2 " + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 92., 89., 202., 89., 78., 92., 78., 97., 356., 103.])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 52 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QCTsPu8_fpHi", + "colab_type": "code", + "outputId": "3f73b9ba-f90b-4de8-ef4b-b90b25aca962", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 272 + } + }, + "source": [ + "# Import a biblioteca seaborn:\n", + "import seaborn as sns\n", + "sns.boxplot(y= m_X2)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 53 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mTRxk17jf0CA", + "colab_type": "code", + "outputId": "96878943-3c29-41c4-c4ab-82ae5c4ae272", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67 + } + }, + "source": [ + "#Q1, Q3\n", + "#IQR = Q3-Q1\n", + "#Lim_Inf = Q1-1.5*IQR\n", + "#Lim_Sup = Q3+1.5*IQR\n", + "\n", + "v_Percentis = np.percentile(m_X2, q=[25, 50, 75])\n", + "print(v_Percentis[0])\n", + "print(v_Percentis[1])\n", + "\n", + "v_LimInf = v_Percentis[0] - 1.5 * (v_Percentis[0] - v_Percentis[2])\n", + "v_LimSup = v_Percentis[2] + 1.5 * (v_Percentis[0] - v_Percentis[2])\n", + "v_Mediana = v_Percentis[1]\n", + "\n", + "f'LimInf = {v_LimInf} e LimSup = {v_LimSup} e Mediana {v_Mediana} '\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "88.64343370711052\n", + "92.3703261730598\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'LimInf = 108.14020505852992 e LimSup = 82.14450992330404 e Mediana 92.3703261730598 '" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 54 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "qAnmCYTnggkx", + "colab_type": "code", + "outputId": "660896f0-b20c-4617-f2d8-8129d38df785", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "#Se m_X2[i] < Lim_Inf então m_X2[i]= Lim_Inf\n", + "#Se m_X2[i] > Lim_Sup então m_X2[i]= Lim_Sup\n", + "\n", + "m_X2[m_X2 < v_LimInf] = v_LimInf\n", + "m_X2[m_X2 > v_LimSup] = v_LimSup\n", + "m_X2\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([82., 82., 82., 82., 82., 82., 82., 82., 82., 82.])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 55 + } + ] + } + ] +} \ No newline at end of file From e860131ff7a7f90b1e0f50b2d472c51ed68df3af Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Tue, 12 Nov 2019 23:33:21 +0000 Subject: [PATCH 14/35] Created using Colaboratory --- dswp_grupo1.ipynb | 1217 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1217 insertions(+) create mode 100644 dswp_grupo1.ipynb diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb new file mode 100644 index 000000000..94cc704f9 --- /dev/null +++ b/dswp_grupo1.ipynb @@ -0,0 +1,1217 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "dswp_grupo1.ipynb", + "provenance": [], + "collapsed_sections": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s7mYPjzGX56N", + "colab_type": "text" + }, + "source": [ + "#**CRISP-DM PROCESS**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IFXrXGs7X5_g", + "colab_type": "text" + }, + "source": [ + "#**1BU - BUSINESS UNDERSTANDING**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-s6WZlGTZP3U", + "colab_type": "text" + }, + "source": [ + "> O que se deseja alcançar a partir de uma perspectiva de negócios." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QGvTW7tjX6Bx", + "colab_type": "text" + }, + "source": [ + "#**2DU - DATA UNDERSTANDING**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7bI9K3YWZehy", + "colab_type": "text" + }, + "source": [ + "> Coleta e exploração dos dados. Exploração do dataframe, descobrir relações e descrever os dados em geral. Utilize-se das técnicas de Data Visualization para detectar relações relevantes entre as variáveis, desequilíbrios de classes e identificar variáveis mais importantes." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BAHN39XGvSAP", + "colab_type": "text" + }, + "source": [ + "###Criar um dataframe a partir do ficheiro" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uRVg-TUebJ7v", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Carrega a library Pandas\n", + "import pandas as pd\n", + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "0YTMtMPRbSYD", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "bff1944b-60d1-442d-9b38-11a1c0c40d7f" + }, + "source": [ + "df_50k = pd.read_csv('https://raw.githubusercontent.com/MathMachado/DSWP/master/Dataframes/adult.data')\n", + "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " + ], + "execution_count": 49, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Ficheiro com 32560 linhas e 15 colunas\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Lxq7B1Vnv1Ys", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 400 + }, + "outputId": "5dd54c0d-44df-4e4d-af49-aa41467e1c8a" + }, + "source": [ + "#Detecção de que o ficheiro não tem header\n", + "df_50k.head(5)" + ], + "execution_count": 50, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " age workclass fnlwgt ... hours-per-week native-country flag_50k\n", + "0 39 State-gov 77516 ... 40 United-States <=50K\n", + "1 50 Self-emp-not-inc 83311 ... 13 United-States <=50K\n", + "2 38 Private 215646 ... 40 United-States <=50K\n", + "3 53 Private 234721 ... 40 United-States <=50K\n", + "4 28 Private 338409 ... 40 Cuba <=50K\n", + "\n", + "[5 rows x 15 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 54 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-P0EGSMywu0d", + "colab_type": "text" + }, + "source": [ + "**Dicionário dos dados** \n", + "* Age: idade da pessoa\n", + "* workclass: classificação do cargo profissional\n", + "* fnlwgt (final weight): ?\n", + "* education: grau de escolaridade\n", + "* education-num: ?\n", + "* marital-status: estado civil\n", + "* occupation: cargo profissional\n", + "* relationship: tipo de relacionamento\n", + "* race: raça\n", + "* sex: sexo\n", + "* capital-gain: ?\n", + "* capital-loss: ?\n", + "* hours-per-week: horas por semana trabalhadas?\n", + "* native-country: país de nascimento\n", + "* flag_50k: indicativo se ganha menos (<=50k) ou mais (>50k) que 50.000 dinheiros\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "m2p6SOzbo99T", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 101 + }, + "outputId": "45563800-4f7c-4973-b8a2-d013946de85e" + }, + "source": [ + "#Colunas do DataFrame\n", + "df_50k.columns" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['age', 'workclass', 'fnlwgt', 'education', 'education-num',\n", + " 'marital-status', 'occupation', 'relationship', 'race', 'sex',\n", + " 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',\n", + " 'flag_50k'],\n", + " dtype='object')" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 23 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "dMDVRA8eo96a", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 286 + }, + "outputId": "a36b8842-a929-43f2-f42c-9386f449c0d6" + }, + "source": [ + "df_50k.dtypes" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "age int64\n", + "workclass object\n", + "fnlwgt int64\n", + "education object\n", + "education-num int64\n", + "marital-status object\n", + "occupation object\n", + "relationship object\n", + "race object\n", + "sex object\n", + "capital-gain int64\n", + "capital-loss int64\n", + "hours-per-week int64\n", + "native-country object\n", + "flag_50k object\n", + "dtype: object" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 24 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5SbUk5Srzqok", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 185 + }, + "outputId": "dd593c3f-b5db-4409-c3c8-92e74c4fc6ec" + }, + "source": [ + "#Valores distintos de WorkClass\n", + "df_50k['workclass'].value_counts()" + ], + "execution_count": 30, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Private 22696\n", + " Self-emp-not-inc 2541\n", + " Local-gov 2093\n", + " ? 1836\n", + " State-gov 1298\n", + " Self-emp-inc 1116\n", + " Federal-gov 960\n", + " Without-pay 14\n", + " Never-worked 7\n", + "Name: workclass, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 30 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VYkfFI451R_m", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 302 + }, + "outputId": "648f0433-d0a0-44df-d05b-2991a577cb12" + }, + "source": [ + "#Valores distintos de Education\n", + "df_50k['education'].value_counts()" + ], + "execution_count": 32, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " HS-grad 10501\n", + " Some-college 7291\n", + " Bachelors 5355\n", + " Masters 1723\n", + " Assoc-voc 1382\n", + " 11th 1175\n", + " Assoc-acdm 1067\n", + " 10th 933\n", + " 7th-8th 646\n", + " Prof-school 576\n", + " 9th 514\n", + " 12th 433\n", + " Doctorate 413\n", + " 5th-6th 333\n", + " 1st-4th 168\n", + " Preschool 51\n", + "Name: education, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 32 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QJly0mWR1R8m", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 151 + }, + "outputId": "f9e901e2-7bd4-4ed1-8943-56f8bc64153c" + }, + "source": [ + "#Valores distintos de marital-status\n", + "df_50k['marital-status'].value_counts()" + ], + "execution_count": 34, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Married-civ-spouse 14976\n", + " Never-married 10683\n", + " Divorced 4443\n", + " Separated 1025\n", + " Widowed 993\n", + " Married-spouse-absent 418\n", + " Married-AF-spouse 23\n", + "Name: marital-status, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 34 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4R_S4oaG1R6_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 286 + }, + "outputId": "ba8bbbb4-a76e-4904-e18e-ddc3c47b5f28" + }, + "source": [ + "#Valores distintos de occupation\n", + "df_50k['occupation'].value_counts()" + ], + "execution_count": 35, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Prof-specialty 4140\n", + " Craft-repair 4099\n", + " Exec-managerial 4066\n", + " Adm-clerical 3770\n", + " Sales 3650\n", + " Other-service 3295\n", + " Machine-op-inspct 2002\n", + " ? 1843\n", + " Transport-moving 1597\n", + " Handlers-cleaners 1370\n", + " Farming-fishing 994\n", + " Tech-support 928\n", + " Protective-serv 649\n", + " Priv-house-serv 149\n", + " Armed-Forces 9\n", + "Name: occupation, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 35 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2q5DBeLM1R3m", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 134 + }, + "outputId": "6936346e-9e6c-411b-a88b-a9beffd2b53f" + }, + "source": [ + "#Valores distintos de relationship\n", + "df_50k['relationship'].value_counts()" + ], + "execution_count": 36, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Husband 13193\n", + " Not-in-family 8305\n", + " Own-child 5068\n", + " Unmarried 3446\n", + " Wife 1568\n", + " Other-relative 981\n", + "Name: relationship, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 36 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3nlhzegs3PD2", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 118 + }, + "outputId": "4a8319d4-ce95-40f3-bba5-2f49dd210b0d" + }, + "source": [ + "#Valores distintos de race\n", + "df_50k['race'].value_counts()" + ], + "execution_count": 37, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " White 27816\n", + " Black 3124\n", + " Asian-Pac-Islander 1039\n", + " Amer-Indian-Eskimo 311\n", + " Other 271\n", + "Name: race, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 37 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-mgIBM783PAl", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67 + }, + "outputId": "1b6d93a0-fc30-4a98-81ee-d30b1aa23a6b" + }, + "source": [ + "#Valores distintos de sex\n", + "df_50k['sex'].value_counts()" + ], + "execution_count": 38, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Male 21790\n", + " Female 10771\n", + "Name: sex, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 38 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EjRcmMm73O-2", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 739 + }, + "outputId": "0c8b12c5-b77b-485d-afc3-d396ff914ac0" + }, + "source": [ + "#Valores distintos de native-country\n", + "df_50k['native-country'].value_counts()" + ], + "execution_count": 42, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " United-States 29170\n", + " Mexico 643\n", + " ? 583\n", + " Philippines 198\n", + " Germany 137\n", + " Canada 121\n", + " Puerto-Rico 114\n", + " El-Salvador 106\n", + " India 100\n", + " Cuba 95\n", + " England 90\n", + " Jamaica 81\n", + " South 80\n", + " China 75\n", + " Italy 73\n", + " Dominican-Republic 70\n", + " Vietnam 67\n", + " Guatemala 64\n", + " Japan 62\n", + " Poland 60\n", + " Columbia 59\n", + " Taiwan 51\n", + " Haiti 44\n", + " Iran 43\n", + " Portugal 37\n", + " Nicaragua 34\n", + " Peru 31\n", + " Greece 29\n", + " France 29\n", + " Ecuador 28\n", + " Ireland 24\n", + " Hong 20\n", + " Cambodia 19\n", + " Trinadad&Tobago 19\n", + " Laos 18\n", + " Thailand 18\n", + " Yugoslavia 16\n", + " Outlying-US(Guam-USVI-etc) 14\n", + " Hungary 13\n", + " Honduras 13\n", + " Scotland 12\n", + " Holand-Netherlands 1\n", + "Name: native-country, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 42 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zkL_oVwT3O73", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67 + }, + "outputId": "36efd24a-9fb7-4b41-a08a-7a7dbdf446de" + }, + "source": [ + "#Valores distintos de flag_50k\n", + "df_50k['flag_50k'].value_counts()" + ], + "execution_count": 43, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " <=50K 24720\n", + " >50K 7841\n", + "Name: flag_50k, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 43 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PYiZL-v7X6EC", + "colab_type": "text" + }, + "source": [ + "#**3DP - DATA PREPARATION**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OX64mjTvZy0n", + "colab_type": "text" + }, + "source": [ + "> Coletar, preparar, transformar e limpar dados: remover duplicatas, corrigir erros, lidar com Missing Values, normalização, conversões de tipo de dados e etc.\n", + "\n", + "* **3DP_Feature Engineering**: derivar variaveis\n", + "* **3DP_Missing Values Handling**: identificar e tratar os Missing Values\n", + "* **3DP_Outliers Handling**: identificar e tratar os Outlier\n", + "* **3DP_Data Transformation**: colocar as variáveis numa mesma escala (RobustScaler?)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9CtlGkaKz-uj", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67 + }, + "outputId": "687f6c5c-2692-449d-95ab-85d3adaafef5" + }, + "source": [ + "#Lista com os nomes da colunas por tipo de dados\n", + "df_50k.select_dtypes(include=['float','number']).columns" + ], + "execution_count": 55, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss',\n", + " 'hours-per-week'],\n", + " dtype='object')" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 55 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vhlHOvTa5Atd", + "colab_type": "code", + "colab": {} + }, + "source": [ + "#criar nova coluna?\n", + "#df_50k['ind_50k'] = df_50k['flag_50k'].apply(lambda x: 0 if x == '<=50K' else 1)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "h_Nh0ctCX6Gk", + "colab_type": "text" + }, + "source": [ + "#**4M - MODELING**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6CGb0YPFaTP4", + "colab_type": "text" + }, + "source": [ + "> Aplicar algoritmos (*Supervised* vs *Unsupervised Learning*)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IJ8WRhPGX6JB", + "colab_type": "text" + }, + "source": [ + "#**5MSE - MODEL SELECTION AND EVALUATE**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "66RGztFNaZ-B", + "colab_type": "text" + }, + "source": [ + "> Aplicação das melhores métricas para avaliar o acurácia dos modelos de ML." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LGrnRo7DX6Lk", + "colab_type": "text" + }, + "source": [ + "#**6D - Deployment**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qB1d4NGBX6OD", + "colab_type": "text" + }, + "source": [ + "> Implementação do Modelo." + ] + } + ] +} \ No newline at end of file From 7efc1717fdb5a2a2f614d4c0b35fa4271b4acf82 Mon Sep 17 00:00:00 2001 From: Marcelo Massao Kataoka Higaskino Date: Wed, 13 Nov 2019 10:05:37 +0000 Subject: [PATCH 15/35] Added files to gitignore --- .gitignore | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/.gitignore b/.gitignore index 894a44cc0..e6591cb7a 100644 --- a/.gitignore +++ b/.gitignore @@ -102,3 +102,15 @@ venv.bak/ # mypy .mypy_cache/ + +.idea/DSWP.iml + +.idea/inspectionProfiles/profiles_settings.xml + +.idea/misc.xml + +.idea/modules.xml + +.idea/vcs.xml + +.idea/workspace.xml From c211dd7f2514a5fb7ecebecc97309f232799df33 Mon Sep 17 00:00:00 2001 From: Marcelo Massao Kataoka Higaskino Date: Wed, 13 Nov 2019 19:14:31 +0000 Subject: [PATCH 16/35] Added basic structure to run Random Forest on raw data. --- dswp_grupo1.ipynb | 1870 +++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 1818 insertions(+), 52 deletions(-) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index 94cc704f9..f2ed71720 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -21,7 +21,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { @@ -104,17 +104,17 @@ "metadata": { "id": "0YTMtMPRbSYD", "colab_type": "code", + "outputId": "bff1944b-60d1-442d-9b38-11a1c0c40d7f", "colab": { "base_uri": "https://localhost:8080/", "height": 34 - }, - "outputId": "bff1944b-60d1-442d-9b38-11a1c0c40d7f" + } }, "source": [ "df_50k = pd.read_csv('https://raw.githubusercontent.com/MathMachado/DSWP/master/Dataframes/adult.data')\n", "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " ], - "execution_count": 49, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -130,17 +130,17 @@ "metadata": { "id": "Lxq7B1Vnv1Ys", "colab_type": "code", + "outputId": "5dd54c0d-44df-4e4d-af49-aa41467e1c8a", "colab": { "base_uri": "https://localhost:8080/", "height": 400 - }, - "outputId": "5dd54c0d-44df-4e4d-af49-aa41467e1c8a" + } }, "source": [ "#Detecção de que o ficheiro não tem header\n", "df_50k.head(5)" ], - "execution_count": 50, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -299,11 +299,11 @@ "metadata": { "id": "HBKqXT9Bo-BC", "colab_type": "code", + "outputId": "777064e5-b82b-4959-9005-6b098ec8d060", "colab": { "base_uri": "https://localhost:8080/", "height": 269 - }, - "outputId": "777064e5-b82b-4959-9005-6b098ec8d060" + } }, "source": [ "#Lista para o header do ficheiro\n", @@ -325,7 +325,7 @@ " ]\n", "l_column" ], - "execution_count": 51, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -375,16 +375,16 @@ "metadata": { "id": "AjswmEdb4R8X", "colab_type": "code", + "outputId": "d78c94ef-c791-45eb-96d5-b68d3a92b769", "colab": { "base_uri": "https://localhost:8080/", "height": 353 - }, - "outputId": "d78c94ef-c791-45eb-96d5-b68d3a92b769" + } }, "source": [ "df_50k.info()" ], - "execution_count": 53, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -419,16 +419,16 @@ "metadata": { "id": "CNyS0K4C4dfp", "colab_type": "code", + "outputId": "32176f45-33bf-44d1-f07f-1fc0bc31d708", "colab": { "base_uri": "https://localhost:8080/", "height": 333 - }, - "outputId": "32176f45-33bf-44d1-f07f-1fc0bc31d708" + } }, "source": [ "df_50k.head(5)" ], - "execution_count": 54, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -615,17 +615,17 @@ "metadata": { "id": "m2p6SOzbo99T", "colab_type": "code", + "outputId": "45563800-4f7c-4973-b8a2-d013946de85e", "colab": { "base_uri": "https://localhost:8080/", "height": 101 - }, - "outputId": "45563800-4f7c-4973-b8a2-d013946de85e" + } }, "source": [ "#Colunas do DataFrame\n", "df_50k.columns" ], - "execution_count": 23, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -650,16 +650,16 @@ "metadata": { "id": "dMDVRA8eo96a", "colab_type": "code", + "outputId": "a36b8842-a929-43f2-f42c-9386f449c0d6", "colab": { "base_uri": "https://localhost:8080/", "height": 286 - }, - "outputId": "a36b8842-a929-43f2-f42c-9386f449c0d6" + } }, "source": [ "df_50k.dtypes" ], - "execution_count": 24, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -695,17 +695,17 @@ "metadata": { "id": "5SbUk5Srzqok", "colab_type": "code", + "outputId": "dd593c3f-b5db-4409-c3c8-92e74c4fc6ec", "colab": { "base_uri": "https://localhost:8080/", "height": 185 - }, - "outputId": "dd593c3f-b5db-4409-c3c8-92e74c4fc6ec" + } }, "source": [ "#Valores distintos de WorkClass\n", "df_50k['workclass'].value_counts()" ], - "execution_count": 30, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -735,17 +735,17 @@ "metadata": { "id": "VYkfFI451R_m", "colab_type": "code", + "outputId": "648f0433-d0a0-44df-d05b-2991a577cb12", "colab": { "base_uri": "https://localhost:8080/", "height": 302 - }, - "outputId": "648f0433-d0a0-44df-d05b-2991a577cb12" + } }, "source": [ "#Valores distintos de Education\n", "df_50k['education'].value_counts()" ], - "execution_count": 32, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -782,17 +782,17 @@ "metadata": { "id": "QJly0mWR1R8m", "colab_type": "code", + "outputId": "f9e901e2-7bd4-4ed1-8943-56f8bc64153c", "colab": { "base_uri": "https://localhost:8080/", "height": 151 - }, - "outputId": "f9e901e2-7bd4-4ed1-8943-56f8bc64153c" + } }, "source": [ "#Valores distintos de marital-status\n", "df_50k['marital-status'].value_counts()" ], - "execution_count": 34, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -820,17 +820,17 @@ "metadata": { "id": "4R_S4oaG1R6_", "colab_type": "code", + "outputId": "ba8bbbb4-a76e-4904-e18e-ddc3c47b5f28", "colab": { "base_uri": "https://localhost:8080/", "height": 286 - }, - "outputId": "ba8bbbb4-a76e-4904-e18e-ddc3c47b5f28" + } }, "source": [ "#Valores distintos de occupation\n", "df_50k['occupation'].value_counts()" ], - "execution_count": 35, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -866,17 +866,17 @@ "metadata": { "id": "2q5DBeLM1R3m", "colab_type": "code", + "outputId": "6936346e-9e6c-411b-a88b-a9beffd2b53f", "colab": { "base_uri": "https://localhost:8080/", "height": 134 - }, - "outputId": "6936346e-9e6c-411b-a88b-a9beffd2b53f" + } }, "source": [ "#Valores distintos de relationship\n", "df_50k['relationship'].value_counts()" ], - "execution_count": 36, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -903,17 +903,17 @@ "metadata": { "id": "3nlhzegs3PD2", "colab_type": "code", + "outputId": "4a8319d4-ce95-40f3-bba5-2f49dd210b0d", "colab": { "base_uri": "https://localhost:8080/", "height": 118 - }, - "outputId": "4a8319d4-ce95-40f3-bba5-2f49dd210b0d" + } }, "source": [ "#Valores distintos de race\n", "df_50k['race'].value_counts()" ], - "execution_count": 37, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -939,17 +939,17 @@ "metadata": { "id": "-mgIBM783PAl", "colab_type": "code", + "outputId": "1b6d93a0-fc30-4a98-81ee-d30b1aa23a6b", "colab": { "base_uri": "https://localhost:8080/", "height": 67 - }, - "outputId": "1b6d93a0-fc30-4a98-81ee-d30b1aa23a6b" + } }, "source": [ "#Valores distintos de sex\n", "df_50k['sex'].value_counts()" ], - "execution_count": 38, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -972,17 +972,17 @@ "metadata": { "id": "EjRcmMm73O-2", "colab_type": "code", + "outputId": "0c8b12c5-b77b-485d-afc3-d396ff914ac0", "colab": { "base_uri": "https://localhost:8080/", "height": 739 - }, - "outputId": "0c8b12c5-b77b-485d-afc3-d396ff914ac0" + } }, "source": [ "#Valores distintos de native-country\n", "df_50k['native-country'].value_counts()" ], - "execution_count": 42, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -1045,17 +1045,17 @@ "metadata": { "id": "zkL_oVwT3O73", "colab_type": "code", + "outputId": "36efd24a-9fb7-4b41-a08a-7a7dbdf446de", "colab": { "base_uri": "https://localhost:8080/", "height": 67 - }, - "outputId": "36efd24a-9fb7-4b41-a08a-7a7dbdf446de" + } }, "source": [ "#Valores distintos de flag_50k\n", "df_50k['flag_50k'].value_counts()" ], - "execution_count": 43, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -1111,17 +1111,17 @@ "metadata": { "id": "9CtlGkaKz-uj", "colab_type": "code", + "outputId": "687f6c5c-2692-449d-95ab-85d3adaafef5", "colab": { "base_uri": "https://localhost:8080/", "height": 67 - }, - "outputId": "687f6c5c-2692-449d-95ab-85d3adaafef5" + } }, "source": [ "#Lista com os nomes da colunas por tipo de dados\n", "df_50k.select_dtypes(include=['float','number']).columns" ], - "execution_count": 55, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -1193,6 +1193,1772 @@ "> Aplicação das melhores métricas para avaliar o acurácia dos modelos de ML." ] }, + { + "cell_type": "code", + "metadata": { + "id": "d0SAY78uAtVl", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix\n", + "\n", + "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n", + "from sklearn.model_selection import cross_val_score\n", + "from time import time\n", + "from operator import itemgetter\n", + "from scipy.stats import randint\n", + "\n", + "from sklearn.tree import export_graphviz\n", + "from sklearn.externals.six import StringIO \n", + "from IPython.display import Image \n", + "import pydotplus\n", + "\n", + "np.set_printoptions(suppress=True)\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "yvLvigcHI2fs", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def make_confusion_matrix(cf,\n", + " group_names=None,\n", + " categories='auto',\n", + " count=True,\n", + " percent=True,\n", + " cbar=True,\n", + " xyticks=False,\n", + " xyplotlabels=True,\n", + " sum_stats=True,\n", + " figsize= (8,8),\n", + " cmap='Blues'):\n", + " '''\n", + " This function will make a pretty plot of an sklearn Confusion Matrix cm using a Seaborn heatmap visualization.\n", + " Arguments\n", + " ---------\n", + " cf: confusion matrix to be passed in\n", + " group_names: List of strings that represent the labels row by row to be shown in each square.\n", + " categories: List of strings containing the categories to be displayed on the x,y axis. Default is 'auto'\n", + " count: If True, show the raw number in the confusion matrix. Default is True.\n", + " normalize: If True, show the proportions for each category. Default is True.\n", + " cbar: If True, show the color bar. The cbar values are based off the values in the confusion matrix.\n", + " Default is True.\n", + " xyticks: If True, show x and y ticks. Default is True.\n", + " xyplotlabels: If True, show 'True Label' and 'Predicted Label' on the figure. Default is True.\n", + " sum_stats: If True, display summary statistics below the figure. Default is True.\n", + " figsize: Tuple representing the figure size. Default will be the matplotlib rcParams value.\n", + " cmap: Colormap of the values displayed from matplotlib.pyplot.cm. Default is 'Blues'\n", + " See http://matplotlib.org/examples/color/colormaps_reference.html\n", + " '''\n", + "\n", + "\n", + " # CODE TO GENERATE TEXT INSIDE EACH SQUARE\n", + " blanks = ['' for i in range(cf.size)]\n", + "\n", + " if group_names and len(group_names)==cf.size:\n", + " group_labels = [\"{}\\n\".format(value) for value in group_names]\n", + " else:\n", + " group_labels = blanks\n", + "\n", + " if count:\n", + " group_counts = [\"{0:0.0f}\\n\".format(value) for value in cf.flatten()]\n", + " else:\n", + " group_counts = blanks\n", + "\n", + " if percent:\n", + " group_percentages = [\"{0:.2%}\".format(value) for value in cf.flatten()/np.sum(cf)]\n", + " else:\n", + " group_percentages = blanks\n", + "\n", + " box_labels = [f\"{v1}{v2}{v3}\".strip() for v1, v2, v3 in zip(group_labels,group_counts,group_percentages)]\n", + " box_labels = np.asarray(box_labels).reshape(cf.shape[0],cf.shape[1])\n", + "\n", + "\n", + " # CODE TO GENERATE SUMMARY STATISTICS & TEXT FOR SUMMARY STATS\n", + " if sum_stats:\n", + " #Accuracy is sum of diagonal divided by total observations\n", + " accuracy = np.trace(cf) / float(np.sum(cf))\n", + "\n", + " #if it is a binary confusion matrix, show some more stats\n", + " if len(cf)==2:\n", + " #Metrics for Binary Confusion Matrices\n", + " precision = cf[1,1] / sum(cf[:,1])\n", + " recall = cf[1,1] / sum(cf[1,:])\n", + " f1_score = 2*precision*recall / (precision + recall)\n", + " stats_text = \"\\n\\nAccuracy={:0.3f}\\nPrecision={:0.3f}\\nRecall={:0.3f}\\nF1 Score={:0.3f}\".format(accuracy,precision,recall,f1_score)\n", + " else:\n", + " stats_text = \"\\n\\nAccuracy={:0.3f}\".format(accuracy)\n", + " else:\n", + " stats_text = \"\"\n", + "\n", + "\n", + " # SET FIGURE PARAMETERS ACCORDING TO OTHER ARGUMENTS\n", + " if figsize==None:\n", + " #Get default figure size if not set\n", + " figsize = plt.rcParams.get('figure.figsize')\n", + "\n", + " if xyticks==False:\n", + " #Do not show categories if xyticks is False\n", + " categories=False\n", + "\n", + "\n", + " # MAKE THE HEATMAP VISUALIZATION\n", + " plt.figure(figsize=figsize)\n", + " sns.heatmap(cf,annot=box_labels,fmt=\"\",cmap=cmap,cbar=cbar,xticklabels=categories,yticklabels=categories)\n", + "\n", + " if xyplotlabels:\n", + " plt.ylabel('True label')\n", + " plt.xlabel('Predicted label' + stats_text)\n", + " else:\n", + " plt.xlabel(stats_text)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "nxo9D0tOASy-", + "colab_type": "code", + "colab": {} + }, + "source": [ + "i_CV= 10 # Número de Cross-Validations\n", + "i_Seed= 20111974 # semente por questões de reproducibilidade\n", + "f_Test_Size= 0.3" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "S3YDfY4nA-bw", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + }, + "outputId": "83b6ea98-6660-4745-a979-beb94e54b053" + }, + "source": [ + "X_train = pd.read_csv('https://raw.githubusercontent.com/massaoHigaskino/DSWP/lectures/project/Execution/adult_50k/datascience_adult_50k_v_t1_adult_data_dataset.csv', index_col= 'id');\n", + "X_train.head()" + ], + "execution_count": 40, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " target\n", + "id \n", + "1 0\n", + "2 0\n", + "3 1\n", + "4 0\n", + "5 0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 49 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DM6ficlv_9oB", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 139 + }, + "outputId": "8796857a-49fb-4b3a-cf7f-2686d535040b" + }, + "source": [ + "# Instancia...\n", + "Model_RF= RandomForestClassifier(n_estimators=100, min_samples_split= 2, max_features=\"auto\", random_state= i_Seed)\n", + "\n", + "# Treina...\n", + "Model_RF.fit(X_train, y_train)" + ], + "execution_count": 41, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=100,\n", + " n_jobs=None, oob_score=False, random_state=20111974,\n", + " verbose=0, warm_start=False)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 41 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DYeWL0k6IVl9", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "outputId": "9f4b5a05-1325-4edc-bb08-8a1db18a6d0a" + }, + "source": [ + "# Cross-Validation com 10 folds\n", + "a_Scores_CV = cross_val_score(Model_RF, X_train, y_train, cv= i_CV)\n", + "print(f'Média das Acurácias calculadas pelo CV....: {100*round(a_Scores_CV.mean(),4)}')\n", + "print(f'std médio das Acurácias calculadas pelo CV: {100*round(a_Scores_CV.std(),4)}')" + ], + "execution_count": 45, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Média das Acurácias calculadas pelo CV....: 85.61999999999999\n", + "std médio das Acurácias calculadas pelo CV: 0.51\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "NrZdl0A8JUTU", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "outputId": "b65a44d6-0dca-4cb8-f7d1-c1e69e7e3e84" + }, + "source": [ + "print(f'Acurácias: {a_Scores_CV}')" + ], + "execution_count": 46, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Acurácias: [0.84801965 0.86179361 0.85841523 0.84674447 0.85995086 0.85227273\n", + " 0.85780098 0.85718673 0.86210074 0.85810811]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "q42UUhYbJVdE", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 538 + }, + "outputId": "f0c5f632-a8a0-46ee-9f04-16b1db300106" + }, + "source": [ + "# Faz predições...\n", + "y_pred = Model_RF.predict(X_test)\n", + "\n", + "# Confusion Matrix\n", + "cf_matrix = confusion_matrix(y_test, y_pred)\n", + "cf_labels = ['True Neg','False Pos','False Neg','True Pos']\n", + "cf_categories = ['Zero', 'One']\n", + "make_confusion_matrix(cf_matrix, group_names= cf_labels, categories= cf_categories)" + ], + "execution_count": 50, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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KVR07M3TwIAB+/P576tY9lS++/o7JH37MiOHD+Pfff3P5LESOTsU0DzmQkEhMrDd8u3jZ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\nbhWAmb0L9EpnH82B7gDOuSQg1sxKHtL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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, { "cell_type": "markdown", "metadata": { From b76b802fa369871f6eba13970df3583bbe5da20e Mon Sep 17 00:00:00 2001 From: Marcelo Massao Kataoka Higaskino Date: Wed, 13 Nov 2019 19:20:57 +0000 Subject: [PATCH 17/35] Added titles. --- dswp_grupo1.ipynb | 70 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 70 insertions(+) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index f2ed71720..9919afd7e 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -1193,6 +1193,16 @@ "> Aplicação das melhores métricas para avaliar o acurácia dos modelos de ML." ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "PpFzDmkhM3ea", + "colab_type": "text" + }, + "source": [ + "##imports" + ] + }, { "cell_type": "code", "metadata": { @@ -1232,6 +1242,16 @@ "execution_count": 0, "outputs": [] }, + { + "cell_type": "markdown", + "metadata": { + "id": "zJzZay89M_At", + "colab_type": "text" + }, + "source": [ + "##Método da matriz de confusão" + ] + }, { "cell_type": "code", "metadata": { @@ -1334,6 +1354,16 @@ "execution_count": 0, "outputs": [] }, + { + "cell_type": "markdown", + "metadata": { + "id": "PWEgWSeDNGr_", + "colab_type": "text" + }, + "source": [ + "##Parametrização" + ] + }, { "cell_type": "code", "metadata": { @@ -1349,6 +1379,26 @@ "execution_count": 0, "outputs": [] }, + { + "cell_type": "markdown", + "metadata": { + "id": "kEc_2hzsNLRw", + "colab_type": "text" + }, + "source": [ + "##Carregamento dos dados (CSV pré processados)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vH3JKvNqMOq9", + "colab_type": "text" + }, + "source": [ + "Massao: fiz uma preparação básica dos dados com PostgreSQL, mas todas as transformações (basicamente adição de dummy columns) podem ser facilmente feitas via Pandas." + ] + }, { "cell_type": "code", "metadata": { @@ -2827,6 +2877,16 @@ } ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "dinkGY6gNT6r", + "colab_type": "text" + }, + "source": [ + "##Random Forest" + ] + }, { "cell_type": "code", "metadata": { @@ -2896,6 +2956,16 @@ } ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "zBLktBfYMnzH", + "colab_type": "text" + }, + "source": [ + "Massao: Como não foi feita nenhuma preparação dos dados, o resultado é mediocre. Pode ser melhorado." + ] + }, { "cell_type": "code", "metadata": { From e0395463ab28cb67e26f6b895be1ea0cdb8adb28 Mon Sep 17 00:00:00 2001 From: Marcelo Massao Kataoka Higaskino Date: Wed, 13 Nov 2019 19:25:38 +0000 Subject: [PATCH 18/35] Added Decision Tree. --- dswp_grupo1.ipynb | 148 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 148 insertions(+) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index 9919afd7e..854192541 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -1234,6 +1234,7 @@ "\n", "np.set_printoptions(suppress=True)\n", "\n", + "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "import warnings\n", @@ -2877,6 +2878,153 @@ } ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "BI3DgCn_N9lV", + "colab_type": "text" + }, + "source": [ + "##Decision Tree" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6NuSOifMN_rk", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 121 + }, + "outputId": "57c72fdb-9092-4ba0-f121-b81e76a36c45" + }, + "source": [ + "# Instancia...\n", + "Model_DT= DecisionTreeClassifier(criterion='gini', splitter='best', \n", + " max_depth=None, min_samples_split=2, \n", + " min_samples_leaf=1, min_weight_fraction_leaf=0.0, \n", + " max_features=None, random_state= i_Seed, \n", + " max_leaf_nodes=None, min_impurity_decrease=0.0, \n", + " min_impurity_split=None, class_weight=None, \n", + " presort=False)\n", + "\n", + "# Treina...\n", + "Model_DT.fit(X_train, y_train)" + ], + "execution_count": 53, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False,\n", + " random_state=20111974, splitter='best')" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 53 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "RsiADVY2OKgY", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "outputId": "4d8fafdf-ad77-4e1e-bc91-7866374957ec" + }, + "source": [ + "# Cross-Validation com 10 folds\n", + "a_Scores_CV = cross_val_score(Model_DT, X_train, y_train, cv= i_CV)\n", + "print(f'Média das Acurácias calculadas pelo CV....: {100*round(a_Scores_CV.mean(),4)}')\n", + "print(f'std médio das Acurácias calculadas pelo CV: {100*round(a_Scores_CV.std(),4)}')" + ], + "execution_count": 54, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Média das Acurácias calculadas pelo CV....: 81.67\n", + "std médio das Acurácias calculadas pelo CV: 0.84\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CBw5nAhlOUWT", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "outputId": "7c8d7ea0-6717-4e1b-fc6b-aa01102d21da" + }, + "source": [ + "print(f'Acurácias: {a_Scores_CV}')" + ], + "execution_count": 55, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Acurácias: [0.80810562 0.81449631 0.81695332 0.80804668 0.82371007 0.81234644\n", + " 0.8215602 0.82463145 0.83261671 0.8046683 ]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gvKT9gZaOZXO", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 538 + }, + "outputId": "f8a97f50-8a2a-4c4b-a11b-c29f2449045f" + }, + "source": [ + "# Faz predições...\n", + "y_pred = Model_DT.predict(X_test)\n", + "\n", + "# Confusion Matrix\n", + "cf_matrix = confusion_matrix(y_test, y_pred)\n", + "cf_labels = ['True Neg','False Pos','False Neg','True Pos']\n", + "cf_categories = ['Zero', 'One']\n", + "make_confusion_matrix(cf_matrix, group_names= cf_labels, categories= cf_categories)" + ], + "execution_count": 56, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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fZWbtA/8cUtHhi8zsV2CV/7mGmb0Z9IAiIiJp4HXga+fc2UANYBnQC5jpnKsM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\nkc9Qx8fHc0uTq3h7yKeUKFk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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, { "cell_type": "markdown", "metadata": { From 49bc3d44222372d8a282d80195f07168e3853d99 Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Wed, 13 Nov 2019 21:50:51 +0000 Subject: [PATCH 19/35] Created using Colaboratory --- dswp_grupo1.ipynb | 76 +++++++++++++++++++++++------------------------ 1 file changed, 38 insertions(+), 38 deletions(-) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index 854192541..a0e4c15b0 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -21,7 +21,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { @@ -51,7 +51,7 @@ "colab_type": "text" }, "source": [ - "> O que se deseja alcançar a partir de uma perspectiva de negócios." + "> O que se deseja alcançar a partir de uma perspectiva de negócios. ..." ] }, { @@ -1405,17 +1405,17 @@ "metadata": { "id": "S3YDfY4nA-bw", "colab_type": "code", + "outputId": "83b6ea98-6660-4745-a979-beb94e54b053", "colab": { "base_uri": "https://localhost:8080/", "height": 287 - }, - "outputId": "83b6ea98-6660-4745-a979-beb94e54b053" + } }, "source": [ "X_train = pd.read_csv('https://raw.githubusercontent.com/massaoHigaskino/DSWP/lectures/project/Execution/adult_50k/datascience_adult_50k_v_t1_adult_data_dataset.csv', index_col= 'id');\n", "X_train.head()" ], - "execution_count": 40, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -2056,17 +2056,17 @@ "metadata": { "id": "jWmSQdveC-NP", "colab_type": "code", + "outputId": "bf9e485f-e926-4183-f0ce-2ddd19052e6f", "colab": { "base_uri": "https://localhost:8080/", "height": 238 - }, - "outputId": "bf9e485f-e926-4183-f0ce-2ddd19052e6f" + } }, "source": [ "y_train = pd.read_csv('https://raw.githubusercontent.com/massaoHigaskino/DSWP/lectures/project/Execution/adult_50k/datascience_adult_50k_v_t1_adult_data_target.csv', index_col= 'id');\n", "y_train.head()" ], - "execution_count": 39, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -2144,17 +2144,17 @@ "metadata": { "id": "NlRs-MXBC-4N", "colab_type": "code", + "outputId": "d57aa85d-f948-4b89-8093-d3b91d50e03d", "colab": { "base_uri": "https://localhost:8080/", "height": 287 - }, - "outputId": "d57aa85d-f948-4b89-8093-d3b91d50e03d" + } }, "source": [ "X_test = pd.read_csv('https://raw.githubusercontent.com/massaoHigaskino/DSWP/lectures/project/Execution/adult_50k/datascience_adult_50k_v_t1_adult_test_dataset.csv', index_col= 'id');\n", "X_test.head()" ], - "execution_count": 38, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -2795,17 +2795,17 @@ "metadata": { "id": "ft_PmPkeC_LD", "colab_type": "code", + "outputId": "3dc4e61c-5f56-4d9b-ca58-24a5495ac495", "colab": { "base_uri": "https://localhost:8080/", "height": 238 - }, - "outputId": "3dc4e61c-5f56-4d9b-ca58-24a5495ac495" + } }, "source": [ "y_test = pd.read_csv('https://raw.githubusercontent.com/massaoHigaskino/DSWP/lectures/project/Execution/adult_50k/datascience_adult_50k_v_t1_adult_test_target.csv', index_col= 'id');\n", "y_test.head()" ], - "execution_count": 49, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -2893,11 +2893,11 @@ "metadata": { "id": "6NuSOifMN_rk", "colab_type": "code", + "outputId": "57c72fdb-9092-4ba0-f121-b81e76a36c45", "colab": { "base_uri": "https://localhost:8080/", "height": 121 - }, - "outputId": "57c72fdb-9092-4ba0-f121-b81e76a36c45" + } }, "source": [ "# Instancia...\n", @@ -2912,7 +2912,7 @@ "# Treina...\n", "Model_DT.fit(X_train, y_train)" ], - "execution_count": 53, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -2938,11 +2938,11 @@ "metadata": { "id": "RsiADVY2OKgY", "colab_type": "code", + "outputId": "4d8fafdf-ad77-4e1e-bc91-7866374957ec", "colab": { "base_uri": "https://localhost:8080/", "height": 52 - }, - "outputId": "4d8fafdf-ad77-4e1e-bc91-7866374957ec" + } }, "source": [ "# Cross-Validation com 10 folds\n", @@ -2950,7 +2950,7 @@ "print(f'Média das Acurácias calculadas pelo CV....: {100*round(a_Scores_CV.mean(),4)}')\n", "print(f'std médio das Acurácias calculadas pelo CV: {100*round(a_Scores_CV.std(),4)}')" ], - "execution_count": 54, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -2967,16 +2967,16 @@ "metadata": { "id": "CBw5nAhlOUWT", "colab_type": "code", + "outputId": "7c8d7ea0-6717-4e1b-fc6b-aa01102d21da", "colab": { "base_uri": "https://localhost:8080/", "height": 52 - }, - "outputId": "7c8d7ea0-6717-4e1b-fc6b-aa01102d21da" + } }, "source": [ "print(f'Acurácias: {a_Scores_CV}')" ], - "execution_count": 55, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -2993,11 +2993,11 @@ "metadata": { "id": "gvKT9gZaOZXO", "colab_type": "code", + "outputId": "f8a97f50-8a2a-4c4b-a11b-c29f2449045f", "colab": { "base_uri": "https://localhost:8080/", "height": 538 - }, - "outputId": "f8a97f50-8a2a-4c4b-a11b-c29f2449045f" + } }, "source": [ "# Faz predições...\n", @@ -3009,7 +3009,7 @@ "cf_categories = ['Zero', 'One']\n", "make_confusion_matrix(cf_matrix, group_names= cf_labels, categories= cf_categories)" ], - "execution_count": 56, + "execution_count": 0, "outputs": [ { "output_type": "display_data", @@ -3040,11 +3040,11 @@ "metadata": { "id": "DM6ficlv_9oB", "colab_type": "code", + "outputId": "8796857a-49fb-4b3a-cf7f-2686d535040b", "colab": { "base_uri": "https://localhost:8080/", "height": 139 - }, - "outputId": "8796857a-49fb-4b3a-cf7f-2686d535040b" + } }, "source": [ "# Instancia...\n", @@ -3053,7 +3053,7 @@ "# Treina...\n", "Model_RF.fit(X_train, y_train)" ], - "execution_count": 41, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -3080,11 +3080,11 @@ "metadata": { "id": "DYeWL0k6IVl9", "colab_type": "code", + "outputId": "9f4b5a05-1325-4edc-bb08-8a1db18a6d0a", "colab": { "base_uri": "https://localhost:8080/", "height": 52 - }, - "outputId": "9f4b5a05-1325-4edc-bb08-8a1db18a6d0a" + } }, "source": [ "# Cross-Validation com 10 folds\n", @@ -3092,7 +3092,7 @@ "print(f'Média das Acurácias calculadas pelo CV....: {100*round(a_Scores_CV.mean(),4)}')\n", "print(f'std médio das Acurácias calculadas pelo CV: {100*round(a_Scores_CV.std(),4)}')" ], - "execution_count": 45, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -3119,16 +3119,16 @@ "metadata": { "id": "NrZdl0A8JUTU", "colab_type": "code", + "outputId": "b65a44d6-0dca-4cb8-f7d1-c1e69e7e3e84", "colab": { "base_uri": "https://localhost:8080/", "height": 52 - }, - "outputId": "b65a44d6-0dca-4cb8-f7d1-c1e69e7e3e84" + } }, "source": [ "print(f'Acurácias: {a_Scores_CV}')" ], - "execution_count": 46, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -3145,11 +3145,11 @@ "metadata": { "id": "q42UUhYbJVdE", "colab_type": "code", + "outputId": "f0c5f632-a8a0-46ee-9f04-16b1db300106", "colab": { "base_uri": "https://localhost:8080/", "height": 538 - }, - "outputId": "f0c5f632-a8a0-46ee-9f04-16b1db300106" + } }, "source": [ "# Faz predições...\n", @@ -3161,7 +3161,7 @@ "cf_categories = ['Zero', 'One']\n", "make_confusion_matrix(cf_matrix, group_names= cf_labels, categories= cf_categories)" ], - "execution_count": 50, + "execution_count": 0, "outputs": [ { "output_type": "display_data", From aaf9290da316a228631b2e0f975e06c6e4acc375 Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Wed, 13 Nov 2019 21:52:51 +0000 Subject: [PATCH 20/35] Created using Colaboratory --- dswp_grupo1.ipynb | 140 ++++++++++++++++++++++++++++++---------------- 1 file changed, 91 insertions(+), 49 deletions(-) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index a0e4c15b0..76faf23a4 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -51,7 +51,17 @@ "colab_type": "text" }, "source": [ - "> O que se deseja alcançar a partir de uma perspectiva de negócios. ..." + "> **Objstivo desta fase:** Compreender o que se deseja alcançar ou conhecer a partir de uma perspectiva de negócios." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aw0OMuB7ndqb", + "colab_type": "text" + }, + "source": [ + "Desenvolver um Modelo de *Machine Learning* que seja capaz de estimar, com significativa acurácia, se uma pessoa ganhará, no ano, mais que 50k dólares." ] }, { @@ -71,7 +81,7 @@ "colab_type": "text" }, "source": [ - "> Coleta e exploração dos dados. Exploração do dataframe, descobrir relações e descrever os dados em geral. Utilize-se das técnicas de Data Visualization para detectar relações relevantes entre as variáveis, desequilíbrios de classes e identificar variáveis mais importantes." + "> **Objetivo desta fase:** Coleta e exploração dos dados. Exploração do dataframe, descobrir relações e descrever os dados em geral. Utilize-se das técnicas de Data Visualization para detectar relações relevantes entre as variáveis, desequilíbrios de classes e identificar variáveis mais importantes." ] }, { @@ -104,7 +114,7 @@ "metadata": { "id": "0YTMtMPRbSYD", "colab_type": "code", - "outputId": "bff1944b-60d1-442d-9b38-11a1c0c40d7f", + "outputId": "d9174f8d-411b-44cc-e861-569fe3e48a24", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -114,7 +124,7 @@ "df_50k = pd.read_csv('https://raw.githubusercontent.com/MathMachado/DSWP/master/Dataframes/adult.data')\n", "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " ], - "execution_count": 0, + "execution_count": 2, "outputs": [ { "output_type": "stream", @@ -130,17 +140,17 @@ "metadata": { "id": "Lxq7B1Vnv1Ys", "colab_type": "code", - "outputId": "5dd54c0d-44df-4e4d-af49-aa41467e1c8a", + "outputId": "e59f8154-f6dd-4a8f-dd52-380686061944", "colab": { "base_uri": "https://localhost:8080/", - "height": 400 + "height": 296 } }, "source": [ "#Detecção de que o ficheiro não tem header\n", "df_50k.head(5)" ], - "execution_count": 0, + "execution_count": 3, "outputs": [ { "output_type": "execute_result", @@ -290,7 +300,7 @@ "metadata": { "tags": [] }, - "execution_count": 50 + "execution_count": 3 } ] }, @@ -299,7 +309,7 @@ "metadata": { "id": "HBKqXT9Bo-BC", "colab_type": "code", - "outputId": "777064e5-b82b-4959-9005-6b098ec8d060", + "outputId": "37dcbfcd-d9f3-4850-8bb3-c76e4f50c701", "colab": { "base_uri": "https://localhost:8080/", "height": 269 @@ -325,7 +335,7 @@ " ]\n", "l_column" ], - "execution_count": 0, + "execution_count": 4, "outputs": [ { "output_type": "execute_result", @@ -351,7 +361,7 @@ "metadata": { "tags": [] }, - "execution_count": 51 + "execution_count": 4 } ] }, @@ -364,8 +374,7 @@ }, "source": [ "#Adição de header ao ficheiro\n", - "df_50k = pd.read_csv('https://raw.githubusercontent.com/MathMachado/DSWP/master/Dataframes/adult.data'\n", - " ,names = l_column)" + "df_50k = pd.read_csv('https://raw.githubusercontent.com/MathMachado/DSWP/master/Dataframes/adult.data',names = l_column)" ], "execution_count": 0, "outputs": [] @@ -375,7 +384,7 @@ "metadata": { "id": "AjswmEdb4R8X", "colab_type": "code", - "outputId": "d78c94ef-c791-45eb-96d5-b68d3a92b769", + "outputId": "24dcab25-c71b-4eed-e187-32369149b52c", "colab": { "base_uri": "https://localhost:8080/", "height": 353 @@ -384,7 +393,7 @@ "source": [ "df_50k.info()" ], - "execution_count": 0, + "execution_count": 7, "outputs": [ { "output_type": "stream", @@ -615,7 +624,7 @@ "metadata": { "id": "m2p6SOzbo99T", "colab_type": "code", - "outputId": "45563800-4f7c-4973-b8a2-d013946de85e", + "outputId": "f6174667-c01d-4b40-889d-7ed02774ef6e", "colab": { "base_uri": "https://localhost:8080/", "height": 101 @@ -625,7 +634,7 @@ "#Colunas do DataFrame\n", "df_50k.columns" ], - "execution_count": 0, + "execution_count": 8, "outputs": [ { "output_type": "execute_result", @@ -641,7 +650,7 @@ "metadata": { "tags": [] }, - "execution_count": 23 + "execution_count": 8 } ] }, @@ -650,7 +659,7 @@ "metadata": { "id": "dMDVRA8eo96a", "colab_type": "code", - "outputId": "a36b8842-a929-43f2-f42c-9386f449c0d6", + "outputId": "725083f9-46d1-4008-ac2e-9f725be621da", "colab": { "base_uri": "https://localhost:8080/", "height": 286 @@ -659,7 +668,7 @@ "source": [ "df_50k.dtypes" ], - "execution_count": 0, + "execution_count": 9, "outputs": [ { "output_type": "execute_result", @@ -686,16 +695,26 @@ "metadata": { "tags": [] }, - "execution_count": 24 + "execution_count": 9 } ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "GG7Xl9IJu8HN", + "colab_type": "text" + }, + "source": [ + "###Verificação do conteúdo das colunas" + ] + }, { "cell_type": "code", "metadata": { "id": "5SbUk5Srzqok", "colab_type": "code", - "outputId": "dd593c3f-b5db-4409-c3c8-92e74c4fc6ec", + "outputId": "54f6557b-bc65-487f-9095-e5af3d0dd5b8", "colab": { "base_uri": "https://localhost:8080/", "height": 185 @@ -705,7 +724,7 @@ "#Valores distintos de WorkClass\n", "df_50k['workclass'].value_counts()" ], - "execution_count": 0, + "execution_count": 13, "outputs": [ { "output_type": "execute_result", @@ -726,7 +745,7 @@ "metadata": { "tags": [] }, - "execution_count": 30 + "execution_count": 13 } ] }, @@ -735,7 +754,7 @@ "metadata": { "id": "VYkfFI451R_m", "colab_type": "code", - "outputId": "648f0433-d0a0-44df-d05b-2991a577cb12", + "outputId": "926b2ca8-c367-44ea-b16b-86409e405991", "colab": { "base_uri": "https://localhost:8080/", "height": 302 @@ -745,7 +764,7 @@ "#Valores distintos de Education\n", "df_50k['education'].value_counts()" ], - "execution_count": 0, + "execution_count": 14, "outputs": [ { "output_type": "execute_result", @@ -773,7 +792,7 @@ "metadata": { "tags": [] }, - "execution_count": 32 + "execution_count": 14 } ] }, @@ -782,7 +801,7 @@ "metadata": { "id": "QJly0mWR1R8m", "colab_type": "code", - "outputId": "f9e901e2-7bd4-4ed1-8943-56f8bc64153c", + "outputId": "f7690474-7982-4808-8295-abf084e3dabc", "colab": { "base_uri": "https://localhost:8080/", "height": 151 @@ -792,7 +811,7 @@ "#Valores distintos de marital-status\n", "df_50k['marital-status'].value_counts()" ], - "execution_count": 0, + "execution_count": 15, "outputs": [ { "output_type": "execute_result", @@ -811,7 +830,7 @@ "metadata": { "tags": [] }, - "execution_count": 34 + "execution_count": 15 } ] }, @@ -820,7 +839,7 @@ "metadata": { "id": "4R_S4oaG1R6_", "colab_type": "code", - "outputId": "ba8bbbb4-a76e-4904-e18e-ddc3c47b5f28", + "outputId": "a3cbca4e-f373-4daa-d25d-a02f8f54c1d9", "colab": { "base_uri": "https://localhost:8080/", "height": 286 @@ -830,7 +849,7 @@ "#Valores distintos de occupation\n", "df_50k['occupation'].value_counts()" ], - "execution_count": 0, + "execution_count": 16, "outputs": [ { "output_type": "execute_result", @@ -857,7 +876,7 @@ "metadata": { "tags": [] }, - "execution_count": 35 + "execution_count": 16 } ] }, @@ -866,7 +885,7 @@ "metadata": { "id": "2q5DBeLM1R3m", "colab_type": "code", - "outputId": "6936346e-9e6c-411b-a88b-a9beffd2b53f", + "outputId": "f9ce12a3-dab3-4078-94b7-a02e34dad5ed", "colab": { "base_uri": "https://localhost:8080/", "height": 134 @@ -876,7 +895,7 @@ "#Valores distintos de relationship\n", "df_50k['relationship'].value_counts()" ], - "execution_count": 0, + "execution_count": 17, "outputs": [ { "output_type": "execute_result", @@ -894,7 +913,7 @@ "metadata": { "tags": [] }, - "execution_count": 36 + "execution_count": 17 } ] }, @@ -903,7 +922,7 @@ "metadata": { "id": "3nlhzegs3PD2", "colab_type": "code", - "outputId": "4a8319d4-ce95-40f3-bba5-2f49dd210b0d", + "outputId": "4e5c3fe1-5fc8-4265-d250-76853053b517", "colab": { "base_uri": "https://localhost:8080/", "height": 118 @@ -913,7 +932,7 @@ "#Valores distintos de race\n", "df_50k['race'].value_counts()" ], - "execution_count": 0, + "execution_count": 18, "outputs": [ { "output_type": "execute_result", @@ -930,7 +949,7 @@ "metadata": { "tags": [] }, - "execution_count": 37 + "execution_count": 18 } ] }, @@ -939,7 +958,7 @@ "metadata": { "id": "-mgIBM783PAl", "colab_type": "code", - "outputId": "1b6d93a0-fc30-4a98-81ee-d30b1aa23a6b", + "outputId": "f1108198-d31b-48d3-ba06-8850a1c63e5e", "colab": { "base_uri": "https://localhost:8080/", "height": 67 @@ -949,7 +968,7 @@ "#Valores distintos de sex\n", "df_50k['sex'].value_counts()" ], - "execution_count": 0, + "execution_count": 19, "outputs": [ { "output_type": "execute_result", @@ -963,7 +982,7 @@ "metadata": { "tags": [] }, - "execution_count": 38 + "execution_count": 19 } ] }, @@ -972,7 +991,7 @@ "metadata": { "id": "EjRcmMm73O-2", "colab_type": "code", - "outputId": "0c8b12c5-b77b-485d-afc3-d396ff914ac0", + "outputId": "7cacb002-dcf8-4e7a-fc4b-b252f8d06050", "colab": { "base_uri": "https://localhost:8080/", "height": 739 @@ -982,7 +1001,7 @@ "#Valores distintos de native-country\n", "df_50k['native-country'].value_counts()" ], - "execution_count": 0, + "execution_count": 20, "outputs": [ { "output_type": "execute_result", @@ -1022,8 +1041,8 @@ " Hong 20\n", " Cambodia 19\n", " Trinadad&Tobago 19\n", - " Laos 18\n", " Thailand 18\n", + " Laos 18\n", " Yugoslavia 16\n", " Outlying-US(Guam-USVI-etc) 14\n", " Hungary 13\n", @@ -1036,7 +1055,7 @@ "metadata": { "tags": [] }, - "execution_count": 42 + "execution_count": 20 } ] }, @@ -1045,7 +1064,7 @@ "metadata": { "id": "zkL_oVwT3O73", "colab_type": "code", - "outputId": "36efd24a-9fb7-4b41-a08a-7a7dbdf446de", + "outputId": "aba41a8a-666c-48e7-9548-28f8e4fc8b6c", "colab": { "base_uri": "https://localhost:8080/", "height": 67 @@ -1055,7 +1074,7 @@ "#Valores distintos de flag_50k\n", "df_50k['flag_50k'].value_counts()" ], - "execution_count": 0, + "execution_count": 21, "outputs": [ { "output_type": "execute_result", @@ -1069,10 +1088,33 @@ "metadata": { "tags": [] }, - "execution_count": 43 + "execution_count": 21 } ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "evJ7TDj6vEu-", + "colab_type": "text" + }, + "source": [ + "###Verificar se há registos em duplicidade" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6mdOurBJvIed", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, { "cell_type": "markdown", "metadata": { From b29b299d6c795e2ea2b6bbd7a2aa89b6530ee30a Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Thu, 14 Nov 2019 01:07:56 +0000 Subject: [PATCH 21/35] Created using Colaboratory --- dswp_grupo1.ipynb | 2008 +++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 1867 insertions(+), 141 deletions(-) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index 76faf23a4..bb5545e55 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -5,7 +5,9 @@ "colab": { "name": "dswp_grupo1.ipynb", "provenance": [], - "collapsed_sections": [], + "collapsed_sections": [ + "evJ7TDj6vEu-" + ], "include_colab_link": true }, "kernelspec": { @@ -114,7 +116,7 @@ "metadata": { "id": "0YTMtMPRbSYD", "colab_type": "code", - "outputId": "d9174f8d-411b-44cc-e861-569fe3e48a24", + "outputId": "5b6e052c-97dc-478f-e5d4-77eaca4dee3d", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -124,7 +126,7 @@ "df_50k = pd.read_csv('https://raw.githubusercontent.com/MathMachado/DSWP/master/Dataframes/adult.data')\n", "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " ], - "execution_count": 2, + "execution_count": 149, "outputs": [ { "output_type": "stream", @@ -140,7 +142,7 @@ "metadata": { "id": "Lxq7B1Vnv1Ys", "colab_type": "code", - "outputId": "e59f8154-f6dd-4a8f-dd52-380686061944", + "outputId": "defbd658-3836-48b8-dfbe-5526a31b113f", "colab": { "base_uri": "https://localhost:8080/", "height": 296 @@ -150,7 +152,7 @@ "#Detecção de que o ficheiro não tem header\n", "df_50k.head(5)" ], - "execution_count": 3, + "execution_count": 151, "outputs": [ { "output_type": "execute_result", @@ -300,7 +302,7 @@ "metadata": { "tags": [] }, - "execution_count": 3 + "execution_count": 151 } ] }, @@ -309,7 +311,7 @@ "metadata": { "id": "HBKqXT9Bo-BC", "colab_type": "code", - "outputId": "37dcbfcd-d9f3-4850-8bb3-c76e4f50c701", + "outputId": "c3994495-e2ca-482d-db9b-5bbaf0edab08", "colab": { "base_uri": "https://localhost:8080/", "height": 269 @@ -335,7 +337,7 @@ " ]\n", "l_column" ], - "execution_count": 4, + "execution_count": 152, "outputs": [ { "output_type": "execute_result", @@ -361,7 +363,7 @@ "metadata": { "tags": [] }, - "execution_count": 4 + "execution_count": 152 } ] }, @@ -384,7 +386,7 @@ "metadata": { "id": "AjswmEdb4R8X", "colab_type": "code", - "outputId": "24dcab25-c71b-4eed-e187-32369149b52c", + "outputId": "7a6f26ec-fa3e-4bdd-cd1d-23fc47ac32a1", "colab": { "base_uri": "https://localhost:8080/", "height": 353 @@ -393,7 +395,7 @@ "source": [ "df_50k.info()" ], - "execution_count": 7, + "execution_count": 154, "outputs": [ { "output_type": "stream", @@ -428,7 +430,7 @@ "metadata": { "id": "CNyS0K4C4dfp", "colab_type": "code", - "outputId": "32176f45-33bf-44d1-f07f-1fc0bc31d708", + "outputId": "1ca0de0b-6db7-4ff5-d8ba-23d1a2961629", "colab": { "base_uri": "https://localhost:8080/", "height": 333 @@ -437,7 +439,7 @@ "source": [ "df_50k.head(5)" ], - "execution_count": 0, + "execution_count": 155, "outputs": [ { "output_type": "execute_result", @@ -587,44 +589,16 @@ "metadata": { "tags": [] }, - "execution_count": 54 + "execution_count": 155 } ] }, - { - "cell_type": "markdown", - "metadata": { - "id": "-P0EGSMywu0d", - "colab_type": "text" - }, - "source": [ - "**Dicionário dos dados** \n", - "* Age: idade da pessoa\n", - "* workclass: classificação do cargo profissional\n", - "* fnlwgt (final weight): ?\n", - "* education: grau de escolaridade\n", - "* education-num: ?\n", - "* marital-status: estado civil\n", - "* occupation: cargo profissional\n", - "* relationship: tipo de relacionamento\n", - "* race: raça\n", - "* sex: sexo\n", - "* capital-gain: ?\n", - "* capital-loss: ?\n", - "* hours-per-week: horas por semana trabalhadas?\n", - "* native-country: país de nascimento\n", - "* flag_50k: indicativo se ganha menos (<=50k) ou mais (>50k) que 50.000 dinheiros\n", - "\n", - "\n", - "\n" - ] - }, { "cell_type": "code", "metadata": { "id": "m2p6SOzbo99T", "colab_type": "code", - "outputId": "f6174667-c01d-4b40-889d-7ed02774ef6e", + "outputId": "0cf40bec-ab22-4a2d-ef4b-6fc0ad4b62a2", "colab": { "base_uri": "https://localhost:8080/", "height": 101 @@ -634,7 +608,7 @@ "#Colunas do DataFrame\n", "df_50k.columns" ], - "execution_count": 8, + "execution_count": 156, "outputs": [ { "output_type": "execute_result", @@ -650,7 +624,7 @@ "metadata": { "tags": [] }, - "execution_count": 8 + "execution_count": 156 } ] }, @@ -659,7 +633,7 @@ "metadata": { "id": "dMDVRA8eo96a", "colab_type": "code", - "outputId": "725083f9-46d1-4008-ac2e-9f725be621da", + "outputId": "fcb7300f-b5e1-47ff-e29a-31066d16b4c2", "colab": { "base_uri": "https://localhost:8080/", "height": 286 @@ -668,7 +642,7 @@ "source": [ "df_50k.dtypes" ], - "execution_count": 9, + "execution_count": 157, "outputs": [ { "output_type": "execute_result", @@ -695,7 +669,7 @@ "metadata": { "tags": [] }, - "execution_count": 9 + "execution_count": 157 } ] }, @@ -754,7 +728,7 @@ "metadata": { "id": "VYkfFI451R_m", "colab_type": "code", - "outputId": "926b2ca8-c367-44ea-b16b-86409e405991", + "outputId": "8625b53b-9ebd-42cd-836e-f95790decfdd", "colab": { "base_uri": "https://localhost:8080/", "height": 302 @@ -764,35 +738,82 @@ "#Valores distintos de Education\n", "df_50k['education'].value_counts()" ], - "execution_count": 14, + "execution_count": 184, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "9 10501\n", + "10 7291\n", + "13 5355\n", + "14 1723\n", + "11 1382\n", + "7 1175\n", + "12 1067\n", + "6 933\n", + "4 646\n", + "15 576\n", + "5 514\n", + "8 433\n", + "16 413\n", + "3 333\n", + "2 168\n", + "1 51\n", + "Name: education-num, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 184 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "q67Wyta2XwZx", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 302 + }, + "outputId": "1110034a-f333-433e-8c9c-dedd540e624e" + }, + "source": [ + "#Valores distintos de Education-num\n", + "df_50k['education-num'].value_counts()" + ], + "execution_count": 185, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - " HS-grad 10501\n", - " Some-college 7291\n", - " Bachelors 5355\n", - " Masters 1723\n", - " Assoc-voc 1382\n", - " 11th 1175\n", - " Assoc-acdm 1067\n", - " 10th 933\n", - " 7th-8th 646\n", - " Prof-school 576\n", - " 9th 514\n", - " 12th 433\n", - " Doctorate 413\n", - " 5th-6th 333\n", - " 1st-4th 168\n", - " Preschool 51\n", - "Name: education, dtype: int64" + "9 10501\n", + "10 7291\n", + "13 5355\n", + "14 1723\n", + "11 1382\n", + "7 1175\n", + "12 1067\n", + "6 933\n", + "4 646\n", + "15 576\n", + "5 514\n", + "8 433\n", + "16 413\n", + "3 333\n", + "2 168\n", + "1 51\n", + "Name: education-num, dtype: int64" ] }, "metadata": { "tags": [] }, - "execution_count": 14 + "execution_count": 185 } ] }, @@ -834,6 +855,43 @@ } ] }, + { + "cell_type": "code", + "metadata": { + "id": "7_Wo0troSfxq", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 134 + }, + "outputId": "68fbc763-b7a0-41e1-d4f8-8ba14fd238e5" + }, + "source": [ + "#Valores distintos de relationship\n", + "df_50k['relationship'].value_counts()" + ], + "execution_count": 173, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Husband 13193\n", + " Not-in-family 8305\n", + " Own-child 5068\n", + " Unmarried 3446\n", + " Wife 1568\n", + " Other-relative 981\n", + "Name: relationship, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 173 + } + ] + }, { "cell_type": "code", "metadata": { @@ -1064,136 +1122,1804 @@ "metadata": { "id": "zkL_oVwT3O73", "colab_type": "code", - "outputId": "aba41a8a-666c-48e7-9548-28f8e4fc8b6c", + "outputId": "e6cf0a8b-da3d-4d20-8b50-ec4879ba2dd9", "colab": { "base_uri": "https://localhost:8080/", - "height": 67 + "height": 34 } }, "source": [ "#Valores distintos de flag_50k\n", - "df_50k['flag_50k'].value_counts()" + "df_50k['flag_50k'].value_counts().keys()" ], - "execution_count": 21, + "execution_count": 182, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - " <=50K 24720\n", - " >50K 7841\n", - "Name: flag_50k, dtype: int64" + "Index([' <=50K', ' >50K'], dtype='object')" ] }, "metadata": { "tags": [] }, - "execution_count": 21 + "execution_count": 182 } ] }, - { - "cell_type": "markdown", - "metadata": { - "id": "evJ7TDj6vEu-", - "colab_type": "text" - }, - "source": [ - "###Verificar se há registos em duplicidade" - ] - }, { "cell_type": "code", "metadata": { - "id": "6mdOurBJvIed", + "id": "2ng16_UcPOCS", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 218 + }, + "outputId": "7934af53-ef4d-419a-c5e3-c6eac24dd288" }, "source": [ - "" + "#Valores distintos de hours-per-week\n", + "df_50k['hours-per-week'].value_counts()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 166, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "40 15217\n", + "50 2819\n", + "45 1824\n", + "60 1475\n", + "35 1297\n", + " ... \n", + "92 1\n", + "94 1\n", + "87 1\n", + "74 1\n", + "82 1\n", + "Name: hours-per-week, Length: 94, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 166 + } + ] }, { "cell_type": "markdown", "metadata": { - "id": "PYiZL-v7X6EC", + "id": "y88wNvojUWmk", "colab_type": "text" }, "source": [ - "#**3DP - DATA PREPARATION**" + "> **Pode-se notar que o valor \"?\" aparece nas colunas \"workclass\", \"occupation\" e \"native-country\".**\n", + "\n", + "* Eliminar registos com \"?\" (já que podemos considerá-lo como um NaN)?\n", + "* Ou substituitr \"?\" pelo valor de maior frequencia (moda)?\n", + "\n" ] }, { "cell_type": "markdown", "metadata": { - "id": "OX64mjTvZy0n", + "id": "x3984I93V1VW", "colab_type": "text" }, "source": [ - "> Coletar, preparar, transformar e limpar dados: remover duplicatas, corrigir erros, lidar com Missing Values, normalização, conversões de tipo de dados e etc.\n", - "\n", - "* **3DP_Feature Engineering**: derivar variaveis\n", - "* **3DP_Missing Values Handling**: identificar e tratar os Missing Values\n", - "* **3DP_Outliers Handling**: identificar e tratar os Outlier\n", - "* **3DP_Data Transformation**: colocar as variáveis numa mesma escala (RobustScaler?)\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "###Criar nova coluna: \"map\" para o indicativo de 50k\n", + " " ] }, { "cell_type": "code", "metadata": { - "id": "9CtlGkaKz-uj", + "id": "YUk0xnfaV4yX", "colab_type": "code", - "outputId": "687f6c5c-2692-449d-95ab-85d3adaafef5", "colab": { "base_uri": "https://localhost:8080/", - "height": 67 - } + "height": 333 + }, + "outputId": "232658ca-2b97-400c-87e0-126989d18c56" }, "source": [ - "#Lista com os nomes da colunas por tipo de dados\n", - "df_50k.select_dtypes(include=['float','number']).columns" + "#Criação de nova coluna, onde 0 = <=50k e 1 = >50k\n", + "df_50k['ind_50k'] = df_50k['flag_50k'].map({' <=50K': 0, ' >50K': 1})\n", + "#df_50k['ind_50k'] = df_50k['flag_50k'].apply(lambda x: 0 if x == ' <=50K' else 1)\n", + "\n", + "df_50k.head(5)" ], - "execution_count": 0, + "execution_count": 183, "outputs": [ { "output_type": "execute_result", "data": { - "text/plain": [ - "Index(['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss',\n", - " 'hours-per-week'],\n", - " dtype='object')" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 55 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "vhlHOvTa5Atd", - "colab_type": "code", - "colab": {} - }, - "source": [ - "#criar nova coluna?\n", - "#df_50k['ind_50k'] = df_50k['flag_50k'].apply(lambda x: 0 if x == '<=50K' else 1)" - ], - "execution_count": 0, - "outputs": [] + "text/html": [ + "
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" + ], + "text/plain": [ + " age workclass fnlwgt ... native-country flag_50k ind_50k\n", + "0 39 State-gov 77516 ... United-States <=50K 0\n", + "1 50 Self-emp-not-inc 83311 ... United-States <=50K 0\n", + "2 38 Private 215646 ... United-States <=50K 0\n", + "3 53 Private 234721 ... United-States <=50K 0\n", + "4 28 Private 338409 ... Cuba <=50K 0\n", + "\n", + "[5 rows x 16 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 183 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3UotJZnQX-1z", + "colab_type": "text" + }, + "source": [ + "###Dicionário de Dados" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-P0EGSMywu0d", + "colab_type": "text" + }, + "source": [ + "**Dicionário dos dados** \n", + "* Age: idade da pessoa\n", + "* workclass: classificação do cargo profissional\n", + "* fnlwgt (final weight): peso atribuido por uma instituição específica\n", + "* education: grau de escolaridade\n", + "* education-num: tempo de estudo em anos (?)\n", + "* marital-status: estado civil\n", + "* occupation: cargo profissional\n", + "* relationship: papel em que a pessoa assume na família\n", + "* race: raça\n", + "* sex: sexo\n", + "* capital-gain: ganhos\n", + "* capital-loss: perdas\n", + "* hours-per-week: horas trabalhadas por semana\n", + "* native-country: país de nascimento\n", + "* flag_50k: marcador de valor: se menor (<=50k) ou maior (>50k) que 50.000 de dólares\n", + "* ind_50k: indicador, onde 0 = <=50k e 1 = >50k\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XlZwcwXDBtxs", + "colab_type": "text" + }, + "source": [ + "###Correlação: Heatmap\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ECXmYAblBvI3", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "#sns.set(font_scale=1)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Vu2p_1-fDH_3", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 366 + }, + "outputId": "5732ce9c-71ed-45ef-ab7c-fd02e4e45b69" + }, + "source": [ + "corr = df_50k.corr()\n", + "sns.heatmap(corr.abs(), \n", + " xticklabels=corr.columns,\n", + " yticklabels=corr.columns,\n", + " cmap=\"RdPu\",\n", + " annot=True,\n", + " fmt= '.2f'\n", + " )" + ], + "execution_count": 187, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 187 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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jEYAdu/fg612BalWq6K/L1xc/H5OugBcIzxYxAITv2UeXDu0L0OVNtSqVddUF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\nqFhEMsitfipyqjoKGCUi/1LVB/yK4xAWiUg1VV3rdyB5TBSRh3A/xjk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" + ], + "text/plain": [ + " age fnlwgt ... hours-per-week ind_50k\n", + "age NaN -0.076646 ... 0.068756 0.234037\n", + "fnlwgt NaN NaN ... -0.018768 -0.009463\n", + "education-num NaN NaN ... 0.148123 0.335154\n", + "capital-gain NaN NaN ... 0.078409 0.223329\n", + "capital-loss NaN NaN ... 0.054256 0.150526\n", + "hours-per-week NaN NaN ... NaN 0.229689\n", + "ind_50k NaN NaN ... NaN NaN\n", + "\n", + "[7 rows x 7 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 188 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-ohsbrRdGmJJ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 118 + }, + "outputId": "d07a691e-1411-47f3-8160-d6e4d96493be" + }, + "source": [ + "set_Colunas_Correlacionadas = set()\n", + "\n", + "for i in range(len(Matrix_Corr.columns)):\n", + " for j in range(i):\n", + " if abs(Matrix_Corr.iloc[i, j]) != 0:\n", + " colname = Matrix_Corr.columns[i]\n", + " set_Colunas_Correlacionadas.add(colname)\n", + "\n", + "set_Colunas_Correlacionadas" + ], + "execution_count": 189, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'capital-gain',\n", + " 'capital-loss',\n", + " 'education-num',\n", + " 'fnlwgt',\n", + " 'hours-per-week',\n", + " 'ind_50k'}" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 189 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "_0MZj06uIhtG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 692 + }, + "outputId": "d0195f71-384e-4525-ca85-06835aa9fe31" + }, + "source": [ + "fig, ax = plt.subplots(figsize=(10, 10)) \n", + "mask = np.zeros_like(df_50k.corr().abs())\n", + "mask[np.triu_indices_from(mask)] = 1\n", + "sns.heatmap(df_50k.corr().abs(), mask= mask, ax= ax, cmap='RdPu', annot= True, fmt= '.2f', center= 0)" + ], + "execution_count": 190, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 190 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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/+Gu6PnIJhWULSQ7cb0CTwkmHQGcPXR/8Gb2/uo/W1x0z4Pm205dSvPvxekY9cpKEtrOW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\nX46IDyaOZjbgvBOild0SSQfVDiQdCJS9LtUZZLXUdiLrmej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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gq4Qg2Lu2miR", + "colab_type": "text" + }, + "source": [ + "###Gráficos" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "g00WmJdX2_o9", + "colab_type": "code", + "colab": {} + }, + "source": [ + "#Subset do DataFrame (Series)\n", + "workclass = df_50k['workclass']\n", + "education = df_50k['education']\n", + "educationN = df_50k['education-num']\n", + "race = df_50k['race']\n", + "sex = df_50k['sex']\n", + "natcountry = df_50k['native-country']\n", + "hoursweek = df_50k['hours-per-week'] \n", + "relationship = df_50k['relationship']" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iQwZoRaMNvAr", + "colab_type": "text" + }, + "source": [ + "**Distribuição da Raça**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "fTUWOuBU30or", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 301 + }, + "outputId": "4607aa92-b753-4123-f578-0274179472ad" + }, + "source": [ + "plt.hist(race)\n", + "plt.title('Distribuição - Raça')\n", + "plt.xlabel('Raça')\n", + "plt.ylabel('Frequencia')\n", + "plt.show()" + ], + "execution_count": 192, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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B6bn4TmB0RGyT3x8AXDLIMjMzq0iRFs73SaMNnAEsRxo/bQrwg/4sHBGnArsBbwGmRsQc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\ndwEz6vb5blLdTSU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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RB8RrJIOP_QE", + "colab_type": "text" + }, + "source": [ + "**Histograma das Horas por Semana**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "OzAQWltWQDVa", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 301 + }, + "outputId": "82876343-91fa-4cd3-ad26-e3e534468b43" + }, + "source": [ + "plt.hist(hoursweek)\n", + "plt.title('Distribuição - Horas por Semana')\n", + "plt.xlabel('Horas por Semana')\n", + "plt.ylabel('Frequencia')\n", + "plt.show()" + ], + "execution_count": 193, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hhavvM_fN3aY", + "colab_type": "text" + }, + "source": [ + "**Relação do Grau de Escolaridade pela Raça**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VE6vRQbl8Yb7", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 481 + }, + "outputId": "3629f987-82a7-4dde-e0ca-ba9df8baee22" + }, + "source": [ + "#Relação da Escolaridade e Raça\n", + "fig, ax = plt.subplots(figsize=(15,7))\n", + "df_50k.groupby(['education','race']).count()['age'].unstack().plot(ax=ax, title='Raça vs Escolaridade')" + ], + "execution_count": 203, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 203 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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p93vk5sd+xPxwUy1kJS20pOut7yKRSEas78pkmbIGLxKJ7PV92GUN3l76k9LO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\n/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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "no6-wtR0aXom", + "colab_type": "text" + }, + "source": [ + "**Relação por Tipo de Familiar pelo Indicativo dos 50k**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lsp3XAlTRd3Y", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 481 + }, + "outputId": "3b1670b5-3f89-4896-d618-560cc6f2f67d" + }, + "source": [ + "#Relação de horas trabalhadas pelo indicatico de valor\n", + "fig, ax = plt.subplots(figsize=(15,7))\n", + "df_50k.groupby(['relationship','ind_50k']).count()['age'].unstack().plot(ax=ax, title='Horas Trabalhadas vs Indicativo do Valor')" + ], + "execution_count": 206, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 206 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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KWwwfPpJ77vlDqZ777rtv57zzzucXvxhaFi/llGhaqchRBIJBCooCFBYFyI/8u7DIT0FR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\n5cE7+X5sfy21nz6lb8ik0+rbO4WhhWm8+/k2jlT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Kda3m07Y1fyDEn19fzd4D1Xxv6iBy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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PYiZL-v7X6EC", + "colab_type": "text" + }, + "source": [ + "#**3DP - DATA PREPARATION**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OX64mjTvZy0n", + "colab_type": "text" + }, + "source": [ + "> **Objetivos desta fase:** Coletar, preparar, transformar e limpar dados: remover duplicatas, corrigir erros, lidar com Missing Values, normalização, conversões de tipo de dados e etc.\n", + "\n", + "* **3DP_Feature Engineering**: derivar variaveis\n", + "* **3DP_Missing Values Handling**: identificar e tratar os Missing Values\n", + "* **3DP_Outliers Handling**: identificar e tratar os Outlier\n", + "* **3DP_Data Transformation**: colocar as variáveis numa mesma escala (RobustScaler?)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o1-4KN7BcBe6", + "colab_type": "text" + }, + "source": [ + "##Estatísticas" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BaRNhWV1cFlr", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 284 + }, + "outputId": "c32846d7-0432-4479-b292-226e6ed97f8d" + }, + "source": [ + "df_50k.describe()" + ], + "execution_count": 211, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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agefnlwgteducation-numcapital-gaincapital-losshours-per-weekind_50k
count32537.0000003.253700e+0432537.00000032537.00000032537.00000032537.00000032537.000000
mean38.5855491.897808e+0510.0818151078.44374187.36822740.4403290.240926
std13.6379841.055565e+052.5716337387.957424403.10183312.3468890.427652
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25%28.0000001.178270e+059.0000000.0000000.00000040.0000000.000000
50%37.0000001.783560e+0510.0000000.0000000.00000040.0000000.000000
75%48.0000002.369930e+0512.0000000.0000000.00000045.0000000.000000
max90.0000001.484705e+0616.00000099999.0000004356.00000099.0000001.000000
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" + ], + "text/plain": [ + " age fnlwgt ... hours-per-week ind_50k\n", + "count 32537.000000 3.253700e+04 ... 32537.000000 32537.000000\n", + "mean 38.585549 1.897808e+05 ... 40.440329 0.240926\n", + "std 13.637984 1.055565e+05 ... 12.346889 0.427652\n", + "min 17.000000 1.228500e+04 ... 1.000000 0.000000\n", + "25% 28.000000 1.178270e+05 ... 40.000000 0.000000\n", + "50% 37.000000 1.783560e+05 ... 40.000000 0.000000\n", + "75% 48.000000 2.369930e+05 ... 45.000000 0.000000\n", + "max 90.000000 1.484705e+06 ... 99.000000 1.000000\n", + "\n", + "[8 rows x 7 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 211 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "KZQOGm7XcrRf", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 166 + }, + "outputId": "7a867ba6-727c-4af1-b2e2-88180a901581" + }, + "source": [ + "df_50k.describe(include=[\"O\"])" + ], + "execution_count": 212, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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count325373253732537325373253732537325373253732537
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freq22673104941497041361318727795217752915324698
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" + ], + "text/plain": [ + " workclass education marital-status ... sex native-country flag_50k\n", + "count 32537 32537 32537 ... 32537 32537 32537\n", + "unique 9 16 7 ... 2 42 2\n", + "top Private HS-grad Married-civ-spouse ... Male United-States <=50K\n", + "freq 22673 10494 14970 ... 21775 29153 24698\n", + "\n", + "[4 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 212 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "evJ7TDj6vEu-", + "colab_type": "text" + }, + "source": [ + "###Verificar se há registos em duplicidade" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6mdOurBJvIed", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "ec658fe1-63de-46cc-e499-4ea51c5119c1" + }, + "source": [ + "#Lista das linhas duplicadas\n", + "df_50k[df_50k.duplicated()]" + ], + "execution_count": 207, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countryflag_50kind_50k
488125Private308144Bachelors13Never-marriedCraft-repairNot-in-familyWhiteMale0040Mexico<=50K0
510490Private52386Some-college10Never-marriedOther-serviceNot-in-familyAsian-Pac-IslanderMale0035United-States<=50K0
917121Private250051Some-college10Never-marriedProf-specialtyOwn-childWhiteFemale0010United-States<=50K0
1163120Private107658Some-college10Never-marriedTech-supportNot-in-familyWhiteFemale0010United-States<=50K0
1308425Private1959941st-4th2Never-marriedPriv-house-servNot-in-familyWhiteFemale0040Guatemala<=50K0
1505921Private243368Preschool1Never-marriedFarming-fishingNot-in-familyWhiteMale0050Mexico<=50K0
1704046Private173243HS-grad9Married-civ-spouseCraft-repairHusbandWhiteMale0040United-States<=50K0
1855530Private144593HS-grad9Never-marriedOther-serviceNot-in-familyBlackMale0040?<=50K0
1869819Private97261HS-grad9Never-marriedFarming-fishingNot-in-familyWhiteMale0040United-States<=50K0
2131819Private138153Some-college10Never-marriedAdm-clericalOwn-childWhiteFemale0010United-States<=50K0
2149019Private146679Some-college10Never-marriedExec-managerialOwn-childBlackMale0030United-States<=50K0
2187549Private312677th-8th4Married-civ-spouseCraft-repairHusbandWhiteMale0040United-States<=50K0
2230025Private1959941st-4th2Never-marriedPriv-house-servNot-in-familyWhiteFemale0040Guatemala<=50K0
2236744Private367749Bachelors13Never-marriedProf-specialtyNot-in-familyWhiteFemale0045Mexico<=50K0
2249449Self-emp-not-inc43479Some-college10Married-civ-spouseCraft-repairHusbandWhiteMale0040United-States<=50K0
2587223Private2401375th-6th3Never-marriedHandlers-cleanersNot-in-familyWhiteMale0055Mexico<=50K0
2631328Private274679Masters14Never-marriedProf-specialtyNot-in-familyWhiteMale0050United-States<=50K0
2823027Private255582HS-grad9Never-marriedMachine-op-inspctNot-in-familyWhiteFemale0040United-States<=50K0
2852242Private204235Some-college10Married-civ-spouseProf-specialtyHusbandWhiteMale0040United-States>50K1
2884639Private30916HS-grad9Married-civ-spouseCraft-repairHusbandWhiteMale0040United-States<=50K0
2915738Private207202HS-grad9Married-civ-spouseMachine-op-inspctHusbandWhiteMale0048United-States>50K1
3084546Private133616Some-college10DivorcedAdm-clericalUnmarriedWhiteFemale0040United-States<=50K0
3199319Private251579Some-college10Never-marriedOther-serviceOwn-childWhiteMale0014United-States<=50K0
3240435Private379959HS-grad9DivorcedOther-serviceNot-in-familyWhiteFemale0040United-States<=50K0
\n", + "
" + ], + "text/plain": [ + " age workclass fnlwgt ... native-country flag_50k ind_50k\n", + "4881 25 Private 308144 ... Mexico <=50K 0\n", + "5104 90 Private 52386 ... United-States <=50K 0\n", + "9171 21 Private 250051 ... United-States <=50K 0\n", + "11631 20 Private 107658 ... United-States <=50K 0\n", + "13084 25 Private 195994 ... Guatemala <=50K 0\n", + "15059 21 Private 243368 ... Mexico <=50K 0\n", + "17040 46 Private 173243 ... United-States <=50K 0\n", + "18555 30 Private 144593 ... ? <=50K 0\n", + "18698 19 Private 97261 ... United-States <=50K 0\n", + "21318 19 Private 138153 ... United-States <=50K 0\n", + "21490 19 Private 146679 ... United-States <=50K 0\n", + "21875 49 Private 31267 ... United-States <=50K 0\n", + "22300 25 Private 195994 ... Guatemala <=50K 0\n", + "22367 44 Private 367749 ... Mexico <=50K 0\n", + "22494 49 Self-emp-not-inc 43479 ... United-States <=50K 0\n", + "25872 23 Private 240137 ... Mexico <=50K 0\n", + "26313 28 Private 274679 ... United-States <=50K 0\n", + "28230 27 Private 255582 ... United-States <=50K 0\n", + "28522 42 Private 204235 ... United-States >50K 1\n", + "28846 39 Private 30916 ... United-States <=50K 0\n", + "29157 38 Private 207202 ... United-States >50K 1\n", + "30845 46 Private 133616 ... United-States <=50K 0\n", + "31993 19 Private 251579 ... United-States <=50K 0\n", + "32404 35 Private 379959 ... United-States <=50K 0\n", + "\n", + "[24 rows x 16 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 207 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "7_xocTl-wz3-", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "1c283687-3bf5-4365-c5e3-fbd799cfd6c8" + }, + "source": [ + "print(f'Há {df_50k[df_50k.duplicated()].count()[0]} registos duplicados')" + ], + "execution_count": 208, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Há 24 registos duplicados\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FESe-vmcxodU", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "0a24eec3-839e-409d-8769-53c4d924d9ee" + }, + "source": [ + "#Exclusão das linhas duplicadas\n", + "df_50k = df_50k.drop_duplicates()\n", + "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " + ], + "execution_count": 209, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Ficheiro com 32537 linhas e 16 colunas\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PBFzzOFhb3Mk", + "colab_type": "text" + }, + "source": [ + "### Missing Values" + ] }, { "cell_type": "markdown", From ef44b3459d8f821e5ba3164d38547f345949645d Mon Sep 17 00:00:00 2001 From: Fagner Candido Date: Thu, 14 Nov 2019 09:38:52 +0000 Subject: [PATCH 22/35] Simple update to another option. --- dswp_grupo1.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index bb5545e55..b06167192 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -1201,6 +1201,7 @@ "\n", "* Eliminar registos com \"?\" (já que podemos considerá-lo como um NaN)?\n", "* Ou substituitr \"?\" pelo valor de maior frequencia (moda)?\n", + "* Uma outra opção seria não fazer nada, a depender do algoritmo, pode-se comparar os resultados.\n", "\n" ] }, @@ -4966,4 +4967,4 @@ ] } ] -} \ No newline at end of file +} From 7468f342398fa4ba4ca01791d152bae67e1e78d1 Mon Sep 17 00:00:00 2001 From: Fagner Candido Date: Thu, 14 Nov 2019 12:36:58 +0000 Subject: [PATCH 23/35] Created using Colaboratory --- dswp_grupo1.ipynb | 1160 ++++++++++++++++++++------------------------- 1 file changed, 521 insertions(+), 639 deletions(-) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index bb5545e55..6db52ca43 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -23,7 +23,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { @@ -116,7 +116,7 @@ "metadata": { "id": "0YTMtMPRbSYD", "colab_type": "code", - "outputId": "5b6e052c-97dc-478f-e5d4-77eaca4dee3d", + "outputId": "126bc93e-2756-4f64-cd0c-10fbd34a89e5", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -126,7 +126,7 @@ "df_50k = pd.read_csv('https://raw.githubusercontent.com/MathMachado/DSWP/master/Dataframes/adult.data')\n", "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " ], - "execution_count": 149, + "execution_count": 2, "outputs": [ { "output_type": "stream", @@ -142,17 +142,17 @@ "metadata": { "id": "Lxq7B1Vnv1Ys", "colab_type": "code", - "outputId": "defbd658-3836-48b8-dfbe-5526a31b113f", + "outputId": "93350141-551c-41be-8704-0b578b6d914b", "colab": { "base_uri": "https://localhost:8080/", - "height": 296 + "height": 204 } }, "source": [ "#Detecção de que o ficheiro não tem header\n", "df_50k.head(5)" ], - "execution_count": 151, + "execution_count": 4, "outputs": [ { "output_type": "execute_result", @@ -302,7 +302,7 @@ "metadata": { "tags": [] }, - "execution_count": 151 + "execution_count": 4 } ] }, @@ -311,10 +311,10 @@ "metadata": { "id": "HBKqXT9Bo-BC", "colab_type": "code", - "outputId": "c3994495-e2ca-482d-db9b-5bbaf0edab08", + "outputId": "ad9f2fe3-0a2a-42f2-8a61-a2ade20ec13c", "colab": { "base_uri": "https://localhost:8080/", - "height": 269 + "height": 272 } }, "source": [ @@ -337,7 +337,7 @@ " ]\n", "l_column" ], - "execution_count": 152, + "execution_count": 5, "outputs": [ { "output_type": "execute_result", @@ -363,7 +363,7 @@ "metadata": { "tags": [] }, - "execution_count": 152 + "execution_count": 5 } ] }, @@ -386,16 +386,16 @@ "metadata": { "id": "AjswmEdb4R8X", "colab_type": "code", - "outputId": "7a6f26ec-fa3e-4bdd-cd1d-23fc47ac32a1", + "outputId": "3600bcbd-6faa-4ccb-df7d-0164296757de", "colab": { "base_uri": "https://localhost:8080/", - "height": 353 + "height": 357 } }, "source": [ "df_50k.info()" ], - "execution_count": 154, + "execution_count": 7, "outputs": [ { "output_type": "stream", @@ -439,7 +439,7 @@ "source": [ "df_50k.head(5)" ], - "execution_count": 155, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -608,7 +608,7 @@ "#Colunas do DataFrame\n", "df_50k.columns" ], - "execution_count": 156, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -642,7 +642,7 @@ "source": [ "df_50k.dtypes" ], - "execution_count": 157, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -698,7 +698,7 @@ "#Valores distintos de WorkClass\n", "df_50k['workclass'].value_counts()" ], - "execution_count": 13, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -738,7 +738,7 @@ "#Valores distintos de Education\n", "df_50k['education'].value_counts()" ], - "execution_count": 184, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -775,17 +775,17 @@ "metadata": { "id": "q67Wyta2XwZx", "colab_type": "code", + "outputId": "1110034a-f333-433e-8c9c-dedd540e624e", "colab": { "base_uri": "https://localhost:8080/", "height": 302 - }, - "outputId": "1110034a-f333-433e-8c9c-dedd540e624e" + } }, "source": [ "#Valores distintos de Education-num\n", "df_50k['education-num'].value_counts()" ], - "execution_count": 185, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -832,7 +832,7 @@ "#Valores distintos de marital-status\n", "df_50k['marital-status'].value_counts()" ], - "execution_count": 15, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -860,17 +860,17 @@ "metadata": { "id": "7_Wo0troSfxq", "colab_type": "code", + "outputId": "68fbc763-b7a0-41e1-d4f8-8ba14fd238e5", "colab": { "base_uri": "https://localhost:8080/", "height": 134 - }, - "outputId": "68fbc763-b7a0-41e1-d4f8-8ba14fd238e5" + } }, "source": [ "#Valores distintos de relationship\n", "df_50k['relationship'].value_counts()" ], - "execution_count": 173, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -907,7 +907,7 @@ "#Valores distintos de occupation\n", "df_50k['occupation'].value_counts()" ], - "execution_count": 16, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -953,7 +953,7 @@ "#Valores distintos de relationship\n", "df_50k['relationship'].value_counts()" ], - "execution_count": 17, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -990,7 +990,7 @@ "#Valores distintos de race\n", "df_50k['race'].value_counts()" ], - "execution_count": 18, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -1026,7 +1026,7 @@ "#Valores distintos de sex\n", "df_50k['sex'].value_counts()" ], - "execution_count": 19, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -1059,7 +1059,7 @@ "#Valores distintos de native-country\n", "df_50k['native-country'].value_counts()" ], - "execution_count": 20, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -1132,7 +1132,7 @@ "#Valores distintos de flag_50k\n", "df_50k['flag_50k'].value_counts().keys()" ], - "execution_count": 182, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -1153,17 +1153,17 @@ "metadata": { "id": "2ng16_UcPOCS", "colab_type": "code", + "outputId": "7934af53-ef4d-419a-c5e3-c6eac24dd288", "colab": { "base_uri": "https://localhost:8080/", "height": 218 - }, - "outputId": "7934af53-ef4d-419a-c5e3-c6eac24dd288" + } }, "source": [ "#Valores distintos de hours-per-week\n", "df_50k['hours-per-week'].value_counts()" ], - "execution_count": 166, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -1220,11 +1220,11 @@ "metadata": { "id": "YUk0xnfaV4yX", "colab_type": "code", + "outputId": "15bb1be0-d464-447e-b683-4767ccae1130", "colab": { "base_uri": "https://localhost:8080/", - "height": 333 - }, - "outputId": "232658ca-2b97-400c-87e0-126989d18c56" + "height": 204 + } }, "source": [ "#Criação de nova coluna, onde 0 = <=50k e 1 = >50k\n", @@ -1233,7 +1233,7 @@ "\n", "df_50k.head(5)" ], - "execution_count": 183, + "execution_count": 8, "outputs": [ { "output_type": "execute_result", @@ -1389,7 +1389,7 @@ "metadata": { "tags": [] }, - "execution_count": 183 + "execution_count": 8 } ] }, @@ -1466,11 +1466,11 @@ "metadata": { "id": "Vu2p_1-fDH_3", "colab_type": "code", + "outputId": "3bd5f300-6dd9-4199-faa1-b4fce5fb61a6", "colab": { "base_uri": "https://localhost:8080/", - "height": 366 - }, - "outputId": "5732ce9c-71ed-45ef-ab7c-fd02e4e45b69" + "height": 355 + } }, "source": [ "corr = df_50k.corr()\n", @@ -1482,24 +1482,24 @@ " fmt= '.2f'\n", " )" ], - "execution_count": 187, + "execution_count": 10, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 187 + "execution_count": 10 }, { "output_type": "display_data", "data": { - "image/png": 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jEYAdu/fg612BalWq6K/L1xc/H5OugBcIzxYxAITv2UeXDu0L0OVNtSqVddUF\ncGLfOW5eu5VvfpPO9QhffggAeegi5ZzL4uLlSIPAmvy5/QzJibdJTrrDn9vP0KCdfl8wEVJS0bsC\nfhUqaH3WqiU7fz1gZuPj5YWoUiXXnnWVfX2p5OsDgIebK67OzlxLuq6PrtNnqOjjjZ+3t6arjT/h\nOaKZ8H376dIuEIDAVi05cOQPjEYjBoOB23fvkpaWzt2UFErZlcKh3GP66Dr7l6m/vDRdLZqz88Ah\nMxsfT09E5cqPPGI4HnUJP1c3/B53w97OjqB6Ddh1+riZjUOZrJufO6kpZj8iDj91DN/HXanmUeFR\nSQa0OTBLj5JAoefAhBCNgWmAoylprJQyTAjxDjAMuAGEZbNvBcyUUjbM5/w14H2TeSrQEbhqqsMV\nKAv8BrwBlAcmAI5CiKPAXinle0III1BeSpkshHgWmAuUA24B70kpDwshKgG/A4uBIOAxYKCU8pc8\n3mO+tgW9H9PrOSa9TYB7wCtACFAHiAS6SSnz//YsJLHx8VTw9Mg89/L04NiJk2Y2cXHxVPD0BMDO\nzo7yDg4kXb+Ovb09S5av5Mv5c/hS52E6TZdnli4Pd46dPGWuKz6nrnJFrssS3HyciI9MyjxPiErC\nzccZVx9nEiITM9OvRiXi6uOsW7txCVfxcnfPPPd0cyPijCx0PRFnJPfupVHRW58vv9j4BCp4ZLvG\n3N05dvq0mU1cQpaNnZ0t5cs5kHT9BoGtWrJz336ad+nO3ZQU/jPkbZwdHdGDuKtX8XJzyzz3dHMj\nQlreXympqQS/9wG2tja83iOYF3QchYi9nkQFJ5csbU7ORERezGW36sBelu/fxb30NL4cOASAWykp\nfLFnB0tee5dlj3D4EKxvM99CRWBCCGdgEdBHStkAzdksFkK0AEYDz0sp66M5HkvqawWMAgKllE8B\nrYHrQLqpjYZoX/y2wGtSyqvAWGCHKep6L0d99sAPwMdSynrAGOAHUzomXQeklM+gOcLpBcgrjG12\nagELpJR1gQPAVuADKWUt0/vqbWE9Rc780KUM6P0S5R7T545YL+Yv+YIBvXuVOF3WQNzVq4ycPoPJ\nH32AjU3xD7AcP3UaG1sb9q5fy441q/jq2++JvHKluGUBEL7sC9bO/YyZIz5iauhS/omOfnAhnenT\ntAVbPgphWGBnFu3aCsDC8M30e7415UqXfuR6/u0R2HNAZeBnIcT9NCPQCgiTUsaa0kLJMcyXDx2A\n5VLKGAApZTKAEMIW+EgI0R7NebkAty2oTwCpUspwU307hBCppvSbQLKUcpPJ9iDwaQF1FcY2O1JK\nedT0+g/gCSlllOn8CFDNwp9Q8osAACAASURBVHoswtPdnejYuMzzmNg4PLPdxQN4eLgTHRuLl6cH\naWlp3ExOxtnJiYiTp9i6cxefzFvAzZvJ2NgYKF3anr49e+ikKzbzPCYuPrcu95y6bmm6Tph0zc+m\ny96evj2DH1qXJSRcvo67X1Zk5ebrTMLlJK5eTqJuqycz0119XTi++6xu7Xq4uRITH595HpuQgKeb\nRfeCACTfusWbH49l6KsDeLpWTd10ebq7ER2X7RqLj8czW+QD4OGm2Xh5uJOWls7NW8k4OzmyaUc4\nzRs1opSdHa4uLtSvW5sTZyR+3t4PrcvD1ZWYhKyp9tiEBDxdLe+v+33rV8GLRvXqcPr8BSpW0Cdq\n9XRyJvp6VrQeez0JT8f8o/WgevWZ+JO2aCci8iLbThzl0y0/cfPuHQwGA/Z2drzcNPc8nt6UgHue\nQlFYuQYgwhT93D/8gKQCyqTlaKeMBe30AZoBzU2RzEILyz2IlGyv0zE5cCHEaNNCkKNCiNYF2fLg\n93M3R7mc57r+dKFurZpc+ieSqMtXSL13j83bd+DformZjX/zZqwP2wzA1p27aPJsAwwGA98sWcTO\nDevYuWEd/Xq/xOAB/XVxXpm6IqOydG3bgX/zZua6WjRnfdjPWboa3tf1OTt/+pGdP/1Iv149Tboe\njfMCOLQhInOFoWhciVvX75AYc4MjW09TP6AGDs5lcXAuS/2AGhzZevoBtVlOXSG4dPkKUdExWp/t\n3kNrCxexpN67x5BxE+nc9gUCc3z+D62rRg0uRV0m6kq0pit8J/7NnjOz8W/2HOu3aBHE1t17aFL/\nGQwGAxU8PTn4x58A3L5zh2MnT1OlYkV9dD1ZnUtXrhAVY+qvvfto3cSylaHXbyaTeu8eAInXb/DH\nqdNUrej3gFKWU8enIv8kxBN1LYHUtDQ2Rxyhdc26ZjaXErJuCvbIkzzhpt3grXhjGNtHjGf7iPG8\n8lwrBrcKeCTOC7StpCw9SgKF/TL9FaguhGgtpdwFYJpz2gOMFEJ4SCnjgIHZylwAqgghXNAcXfYh\ntDBgqRBisZQyVgjhgOYgnIEEKeVNIYQTmkP73VTmBuCUjz4J2N/XJ4TwB0qZ0vO95ZNSTgYm3z83\nzYHlR0Hv55FjZ2fHmBEfMvC9oWSkZ9D9xY5Ur1qFuYtCqVOzJv4tmxPcuRMjQsYT0DUYJ0dHPps8\n8dHoGv4BA98bRkZGOt07mXQtXkKdmjXwb9Gc4Bc7MiJkAgHdeph0TShyXQAjVr1KvVbVcXRzYHnk\nJFaGhGFXyhaAzYt/4fDmkzwbVJsvzo0j5XYqs15dCUBy4m1WT9zC7MMjAVg94WeSEy0ZGLAMO1tb\nPn73bQb9dzQZGRl0CwygeqVKzF22nDpPVsf/uaYcl5Ih4yZyI/kmuw4eYt7yFWxaGsqWPXv5/fhx\nkm7cYP3W7QBMGf4hNatVfXhddraMGfYeAz8cQUZGBt07tKd65crMXfoldWoI/Js9T3CHDoyYNIWA\nXi9rn+W4MQD06dqFUVOn0/GVARiN0C2oHUIHTWDqr7feYNDH47T+CniB6k9UZO6Kb6hTvRr+TRpz\n/OxfDJk4hRvJyew6dJh5K1exadECLkRGEjJvITY2BjIyjLzeozvVdHKs97WNfrEHg79aSIbRSNcG\nTajmWYF528Oo7VsR/5p1WXVgLwfOS+xsbXEs8xhTgl/Rrf3/FWv7HZjh/qovSzE5rE/QhvXs0b7Q\nOwFvAUPRHMxm4C0ppZupzBjgVSAWzdm9kG0RxEBTuQy0qKcTWtTyA+ALxAEngbJSygEmh/Yz2iKN\nPYVdxJFNk9l5jvdYoG1+7yePBR4DgI5SymDT+TjAQUr5kSV9bbxxrXAfzqOikNfMo6KD89jilpAv\nmy5Z9JE/cgxlHv08iyUYb94sbgl5knH00oONigm77gEP7X1O1Zlv8T93rRPvFru3K7QDUzw6lAMr\nHMqBFR7lwArHv92Bna5nuQOrGVH8DkxtJaVQKBQKwPqW0SsHplAoFApAbearUCgUCiulpKwutBTl\nwBQKhUIBgMG6/JdyYAqFQqHQsLIpMOXAFAqFQqFRVHNgQognga/Rtui7CvSTUv6Vh11PtC0ADWi7\nPL2QbYenXFhZwKhQKBSKosJgY/lRSBah7RH7JLAAbaN0M4QQDYFxQFspZR203ZgKfKSCisAUCoVC\nARRuCNG0uXteGzwmSSmTstl5APWBtqak1cB8IYS7lDI+W7lhaBtB3N8b94HPA1IRmEKhUCgAMNga\nLD7QdlD6O49jaI5q/YDLUsp0ANPfK6b07NRC26ZvrxDiDyHEx0KIAl2qisAUCoVCARR6aHA2sCyP\n9II2dy8IW6AeWqRmD2wB/gGW51dAOTCFQqFQAIVbxGEaJrTEWUUCPkIIWylluulxWd6m9Oz8A6yV\nUqYAKUKIn4BGKAdmpdxNebBNMWBwLF/cEvKkpO43CNDxiZnFLSFPNt+YVNwS8qFkruc2PFEyr329\nKIpl9FLKOCHEUbQnd6w0/f0zx/wXwCogSAixAs03tQHWFlS3mgNTKBQKhYZNIY7C8SYwRAhxFhhi\nOkcIsdm0+hDgW7Snj5wCjqI9heSLgipVEZhCoVAoALCxLZrIV0p5Bsj1tFEpZVC21xnAB6bDIpQD\nUygUCgWgtpJSKBQKhZVibU9kVg5MoVAoFICKwBQKhUJhrSgHplAoFAprxMpGEJUDUygUCoWGjZ11\neTDlwBQKhUIBqAhMoVAoFNZKET0PrKhQDkyhUCgUgFqFqFAoFAorRQ0hKhQKhcIqKcxu9CUB5cAU\nCoVCAYCNbXErKBzKgf0L2HfoEJPnzCcjI53gjh0Y3Pdls/zU1FRGTp7KSSlxdnTis/Fj8a1QgXtp\naXw8/RNOnT1Leno6nQMDeeOVl/NppfDs3f8rk6fPJCMjnR5duzB44Ku5dI0YPZaTp0/j7OTErBnT\n8PXxBmDxF1+ydt1P2NjY8vHIj2j+/HO66dp3+HemLPycjIwMgtu34/VeL5nlH444ztTPF3H2wt98\nOvq/BLZoDsDpc+cZP3ceybdvY2tjwxt9ehPUqqVuuoZ+0ZdGHeuQFHeTt+tOztPmjTk9eDaoNim3\nU/lswArO/6k9UqlNv8b0+rgdAN9O2kL48kO66QLY++sBJs+cRUZGBj26vMjgAf3M8lNTUxkRMp6T\npyXOTo7MmjoJX29vIk6cZMyUaQAYjUaGDB5E29atdNNVUq99gH3HIpi6YhXpGRkEt2rB6y92NMtf\ntnkLa3ftxc7WBhfH8kx6fSA+7m6cvniJCV8tJ/nOHe0669yJ9k1z7YNbNFhZBGZlU3a5EUJ4CyF2\nZTsfJ4Swt7DsRSFEHZ31HBVClNWzzoJIT09nwmdzWDJzOptWfE3Yjp2c+/uimc3asM04lndg27er\n6N8zmE8XhQKwZddu7qWmsvHrr/hhaSjfbdhAVHS0frqmTGPpwrmErVvLpi1bOXf+gpnN9+vW4+jo\nyPZNPzGg78vMnD0XgHPnLxC2ZRthP37P0oXzGD9lGunp6brpmjhvAaFTJrFxaShhu3Zz7tIlMxtv\nD3emDv+QDv6tzdLLlCnNtBHD2bQ0lCVTJjP180XcSE7WRRfAjmUHGdNuQb75DdvXxqe6O4Oqj2Pu\n4FW8+3kvABxcHqNPSBDDGn/CsEYz6BMShIOzfpdgeno6E6bPZOncWYR9v5pNW7dx7sLfZjbf/7QB\nx/KObF+/lgF9ejNznvY+qleryg/Lv+KnVStYOm82Y6dMJy0tTT9dJfDaB0jPyGDSshUsHvEBG2dM\nYfOBQ5yLumxmU/OJJ/h+Ugjrp00isNGzfLp6DQBlS5dm6luvs3HGFEJHfsjUlau4ceuWbtoKwmCw\n/CgJWL0Dk1JekVJm/6YJQXscdXHpeVpKeedRtRdx+gwVfXzw8/bGvlQpgtr4E/7LfjOb8H376dJO\nuzsPbNWSA0eOYDQaMRgM3L57l7S0NO6mpFDKrhQO5crpo+vESZ7w88PP1xf7UqXo0C6A8N27zWx2\n7tpDV9NdaWDbNhz47TeMRiPhu3fToV0A9vb2+Pn68ISfHxEnTuqjS0oqelfAr0IFrb9atWTnrwfM\nbHy8vBBVqmCT47+0sq8vlXx9APBwc8XV2ZlrSdd10QVwYt85bl7L/4uqSed6mZGVPHSRcs5lcfFy\npEFgTf7cfobkxNskJ93hz+1naNCulm66Ik6e4gk/X/x8fbTPMqAt4Xv2mtns3LOPrh21J2MEtmnN\ngd9+x2g0UrZMGezstIGelJRUXb/4Suq1D3D8/AUqenri5+GBvZ0d7Zs0ZueRP81sGteuSdnSpQGo\nV60qsdeuAVCpgheVvLwA8HBxwdXRkWs3b+qmrSAMNpYfJYFiG0IUQjQFPgHuP+J0OBAAtERzQAnA\na1LKS0KISsDvwNdAW7THtb4tpdx3P09K6SaEuH/7+qsQIgNoBQQB75Pl1D6SUoZboK8W8BVQDu3h\natWASVLKTUKID4FeaP13F3hLSnnUVM4IlJdSJgshLqI9DrstUAGYKaWcX8iuKpDY+HgqeLhnnnu5\nu3Ps9Ckzm7iELBs7OzvKl3Mg6fp1Alu1ZOe+X2jepTt3U1L4z5B3cHZ01EdXXBxeXp6Z554enkQc\nP5HDJp4KJhs7OzvKOziQmJREbGw8T9Wrm1XW05PYuDhddMUlXMXLPau/PN3ciDgjC11PxBnJvXtp\nVPSuoIsuS3DzcSI+MusJ7glRSbj5OOPq40xCZGJm+tWoRFx9nHVrNzYuHi9Pj8xzTw+PXDcUsXHx\nVPDM8Vlev87jzs4cO3GCURMmcyU6hhkTQjId2kPrKqHXPkDstUS8XB/P0va4CxE5RiCy8+PuvTR/\nql6u9IjzF7iXlkZFD488SumPtS3iKBY/KoR4HFgHjJBSPgXUBw4D06SUz5rSVgPTsxVzBY5JKeuh\nPdFztRCidPZ6pZTvmF4+Z4qEkoCtQBMp5TNoTudrC2WuAOZJKesAs4Fns+UtN+l8BhgDLCqgnsek\nlE3RnOk0IYSDhe0XOcdPncbG1pa9639gx5rVfPXtGiKvXCluWSWeuKtXGTl9BpM/+gAbmxJyK1qC\neapOHcLWrGbt8i9Z/NVyUlJSiltSibr2N/zyKycu/M1rHdubpccnJvGfz0OZPHjgI7vODLaWHyWB\n4vrvawqcklL+CiClTJdSJgLthRAHhRAngI+Ap7OVSQVWmux3A3cAYUFbVYGtQoiTwHeAlxDCq6AC\nQghHoA6wytTe70BENpMGQoi9Jp2f5dCZk29NdVwEEgFfCzRbjKe7O9Fx8ZnnMfHxeLq5m9l4uGXZ\npKWlcfNWMs5OTmzaEU7zRo0oZWeHq4sL9evW4cT/EI3kqcvDg5iY2Mzz2LhYPD3dc9i4E22ySUtL\n42ZyMi7Oznh6uhMTG5NVNjYWT53uQD3cXImJz+qv2IQEPN1cLS6ffOsWb348lqGvDuDpWjV10WQp\nCZev4+6XFVm5+TqTcDmJq5eTcPNzyUx39XXh6uWkvKr4n/D0cCcmNisCjo2Lw9Mjj88yNsdn6eRk\nZlO1cmUee6wsZwuIRAqlq4Re+wCej7sQc/ValrZriXi4uOSy+/XESUJ/2siCD4diX6pUZnry7Tu8\nOXMW7/fozlPVq+mm64HYGCw/SgAl5vZRCPEEMAvobYp6XgPK6FD1amChlLI2WqSXlrNeIURd0+KL\no0KIWdmyjHnotAfWAkNNOtsBpXPaZeNuttfp6DxsW7eG4FJUFFFXokm9d4/N4Tvxb2a+Ys+/2XOs\n37IFgK2799Ckfn0MBgMVPD04+McfANy+c4djJ09RpWJFfXTVrsXFfyKJjLpM6r17hG3Zhn9L8xV7\n/q1asm7DJk3X9nCaNHoWg8GAf8uWhG3ZRmpqKpFRl7n4TyT16tTWR5cQXLp8hajoGK2/du+hddMm\nFpVNvXePIeMm0rntC5krEx8lhzZE0KafthpNNK7Eret3SIy5wZGtp6kfUAMH57I4OJelfkANjmw9\nrVu7dWvV5GJkJJGXr2if5bbt+Od4//4tmrNu02YAtobvosmzDTEYDERevpK5aONydDQXLl7CR6dh\n15J67QPUqVKZSzGxRMXFk5qWxs8HD9G6wTNmNqcuXmL8F8uY/+H7uDplDV+mpqUxZPZcOjd7jsDG\nz+asumixslUcxTUHdgCoJYRoKqU8IISwBSqiRVkxQggb4M0cZeyBPsBKIURzoCxwBvDOYXcTcALu\nLw9zBu4vmXqNPJyNlPI4OaIoU8TWG1glhKgP3J+UKYPWb5Gm87ctfdNFgZ2dHWOGvc/AD4eTkZFB\n9w7tqV65MnOXfkmdGgL/Zs8T3CGIEZOmENCrD06Ojnw2biwAfbp2YdTU6XR8ZQBGo5FuQe0R1arq\npmvsf0cw6K13Sc9Ip3uXzlSvVpU5Cz6nTu1atGnVkuCunRk+egxtO3bGydGJWTOmANrKtfYBbQnq\nGoytrR1jR43E1lafMQs7W1s+fvdtBv13NBkZGXQLDKB6pUrMXbacOk9Wx/+5phyXkiHjJnIj+Sa7\nDh5i3vIVbFoaypY9e/n9+HGSbtxg/dbtAEwZ/iE1deqzEatepV6r6ji6ObA8chIrQ8KwK6W9782L\nf+Hw5pM8G1SbL86NI+V2KrNeXQlAcuJtVk/cwuzDIwFYPeFnkhNv66IJTJ/l8I8YNOR90tMz6P5i\nR6pXrcKcRaHUqVmDNi1bENy5E8PHjqdtl2CcHB2ZNWUiAEeOHmPJ18uxs7PDxmBg3H+G87izPvNz\nJfXaB+06Gz2gL69Pn0lGRgZdWzanuq8P89b+SO3KlfFv8AwzV33H7bspDJujTd17u7my4MOhbDn4\nG0fOnCXpZjLr9v4CwJQ3BlGz0hO66cuXEhPSWIbBaMwVZDwShBDPAZ+iLZLIQBsy7AS8iLaAYzPQ\nX0pZKdsijmVoCz3yXMRhqjcEzdHdQZt36gRMQBu+2wIMBhpKKS+aFll0lFKary7Q6qkDfInmKI8D\nNYH3TG2OQHNcV9GisSlSSoOpXM5FHJn1F9ReXhjjoovnw3kABsfyDzYqBjKyDSeVNDo+MbO4JeTJ\n5huTiltCnhjv3H2wUTGQ8c/F4paQL7YNmz50WHR32DcWf+eUmfVysYdhxebACkNOJ/WI2nQAbkkp\njaYVibsBYZqreyQoB1Y4lAMrPMqBFY5/uwNL+cByB1b6s+J3YGonjvx5DvhECHH/Q3r9UTovhUKh\neOTYWdcYolU4MNMKvkcWfZna3AZse5RtKhQKRbFiXf7LOhyYQqFQKB4BJWR1oaUoB6ZQKBQKjRLy\n+y5LUQ5MoVAoFBpqCFGhUCgUVomtisAUCoVCYY2oIUSFQqFQWCXKgSkUCoXCGrGyRYjKgSkUCoXC\nhIrAFAqFQmGVKAemUCgUCqtErUJU6IZdCXnsaU5K6AbQhjIFPZateCmpm+YGOX5c3BLyJOzauOKW\nkCcG59wPpfxXoX4HplAoFAqrpIiGEIUQTwJfA65oj6HqJ6X8Kx9bAfyJ9iDijwqq18r8rUKhUCiK\nDBuD5UfhWAQskFI+CSwAFudlZHq48WJgvSWVqghMoVAoFBqF8EtCCGe0J97nJElKmZTNzgOoD7Q1\nJa0G5gsh3KWUOR/i9x9gE+BgOgpERWAKhUKh0ChcBDYU+DuPY2iOWv2Ay1LKdADT3yum9EyEEE8B\ngcAsS+WqCEyhUCgUABgKtwpxNrAsj/SkPNIKRAhRCggFXpVSpmvTYA9GOTCFQqFQaBRibss0TGiJ\ns4oEfIQQtibnZAt4m9LvUwGoCmw2OS9nwCCEcJRSDs6vYuXAFAqFQqFRBKsQpZRxQoijQG9gpenv\nn9nnv6SU/wBu98+FEOMAB7UKUaFQKBSWYVOIo3C8CQwRQpwFhpjOEUJsFkI0/F/lqghMoVAoFBpF\ntJuvlPIM0DiP9KB87MdZUq9yYAqFQqHQUFtJKRQKhcIqsS7/pRyYQqFQKEwoB6ZQKBQKq8TKHqei\nViH+C9h34BDtXupDQHAvQpevzJWfmprKsI9DCAjuRc+Bg4mKjs7Mk+fO8dLrb9Kxzyt0erk/KSkp\nuunau/9XAjt3o22nLoR+uSxPXUNH/Je2nbrQo29/oi5fASAxKYlXBr3BM02bM2HqdN30ZGffod9o\n16cfAb1eJnTlqjy1DQsZT0Cvl+k5+C2iomMAuJeWxsjJU+nU/zWC+vZn8YpvdNW199cDBHbrSdsu\nwYQuW56nrqH/HU3bLsH06P8aUVe0Pos4cZLOfV6hc59XeLF3X7bv2q2rrqFf9GVV7DQWHh+dr80b\nc3qw9K9xLDg2iqrPZG2y0KZfY5acDWHJ2RDa9Ms1j/9Q7DtwkHY9exMQ/BKhy1fkyk9NTWXY6LEE\nBL9Ez9deJ+qKdu1HXYnmqZb+dHllAF1eGUDI9E901QWw7/c/aD/4LQIHvcGSNWtz5R8+cZJu7w2j\nTqeubP1lv1le7U5d6fruULq+O5S3xz/CJxkYCnGUAP5VDkwI4S2E2JXtfJwQwt7CsheFEHXyydst\nhOiol049SU9PZ8Knn7Hks5lsWr2CsO07OPf332Y2azeG4Vi+PNvWfkv/Xj35dMEiANLS0hg+biLj\nR3zEplUrWL5wLnZ2+gTl6enpTJg6naUL5hL24/ds2rKVc+cvmNl8v+4nHB3Ls33jegb07cPMOfMA\nKF26NO+/8xYjPnhfFy15avtsDktmTmPTimWE7Qjn3N8XzWzWhm3W+uzbb+jfswefLtL2Ht2yazf3\nUu+x8esv+WHpYr7bsDHTuemia/pMls6dRdj3q9m0dRvnLph/lt//tAHH8o5sX7+WAX16M3PeAgCq\nV6vKD8u/4qdVK1g6bzZjp0wnLS1NF10AO5YdZEy7BfnmN2xfG5/q7gyqPo65g1fx7ue9AHBweYw+\nIUEMa/wJwxrNoE9IEA7OZXXRlJ6ezoSZn7Fk1kw2rV5J2LY8rv0Nm3B0LM+2td/Rv/dLfLrg88y8\nij4+rF+xjPUrljF+5HBdNGXXNvHzxYSOD2Hj5/MJ27uPc//8Y2bj7e7G1GHv06FVi1zly9jbs27+\nbNbNn83CkEf4yBvlwIoPKeUVKWXrbEkhgEUOzFqJOHWair4++Pl4Y1+qFEEvtCF87y9mNuH79tEl\nqB0Aga1bceD3IxiNRvb/dhhRrSo1qlcDwMXJCVtbfZ5BFnHiJE/4+eHn64t9qVJ0CAwgfPceM5ud\nu/fQtZN2XxD4QhsO/PYbRqORx8qWpeEzT1Pavmie7xVx+gwVfbzx8zb1WRt/wnPcAYfv20+XdoGa\ntlYtOXDkD4xGIwaDgdt375KWls7dlBRK2ZXCodxj+ug6eYon/Hzx8/XR+iygLeF79prZ7Nyzj64d\ntZXHgW1ac+C33zEajZQtUybz5iMlJVX31dAn9p3j5rVb+eY36VyP8OWHAJCHLlLOuSwuXo40CKzJ\nn9vPkJx4m+SkO/y5/QwN2tXSRZN27fvi56P1V1DbF/K49n+hS1B7wPzaL2oizv5FRW8v/Cp4adpa\nNGfnwd/MbHw8PRGVK2FjKDlfwwYbg8VHSaBEzIEJIZoCnwDlTUnDgQCgJZoDSgBek1JeEkJUAn5H\ne7ZMW7R7gbellPvu50kp3YQQ928XfxVCZACtgCDgfbKc2kdSyvBCavVEezRAVVPbn0gplwshbID5\ngD+QAiRLKZ837cS8CvA0VbFDSjmsMG0WRGx8PBU8PDLPvTzcOXbytJlNXHwCFTw1Gzs7O8o7lCPp\n+nUu/hOJwWBg4NAPSExMIqhtGwb1fVkfXXFxeHl5Zp57enoQcfxELpsKJhtNlwOJSdd53CWvDa71\nIzY+wbzP3N05djpHnyVk2djZ2VK+nANJ128Q2KolO/ftp3mX7txNSeE/Q97G2dFRH11x8Xh5Zuny\n9PAg4sTJXDYVPHP02fXrPO7szLETJxg1YTJXomOYMSFEt2jaEtx8nIiPzNpVKCEqCTcfZ1x9nEmI\nTMxMvxqViKuPPp9v3tf+KTObuPj4PK990IYRu/Z7lXLlyjH0jddp+PRTuugCiLt6FS+3zI0l8HRz\nJUKetbh8Smoqwe9/gK2tLa/36M4LTZvopq1ASoZfsphid2BCiMeBdUA3KeWvpn2yHIE/7m8jIoQY\nBEwHepmKuQLHpJQfCiFaAauFEFWz1yulfEcI8TbwnJQy2VTPVmC1lNJoemhaOOBbSMlzgRNSyq5C\niArAESHEH0ApoDVQS0qZIYS4/+jWl4HzUsoXTBpKzCNd09LTOXLsOGu/DKVMmTIMGDKU2kLQ9Nn/\n+Yfx/3qOnzqNja0Ne9ev5cbNm7z8zvs817ABft7exS2Np+rUIWzNas7//TcjQybS4rmmlC5dcp9S\nXZx4uLmy86cfcHFy4sSZM7w7YhSbVq/AoVy54pYGQPhXS/F0cyUyOoYBo8bwZKUnqFihQtE3XEIi\nK0spCbFrU+CUlPJX0Lbal1ImAu2FEAeFECeAj4Cns5VJRdtTCynlbuAOYMn2xVWBrUKIk8B3gJcQ\nwquQel/A9DA2KWU0sBnNcV1Ac2JfCCFeyWZ/0PRePjHNoyUXsr0C8XR3JzouLvM8Ji4eT3c3MxsP\ndzeiYzWbtLQ0bibfwtnJCS8Pdxo+/RQuzs6ULVOGlk2bcKoQd4kF6vLwICYmNvM8NjYOz2x3y/dt\nok02mq5kXJyddGm/QG3ubuZ9Fh+Pp1uOPnPLsklLS+fmrWScnRzZtCOc5o0aUcrODlcXF+rXrc2J\nM1IfXR7uxMRm6YqNi8PTwz2XTXRsjj5zMu+zqpUr89hjZTmbY86xKEm4fB13v6zIys3XmYTLSVy9\nnISbX9Y9m6uvC1cvF3qz8jzJ+9o37y8P9/9r777Do6rSB45/AyGAQggQEiCA1HlRigpSlA4KSrGs\n0cVe0Z9rR0R3RanKWkBR7L1iWzsI0sEVEFHpviosShWp0ksyvz/OnWQmJBBkyJ2J7+d55pm5ZW7e\n3Lkz7z3nnntOlXyPKTvxNwAAIABJREFU/aSkpJz91rhhQ2pmVOd/v64kWtIqV2bdhg05079t2Eh6\n5cqFfn96qlu3ZrWqtGzSmKVF9VnaNbAjJyLH4caEuUhVGwNXA2WisOkxuGGqG+EGWNufd7si0kRE\nvvcehR6XRlW3Ao2At4GmwGIRqaqqs4CTgXnAZcDUgrdy+Joc35BfVq5i1Zo17N23j3GTJtO5XduI\ndTq3bctH48YDMGHqNFo3b0ZCQgJtW7Xip2XL2LV7N/v372fud99Tr07t6MTV6ARW/LqSlatXs3ff\nPsZO+ILOHSIvVnfu0J4PP/3MxTVpMq1btCDhKHVlExFbw4b8smo1q9asdfts8hQ6tz0tMra2p/HR\n+AkutmnTad3sZBISEqiWns7sb78DYOeuXcxfvJS6tWpFJ64TjmfFypWsXO0+y7FfTKRz+3aRcbVv\nx4efjXNxTZ5K6xankJCQwMrVa3Iabaxeu5blK34ho3oRnLF75nyyIKeFobSqzY6tu9i87g/mTVhK\ns64NKZdSlnIpZWnWtSHzJiw9xNYKxx37K3OP/YmT6NyuTcQ6ndu14aNxnwPesX+KO/Y3bd5MVlYW\nACtXr+aXVauiWopuEmjAL6vXsmrdby62GTPp1Kplod67ddt29u7bB8DmrX/w7dKl1KtV8xDvipI4\nS2C+VyECs4ATRORUVZ3lVSHWwpWy1nnXlv4vz3uSgIuBN0SkHVAW+AHXRX+4bUAFcks9KbgB18Al\nxQPqV1R1IZGlvbwmAX2AgV7prTvwqIhUAfar6gQRmQT0BOqKSFlglaq+LSIzgZ9FpISqZh9ivxRK\nYmIi995xO9fcdgfZ2dmc37MHDerW4fHnXqDx8Q3p3K4tmb160H/wMLpm9qZCcjIjhw4CoEJyea68\n6O9ccHUfEhISaH9qazq2Oe3gf/Aw4rrv7ju59oabycrO4vxzzqZB/XqMeuoZGp9wPF06diDzvHO4\n8577OKPXuVRITubRBx/IeX/ns3qxfccO9u3bx6Sp03np6dHUr1c3SrGV5N7bb+GaO/q7fdbjLBrU\nqcPjL7xE44ZC57ZtyOzRg/7DHqBr70vcPht0LwAXn3cu/xr+ID0vu5JgEP7W/Uykfr1D/MXCxpXI\nfXf249qbbyUrK5vzz+5Jg3p1GfXMczQ+viFdOrQn85xe3HnfYM44N9PtsweGAjDv+/k8/+prJCYm\nUiIhgUF330mllOhdS+z/1lU07diA5NRyvLZyGG8MHEtiKdfgZ9yzXzJ33GJadG/Eiz8PYs/OvTx6\nlbudY/vmnYwZOp7H5t4FwJghn7N9886oxJSYmMi9/fpyza19w479uu7Yb9iQzu3bktmrJ/0HD6Vr\n5t8jjv25383niedf8PZXCQb170dKhehcywRILFmSATdcx7X3DiI7O5u/ndGFBsfV4vHX36Rxg/p0\nbt2KhT/+xM3DhvPH9u1M/XouT7w5hs+eHs3ylSsZOPppSpRIIDs7SJ/M86kfpZOkQyqCE8hoSiiK\nFjmHIiKnASOAY4FsXJVhL+BsXAOOccAVqlo7rBHHK7iGHvk24vC2OxCX6HbhGnH0AoYAm4HxwHXA\nKaq6QkRWAD1VNbKlgdvONOARVf3Ma8TxLFCXyEYczYDncScFicAE7/+4AugLZOFKvCNU9dXC7Jfg\npvX+fzj5SCgbnWbQ0Rbcts3vEAoUq/use3IRNtE+DGM3DfI7hHwFN270O4QClajf8Iizz/5PJxb6\nNyex1xm+Z7uYSGCHI2+SKs4sgR0eS2CHzxLY4Sn2CeyzSYVPYD1P9z2BxUIVojHGmFjge0o6PHGX\nwFR1BWEjdxpjjIkSS2DGGGPiUpw14rAEZowxxolOT3JFxhKYMcYYx0pgxhhj4lJ85S9LYMYYY5w4\nK4BZAjPGGOOJs858LYEZY4xx4it/WQIzxhjjsRKYMcaYuBRf+csSmDHGGI+VwEzUZEdlxJXoi9G4\nYrkz31g9tY3VTnN7VBrkdwj5+vTrS/wO4eiKzcO0QJbAjDHGOHHWjt4SmDHGGI8lMGOMMfHIroEZ\nY4yJS1aFaIwxJi5ZAjPGGBOXLIEZY4yJS5bAjDHGxKWEEkdlsyISAF4FKgMbgctV9ac869wL9Aay\ngH3Av1R1wsG2e3SiNcYYE38SEgr/ODzPAE+qagB4Eng2n3W+BlqoalPgauAdESl7sI1aCcwYY4xz\nGIlJRFKAlHwWbVHVLWHrpQHNgDO8WWOA0SJSRVV/D62Xp7S1AHdTWmVgVUExWAnMGGOMc3glsNuA\n/+XzuC3PVmsCq1U1C8B7XuPNL8jlwDJVLTB5gZXAjDHGhBxe1eBjwCv5zN+Sz7xCE5EOwFByS2wF\nsgRmjDHmsHnVhIVJViuBDBEpqapZIlISqO7NjyAipwJvAOeoqh5qw5bAioGZs+dw/2NPkJ2dTWav\nHlx3WWSP2Xv37uWuoQ+wWH8kpUIyI4cMpEa1anw6YSIvvvV2znq6bBkfvPQ8xwcaRCWuGf+dxf2P\njCA7K5sLzjuH66664oC4+t87iMVLfyAlpQKP/vt+alSvzn9nz2HE40+yb/8+SiWW4s7bbubUli2i\nEhPAzG/m8cCzL5CdnUVmt670uTAzYvnchYsY/twL/Pi/FYy4+066tW2Ts6xRz3MJ1D4OgGpVqvDU\nwAFRiwtg5pw53D9qtIutZw+uuzSfz/L+4SxWJSW5AiMH30eNatXYt38/Ax58mCU//khWVhbndOvG\n9ZdFr+f0mbNmc/+jo9wxdnZPrrv8sgPjGjzMiyuZkcOGUKN6NVatWUuPiy6hTq1aAJzYuBGD77oz\nanHd9uKltOzZmC3rt/GPJvfnu871oy6gRfdG7Nm5l5FXvs6y79zvZpfLW9F7wJkAvD1sPJNfmxO1\nuABmzl/I8NffIis7m8yO7elzdo+I5a+Mm8D702aQWLIEFcuXZ9h1V5ORmsrqDRu45dEnyA4G2Z+V\nxSVdT6d3l05Rja0gCSWif1VJVdeLyPfARbjkdBHwXfj1LwARaQG8A2Sq6reF2fYhE5iIBIHyqrr9\nsCM3iMg04BFV/exobD8rK4shIx7jpcdGkJ5WhQuuvZ7ObdtQv07tnHXe/2wsyeXL88W7bzF20mRG\nPPUsjw4dRK9uZ9Crmyul67Jl3HT3gKglr6ysLIY8+BAvPzWa9PQ0Mi+9gs4d2lG/bt2cdd776BOS\nk8sz8ZMPGDvhCx4ZNZrHHnyAiikpPD1qBOlVqvDjz8u45sZbmDlhbNTiGvrUs7x4/xDSUytz4W13\n0Kl1S+p7P7AA1dOqMLzvrbz0n48OeH+ZpCQ+HD0qKrHkF9uQkaN46dFHSK9ShQv6/B+d2+T5LMeO\nI7l8Ob542/ssn3mORwcPZPzUaezbu5dPX32ZXbt30+OyK+hxemdqVKsWnbgeGclLjz9KeloaF1x1\nLZ3btaV+nTq5cX3yGcnJ5fni/XcYO3ESI558mkfvHwJArYwMPnr9lSOOIz+TXpnNp6Onc8drl+e7\n/JSzGpHRoArXNhiEtKrNTU/35vbWD1Ou4jFcPLA7t57yIASDjJp3N3M+WcD2LbuiEldWdjbDXn2d\nF+7uR3qlSvz9viF0an4S9TMyctY5vnYt3ht6H2VLl+btSVMYMeZdRt78D6qkpDBm0ACSSpVix+7d\nnHP3ADo3O4m0ihWjEttBHb37wP4PeFVE7gM2465xISLjgPtU9RvgKaAs8KyIhN53maouLGijvjfi\n8IqTRfn3ilWpc8HSpdSqkUHNjOoklSpF9y6dmTzzy4h1Js/8L+d27wZAt44dmDXvW4LBYMQ6YydO\npvvpnaMX16LFHFejBjVrZJBUqhQ9unVl8rQZEetMmTad83q6s9JuXToza+5cgsEgJzQU0qtUAaBB\nvbrs2bOHvXv3RieuH3+iVvVq1KxW1e2v9u2YMivyzDsjPR2pU4cSRdyx6YKlP1ArI4Oa1cM+yy//\nG7HO5Jn/5dwzXanBfZbzCAaDJCQksHP3bvbv38/uPXsolViKcsceG524liylVo0a1Mxwn2X3M05n\n8oy8x9iXnNv9LBdXp47M+mbeAcfY0bBo5s9s27SjwOWtz2maU7LSOSs4NqUsFasm07zb8Xw38Qe2\nb97J9i27+G7iDzQ/84SoxbVw2XJqpadRMy2NpMREzmrdkinzvotYp9UJx1O2dGkAmtavx2+bNgOQ\nlJhIUqlSAOzbt5/sItiPOY5SM3pV/UFVW6lqwHtWb353L3mhqi1UtYqqnhT2KDB5QeGrEG8RkfNw\nTRrvVNX/AIjImcBwoCTwO3C9qv4sIlcCPVU101svZ9p7fSmwDWgAXCoi5+CKlbuBINApvBlmiFca\nHAKcg8vU/wqLpRXwbyDZW/0+VR0rIrWBb3AXGzsDz+HuSQjf7mrgZK+oOw4IqmoPr/nnt6paQ0SS\ngPuBDkBpXDPPG1R1u4gkAyOBpkAZYCrQN9TqJuzv9AbuAM47VOuawvrt9w1US0vLma6aVoX5i5dG\nrLM+bJ3ExETKH3ssW7ZupWJKbgvYzydP5ckH86+C+XNx/U7Vquk50+lpaSxYtPiAdap56yQmJlK+\nXDk2b9lKpYq5cU2YPIUTGgpJSUlRiWv9xo1UTU3NjSs1lQWHrmrPsWfvXjJv6UvJkiXoc0Emp5/W\nOipxgbc/0qrkTFetUoX5S5dErLN+Q+467rMsx5atW+nWsQNTZn5Ju3PPZ/eePdx9842kJCcTDS6u\nvMdYnrh+/51q6WHHWDl3jAGsWrOW8y6/imOPPZbbru/DKSedGJW4CiM1owK/r8z9KdmwagupGSlU\nzkhhw8rNOfM3rtpM5Yz8WoT/Ob9t3kzVSpVypqtWqsSCZcsKXP+D6TNod2KTnOm1GzdywyOP8etv\n6+l30YVFU/qCuOuJo7AlsD9UtQVwGfA45LTtfx24xLvx7C3gzUJurzXQT1UbA78Ct+MSyElAe+Bg\n1ZVZ3npnA8+JSJp3P8IzwMWq2hzoiSuGho7IysBcVW2mqs/ks82pQGcRKQXUAep4r7t4ywD6A1tV\ntaWqnohrBvpPb9lIYLqqtgROAtJwN+LlEJH+wLXA6dFKXtEyf/ESypQpTSCsei8W/LRsGY88Ppoh\n9/zz0CsXkcmvvMj7j4/kkf79GP7cC/y6dq3fIQGwcMlSSpQsyYyP/sOkd8fw8tvvsnLNGr/DIi21\nMlM+/g8fvvYyd996E/3uG8z2HQWXmP6KPvnyKxYtX8HVPc7KmVetcmU+Gj6U8SP+zccz/8sG72Tg\nqDt6NzIfFYVNYKEr/bOB6iJSBmgFzFfV0KnYy8BJIlK+ENv7UlVDpyNbgZ+B10SkD1BOVfcf5L0v\nAnhF0G9xyfA0XOL53LtY+DmuJFffe89u4N2DbHMycLq3rdm4O8JbefOmeOucjSstfu/9jbOBemHL\n7vTmfws0BwJh2x/kbbu7qkb1SEyvksra9etzptet/530KqkR66SFrbN//3627dhBSoUKOcvHTZpC\nj9O7RDMs0qtUYd2633Kmf1u/nvSw0kVonbXeOvv372fb9u1UTHFxrfvtN266oz8PDhlErZo1ohZX\nWuXKrNuwITeuDRtIr1y50O9PT3Xr1qxWlZZNG7N02fKoxZZepQpr1+de1173+++kp0bus7TU3HXc\nZ7mdlAoV+GzSZNq1bEmpxEQqV6xIsyaNWfRD4UuWh44r7zGWJ64qVVj7W9gxtt0dY0lJSVT0jrXG\nDRtSM6M6//v1gMZnR82G1VupUjO3ZJVaI4UNq7ewcfUWUmvmlmoq16jIxtVH1Po7QnrFiqzbtCln\net2mTfmWor5atJjnPvmMJ/vemlNtGC6tYkXq18hgnv4YtdgOKqFE4R8xoLBR7IacG9Dg0FWP+/Ns\nu0ye5TklLG+brYHRQA1gnog0FZGrQslCRA7VnCoBWJCn7rRmqG4V2KGqORXJIjLH2+5Mb9YUXGmr\nCy6ZTc4zHfob/wjb/vGq2jts2blhywKqGt7UajbQCDjuEP/HYWvSsCG/rFrFqjVr2btvH+MmT6Fz\nWKs5gM5t2/DROHeT+4Rp02nd/GQSvDOo7OxsPp8yNeoJrEmjE1ixciUrV69m7759jJ3wBZ07tIuM\nq0N7PvzMNc6YMHkKrVucQkJCAn9s28Z1t9zOHTffRPMoVzc1CTTglzVrWLVundtfM2bSqXWrQr13\n67bt7N23D4DNW//g2yVLqVfrYPdiHmZsDSWfz/K0iHU6tz2Nj8aPB7zPslkzEhISqJaexuxvXcOt\nnbt2MX/xEuqGNUw5oriOb8gvK1eyas0aF9fESXRul+cYa9eGj8Z97uKaOo3Wp7i4Nm3eTFaW+9lY\nuXo1v6xaRc3q1aMSV2HM+WQBXS53n6+0qs2OrbvYvO4P5k1YSrOuDSmXUpZyKWVp1rUh8yYsPcTW\nCq9x3Tr8sm49q9b/zt79+/l89td0anZyxDpLVvzC4JdeZXTfW6hcIbe6d93GTez2rvlu3bGDb3/8\niTrVqkYttoOKsxLYkTRomA28JCINVfUH4Apc08htIvIz0FRESuNKQpkUcL+AV2Irp6rTgenefQCN\nVfVlXKkur6uAYSLSADjZi2Mf0EBEOqnqVG+7LXDXvg6gqq3yTP8iIlne/3AaLiENAfap6q/eap8A\nfUVklqru8uKuoapLvWV3i8gN3n0OqbiWm//z3jse+AAYJyLnqmrkxaAjkJiYyL2338Y1ffuRnZXN\n+T2706BuHR5//kUaN2xI53ZtyOzZnf5D76frhRdTIbk8IwcPzHn/3O/nUy0tjZoZ0f1RSUxM5L67\n7uTaG28hKzub88/uRYN69Rj19LM0PuF4unRoT+a5Z3PnvQM54+y/UaFCMo8Od9fg3njnXX5duYon\nn3+BJ59/AYCXnnqCymHXFP50XCVLMuCG67l2wCCys7P5W9fTaXBcLR5//U0aN6hP59atWPjjT9w8\n9AH+2L6dqXPm8sQbb/HZM0+yfOVKBj7xFCVKJJCdHaTPBedHtF484tgSE7n39lu55o47yc7O5vwe\nZ9GgTh0ef+ElGjcUOrdtQ2aP7vQf9gBde19MheRkRg66D4CLzzuXfw1/kJ6XXUkwGORv3c9C6tc7\nxF88jLj69eWaW/u6uHr2oEHdujz+3AvuGGvflsxePek/eChdM//u4ho6CIC5383niedfIDExkRIJ\nJRjUvx8pFaJzbQ6g/1tX0bRjA5JTy/HaymG8MXAsiaVcu7Bxz37J3HGLadG9ES/+PIg9O/fy6FVv\nALB9807GDB3PY3PvAmDMkM/Zvnln1OJKLFmSe664hD4PjSA7O5vzOrSjQY0Mnnj/QxrVqU3n5ifz\nyJh32bl7D7c//hQA1StX5sk7bmX5mrU89NbbJCQkEAwGuar7mQRqRu9E6aBiJDEVVsKhWgrlbUYf\nPu014ngAlwhzGnF46z2Dq4JbA8wHqoU14ghv4FED+A+uUUYJXBXcdaq6u4BYBuMacRxDZCOOFsDD\nQEUgCVgO9AJqAd+oamre7eXZ9rNAW1Vt5E0vAWaq6vXedClcVeA5QDYuMQ9W1Q+8ZPYQ0M6bvwe4\nTVW/DG9GLyJtgNdw9zlENknKR3DDuiJsflR4CWUP2r+mb7LXrfM7hAIllI/ej3ZUlYrNRrk9Kg3y\nO4R8ffp19O6ti7aSLU474uyT/fMPhf7NKVG/oe/Z7pAJLJb81e5JswR2eCyB/QmWwA5LsU9gy7Tw\nCaye+J7AYvPoNcYYU/TirAoxrhKYqsbX3jXGmHgSI60LCyuuEpgxxpijyEpgxhhj4pIlMGOMMXEp\nzhJYfFV4GmOMMR4rgRljjHHirARmCcwYY4xTxEMIHSlLYMYYYzyWwIwxxsQjq0I0xhgTl+Irf1kC\nM8YY4yTEWQazBBbDspcXPAS5n0o2aep3CPnK/v4Xv0MoUMJxhRnnteglpBTRUPWHKVY7ze3VsrCD\nzhe9ccHTDr3SoZSIrzurLIEZY4xx4qsAZgnMGGNMSHxlMEtgxhhjHGuFaIwxJi7FV/6yBGaMMSYk\nvjKYJTBjjDGOdSVljDEmLtk1MGOMMfHJEpgxxph4FF/5yxKYMcYYj1UhGmOMiUuWwIwxxsSjBEtg\nxhhj4pIlMGOMMfHJEpgpYjMXLGT462+RlR0ks2M7+vTqEbH8lc8n8P60GSSWLEnF8uUZ1ucqMlJT\nWb1hA7c8NprsYJD9WVlcckYXenfpFLW4Zvz3K+5/6BGys7O54Lxzue7qKyOW7927l/4DBrJ46VJS\nKlTg0QeHUyOjOpu3bOGWfnexaPESzju7J/f9866oxRQy88cl/Puz/5CVnc35LU6lT4euEcvfmfMl\nY2bPoESJEhyTVJpB5/amfnq1nOVrtmzi7Mfu58Yu3bmqXZfoxTV/gfdZZpPZsT19zu4ZsfyVceN5\nf+oMEkuWoGJyeYb1uYaMKqksXfELQ15+je27dlGyRAmuP6cXZ53aKnpxffMtDzz3PNnZ2WR2PYM+\nF2ZGLJ+7aDHDn3uBH/+3ghF39aNb2zY5yxr1Oo/AcccBUK1KKk8NHBC9uOYvzLO/8hz740LHfgl3\n7F93de6x/+gTucd+19Ojeuzf9uKltOzZmC3rt/GPJvfnu871oy6gRfdG7Nm5l5FXvs6y71YC0OXy\nVvQecCYAbw8bz+TX5kQtrkOKr/xlCSzeZWVnM+zVN3jhrjtIr1SJv983hE7NTqJ+RkbOOscfV4v3\nhtxH2dKleXvSVEa8/R4jb7qBKikpjBl4D0mlSrFj927O+ee9dG52EmkVj3yMqKysLIYMf5CXn3mS\n9PR0Mi+5nM4d2lO/Xt2cdd778GOSk8sz8dOPGDt+Ao+MeoLHHhpO6dKlufXGG/jp55/56efoj4mW\nlZ3N/Z+8x/NX30h6cgp/f+phOjVsEpGgepzYnL+3agvAlKULeWjchzx31T9ylj809kPaBU6IelzD\nXnmdF/55p/ss7x1Mp2YnU79G+Gd5HO8NG+h9llMYMeZdRt7yD8qWLs3wG/pQu2pV1m/eTOaAQbRp\n2pjkY4898riyshj69LO8OGww6amVufD2fnRq3ZL6tWrlrFO9SirDb7+Vlz748ID3l0lK4sPRjx1x\nHAfElZ3NsFdf54W7++Ue+83zHPu1a/He0Psi99fN/3DH/qABucf+3QOiduwDTHplNp+Ons4dr12e\n7/JTzmpERoMqXNtgENKqNjc93ZvbWz9MuYrHcPHA7tx6yoMQDDJq3t3M+WQB27fsikpchxRnVYjx\nNXpZIYjI9yJS9k+8b4WIND7I8o4istPb/vciMifP8ntFZJn3uDds/iAReeRw4ymshcuWUys9jZpp\naSQlJnJW61ZMmfd9xDqtTjiesqVLA9C0fl1+27QZgKTERJJKlQJg3779ZAeDUYtrwaLFHFezJjVr\n1CCpVCl6dOvK5GnTI9aZMm065/VyJYxup3dh1tdfEwwGOaZsWU45+SRKJ5WOWjzhFq76hZqVU6lZ\nKZWkxES6N23O1KULI9YpVyb3ENq1d0/E93rykvnUqFSZ+mnViCb3Wabn+Sy/i1inVaPwz7Iev23a\nBEDtalWpXbUqAGkVK1I5OZlN27ZFJa4FP/5ErepVqVmtKkmlStG9fTumzP46Yp2M9HSkTm1KJBTd\nT8qBx37LA/fXCXn319E/9gEWzfyZbZt2FLi89TlNc0pWOmcFx6aUpWLVZJp3O57vJv7A9s072b5l\nF99N/IHmZ0b3ROmgEhIK/4gBxa4EpqonHcXNL1HVU/LOFJH2wAVAKAHOEZHpqjrjKMYCwG+bt1C1\nUqWc6aqVKrJg2fIC1/9g+kzaNW2SM7124yZuGPEYv/62nn69L4jaGehv69dTtWp6znR6ehoLFi46\nYJ1q3jqJiYmUL1eOzVu2UqliSlRiKDC2rVuoViH3/0yvkMKClSsOWO+tWTN47b9T2Ze1n5euuRmA\nHXv28OL0STx/9U28MnNydOPatJmqlQ/js5w2g3YnHjg69oJly9m3fz+10tKiEtf6jRupmpqaM52e\nWpkF+mOh379n714yb+1LyZIl6XPB+Zx+auuoxPXb5s15jv1KLFhWcIn9g+kzaHdi+LG/kRse8Y79\niy6M2rFfGKkZFfh95Zac6Q2rtpCakULljBQ2rNycM3/jqs1Uzji634cIMZKYCi0YDBarRyAQCAYC\ngXLe6xWBQGBIIBCY5b2+KWy9doFAYKH3GB0IBH4JBAKND7LdjoFA4JsClj0ZCAT6hU33CwQCT3qv\nBwUCgUe8100CgcCCQCDQIYr/b2YgEHghbPqyQCAwuoB1Lw0EArMDgUDpfJZVDwQCXwcCgfSiiisQ\nCCwKBAI1wqaXBQKB1LDpKwv6X4pqn3nLLw4EAq96rx8JBAIXhn22/fyIq6DPMhAIVAsEAhoIBFr7\nFNcrgUAgM8+8DO+5rvc9rBcr+8tbFtVjP+xROxgMLipg2WfBYLBt2PTkYDB4SjAY7BcMBgeEzb/X\nmxfV70BxeRS7KsR8HKOqpwIdgX+LSDkRKQ28Ddysqk2AGUCtg2wjJCAi34rIHBG5Imx+LeCXsOlf\ngZrhbxSR04G3gN6qGlmXdmRW5/lbNbx5Eby/fw9wtqruybtcVdcAi4B2RRhXzjoikghUADZG6e8f\naWzh3gbO9V63Ah4SkRXAbcC/ROSmooyroM9SRJKBscA9qjo7SjEVOq6CqOpq73k5MA04uSjj8uHY\nL4yCYj+iff1X81dIYG8DqOoKYDPugBBgp6pO85a9C2w9xHa+BWqqajOgN3Cf98UojK7AY0A3VV1y\nuP/AIcwFGohIHRFJ8mL7JHwFETkZeBb3BV4fNr9G6HqhiFQE2gJaVHF506ETgUxgiqpG92LEn4xN\nRBqETfYAfgJQ1XaqWltVa+M+0wdUdXQRxlXQZ5kEfAi8pqrvRymeQsdVEBGp6J0wIiKpQBsgWt+B\nWD32C+MT4HJcu7/WuN+ftcAE3O9FRe/R1Ztn8vFXSGC7w15nUfB1v4P+cKrqH6q61Xv9P+Aj3JcR\nXInruLDVawHIp9DgAAAVY0lEQVQrw6Z/BEoCB1w/O1Kquh+4CXeQLwXeVdXFIjJERM72VnsYKAe8\n5zVACX3Jj8ddr5sPTAceUdWFREEh43oRqCwiPwN9gbtD7/dKOCOBK0VklYhE7Up2IWO7SUQWi8j3\nXmxXFLC5qDnCz/JCoD1uf4UaGkXlenBh4hKRFiKyCnct+FkRWey9/XjgG+8Ymwr8O1oncbF67HvG\nALNwJ8urgGuA//MeAOOA5cDPwPNAqInrJmAoLjnPBYZ480w+EoLBojjhLToiEgTKq+p270ewp6ou\n8patAHrizqaXARep6kwRyQTeA5qE1s1nu9WAdaoaFJFKuIN+gKp+LCIdgcdx1UsAc3DVk9NFZBDu\nCzQKGA8MUdV3ov6PG2PMX8xfoQR2AK8e/CLgKRFZgLs+9ush3nY+sMg7I5+Bq6r52NveNOADYLH3\n+CDvdS5VXQl0wV0zuTJq/4wxxvxFFbsSmDHGmL+Gv2QJzBhjTPwrdjcyHykR+YYD98tsVf2//NY3\nxhjjD6tCNMYYE5esCtEYY0xcsgRmjDEmLlkCM0VORBoWZp6JJCLHiEhARE4IPfyOKZaJSPV85p3o\nRyx5YjigyyoRyX/cFXNQ1oijGPO6Q3oZyFDVOiLSDNelziB/I+MtoFkh5hU5r3uhi4D6hH0/VLW/\nb0EBInIj8G9crwzZ3uwgULfANxUBERmB6y1iB66njWbA9ar6hp9xeT4UkS6quh3AS/jvAw0O/raj\nbrSIXKiqCiAiFwK3A6/5G1b8sQRWvD0NDMP98AF8D7wODPIjGK8vvDSgjIgcT+74rxWAIx95MTo+\nwCWIecABHb/66A6gsar+csg1i9bpqnqHiPTAdTr7d1w3SbGQwB4FPhCR7kBtXPdvR71LsEK4FHhX\nRLoCLYH7cJ0cmMNkCax4q6Cq40VkOICqZovIXh/juQTXg3t13I9cyFbgIV8iOlAtVW3kdxD5WBeD\nyStce1wPNGu87tx8p6pvi0hNXIfejYDrVPUrn8NCVReKSF9gIq6P1K6q+pvPYcUlS2DFW5aIlMLr\nqFhEMsitfipyqjoKGCUi/1LVB/yK4xAWiUg1VV3rdyB5TBSRh3A/xjkdVB+F0Q0O13oReRo4Czdc\nUSLuR9k3XokrZAmuo+OJwDEi0l1Vx+X/zqMeV96TtCAuvltFxPdq6nhkCax4ewo3xEaq16nw5bhx\nkfz2UT4NELaGxo3y2WBcL+XfE5koLvQvJMB9duB6ew/x/RoYcDGuZP2qqm4Wkdq4UQT8dGee6e1A\nE+8RJLL0X5R25Jn+wJcoihG7kbmYE5G2QC/c9aZPVXWmzyGFRgWoSe4YbBWA9biEcVGUB2M8LF5P\nLLNx479lhear6qt+xRQvRCQNqOvn52f+WqwEVsyp6pfAl37HkcdHwDRV/QhARM4BOuNKi6PIHZbG\nD0mqGq0Rlo+YiJRW1T0ickx+y1V1Z1HHFE5EZuKGKEoAvgO2iMg4Vc1bCipyIpIAXA0EVPUur3RY\n3c/rYN4J5d/JHXV5JfCO9z01h8nuAyvGRGSuiHyd5zHRG/CvnI+hdQwlLwBvWJoO3rA0ZX2Lypkt\nIk18jiHcLO95O7DNe94eNu23ct5Arz2BN3HVdGf6G1KOkbjWfed409two2j7QkQGAE8CK3D76k3v\n9ZMicq9fccUzK4EVb5Nx97yEqr8uA9YAGbgm9pf5FFcJETktdCYsIqeSezLlWyMTT0vcCMJK5DWw\nln4Eo6rNvOdYPdks7T13At72Wrru9zOgMJ2Ak3HVwajqRhEp42M8V+AGzQ0fJR4ReQpYiBuJ2RwG\nS2DFWwdVPTU0ISKfAV8Bp+JaP/nlRuAdEQlVfx0DXOyVCh/1LywAbvX578ebaSKyBPdb8n8ikkLY\ntUOf7fZGUAdAREqQe++hHxLI/wQtiL9xxS1LYMVbqoiUCTvjKw1U8r7Uu/wKSlVnikg9QHJnaej+\nNF8bS+QdSTtWeF0gPQOcSG6pB1X1tck67mTkRGC5qu7zmtH38TmmkIUicgmQ4F3/+ifgZyOmV4Gv\nReQ1IHRP33G4FqbWSOhPsARWvL0LzBKRd73pC4D3vZLOCr+CEpFhwCTgq7DEFRNEZC7efXPh/KpC\nDPMUMAB3XedMXOLw/RqYdzK0F7jMK+lMUdXvfA4rpC9uf1UD5gCf4Ho08YWqDhWRabhGHB292b8C\nt8bqiVOss2b0xZyI9MRdCwjiWv595nNIiMiduIvrLXAt1ybjfvjm+BoYICIdwibL4PpFXKOq//Ip\nJABEZJ6qNheRharaxJs3V1Vb+BzXZbiuykL3Vp0F3KWqb/oXlfmrsBJYMSYiFYA2wPG41n3NRKSv\nqnb2My5VfRh4WESSgN64m4eH4XMPDnBgFaKIfEFs3IYQahixyatOXAWk+hhPSD+guaquAxCRqsAE\nXAs7X3m3HvwTd2/aJd6IBw3DW8D6SUQ6476f36vqp37HE49itWWTiY6XcD98AeA53MX1r32NCBCR\n872WV3NxLSGfBVr7G1WBkoGqfgeBa/RSGRiOS6grcU2yfRdKXnlfx4CngVLASd70KmCgX8GIyKyw\n15fjvpMVgPu9vhHNYbISWPFWX1XPF5FzVHWMiHyAG/LCb+/i7m+6E1d1GCvNrvNeAyuB66pphH8R\nOaoa6p5pvIhUAsqoqu/XwIBlIjIYdxICrgHHch/jCddUVa8QkW4Aqrrda4nol/Am/DfgevJf4X2e\n0/C/C664YwmseAsNB7LX+5JsBqr4GE9INVzPGxcCI0RkJTAp7EfaT/3CXu/Hta7zvWPfPB3UhuZt\nBRZ5NxL75f+Ax4EFuMQ/Cbjex3jCRQyH490D5mcCC29wUEpVVwCo6qYYuncurlgCK95+9BLXW7j+\n/bbgxrnylaquF5H3cNVgvwJXAW2JgTPQGG4Ndi9wCu6GV3A9XiwAMkTkWr8a56jqetx1zFg0Q0T+\nBZQWkY64Vokf+xiPiMjXuHu+6otI+bBSdJKPccUtS2DFmKpe6r0c6X1xUoDxPoYE5NxQ3QpYBEzB\nDfDnewtEABE5DTc2WV3c9yMBCKpqmq+Bwc/ATao6D0Dc6Np9cftuDFCkCSy/EmE4v4YsyeMeoD/u\ndoOHcM3o/33QdxxdefdZaJijqrjrdeYwWTN6U+S8kWhnqqpvN1MXRESW4rr0mU1kb/S+DiYpIgtU\ntWmeefNV9cTQcxHHc7BrqUG/W7rGMxF5SlX/4Xcc8cBKYKbIhPWo/iWud4SIHtb97lnds0tV3/I7\niHzsFJGLVHUMgIhcBIROAIr8LFRVOxX13zxc3rXVKd5jsqqu8jmkworVFrkxx5rRm6IU6kE9vFf1\nbcROz+oA40TkLL+DyMdVwB0issvrBuwO4GoROZYDB3D0hYj43lozj2bA50A73PUw9W7fMMWEVSEa\nE0ZEfgcq4xLqHmLnGhgAIlIeIEaa0EcQkW9DvefHChEpiSvRdAGuBHaqamNfgzqEWNyPscqqEI1v\nvJ44co7BGKlCPMXvAA5GVbd5JR3f+vQ7iJjqUd1rLFQbd/P+ZKBNLNwSYaLHEpgpciLyN9y9Q9XJ\nHUoiSGx0JZXTWENEOsRos/pYvf7k1/hyBSlB7mWSIP6PNVdYsTIcTcyzKkRT5ETkZ9wQErNVNWZ/\nVGK1KkdEvlPVk32O4YSDLVdVP8eby+EN79Iad+P8FcCOvK05izCWuNhn8cRKYMYPm0KjMce4mKoS\nCxMLJZ2xB1kWxN1H5ysRScUlrjNw18CycAO6+mUsuTUOtYA/vOkKuBv66/gXWnyyBGb88KGI3AC8\nA+QMrx4j18DCved3AAWctWeH5vt11q6q8fBj+z25zeiHquqvfgYT2mci8gQwQ1Xf86YzgfZ+xhav\nrArRFBkRaaCqP4lIvsOqx8DowjFHRP53kMVBVfW9pAMgImmEdVbrd7KIZfndeC4i36vqSQW9x+TP\nSmCmKL0NNMcNrBmTPTXEWldSsV7S8ca0ehVIx1XRJQEbgZi47SBERKaraodDr1kkEkSknarOBBCR\nNtg9uX+KJTBTlMqKyPlALe9m4YhrTDHSf96L5NOVVKyIwZLOw7jrS+/gbhy+Btd0PdaU9zuAMDcC\nY0RkhzddFjfytzlMlsBMUfonbqiNdFwnq+GC5A5L76eY7Eoqlks6qvqjiJRS1SDwgoh8AwzwO648\n9vodQIiqzhSRuoDkztKYiS+eWAIzRUZVPwY+FpGRqhqrI9COE5GzVPVzvwPJI1ZLOvu859Ui0gtY\nAVTyLxzH64HjGlV9DkBVY61/wURcTy+JuKFVrBn9n2CNOIwJE6tdSYnIPFVtLiKLQl0hicg3qupr\nzyFep8Ljgfq4YV0qALer6ht+xgWxsX/yIyI34oZ12UTuzdUx0yAnnlgJzJhIMfeD54nJkg4wVlX/\nAObikhgikuxvSDmmikimqr7vdyB53AE09nuInuLAEpgxYVT1F6/3hvDrE7Ew3PsoEamIu7aUU9Lx\nNyQApuGqNA81zw9X4vXgD+wgRkrTwDpLXtFhCcyYMCJyCvAfcqsPE0XkfFX91t/IYquk4yX5JKCE\niJQlt0VpBeCYAt9YtGK1ND1RRB7C3VYSfiO/XQM7TJbAjIk0CrhaVSdDTuu/J4A2vkYVeyWde4CB\nuNajO8Lm/wHExLhgXmk6GagfAycg4S73ni8ImxcT3W/FG2vEYUyY/HpE8LOXhLCSzlfAqUSWdKaq\nakM/4goRkdGqepOfMRRERLoDzwJZqlrbK10PVNVePodmosTu/jYm0k4R6RiaEJEOgJ99NN6DG7G6\nCa6kExrJeinwpo9xARCrycszGGgBbAZQ1W+Aen4FIyKlvedj8nv4FVc8sypEYyLdCrwvInu86STg\nfL+CUdXBwOBYK+mIyGRV7eLddhBejRMrDSUAUNV1IhI+a09B6xaBWbgq3+3k9kofEhPj4cUbS2DG\nhFHVuSJSn8hWiPsO9p6iEEvJy3Op9xyrDSUAtolIOl6C9UrWW/wKJjS2nKpazVeU2DUwY3DVO6q6\np6CqHL+GeomHko6IlCI34f8QI7cdICItgWdw42zNBxoAZ6vqPF8DM1FjJTBjnLzVOyEJ+Fu9E9Ml\nHRFpi7svbSduX5URkd6xMGCpqn4tIp2A03CxfaWqvpXATPRZCcyYOBGLJR0RmQ/coqrTvel2wOi8\n4135xRuVOdQP4mxV3eBnPCa6rC7WmDAi8lhh5hU1r6SzHHeT9QfAcm/sMt+Fkpf3eqafsYQTkb8B\nPwA3A7cAS0TkXH+jMtFkCcyYSPkN7R4LAyE+CVyqqqKqAeAS4GmfYwLXq8QloQkRuRiY4GM84e4H\nTlPVbqraFXcz+nCfYzJRZNfAjAFE5ALgQqC2iLwbtqgC/t4HliNvSSdP83C/XAH0FZEXvOnSwEYR\nuQr/G5nsVtUfQxOq+pPXL6IpJiyBGeP8CIwFWnrPIX8Ak32JKNJEEblEVd+EmCrpxGTjEs/HInIP\nbpTtBOAq4KNQ341+tSw10WONOIwJIyKVVHWT33HkFTZOWehG3NK4EZnB/5JOTBKR7IMsDqqq3Tgc\n56wEZkykP0TkOuAkoExopqpe7V9IQIyWdESkJvAQcCKR+8v3jmnthuHizxKYMZGexX0vOuEaSVwM\nzPA1IlzP6n7HUICXcMOCnIRrWHIDsMzXiPIhIhep6hi/4zDRZWcoxkRqqapXAFtUdTjQFmjkc0yI\nSE0RGSMiS0Rkeejhd1xAqqq+COxX1Vm4QSS7+xtSvu70OwATfZbAjIkUaqWWJSLHqOpWIBauL70E\nTMI1RrgE+BJ41deInL3e83YRqQWUAqr4GE9BEg69iok3lsCMibRJRCoC44HPReQ/wGqfY4LYLenM\nEJFKwFPAPFz14cf+hpSvUX4HYKLPEpgxkXqo6mbcOFzPA1PxcTiVMLFa0pmAGzDydaA50Bv41N+Q\nHBFJFpHQb9w3ItJbRJJ8DcpElSUwYyJVEpEkVc1W1TdwSay030ERuyWdh3H3yqG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AMO84QQmP4niWdqNW8yrsWXeElPNXSEm+yp51R6jVwriLxD6zmbJ+ZQgsU8bS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\n+qNXmkoS1idU8jgGx730nrt+GfArcJKInKOqd3ukK4mcmcwPAsmquk9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MImJGyIh1Drrz\n0CiAiFQDQt5KMorACuAJoAGQAOwELgaWeCnK+OuYETJinVHAeCBJRIYAaUBfbyUZReBjYAewAFjv\nsRYjClhgghHziEhjnJlVBZimqis8lmT8RURkmao281qHET2sJWTEPKr6I/Cj1zqMqPCdiJyoqku9\nFmJEB2sJGYbhe0RkKU6/XhzQCFgF7Mdp3aqqNvdQnlEEzAgZhuF7ROT4I+1X1TXFpcWILmaEDMMw\nDM+wwaqGYRiGZ5gRMgzDMDzDjJBhGIbhGWaEDMMwDM/4f6og/ghpuueLAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -1525,18 +1525,18 @@ "metadata": { "id": "RNLaUYs3CHjQ", "colab_type": "code", + "outputId": "c1ed0d9e-d8cf-4c76-e3ec-a6a659a31c13", "colab": { "base_uri": "https://localhost:8080/", - "height": 254 - }, - "outputId": "6a1973a5-7909-454b-f17e-f2ae10c029eb" + "height": 266 + } }, "source": [ "#Matriz de correlação\n", "Matrix_Corr = df_50k.corr().where(np.triu(np.ones(df_50k.corr().shape), k=1).astype(np.bool))\n", "Matrix_Corr" ], - "execution_count": 188, + "execution_count": 11, "outputs": [ { "output_type": "execute_result", @@ -1660,7 +1660,7 @@ "metadata": { "tags": [] }, - "execution_count": 188 + "execution_count": 11 } ] }, @@ -1669,11 +1669,11 @@ "metadata": { "id": "-ohsbrRdGmJJ", "colab_type": "code", + "outputId": "33c7a852-b1e8-42bc-92c5-60213abd6ae2", "colab": { "base_uri": "https://localhost:8080/", - "height": 118 - }, - "outputId": "d07a691e-1411-47f3-8160-d6e4d96493be" + "height": 119 + } }, "source": [ "set_Colunas_Correlacionadas = set()\n", @@ -1686,7 +1686,7 @@ "\n", "set_Colunas_Correlacionadas" ], - "execution_count": 189, + "execution_count": 12, "outputs": [ { "output_type": "execute_result", @@ -1703,7 +1703,7 @@ "metadata": { "tags": [] }, - "execution_count": 189 + "execution_count": 12 } ] }, @@ -1712,11 +1712,11 @@ "metadata": { "id": "_0MZj06uIhtG", "colab_type": "code", + "outputId": "1e569328-475c-4f04-e933-31af809d006e", "colab": { "base_uri": "https://localhost:8080/", - "height": 692 - }, - "outputId": "d0195f71-384e-4525-ca85-06835aa9fe31" + "height": 681 + } }, "source": [ "fig, ax = plt.subplots(figsize=(10, 10)) \n", @@ -1724,24 +1724,24 @@ "mask[np.triu_indices_from(mask)] = 1\n", "sns.heatmap(df_50k.corr().abs(), mask= mask, ax= ax, cmap='RdPu', annot= True, fmt= '.2f', center= 0)" ], - "execution_count": 190, + "execution_count": 13, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 190 + "execution_count": 13 }, { "output_type": "display_data", "data": { - "image/png": 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76frqivLoS1uB9ncsJz93GpDQs3IV3ZfdM+TnDceEqfmavC9A7vC5FF5XWZp9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\nX46IDyaOZjbgvBOild0SSQfVDiQdCJS9LtUZZLXUdiLrmej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49fouSRp/Wjphid27yJWbp3dyVQ8xbdL225SSXN8Pkzuhs42Olx9C34LbRjPkERPTtux7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\nG60MfNcAp5CtL1G6bktJXwS+CbzKivIb2DlZqNY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" ] @@ -1798,11 +1798,11 @@ "metadata": { "id": "fTUWOuBU30or", "colab_type": "code", + "outputId": "360e667c-8063-4791-8723-e7adc992cea4", "colab": { "base_uri": "https://localhost:8080/", - "height": 301 - }, - "outputId": "4607aa92-b753-4123-f578-0274179472ad" + "height": 295 + } }, "source": [ "plt.hist(race)\n", @@ -1811,12 +1811,12 @@ "plt.ylabel('Frequencia')\n", "plt.show()" ], - "execution_count": 192, + "execution_count": 15, "outputs": [ { "output_type": "display_data", "data": { - "image/png": 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B6bn4TmB0RGyT3x8AXDLIMjMzq0iRFs73SaMNnAEsRxo/bQrwg/4sHBGnArsBbwGmRsQc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\ndwEz6vb5blLdTSU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" ] @@ -1842,11 +1842,11 @@ "metadata": { "id": "OzAQWltWQDVa", "colab_type": "code", + "outputId": "8dcd7e19-9513-4b40-803f-71be01d76a7e", "colab": { "base_uri": "https://localhost:8080/", - "height": 301 - }, - "outputId": "82876343-91fa-4cd3-ad26-e3e534468b43" + "height": 295 + } }, "source": [ "plt.hist(hoursweek)\n", @@ -1855,12 +1855,12 @@ "plt.ylabel('Frequencia')\n", "plt.show()" ], - "execution_count": 193, + "execution_count": 16, "outputs": [ { "output_type": "display_data", "data": { - "image/png": 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Z8OFAY2ZmhXKgMTOzQjnQmJlZoRr1ezQ10SwDd0bEBOBSYEdgHXAP8BFJKyJi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\nmRXKgcbMzArlQGNmZoVyoDEzs0L9f8OIBJ6VmJSmAAAAAElFTkSuQmCC\n", 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" ] @@ -1886,35 +1886,35 @@ "metadata": { "id": "VE6vRQbl8Yb7", "colab_type": "code", + "outputId": "eae4c225-482d-41d5-bdde-8e6b13edb0f8", "colab": { "base_uri": "https://localhost:8080/", - "height": 481 - }, - "outputId": "3629f987-82a7-4dde-e0ca-ba9df8baee22" + "height": 475 + } }, "source": [ "#Relação da Escolaridade e Raça\n", "fig, ax = plt.subplots(figsize=(15,7))\n", "df_50k.groupby(['education','race']).count()['age'].unstack().plot(ax=ax, title='Raça vs Escolaridade')" ], - "execution_count": 203, + "execution_count": 17, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 203 + "execution_count": 17 }, { "output_type": "display_data", "data": { - "image/png": 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p93vk5sd+xPxwUy1kJS20pOut7yKRSEas78pkmbIGLxKJ7PV92GUN3l76k9LO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\n/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awtl8O3km4OPVI+Qr++ms23gfeKyBuBw1V18xzq0MtVtaWqHXxAWJwLT8LXlYS68+v4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\n+XAAAAAASUVORK5CYII=\n", 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Mnj2blStXYoyhqKiI/fv3A/DCCy/w9NNPh9aRtba2cuDAAQDOO++8PgU5gC984QsALFy4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" ] @@ -1940,35 +1940,35 @@ "metadata": { "id": "lsp3XAlTRd3Y", "colab_type": "code", + "outputId": "058f4e57-bf02-4302-b7d3-3b6221f8856b", "colab": { "base_uri": "https://localhost:8080/", - "height": 481 - }, - "outputId": "3b1670b5-3f89-4896-d618-560cc6f2f67d" + "height": 475 + } }, "source": [ "#Relação de horas trabalhadas pelo indicatico de valor\n", "fig, ax = plt.subplots(figsize=(15,7))\n", "df_50k.groupby(['relationship','ind_50k']).count()['age'].unstack().plot(ax=ax, title='Horas Trabalhadas vs Indicativo do Valor')" ], - "execution_count": 206, + "execution_count": 18, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 206 + "execution_count": 18 }, { "output_type": "display_data", "data": { - "image/png": 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KWwwfPpJ77vlDqZ777rtv57zzzucXvxhaFi/llGhaqchRBIJBCooCFBYFyI/8u7DIT0FR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\n5cE7+X5sfy21nz6lb8ik0+rbO4WhhWm8+/k2jlT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Kda3m07Y1fyDEn19fzd4D1Xxv6iBy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\nM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NZmYm5cqVo3Tp0ixfvpzVq1cTGxvL6NGj+fDDD4+s9/jjj/P4448zYMAAXnvttbzereNS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ZzGXT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\nIaI+0AAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -2027,16 +2027,16 @@ "metadata": { "id": "BaRNhWV1cFlr", "colab_type": "code", + "outputId": "c31f9ec4-aa8a-4442-b9d1-ab05a19bd486", "colab": { "base_uri": "https://localhost:8080/", - "height": 284 - }, - "outputId": "c32846d7-0432-4479-b292-226e6ed97f8d" + "height": 297 + } }, "source": [ "df_50k.describe()" ], - "execution_count": 211, + "execution_count": 19, "outputs": [ { "output_type": "execute_result", @@ -2072,33 +2072,33 @@ " \n", " \n", " count\n", - " 32537.000000\n", - " 3.253700e+04\n", - " 32537.000000\n", - " 32537.000000\n", - " 32537.000000\n", - " 32537.000000\n", - " 32537.000000\n", + " 32561.000000\n", + " 3.256100e+04\n", + " 32561.000000\n", + " 32561.000000\n", + " 32561.000000\n", + " 32561.000000\n", + " 32561.000000\n", " \n", " \n", " mean\n", - " 38.585549\n", - " 1.897808e+05\n", - " 10.081815\n", - " 1078.443741\n", - " 87.368227\n", - " 40.440329\n", - " 0.240926\n", + " 38.581647\n", + " 1.897784e+05\n", + " 10.080679\n", + " 1077.648844\n", + " 87.303830\n", + " 40.437456\n", + " 0.240810\n", " \n", " \n", " std\n", - " 13.637984\n", - " 1.055565e+05\n", - " 2.571633\n", - " 7387.957424\n", - " 403.101833\n", - " 12.346889\n", - " 0.427652\n", + " 13.640433\n", + " 1.055500e+05\n", + " 2.572720\n", + " 7385.292085\n", + " 402.960219\n", + " 12.347429\n", + " 0.427581\n", " \n", " \n", " min\n", @@ -2133,7 +2133,7 @@ " \n", " 75%\n", " 48.000000\n", - " 2.369930e+05\n", + " 2.370510e+05\n", " 12.000000\n", " 0.000000\n", " 0.000000\n", @@ -2156,13 +2156,13 @@ ], "text/plain": [ " age fnlwgt ... hours-per-week ind_50k\n", - "count 32537.000000 3.253700e+04 ... 32537.000000 32537.000000\n", - "mean 38.585549 1.897808e+05 ... 40.440329 0.240926\n", - "std 13.637984 1.055565e+05 ... 12.346889 0.427652\n", + "count 32561.000000 3.256100e+04 ... 32561.000000 32561.000000\n", + "mean 38.581647 1.897784e+05 ... 40.437456 0.240810\n", + "std 13.640433 1.055500e+05 ... 12.347429 0.427581\n", "min 17.000000 1.228500e+04 ... 1.000000 0.000000\n", "25% 28.000000 1.178270e+05 ... 40.000000 0.000000\n", "50% 37.000000 1.783560e+05 ... 40.000000 0.000000\n", - "75% 48.000000 2.369930e+05 ... 45.000000 0.000000\n", + "75% 48.000000 2.370510e+05 ... 45.000000 0.000000\n", "max 90.000000 1.484705e+06 ... 99.000000 1.000000\n", "\n", "[8 rows x 7 columns]" @@ -2171,7 +2171,7 @@ "metadata": { "tags": [] }, - "execution_count": 211 + "execution_count": 19 } ] }, @@ -2180,16 +2180,16 @@ "metadata": { "id": "KZQOGm7XcrRf", "colab_type": "code", + "outputId": "3e4fbbd2-25a4-47d8-b81c-ecc6a56f63bc", "colab": { "base_uri": "https://localhost:8080/", - "height": 166 - }, - "outputId": "7a867ba6-727c-4af1-b2e2-88180a901581" + "height": 173 + } }, "source": [ "df_50k.describe(include=[\"O\"])" ], - "execution_count": 212, + "execution_count": 20, "outputs": [ { "output_type": "execute_result", @@ -2227,15 +2227,15 @@ " \n", " \n", " count\n", - " 32537\n", - " 32537\n", - " 32537\n", - " 32537\n", - " 32537\n", - " 32537\n", - " 32537\n", - " 32537\n", - " 32537\n", + " 32561\n", + " 32561\n", + " 32561\n", + " 32561\n", + " 32561\n", + " 32561\n", + " 32561\n", + " 32561\n", + " 32561\n", " \n", " \n", " unique\n", @@ -2263,15 +2263,15 @@ " \n", " \n", " freq\n", - " 22673\n", - " 10494\n", - " 14970\n", - " 4136\n", - " 13187\n", - " 27795\n", - " 21775\n", - " 29153\n", - " 24698\n", + " 22696\n", + " 10501\n", + " 14976\n", + " 4140\n", + " 13193\n", + " 27816\n", + " 21790\n", + " 29170\n", + " 24720\n", " \n", " \n", "\n", @@ -2279,10 +2279,10 @@ ], "text/plain": [ " workclass education marital-status ... sex native-country flag_50k\n", - "count 32537 32537 32537 ... 32537 32537 32537\n", + "count 32561 32561 32561 ... 32561 32561 32561\n", "unique 9 16 7 ... 2 42 2\n", "top Private HS-grad Married-civ-spouse ... Male United-States <=50K\n", - "freq 22673 10494 14970 ... 21775 29153 24698\n", + "freq 22696 10501 14976 ... 21790 29170 24720\n", "\n", "[4 rows x 9 columns]" ] @@ -2290,7 +2290,7 @@ "metadata": { "tags": [] }, - "execution_count": 212 + "execution_count": 20 } ] }, @@ -2309,17 +2309,17 @@ "metadata": { "id": "6mdOurBJvIed", "colab_type": "code", + "outputId": "722888a6-658c-4eca-ff4b-a8238870982e", "colab": { "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "ec658fe1-63de-46cc-e499-4ea51c5119c1" + "height": 49 + } }, "source": [ "#Lista das linhas duplicadas\n", "df_50k[df_50k.duplicated()]" ], - "execution_count": 207, + "execution_count": 24, "outputs": [ { "output_type": "execute_result", @@ -2362,500 +2362,20 @@ " \n", " \n", " \n", - " \n", - " 4881\n", - " 25\n", - " Private\n", - " 308144\n", - " Bachelors\n", - " 13\n", - " Never-married\n", - " Craft-repair\n", - " Not-in-family\n", - " White\n", - " Male\n", - " 0\n", - " 0\n", - " 40\n", - " Mexico\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 5104\n", - " 90\n", - " Private\n", - " 52386\n", - " Some-college\n", - " 10\n", - " Never-married\n", - " Other-service\n", - " Not-in-family\n", - " Asian-Pac-Islander\n", - " Male\n", - " 0\n", - " 0\n", - " 35\n", - " United-States\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 9171\n", - " 21\n", - " Private\n", - " 250051\n", - " Some-college\n", - " 10\n", - " Never-married\n", - " Prof-specialty\n", - " Own-child\n", - " White\n", - " Female\n", - " 0\n", - " 0\n", - " 10\n", - " United-States\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 11631\n", - " 20\n", - " Private\n", - " 107658\n", - " Some-college\n", - " 10\n", - " Never-married\n", - " Tech-support\n", - " Not-in-family\n", - " White\n", - " Female\n", - " 0\n", - " 0\n", - " 10\n", - " United-States\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 13084\n", - " 25\n", - " Private\n", - " 195994\n", - " 1st-4th\n", - " 2\n", - " Never-married\n", - " Priv-house-serv\n", - " Not-in-family\n", - " White\n", - " Female\n", - " 0\n", - " 0\n", - " 40\n", - " Guatemala\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 15059\n", - " 21\n", - " Private\n", - " 243368\n", - " Preschool\n", - " 1\n", - " Never-married\n", - " Farming-fishing\n", - " Not-in-family\n", - " White\n", - " Male\n", - " 0\n", - " 0\n", - " 50\n", - " Mexico\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 17040\n", - " 46\n", - " Private\n", - " 173243\n", - " HS-grad\n", - " 9\n", - " Married-civ-spouse\n", - " Craft-repair\n", - " Husband\n", - " White\n", - " Male\n", - " 0\n", - " 0\n", - " 40\n", - " United-States\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 18555\n", - " 30\n", - " Private\n", - " 144593\n", - " HS-grad\n", - " 9\n", - " Never-married\n", - " Other-service\n", - " Not-in-family\n", - " Black\n", - " Male\n", - " 0\n", - " 0\n", - " 40\n", - " ?\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 18698\n", - " 19\n", - " Private\n", - " 97261\n", - " HS-grad\n", - " 9\n", - " Never-married\n", - " Farming-fishing\n", - " Not-in-family\n", - " White\n", - " Male\n", - " 0\n", - " 0\n", - " 40\n", - " United-States\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 21318\n", - " 19\n", - " Private\n", - " 138153\n", - " Some-college\n", - " 10\n", - " Never-married\n", - " Adm-clerical\n", - " Own-child\n", - " White\n", - " Female\n", - " 0\n", - " 0\n", - " 10\n", - " United-States\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 21490\n", - " 19\n", - " Private\n", - " 146679\n", - " Some-college\n", - " 10\n", - " Never-married\n", - " Exec-managerial\n", - " Own-child\n", - " Black\n", - " Male\n", - " 0\n", - " 0\n", - " 30\n", - " United-States\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 21875\n", - " 49\n", - " Private\n", - " 31267\n", - " 7th-8th\n", - " 4\n", - " Married-civ-spouse\n", - " Craft-repair\n", - " Husband\n", - " White\n", - " Male\n", - " 0\n", - " 0\n", - " 40\n", - " United-States\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 22300\n", - " 25\n", - " Private\n", - " 195994\n", - " 1st-4th\n", - " 2\n", - " Never-married\n", - " Priv-house-serv\n", - " Not-in-family\n", - " White\n", - " Female\n", - " 0\n", - " 0\n", - " 40\n", - " Guatemala\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 22367\n", - " 44\n", - " Private\n", - " 367749\n", - " Bachelors\n", - " 13\n", - " Never-married\n", - " Prof-specialty\n", - " Not-in-family\n", - " White\n", - " Female\n", - " 0\n", - " 0\n", - " 45\n", - " Mexico\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 22494\n", - " 49\n", - " Self-emp-not-inc\n", - " 43479\n", - " Some-college\n", - " 10\n", - " Married-civ-spouse\n", - " Craft-repair\n", - " Husband\n", - " White\n", - " Male\n", - " 0\n", - " 0\n", - " 40\n", - " United-States\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 25872\n", - " 23\n", - " Private\n", - " 240137\n", - " 5th-6th\n", - " 3\n", - " Never-married\n", - " Handlers-cleaners\n", - " Not-in-family\n", - " White\n", - " Male\n", - " 0\n", - " 0\n", - " 55\n", - " Mexico\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 26313\n", - " 28\n", - " Private\n", - " 274679\n", - " Masters\n", - " 14\n", - " Never-married\n", - " Prof-specialty\n", - " Not-in-family\n", - " White\n", - " Male\n", - " 0\n", - " 0\n", - " 50\n", - " United-States\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 28230\n", - " 27\n", - " Private\n", - " 255582\n", - " HS-grad\n", - " 9\n", - " Never-married\n", - " Machine-op-inspct\n", - " Not-in-family\n", - " White\n", - " Female\n", - " 0\n", - " 0\n", - " 40\n", - " United-States\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 28522\n", - " 42\n", - " Private\n", - " 204235\n", - " Some-college\n", - " 10\n", - " Married-civ-spouse\n", - " Prof-specialty\n", - " Husband\n", - " White\n", - " Male\n", - " 0\n", - " 0\n", - " 40\n", - " United-States\n", - " >50K\n", - " 1\n", - " \n", - " \n", - " 28846\n", - " 39\n", - " Private\n", - " 30916\n", - " HS-grad\n", - " 9\n", - " Married-civ-spouse\n", - " Craft-repair\n", - " Husband\n", - " White\n", - " Male\n", - " 0\n", - " 0\n", - " 40\n", - " United-States\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 29157\n", - " 38\n", - " Private\n", - " 207202\n", - " HS-grad\n", - " 9\n", - " Married-civ-spouse\n", - " Machine-op-inspct\n", - " Husband\n", - " White\n", - " Male\n", - " 0\n", - " 0\n", - " 48\n", - " United-States\n", - " >50K\n", - " 1\n", - " \n", - " \n", - " 30845\n", - " 46\n", - " Private\n", - " 133616\n", - " Some-college\n", - " 10\n", - " Divorced\n", - " Adm-clerical\n", - " Unmarried\n", - " White\n", - " Female\n", - " 0\n", - " 0\n", - " 40\n", - " United-States\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 31993\n", - " 19\n", - " Private\n", - " 251579\n", - " Some-college\n", - " 10\n", - " Never-married\n", - " Other-service\n", - " Own-child\n", - " White\n", - " Male\n", - " 0\n", - " 0\n", - " 14\n", - " United-States\n", - " <=50K\n", - " 0\n", - " \n", - " \n", - " 32404\n", - " 35\n", - " Private\n", - " 379959\n", - " HS-grad\n", - " 9\n", - " Divorced\n", - " Other-service\n", - " Not-in-family\n", - " White\n", - " Female\n", - " 0\n", - " 0\n", - " 40\n", - " United-States\n", - " <=50K\n", - " 0\n", - " \n", " \n", "\n", "" ], "text/plain": [ - " age workclass fnlwgt ... native-country flag_50k ind_50k\n", - "4881 25 Private 308144 ... Mexico <=50K 0\n", - "5104 90 Private 52386 ... United-States <=50K 0\n", - "9171 21 Private 250051 ... United-States <=50K 0\n", - "11631 20 Private 107658 ... United-States <=50K 0\n", - "13084 25 Private 195994 ... Guatemala <=50K 0\n", - "15059 21 Private 243368 ... Mexico <=50K 0\n", - "17040 46 Private 173243 ... United-States <=50K 0\n", - "18555 30 Private 144593 ... ? <=50K 0\n", - "18698 19 Private 97261 ... United-States <=50K 0\n", - "21318 19 Private 138153 ... United-States <=50K 0\n", - "21490 19 Private 146679 ... United-States <=50K 0\n", - "21875 49 Private 31267 ... United-States <=50K 0\n", - "22300 25 Private 195994 ... Guatemala <=50K 0\n", - "22367 44 Private 367749 ... Mexico <=50K 0\n", - "22494 49 Self-emp-not-inc 43479 ... United-States <=50K 0\n", - "25872 23 Private 240137 ... Mexico <=50K 0\n", - "26313 28 Private 274679 ... United-States <=50K 0\n", - "28230 27 Private 255582 ... United-States <=50K 0\n", - "28522 42 Private 204235 ... United-States >50K 1\n", - "28846 39 Private 30916 ... United-States <=50K 0\n", - "29157 38 Private 207202 ... United-States >50K 1\n", - "30845 46 Private 133616 ... United-States <=50K 0\n", - "31993 19 Private 251579 ... United-States <=50K 0\n", - "32404 35 Private 379959 ... United-States <=50K 0\n", - "\n", - "[24 rows x 16 columns]" + "Empty DataFrame\n", + "Columns: [age, workclass, fnlwgt, education, education-num, marital-status, occupation, relationship, race, sex, capital-gain, capital-loss, hours-per-week, native-country, flag_50k, ind_50k]\n", + "Index: []" ] }, "metadata": { "tags": [] }, - "execution_count": 207 + "execution_count": 24 } ] }, @@ -2864,21 +2384,21 @@ "metadata": { "id": "7_xocTl-wz3-", "colab_type": "code", + "outputId": "33f77ec1-db3d-4659-b102-e5d8f6583a0f", "colab": { "base_uri": "https://localhost:8080/", "height": 34 - }, - "outputId": "1c283687-3bf5-4365-c5e3-fbd799cfd6c8" + } }, "source": [ "print(f'Há {df_50k[df_50k.duplicated()].count()[0]} registos duplicados')" ], - "execution_count": 208, + "execution_count": 25, "outputs": [ { "output_type": "stream", "text": [ - "Há 24 registos duplicados\n" + "Há 0 registos duplicados\n" ], "name": "stdout" } @@ -2889,18 +2409,18 @@ "metadata": { "id": "FESe-vmcxodU", "colab_type": "code", + "outputId": "cd262f3c-54e8-4bef-bcc8-d45c78e06220", "colab": { "base_uri": "https://localhost:8080/", "height": 34 - }, - "outputId": "0a24eec3-839e-409d-8769-53c4d924d9ee" + } }, "source": [ "#Exclusão das linhas duplicadas\n", "df_50k = df_50k.drop_duplicates()\n", "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " ], - "execution_count": 209, + "execution_count": 26, "outputs": [ { "output_type": "stream", @@ -3148,6 +2668,368 @@ "execution_count": 0, "outputs": [] }, + { + "cell_type": "markdown", + "metadata": { + "id": "PTxg_-lIubtq", + "colab_type": "text" + }, + "source": [ + "##Carregamento dos dados(Sem CSV :( )" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tMH_RurjvpKN", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 343 + }, + "outputId": "8c847dfa-df0c-466d-8985-7ee08a9a7585" + }, + "source": [ + "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n", + "\n", + "copyDataframe = df_50k\n", + "copyDataframe.head()\n", + "\n", + "copyDataframe['sex'].unique()\n", + "copyDataframe['race'].unique()\n", + "\n", + "le = LabelEncoder()\n", + "copyDataframe['sex_le'] = le.fit_transform(copyDataframe['sex'])\n", + "copyDataframe['race_le'] = le.fit_transform(copyDataframe['race'])\n", + "copyDataframe['marital_status_le'] = le.fit_transform(copyDataframe['marital-status'])\n", + "copyDataframe['relationship_le'] = le.fit_transform(copyDataframe['relationship'])\n", + "copyDataframe['flag_50k_le'] = le.fit_transform(copyDataframe['flag_50k'])\n", + "\n", + "\n", + "dummiesSex= pd.get_dummies(copyDataframe['sex'])\n", + "copyDataframe= pd.concat([copyDataframe, dummiesSex], axis= 1)\n", + "\n", + "dummiesRace= pd.get_dummies(copyDataframe['race'])\n", + "copyDataframe= pd.concat([copyDataframe, dummiesRace], axis= 1)\n", + "\n", + "dummiesMaritalStatus= pd.get_dummies(copyDataframe['marital-status'])\n", + "copyDataframe= pd.concat([copyDataframe, dummiesMaritalStatus], axis= 1)\n", + "\n", + "dummiesRelationship= pd.get_dummies(copyDataframe['relationship'])\n", + "copyDataframe= pd.concat([copyDataframe, dummiesRelationship], axis= 1)\n", + "\n", + "copyDataframe.head()" + ], + "execution_count": 38, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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039State-gov77516Bachelors13Never-marriedAdm-clericalNot-in-familyWhiteMale2174040United-States<=50K01441001000010000100010000
150Self-emp-not-inc83311Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale0013United-States<=50K01420001000010010000100000
238Private215646HS-grad9DivorcedHandlers-cleanersNot-in-familyWhiteMale0040United-States<=50K01401001000011000000010000
353Private23472111th7Married-civ-spouseHandlers-cleanersHusbandBlackMale0040United-States<=50K01220001001000010000100000
428Private338409Bachelors13Married-civ-spouseProf-specialtyWifeBlackFemale0040Cuba<=50K00225010001000010000000001
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" + ], + "text/plain": [ + " age workclass fnlwgt ... Own-child Unmarried Wife\n", + "0 39 State-gov 77516 ... 0 0 0\n", + "1 50 Self-emp-not-inc 83311 ... 0 0 0\n", + "2 38 Private 215646 ... 0 0 0\n", + "3 53 Private 234721 ... 0 0 0\n", + "4 28 Private 338409 ... 0 0 1\n", + "\n", + "[5 rows x 41 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 38 + } + ] + }, { "cell_type": "markdown", "metadata": { From c31358636e2aad44f1e87ce1463c1423ca26c5f5 Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Thu, 14 Nov 2019 21:21:17 +0000 Subject: [PATCH 24/35] Created using Colaboratory --- dswp_grupo1.ipynb | 46 +++++++++++++++++++++++----------------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index 7302f84c6..90a62726f 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -23,7 +23,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { @@ -126,7 +126,7 @@ "df_50k = pd.read_csv('https://raw.githubusercontent.com/MathMachado/DSWP/master/Dataframes/adult.data')\n", "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " ], - "execution_count": 2, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -152,7 +152,7 @@ "#Detecção de que o ficheiro não tem header\n", "df_50k.head(5)" ], - "execution_count": 4, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -337,7 +337,7 @@ " ]\n", "l_column" ], - "execution_count": 5, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -395,7 +395,7 @@ "source": [ "df_50k.info()" ], - "execution_count": 7, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -1234,7 +1234,7 @@ "\n", "df_50k.head(5)" ], - "execution_count": 8, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -1483,7 +1483,7 @@ " fmt= '.2f'\n", " )" ], - "execution_count": 10, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -1537,7 +1537,7 @@ "Matrix_Corr = df_50k.corr().where(np.triu(np.ones(df_50k.corr().shape), k=1).astype(np.bool))\n", "Matrix_Corr" ], - "execution_count": 11, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -1687,7 +1687,7 @@ "\n", "set_Colunas_Correlacionadas" ], - "execution_count": 12, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -1725,7 +1725,7 @@ "mask[np.triu_indices_from(mask)] = 1\n", "sns.heatmap(df_50k.corr().abs(), mask= mask, ax= ax, cmap='RdPu', annot= True, fmt= '.2f', center= 0)" ], - "execution_count": 13, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -1812,7 +1812,7 @@ "plt.ylabel('Frequencia')\n", "plt.show()" ], - "execution_count": 15, + "execution_count": 0, "outputs": [ { "output_type": "display_data", @@ -1856,7 +1856,7 @@ "plt.ylabel('Frequencia')\n", "plt.show()" ], - "execution_count": 16, + "execution_count": 0, "outputs": [ { "output_type": "display_data", @@ -1898,7 +1898,7 @@ "fig, ax = plt.subplots(figsize=(15,7))\n", "df_50k.groupby(['education','race']).count()['age'].unstack().plot(ax=ax, title='Raça vs Escolaridade')" ], - "execution_count": 17, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -1952,7 +1952,7 @@ "fig, ax = plt.subplots(figsize=(15,7))\n", "df_50k.groupby(['relationship','ind_50k']).count()['age'].unstack().plot(ax=ax, title='Horas Trabalhadas vs Indicativo do Valor')" ], - "execution_count": 18, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -2037,7 +2037,7 @@ "source": [ "df_50k.describe()" ], - "execution_count": 19, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -2190,7 +2190,7 @@ "source": [ "df_50k.describe(include=[\"O\"])" ], - "execution_count": 20, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -2320,7 +2320,7 @@ "#Lista das linhas duplicadas\n", "df_50k[df_50k.duplicated()]" ], - "execution_count": 24, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -2394,7 +2394,7 @@ "source": [ "print(f'Há {df_50k[df_50k.duplicated()].count()[0]} registos duplicados')" ], - "execution_count": 25, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -2421,7 +2421,7 @@ "df_50k = df_50k.drop_duplicates()\n", "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " ], - "execution_count": 26, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -2684,11 +2684,11 @@ "metadata": { "id": "tMH_RurjvpKN", "colab_type": "code", + "outputId": "8c847dfa-df0c-466d-8985-7ee08a9a7585", "colab": { "base_uri": "https://localhost:8080/", "height": 343 - }, - "outputId": "8c847dfa-df0c-466d-8985-7ee08a9a7585" + } }, "source": [ "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n", @@ -2721,7 +2721,7 @@ "\n", "copyDataframe.head()" ], - "execution_count": 38, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -4849,4 +4849,4 @@ ] } ] -} +} \ No newline at end of file From 0f37ecd16b11b8d35a5040920f3dfdfe07179c39 Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Thu, 14 Nov 2019 21:22:44 +0000 Subject: [PATCH 25/35] Created using Colaboratory From 90243af52bc3fadf09269756d18248527d4b3dbe Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Thu, 14 Nov 2019 21:23:21 +0000 Subject: [PATCH 26/35] Created using Colaboratory --- dswp_grupo1.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index 90a62726f..59456850a 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -33,7 +33,7 @@ "colab_type": "text" }, "source": [ - "#**CRISP-DM PROCESS**" + "#**CRISP-DM PROCESS.**" ] }, { From 6c963bae2aa0e5dfe1a68eac16b4936aa80dea79 Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Thu, 14 Nov 2019 21:29:55 +0000 Subject: [PATCH 27/35] Created using Colaboratory --- dswp_grupo1.ipynb | 221 +++++++++++++++++++++++----------------------- 1 file changed, 110 insertions(+), 111 deletions(-) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index 59456850a..0389aba64 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -33,7 +33,7 @@ "colab_type": "text" }, "source": [ - "#**CRISP-DM PROCESS.**" + "#**CRISP-DM PROCESS**" ] }, { @@ -116,7 +116,7 @@ "metadata": { "id": "0YTMtMPRbSYD", "colab_type": "code", - "outputId": "126bc93e-2756-4f64-cd0c-10fbd34a89e5", + "outputId": "85543ac5-ac32-4d09-ec4c-1bd8bc6cd131", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -126,7 +126,7 @@ "df_50k = pd.read_csv('https://raw.githubusercontent.com/MathMachado/DSWP/master/Dataframes/adult.data')\n", "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " ], - "execution_count": 0, + "execution_count": 132, "outputs": [ { "output_type": "stream", @@ -142,17 +142,17 @@ "metadata": { "id": "Lxq7B1Vnv1Ys", "colab_type": "code", - "outputId": "93350141-551c-41be-8704-0b578b6d914b", + "outputId": "12a84488-fae9-4294-dd1d-5ba13117eed4", "colab": { "base_uri": "https://localhost:8080/", - "height": 204 + "height": 296 } }, "source": [ "#Detecção de que o ficheiro não tem header\n", "df_50k.head(5)" ], - "execution_count": 0, + "execution_count": 133, "outputs": [ { "output_type": "execute_result", @@ -302,7 +302,7 @@ "metadata": { "tags": [] }, - "execution_count": 4 + "execution_count": 133 } ] }, @@ -311,10 +311,10 @@ "metadata": { "id": "HBKqXT9Bo-BC", "colab_type": "code", - "outputId": "ad9f2fe3-0a2a-42f2-8a61-a2ade20ec13c", + "outputId": "139d1e45-34ca-411a-c9b2-6be4f06ce0a9", "colab": { "base_uri": "https://localhost:8080/", - "height": 272 + "height": 269 } }, "source": [ @@ -337,7 +337,7 @@ " ]\n", "l_column" ], - "execution_count": 0, + "execution_count": 134, "outputs": [ { "output_type": "execute_result", @@ -363,7 +363,7 @@ "metadata": { "tags": [] }, - "execution_count": 5 + "execution_count": 134 } ] }, @@ -386,16 +386,16 @@ "metadata": { "id": "AjswmEdb4R8X", "colab_type": "code", - "outputId": "3600bcbd-6faa-4ccb-df7d-0164296757de", + "outputId": "2c9d3826-95aa-4ca2-8f54-2edd5113c0a0", "colab": { "base_uri": "https://localhost:8080/", - "height": 357 + "height": 353 } }, "source": [ "df_50k.info()" ], - "execution_count": 0, + "execution_count": 136, "outputs": [ { "output_type": "stream", @@ -430,7 +430,7 @@ "metadata": { "id": "CNyS0K4C4dfp", "colab_type": "code", - "outputId": "1ca0de0b-6db7-4ff5-d8ba-23d1a2961629", + "outputId": "1c272dba-bc70-4a2f-a69c-22d406471e35", "colab": { "base_uri": "https://localhost:8080/", "height": 333 @@ -439,7 +439,7 @@ "source": [ "df_50k.head(5)" ], - "execution_count": 0, + "execution_count": 137, "outputs": [ { "output_type": "execute_result", @@ -589,7 +589,7 @@ "metadata": { "tags": [] }, - "execution_count": 155 + "execution_count": 137 } ] }, @@ -598,7 +598,7 @@ "metadata": { "id": "m2p6SOzbo99T", "colab_type": "code", - "outputId": "0cf40bec-ab22-4a2d-ef4b-6fc0ad4b62a2", + "outputId": "3d6aae12-a2a3-4d40-ba25-9194db6bdcab", "colab": { "base_uri": "https://localhost:8080/", "height": 101 @@ -608,7 +608,7 @@ "#Colunas do DataFrame\n", "df_50k.columns" ], - "execution_count": 0, + "execution_count": 138, "outputs": [ { "output_type": "execute_result", @@ -624,7 +624,7 @@ "metadata": { "tags": [] }, - "execution_count": 156 + "execution_count": 138 } ] }, @@ -633,7 +633,7 @@ "metadata": { "id": "dMDVRA8eo96a", "colab_type": "code", - "outputId": "fcb7300f-b5e1-47ff-e29a-31066d16b4c2", + "outputId": "722b903e-ddfb-4a69-b3aa-601ffba6e120", "colab": { "base_uri": "https://localhost:8080/", "height": 286 @@ -642,7 +642,7 @@ "source": [ "df_50k.dtypes" ], - "execution_count": 0, + "execution_count": 139, "outputs": [ { "output_type": "execute_result", @@ -669,7 +669,7 @@ "metadata": { "tags": [] }, - "execution_count": 157 + "execution_count": 139 } ] }, @@ -688,7 +688,7 @@ "metadata": { "id": "5SbUk5Srzqok", "colab_type": "code", - "outputId": "54f6557b-bc65-487f-9095-e5af3d0dd5b8", + "outputId": "14c9ae12-3ce3-44e2-f154-ce8c6c821c17", "colab": { "base_uri": "https://localhost:8080/", "height": 185 @@ -698,7 +698,7 @@ "#Valores distintos de WorkClass\n", "df_50k['workclass'].value_counts()" ], - "execution_count": 0, + "execution_count": 140, "outputs": [ { "output_type": "execute_result", @@ -719,7 +719,7 @@ "metadata": { "tags": [] }, - "execution_count": 13 + "execution_count": 140 } ] }, @@ -728,7 +728,7 @@ "metadata": { "id": "VYkfFI451R_m", "colab_type": "code", - "outputId": "8625b53b-9ebd-42cd-836e-f95790decfdd", + "outputId": "2b959853-ea48-4d06-a4f8-5100e1dba513", "colab": { "base_uri": "https://localhost:8080/", "height": 302 @@ -738,35 +738,35 @@ "#Valores distintos de Education\n", "df_50k['education'].value_counts()" ], - "execution_count": 0, + "execution_count": 141, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "9 10501\n", - "10 7291\n", - "13 5355\n", - "14 1723\n", - "11 1382\n", - "7 1175\n", - "12 1067\n", - "6 933\n", - "4 646\n", - "15 576\n", - "5 514\n", - "8 433\n", - "16 413\n", - "3 333\n", - "2 168\n", - "1 51\n", - "Name: education-num, dtype: int64" + " HS-grad 10501\n", + " Some-college 7291\n", + " Bachelors 5355\n", + " Masters 1723\n", + " Assoc-voc 1382\n", + " 11th 1175\n", + " Assoc-acdm 1067\n", + " 10th 933\n", + " 7th-8th 646\n", + " Prof-school 576\n", + " 9th 514\n", + " 12th 433\n", + " Doctorate 413\n", + " 5th-6th 333\n", + " 1st-4th 168\n", + " Preschool 51\n", + "Name: education, dtype: int64" ] }, "metadata": { "tags": [] }, - "execution_count": 184 + "execution_count": 141 } ] }, @@ -775,7 +775,7 @@ "metadata": { "id": "q67Wyta2XwZx", "colab_type": "code", - "outputId": "1110034a-f333-433e-8c9c-dedd540e624e", + "outputId": "110338bf-17de-48c9-b88c-af4219927cc8", "colab": { "base_uri": "https://localhost:8080/", "height": 302 @@ -785,7 +785,7 @@ "#Valores distintos de Education-num\n", "df_50k['education-num'].value_counts()" ], - "execution_count": 0, + "execution_count": 142, "outputs": [ { "output_type": "execute_result", @@ -813,7 +813,7 @@ "metadata": { "tags": [] }, - "execution_count": 185 + "execution_count": 142 } ] }, @@ -822,7 +822,7 @@ "metadata": { "id": "QJly0mWR1R8m", "colab_type": "code", - "outputId": "f7690474-7982-4808-8295-abf084e3dabc", + "outputId": "4e4b87aa-e6f7-496a-f6d0-a04ecf3cae40", "colab": { "base_uri": "https://localhost:8080/", "height": 151 @@ -832,7 +832,7 @@ "#Valores distintos de marital-status\n", "df_50k['marital-status'].value_counts()" ], - "execution_count": 0, + "execution_count": 143, "outputs": [ { "output_type": "execute_result", @@ -851,7 +851,7 @@ "metadata": { "tags": [] }, - "execution_count": 15 + "execution_count": 143 } ] }, @@ -860,7 +860,7 @@ "metadata": { "id": "7_Wo0troSfxq", "colab_type": "code", - "outputId": "68fbc763-b7a0-41e1-d4f8-8ba14fd238e5", + "outputId": "3e08b64f-e1aa-497e-855f-f5e782d8d3df", "colab": { "base_uri": "https://localhost:8080/", "height": 134 @@ -870,7 +870,7 @@ "#Valores distintos de relationship\n", "df_50k['relationship'].value_counts()" ], - "execution_count": 0, + "execution_count": 144, "outputs": [ { "output_type": "execute_result", @@ -888,7 +888,7 @@ "metadata": { "tags": [] }, - "execution_count": 173 + "execution_count": 144 } ] }, @@ -897,7 +897,7 @@ "metadata": { "id": "4R_S4oaG1R6_", "colab_type": "code", - "outputId": "a3cbca4e-f373-4daa-d25d-a02f8f54c1d9", + "outputId": "b0a75f1e-95e0-4c0a-b2ec-231eb8b1f53d", "colab": { "base_uri": "https://localhost:8080/", "height": 286 @@ -907,7 +907,7 @@ "#Valores distintos de occupation\n", "df_50k['occupation'].value_counts()" ], - "execution_count": 0, + "execution_count": 145, "outputs": [ { "output_type": "execute_result", @@ -934,7 +934,7 @@ "metadata": { "tags": [] }, - "execution_count": 16 + "execution_count": 145 } ] }, @@ -943,7 +943,7 @@ "metadata": { "id": "2q5DBeLM1R3m", "colab_type": "code", - "outputId": "f9ce12a3-dab3-4078-94b7-a02e34dad5ed", + "outputId": "ae419da2-0029-42d6-ce39-3bb87d26d275", "colab": { "base_uri": "https://localhost:8080/", "height": 134 @@ -953,7 +953,7 @@ "#Valores distintos de relationship\n", "df_50k['relationship'].value_counts()" ], - "execution_count": 0, + "execution_count": 146, "outputs": [ { "output_type": "execute_result", @@ -971,7 +971,7 @@ "metadata": { "tags": [] }, - "execution_count": 17 + "execution_count": 146 } ] }, @@ -980,7 +980,7 @@ "metadata": { "id": "3nlhzegs3PD2", "colab_type": "code", - "outputId": "4e5c3fe1-5fc8-4265-d250-76853053b517", + "outputId": "f7eef8c4-8fb0-43d9-c992-de00eff21a5b", "colab": { "base_uri": "https://localhost:8080/", "height": 118 @@ -990,7 +990,7 @@ "#Valores distintos de race\n", "df_50k['race'].value_counts()" ], - "execution_count": 0, + "execution_count": 147, "outputs": [ { "output_type": "execute_result", @@ -1007,7 +1007,7 @@ "metadata": { "tags": [] }, - "execution_count": 18 + "execution_count": 147 } ] }, @@ -1016,7 +1016,7 @@ "metadata": { "id": "-mgIBM783PAl", "colab_type": "code", - "outputId": "f1108198-d31b-48d3-ba06-8850a1c63e5e", + "outputId": "f0f27779-a373-427c-dbf9-267ba11d2582", "colab": { "base_uri": "https://localhost:8080/", "height": 67 @@ -1026,7 +1026,7 @@ "#Valores distintos de sex\n", "df_50k['sex'].value_counts()" ], - "execution_count": 0, + "execution_count": 148, "outputs": [ { "output_type": "execute_result", @@ -1040,7 +1040,7 @@ "metadata": { "tags": [] }, - "execution_count": 19 + "execution_count": 148 } ] }, @@ -1049,7 +1049,7 @@ "metadata": { "id": "EjRcmMm73O-2", "colab_type": "code", - "outputId": "7cacb002-dcf8-4e7a-fc4b-b252f8d06050", + "outputId": "977e7f03-8027-4df4-85e2-331d502c7335", "colab": { "base_uri": "https://localhost:8080/", "height": 739 @@ -1059,7 +1059,7 @@ "#Valores distintos de native-country\n", "df_50k['native-country'].value_counts()" ], - "execution_count": 0, + "execution_count": 149, "outputs": [ { "output_type": "execute_result", @@ -1092,8 +1092,8 @@ " Portugal 37\n", " Nicaragua 34\n", " Peru 31\n", - " Greece 29\n", " France 29\n", + " Greece 29\n", " Ecuador 28\n", " Ireland 24\n", " Hong 20\n", @@ -1103,8 +1103,8 @@ " Laos 18\n", " Yugoslavia 16\n", " Outlying-US(Guam-USVI-etc) 14\n", - " Hungary 13\n", " Honduras 13\n", + " Hungary 13\n", " Scotland 12\n", " Holand-Netherlands 1\n", "Name: native-country, dtype: int64" @@ -1113,7 +1113,7 @@ "metadata": { "tags": [] }, - "execution_count": 20 + "execution_count": 149 } ] }, @@ -1122,7 +1122,7 @@ "metadata": { "id": "zkL_oVwT3O73", "colab_type": "code", - "outputId": "e6cf0a8b-da3d-4d20-8b50-ec4879ba2dd9", + "outputId": "e6ab7553-7426-42ed-dd4c-1ea83238f0aa", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -1132,7 +1132,7 @@ "#Valores distintos de flag_50k\n", "df_50k['flag_50k'].value_counts().keys()" ], - "execution_count": 0, + "execution_count": 150, "outputs": [ { "output_type": "execute_result", @@ -1144,7 +1144,7 @@ "metadata": { "tags": [] }, - "execution_count": 182 + "execution_count": 150 } ] }, @@ -1153,7 +1153,7 @@ "metadata": { "id": "2ng16_UcPOCS", "colab_type": "code", - "outputId": "7934af53-ef4d-419a-c5e3-c6eac24dd288", + "outputId": "7a171bb4-3fb6-4ed8-c29d-f304530acf09", "colab": { "base_uri": "https://localhost:8080/", "height": 218 @@ -1163,7 +1163,7 @@ "#Valores distintos de hours-per-week\n", "df_50k['hours-per-week'].value_counts()" ], - "execution_count": 0, + "execution_count": 151, "outputs": [ { "output_type": "execute_result", @@ -1186,7 +1186,7 @@ "metadata": { "tags": [] }, - "execution_count": 166 + "execution_count": 151 } ] }, @@ -1221,10 +1221,10 @@ "metadata": { "id": "YUk0xnfaV4yX", "colab_type": "code", - "outputId": "15bb1be0-d464-447e-b683-4767ccae1130", + "outputId": "536d095c-6637-43d0-845d-78d71f79aa4c", "colab": { "base_uri": "https://localhost:8080/", - "height": 204 + "height": 333 } }, "source": [ @@ -1234,7 +1234,7 @@ "\n", "df_50k.head(5)" ], - "execution_count": 0, + "execution_count": 152, "outputs": [ { "output_type": "execute_result", @@ -1390,7 +1390,7 @@ "metadata": { "tags": [] }, - "execution_count": 8 + "execution_count": 152 } ] }, @@ -1411,7 +1411,6 @@ "colab_type": "text" }, "source": [ - "**Dicionário dos dados** \n", "* Age: idade da pessoa\n", "* workclass: classificação do cargo profissional\n", "* fnlwgt (final weight): peso atribuido por uma instituição específica\n", @@ -1467,7 +1466,7 @@ "metadata": { "id": "Vu2p_1-fDH_3", "colab_type": "code", - "outputId": "3bd5f300-6dd9-4199-faa1-b4fce5fb61a6", + "outputId": "4a1a15fc-66d8-4350-9761-651d81c8dbe7", "colab": { "base_uri": "https://localhost:8080/", "height": 355 @@ -1483,19 +1482,19 @@ " fmt= '.2f'\n", " )" ], - "execution_count": 0, + "execution_count": 154, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 10 + "execution_count": 154 }, { "output_type": "display_data", @@ -1526,10 +1525,10 @@ "metadata": { "id": "RNLaUYs3CHjQ", "colab_type": "code", - "outputId": "c1ed0d9e-d8cf-4c76-e3ec-a6a659a31c13", + "outputId": "a58648db-0534-4add-c35a-155cd5e8238d", "colab": { "base_uri": "https://localhost:8080/", - "height": 266 + "height": 254 } }, "source": [ @@ -1537,7 +1536,7 @@ "Matrix_Corr = df_50k.corr().where(np.triu(np.ones(df_50k.corr().shape), k=1).astype(np.bool))\n", "Matrix_Corr" ], - "execution_count": 0, + "execution_count": 155, "outputs": [ { "output_type": "execute_result", @@ -1661,7 +1660,7 @@ "metadata": { "tags": [] }, - "execution_count": 11 + "execution_count": 155 } ] }, @@ -1670,10 +1669,10 @@ "metadata": { "id": "-ohsbrRdGmJJ", "colab_type": "code", - "outputId": "33c7a852-b1e8-42bc-92c5-60213abd6ae2", + "outputId": "bb7b9356-f6a9-4e17-fa3a-be982b9b70f2", "colab": { "base_uri": "https://localhost:8080/", - "height": 119 + "height": 118 } }, "source": [ @@ -1687,7 +1686,7 @@ "\n", "set_Colunas_Correlacionadas" ], - "execution_count": 0, + "execution_count": 156, "outputs": [ { "output_type": "execute_result", @@ -1704,7 +1703,7 @@ "metadata": { "tags": [] }, - "execution_count": 12 + "execution_count": 156 } ] }, @@ -1713,7 +1712,7 @@ "metadata": { "id": "_0MZj06uIhtG", "colab_type": "code", - "outputId": "1e569328-475c-4f04-e933-31af809d006e", + "outputId": "95cc6750-f6e6-4652-e44c-8fd84d5d1805", "colab": { "base_uri": "https://localhost:8080/", "height": 681 @@ -1725,19 +1724,19 @@ "mask[np.triu_indices_from(mask)] = 1\n", "sns.heatmap(df_50k.corr().abs(), mask= mask, ax= ax, cmap='RdPu', annot= True, fmt= '.2f', center= 0)" ], - "execution_count": 0, + "execution_count": 157, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 13 + "execution_count": 157 }, { "output_type": "display_data", @@ -1799,7 +1798,7 @@ "metadata": { "id": "fTUWOuBU30or", "colab_type": "code", - "outputId": "360e667c-8063-4791-8723-e7adc992cea4", + "outputId": "4f6a8e79-6530-469d-e7b9-0b90e76a1bb3", "colab": { "base_uri": "https://localhost:8080/", "height": 295 @@ -1812,7 +1811,7 @@ "plt.ylabel('Frequencia')\n", "plt.show()" ], - "execution_count": 0, + "execution_count": 159, "outputs": [ { "output_type": "display_data", @@ -1843,7 +1842,7 @@ "metadata": { "id": "OzAQWltWQDVa", "colab_type": "code", - "outputId": "8dcd7e19-9513-4b40-803f-71be01d76a7e", + "outputId": "294e4b42-159f-46fe-e11c-220aef8c339d", "colab": { "base_uri": "https://localhost:8080/", "height": 295 @@ -1856,7 +1855,7 @@ "plt.ylabel('Frequencia')\n", "plt.show()" ], - "execution_count": 0, + "execution_count": 160, "outputs": [ { "output_type": "display_data", @@ -1887,7 +1886,7 @@ "metadata": { "id": "VE6vRQbl8Yb7", "colab_type": "code", - "outputId": "eae4c225-482d-41d5-bdde-8e6b13edb0f8", + "outputId": "6488f751-4c14-4dc2-cf7f-bc407932fe78", "colab": { "base_uri": "https://localhost:8080/", "height": 475 @@ -1898,19 +1897,19 @@ "fig, ax = plt.subplots(figsize=(15,7))\n", "df_50k.groupby(['education','race']).count()['age'].unstack().plot(ax=ax, title='Raça vs Escolaridade')" ], - "execution_count": 0, + "execution_count": 161, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 17 + "execution_count": 161 }, { "output_type": "display_data", @@ -1941,7 +1940,7 @@ "metadata": { "id": "lsp3XAlTRd3Y", "colab_type": "code", - "outputId": "058f4e57-bf02-4302-b7d3-3b6221f8856b", + "outputId": "a2e1ab0a-bf92-4299-d8cf-6035c1c423b4", "colab": { "base_uri": "https://localhost:8080/", "height": 475 @@ -1952,19 +1951,19 @@ "fig, ax = plt.subplots(figsize=(15,7))\n", "df_50k.groupby(['relationship','ind_50k']).count()['age'].unstack().plot(ax=ax, title='Horas Trabalhadas vs Indicativo do Valor')" ], - "execution_count": 0, + "execution_count": 162, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 18 + "execution_count": 162 }, { "output_type": "display_data", From e9172c1270234db2332a883d3d67676cf7334220 Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Thu, 14 Nov 2019 21:54:54 +0000 Subject: [PATCH 28/35] Created using Colaboratory --- dswp_grupo1.ipynb | 1137 +++++++++++++++++++++++++++++++++------------ 1 file changed, 830 insertions(+), 307 deletions(-) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index 0389aba64..f9eaefa31 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -1755,68 +1755,318 @@ { "cell_type": "markdown", "metadata": { - "id": "gq4Qg2Lu2miR", + "id": "o1-4KN7BcBe6", "colab_type": "text" }, "source": [ - "###Gráficos" + "##Estatísticas" ] }, { "cell_type": "code", "metadata": { - "id": "g00WmJdX2_o9", + "id": "BaRNhWV1cFlr", "colab_type": "code", - "colab": {} + "outputId": "a49ce253-ad8a-4f7f-d778-36317dba3d66", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 284 + } }, "source": [ - "#Subset do DataFrame (Series)\n", - "workclass = df_50k['workclass']\n", - "education = df_50k['education']\n", - "educationN = df_50k['education-num']\n", - "race = df_50k['race']\n", - "sex = df_50k['sex']\n", - "natcountry = df_50k['native-country']\n", - "hoursweek = df_50k['hours-per-week'] \n", - "relationship = df_50k['relationship']" + "df_50k.describe()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 185, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " workclass education marital-status ... sex native-country flag_50k\n", + "count 32537 32537 32537 ... 32537 32537 32537\n", + "unique 9 16 7 ... 2 42 2\n", + "top Private HS-grad Married-civ-spouse ... Male United-States <=50K\n", + "freq 22673 10494 14970 ... 21775 29153 24698\n", + "\n", + "[4 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 186 + } ] }, { "cell_type": "code", "metadata": { - "id": "fTUWOuBU30or", + "id": "3hL2PFjl3V6q", "colab_type": "code", - "outputId": "4f6a8e79-6530-469d-e7b9-0b90e76a1bb3", "colab": { "base_uri": "https://localhost:8080/", - "height": 295 - } + "height": 297 + }, + "outputId": "7b06937b-6252-4cab-eb3f-c40986e4b2c0" }, "source": [ - "plt.hist(race)\n", - "plt.title('Distribuição - Raça')\n", - "plt.xlabel('Raça')\n", - "plt.ylabel('Frequencia')\n", - "plt.show()" + "#Indicador dos 50K por Genero\n", + "sns.countplot(x='ind_50k', hue='sex', data=df_50k)" ], - "execution_count": 159, + "execution_count": 187, "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 187 + }, { "output_type": "display_data", "data": { - "image/png": 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TgZ6S9gCuBG5o78ok9St8PJh0WzHAWOCzktaRtDUwELiHdC1mYL5Da23SBfmx+fcsE4FD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\nS+RBREnXSFoK5a4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" ] @@ -1830,20 +2080,95 @@ { "cell_type": "markdown", "metadata": { - "id": "RB8RrJIOP_QE", + "id": "gq4Qg2Lu2miR", "colab_type": "text" }, "source": [ - "**Histograma das Horas por Semana**" + "###Gráficos" ] }, { "cell_type": "code", "metadata": { - "id": "OzAQWltWQDVa", + "id": "g00WmJdX2_o9", "colab_type": "code", - "outputId": "294e4b42-159f-46fe-e11c-220aef8c339d", - "colab": { + "colab": {} + }, + "source": [ + "#Subset do DataFrame (Series)\n", + "workclass = df_50k['workclass']\n", + "education = df_50k['education']\n", + "educationN = df_50k['education-num']\n", + "race = df_50k['race']\n", + "sex = df_50k['sex']\n", + "natcountry = df_50k['native-country']\n", + "hoursweek = df_50k['hours-per-week'] \n", + "relationship = df_50k['relationship']" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iQwZoRaMNvAr", + "colab_type": "text" + }, + "source": [ + "**Distribuição da Raça**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "fTUWOuBU30or", + "colab_type": "code", + "outputId": "4f6a8e79-6530-469d-e7b9-0b90e76a1bb3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + } + }, + "source": [ + "plt.hist(race)\n", + "plt.title('Distribuição - Raça')\n", + "plt.xlabel('Raça')\n", + "plt.ylabel('Frequencia')\n", + "plt.show()" + ], + "execution_count": 159, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RB8RrJIOP_QE", + "colab_type": "text" + }, + "source": [ + "**Histograma das Horas por Semana**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "OzAQWltWQDVa", + "colab_type": "code", + "outputId": "294e4b42-159f-46fe-e11c-220aef8c339d", + "colab": { "base_uri": "https://localhost:8080/", "height": 295 } @@ -2015,28 +2340,29 @@ { "cell_type": "markdown", "metadata": { - "id": "o1-4KN7BcBe6", + "id": "evJ7TDj6vEu-", "colab_type": "text" }, "source": [ - "##Estatísticas" + "###Verificar se há registos em duplicidade" ] }, { "cell_type": "code", "metadata": { - "id": "BaRNhWV1cFlr", + "id": "6mdOurBJvIed", "colab_type": "code", - "outputId": "c31f9ec4-aa8a-4442-b9d1-ab05a19bd486", + "outputId": "8e6ff915-bfc9-4f94-c459-11f5af6e6b9f", "colab": { "base_uri": "https://localhost:8080/", - "height": 297 + "height": 1000 } }, "source": [ - "df_50k.describe()" + "#Lista das linhas duplicadas\n", + "df_50k[df_50k.duplicated()]" ], - "execution_count": 0, + "execution_count": 175, "outputs": [ { "output_type": "execute_result", @@ -2061,321 +2387,518 @@ " \n", " \n", " age\n", + " workclass\n", " fnlwgt\n", + " education\n", " education-num\n", + " marital-status\n", + " occupation\n", + " relationship\n", + " race\n", + " sex\n", " capital-gain\n", " capital-loss\n", " hours-per-week\n", + " native-country\n", + " flag_50k\n", " ind_50k\n", " \n", " \n", " \n", " \n", - " count\n", - " 32561.000000\n", - " 3.256100e+04\n", - " 32561.000000\n", - " 32561.000000\n", - " 32561.000000\n", - " 32561.000000\n", - " 32561.000000\n", + " 4881\n", + " 25\n", + " Private\n", + " 308144\n", + " Bachelors\n", + " 13\n", + " Never-married\n", + " Craft-repair\n", + " Not-in-family\n", + " White\n", + " Male\n", + " 0\n", + " 0\n", + " 40\n", + " Mexico\n", + " <=50K\n", + " 0\n", " \n", " \n", - " mean\n", - " 38.581647\n", - " 1.897784e+05\n", - " 10.080679\n", - " 1077.648844\n", - " 87.303830\n", - " 40.437456\n", - " 0.240810\n", + " 5104\n", + " 90\n", + " Private\n", + " 52386\n", + " Some-college\n", + " 10\n", + " Never-married\n", + " Other-service\n", + " Not-in-family\n", + " Asian-Pac-Islander\n", + " Male\n", + " 0\n", + " 0\n", + " 35\n", + " United-States\n", + " <=50K\n", + " 0\n", " \n", " \n", - " std\n", - " 13.640433\n", - " 1.055500e+05\n", - " 2.572720\n", - " 7385.292085\n", - " 402.960219\n", - " 12.347429\n", - " 0.427581\n", + " 9171\n", + " 21\n", + " Private\n", + " 250051\n", + " Some-college\n", + " 10\n", + " Never-married\n", + " Prof-specialty\n", + " Own-child\n", + " White\n", + " Female\n", + " 0\n", + " 0\n", + " 10\n", + " United-States\n", + " <=50K\n", + " 0\n", " \n", " \n", - " min\n", - " 17.000000\n", - " 1.228500e+04\n", - " 1.000000\n", - " 0.000000\n", - " 0.000000\n", - " 1.000000\n", - " 0.000000\n", + " 11631\n", + " 20\n", + " Private\n", + " 107658\n", + " Some-college\n", + " 10\n", + " Never-married\n", + " Tech-support\n", + " Not-in-family\n", + " White\n", + " Female\n", + " 0\n", + " 0\n", + " 10\n", + " United-States\n", + " <=50K\n", + " 0\n", " \n", " \n", - " 25%\n", - " 28.000000\n", - " 1.178270e+05\n", - " 9.000000\n", - " 0.000000\n", - " 0.000000\n", - " 40.000000\n", - " 0.000000\n", + " 13084\n", + " 25\n", + " Private\n", + " 195994\n", + " 1st-4th\n", + " 2\n", + " Never-married\n", + " Priv-house-serv\n", + " Not-in-family\n", + " White\n", + " Female\n", + " 0\n", + " 0\n", + " 40\n", + " Guatemala\n", + " <=50K\n", + " 0\n", " \n", " \n", - " 50%\n", - " 37.000000\n", - " 1.783560e+05\n", - " 10.000000\n", - " 0.000000\n", - " 0.000000\n", - " 40.000000\n", - " 0.000000\n", + " 15059\n", + " 21\n", + " Private\n", + " 243368\n", + " Preschool\n", + " 1\n", + " Never-married\n", + " Farming-fishing\n", + " Not-in-family\n", + " White\n", + " Male\n", + " 0\n", + " 0\n", + " 50\n", + " Mexico\n", + " <=50K\n", + " 0\n", " \n", " \n", - " 75%\n", - " 48.000000\n", - " 2.370510e+05\n", - " 12.000000\n", - " 0.000000\n", - " 0.000000\n", - " 45.000000\n", - " 0.000000\n", + " 17040\n", + " 46\n", + " Private\n", + " 173243\n", + " HS-grad\n", + " 9\n", + " Married-civ-spouse\n", + " Craft-repair\n", + " Husband\n", + " White\n", + " Male\n", + " 0\n", + " 0\n", + " 40\n", + " United-States\n", + " <=50K\n", + " 0\n", " \n", " \n", - " max\n", - " 90.000000\n", - " 1.484705e+06\n", - " 16.000000\n", - " 99999.000000\n", - " 4356.000000\n", - " 99.000000\n", - " 1.000000\n", + " 18555\n", + " 30\n", + " Private\n", + " 144593\n", + " HS-grad\n", + " 9\n", + " Never-married\n", + " Other-service\n", + " Not-in-family\n", + " Black\n", + " Male\n", + " 0\n", + " 0\n", + " 40\n", + " ?\n", + " <=50K\n", + " 0\n", " \n", - " \n", - "\n", - "" - ], - "text/plain": [ - " age fnlwgt ... hours-per-week ind_50k\n", - "count 32561.000000 3.256100e+04 ... 32561.000000 32561.000000\n", - "mean 38.581647 1.897784e+05 ... 40.437456 0.240810\n", - "std 13.640433 1.055500e+05 ... 12.347429 0.427581\n", - "min 17.000000 1.228500e+04 ... 1.000000 0.000000\n", - "25% 28.000000 1.178270e+05 ... 40.000000 0.000000\n", - "50% 37.000000 1.783560e+05 ... 40.000000 0.000000\n", - "75% 48.000000 2.370510e+05 ... 45.000000 0.000000\n", - "max 90.000000 1.484705e+06 ... 99.000000 1.000000\n", - "\n", - "[8 rows x 7 columns]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 19 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "KZQOGm7XcrRf", - "colab_type": "code", - "outputId": "3e4fbbd2-25a4-47d8-b81c-ecc6a56f63bc", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 173 - } - }, - "source": [ - "df_50k.describe(include=[\"O\"])" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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1869819Private97261HS-grad9Never-marriedFarming-fishingNot-in-familyWhiteMale0040United-States<=50K0
2131819Private138153Some-college10Never-marriedAdm-clericalOwn-childWhiteFemale0010United-States<=50K0
2149019Private146679Some-college10Never-marriedExec-managerialOwn-childBlackMale0030United-States<=50K0
2187549Private312677th-8th4Married-civ-spouseCraft-repairHusbandWhiteMale0040United-States<=50K0
count3256132561325613256132561325613256132561325612230025Private1959941st-4th2Never-marriedPriv-house-servNot-in-familyWhiteFemale0040Guatemala<=50K0
unique2236744Private367749Bachelors13Never-marriedProf-specialtyNot-in-familyWhiteFemale0045Mexico<=50K0
2249449Self-emp-not-inc43479Some-college10Married-civ-spouseCraft-repairHusbandWhiteMale0040United-States<=50K0
2587223Private2401375th-6th3Never-marriedHandlers-cleanersNot-in-familyWhiteMale0055Mexico<=50K0
2631328Private274679Masters14Never-marriedProf-specialtyNot-in-familyWhiteMale0050United-States<=50K0
2823027Private255582HS-grad916715652Never-marriedMachine-op-inspctNot-in-familyWhiteFemale0040United-States<=50K0
28522422Private204235Some-college10Married-civ-spouseProf-specialtyHusbandWhiteMale0040United-States>50K1
top2884639Private30916HS-grad9Married-civ-spouseProf-specialtyCraft-repairHusbandWhiteMale0040United-States<=50K0
freq226961050114976414013193278162179029170247202915738Private207202HS-grad9Married-civ-spouseMachine-op-inspctHusbandWhiteMale0048United-States>50K1
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" - ], - "text/plain": [ - " workclass education marital-status ... sex native-country flag_50k\n", - "count 32561 32561 32561 ... 32561 32561 32561\n", - "unique 9 16 7 ... 2 42 2\n", - "top Private HS-grad Married-civ-spouse ... Male United-States <=50K\n", - "freq 22696 10501 14976 ... 21790 29170 24720\n", - "\n", - "[4 rows x 9 columns]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 20 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "evJ7TDj6vEu-", - "colab_type": "text" - }, - "source": [ - "###Verificar se há registos em duplicidade" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "6mdOurBJvIed", - "colab_type": "code", - "outputId": "722888a6-658c-4eca-ff4b-a8238870982e", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 49 - } - }, - "source": [ - "#Lista das linhas duplicadas\n", - "df_50k[df_50k.duplicated()]" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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3084546Private133616Some-college10DivorcedAdm-clericalUnmarriedWhiteFemale0040United-States<=50K0
3199319Private251579Some-college10Never-marriedOther-serviceOwn-childWhiteMale0014United-States<=50K0
3240435Private379959HS-grad9DivorcedOther-serviceNot-in-familyWhiteFemale0040United-States<=50K0
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" ], "text/plain": [ - "Empty DataFrame\n", - "Columns: [age, workclass, fnlwgt, education, education-num, marital-status, occupation, relationship, race, sex, capital-gain, capital-loss, hours-per-week, native-country, flag_50k, ind_50k]\n", - "Index: []" + " age workclass fnlwgt ... native-country flag_50k ind_50k\n", + "4881 25 Private 308144 ... Mexico <=50K 0\n", + "5104 90 Private 52386 ... United-States <=50K 0\n", + "9171 21 Private 250051 ... United-States <=50K 0\n", + "11631 20 Private 107658 ... United-States <=50K 0\n", + "13084 25 Private 195994 ... Guatemala <=50K 0\n", + "15059 21 Private 243368 ... Mexico <=50K 0\n", + "17040 46 Private 173243 ... United-States <=50K 0\n", + "18555 30 Private 144593 ... ? <=50K 0\n", + "18698 19 Private 97261 ... United-States <=50K 0\n", + "21318 19 Private 138153 ... United-States <=50K 0\n", + "21490 19 Private 146679 ... United-States <=50K 0\n", + "21875 49 Private 31267 ... United-States <=50K 0\n", + "22300 25 Private 195994 ... Guatemala <=50K 0\n", + "22367 44 Private 367749 ... Mexico <=50K 0\n", + "22494 49 Self-emp-not-inc 43479 ... United-States <=50K 0\n", + "25872 23 Private 240137 ... Mexico <=50K 0\n", + "26313 28 Private 274679 ... United-States <=50K 0\n", + "28230 27 Private 255582 ... United-States <=50K 0\n", + "28522 42 Private 204235 ... United-States >50K 1\n", + "28846 39 Private 30916 ... United-States <=50K 0\n", + "29157 38 Private 207202 ... United-States >50K 1\n", + "30845 46 Private 133616 ... United-States <=50K 0\n", + "31993 19 Private 251579 ... United-States <=50K 0\n", + "32404 35 Private 379959 ... United-States <=50K 0\n", + "\n", + "[24 rows x 16 columns]" ] }, "metadata": { "tags": [] }, - "execution_count": 24 + "execution_count": 175 } ] }, @@ -2384,7 +2907,7 @@ "metadata": { "id": "7_xocTl-wz3-", "colab_type": "code", - "outputId": "33f77ec1-db3d-4659-b102-e5d8f6583a0f", + "outputId": "6b363259-90d7-4e6b-d2a0-bb231120f80f", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -2393,12 +2916,12 @@ "source": [ "print(f'Há {df_50k[df_50k.duplicated()].count()[0]} registos duplicados')" ], - "execution_count": 0, + "execution_count": 176, "outputs": [ { "output_type": "stream", "text": [ - "Há 0 registos duplicados\n" + "Há 24 registos duplicados\n" ], "name": "stdout" } @@ -2409,7 +2932,7 @@ "metadata": { "id": "FESe-vmcxodU", "colab_type": "code", - "outputId": "cd262f3c-54e8-4bef-bcc8-d45c78e06220", + "outputId": "52954989-a3d0-4862-e368-f97cac22be31", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -2420,7 +2943,7 @@ "df_50k = df_50k.drop_duplicates()\n", "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " ], - "execution_count": 0, + "execution_count": 177, "outputs": [ { "output_type": "stream", From e5a4057930e5221d9ddef149660465013e18ffd3 Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Thu, 14 Nov 2019 22:26:08 +0000 Subject: [PATCH 29/35] Created using Colaboratory --- dswp_grupo1.ipynb | 1090 +++++++++++++++++++++++++++++---------------- 1 file changed, 703 insertions(+), 387 deletions(-) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index f9eaefa31..aedd04324 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -116,7 +116,7 @@ "metadata": { "id": "0YTMtMPRbSYD", "colab_type": "code", - "outputId": "85543ac5-ac32-4d09-ec4c-1bd8bc6cd131", + "outputId": "fc2ba622-4670-49a5-b931-091b925893be", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -126,7 +126,7 @@ "df_50k = pd.read_csv('https://raw.githubusercontent.com/MathMachado/DSWP/master/Dataframes/adult.data')\n", "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " ], - "execution_count": 132, + "execution_count": 193, "outputs": [ { "output_type": "stream", @@ -142,7 +142,7 @@ "metadata": { "id": "Lxq7B1Vnv1Ys", "colab_type": "code", - "outputId": "12a84488-fae9-4294-dd1d-5ba13117eed4", + "outputId": "5dfb92c9-31f7-4679-f1d0-fe6d2a12465e", "colab": { "base_uri": "https://localhost:8080/", "height": 296 @@ -152,7 +152,7 @@ "#Detecção de que o ficheiro não tem header\n", "df_50k.head(5)" ], - "execution_count": 133, + "execution_count": 194, "outputs": [ { "output_type": "execute_result", @@ -302,7 +302,7 @@ "metadata": { "tags": [] }, - "execution_count": 133 + "execution_count": 194 } ] }, @@ -311,7 +311,7 @@ "metadata": { "id": "HBKqXT9Bo-BC", "colab_type": "code", - "outputId": "139d1e45-34ca-411a-c9b2-6be4f06ce0a9", + "outputId": "5af62eef-b78d-40ae-ef12-51cba12cbc25", "colab": { "base_uri": "https://localhost:8080/", "height": 269 @@ -337,7 +337,7 @@ " ]\n", "l_column" ], - "execution_count": 134, + "execution_count": 195, "outputs": [ { "output_type": "execute_result", @@ -363,7 +363,7 @@ "metadata": { "tags": [] }, - "execution_count": 134 + "execution_count": 195 } ] }, @@ -386,7 +386,7 @@ "metadata": { "id": "AjswmEdb4R8X", "colab_type": "code", - "outputId": "2c9d3826-95aa-4ca2-8f54-2edd5113c0a0", + "outputId": "bb59ea42-8641-4545-8130-4868f973285e", "colab": { "base_uri": "https://localhost:8080/", "height": 353 @@ -395,7 +395,7 @@ "source": [ "df_50k.info()" ], - "execution_count": 136, + "execution_count": 197, "outputs": [ { "output_type": "stream", @@ -430,7 +430,7 @@ "metadata": { "id": "CNyS0K4C4dfp", "colab_type": "code", - "outputId": "1c272dba-bc70-4a2f-a69c-22d406471e35", + "outputId": "bc23a584-3023-4fdd-f8c1-042ed6d6e11c", "colab": { "base_uri": "https://localhost:8080/", "height": 333 @@ -439,7 +439,7 @@ "source": [ "df_50k.head(5)" ], - "execution_count": 137, + "execution_count": 198, "outputs": [ { "output_type": "execute_result", @@ -589,7 +589,7 @@ "metadata": { "tags": [] }, - "execution_count": 137 + "execution_count": 198 } ] }, @@ -598,7 +598,7 @@ "metadata": { "id": "m2p6SOzbo99T", "colab_type": "code", - "outputId": "3d6aae12-a2a3-4d40-ba25-9194db6bdcab", + "outputId": "6a971332-b3ff-4fc5-b262-cfd77692db4f", "colab": { "base_uri": "https://localhost:8080/", "height": 101 @@ -608,7 +608,7 @@ "#Colunas do DataFrame\n", "df_50k.columns" ], - "execution_count": 138, + "execution_count": 199, "outputs": [ { "output_type": "execute_result", @@ -624,7 +624,7 @@ "metadata": { "tags": [] }, - "execution_count": 138 + "execution_count": 199 } ] }, @@ -633,7 +633,7 @@ "metadata": { "id": "dMDVRA8eo96a", "colab_type": "code", - "outputId": "722b903e-ddfb-4a69-b3aa-601ffba6e120", + "outputId": "9c509a16-eb47-4966-8c1d-c8878ae2b702", "colab": { "base_uri": "https://localhost:8080/", "height": 286 @@ -642,7 +642,7 @@ "source": [ "df_50k.dtypes" ], - "execution_count": 139, + "execution_count": 200, "outputs": [ { "output_type": "execute_result", @@ -669,7 +669,7 @@ "metadata": { "tags": [] }, - "execution_count": 139 + "execution_count": 200 } ] }, @@ -688,7 +688,7 @@ "metadata": { "id": "5SbUk5Srzqok", "colab_type": "code", - "outputId": "14c9ae12-3ce3-44e2-f154-ce8c6c821c17", + "outputId": "d3644a2d-5791-4ffb-adc6-ee22061902a1", "colab": { "base_uri": "https://localhost:8080/", "height": 185 @@ -698,7 +698,7 @@ "#Valores distintos de WorkClass\n", "df_50k['workclass'].value_counts()" ], - "execution_count": 140, + "execution_count": 201, "outputs": [ { "output_type": "execute_result", @@ -719,7 +719,7 @@ "metadata": { "tags": [] }, - "execution_count": 140 + "execution_count": 201 } ] }, @@ -728,7 +728,7 @@ "metadata": { "id": "VYkfFI451R_m", "colab_type": "code", - "outputId": "2b959853-ea48-4d06-a4f8-5100e1dba513", + "outputId": "679ebc02-3c6d-4160-809e-c68f8373b63f", "colab": { "base_uri": "https://localhost:8080/", "height": 302 @@ -738,7 +738,7 @@ "#Valores distintos de Education\n", "df_50k['education'].value_counts()" ], - "execution_count": 141, + "execution_count": 202, "outputs": [ { "output_type": "execute_result", @@ -766,7 +766,7 @@ "metadata": { "tags": [] }, - "execution_count": 141 + "execution_count": 202 } ] }, @@ -775,7 +775,7 @@ "metadata": { "id": "q67Wyta2XwZx", "colab_type": "code", - "outputId": "110338bf-17de-48c9-b88c-af4219927cc8", + "outputId": "2af6ff33-3f27-4cd9-c8ad-63d87f0f40d5", "colab": { "base_uri": "https://localhost:8080/", "height": 302 @@ -785,7 +785,7 @@ "#Valores distintos de Education-num\n", "df_50k['education-num'].value_counts()" ], - "execution_count": 142, + "execution_count": 203, "outputs": [ { "output_type": "execute_result", @@ -813,7 +813,7 @@ "metadata": { "tags": [] }, - "execution_count": 142 + "execution_count": 203 } ] }, @@ -822,7 +822,7 @@ "metadata": { "id": "QJly0mWR1R8m", "colab_type": "code", - "outputId": "4e4b87aa-e6f7-496a-f6d0-a04ecf3cae40", + "outputId": "cd1e8686-c533-4418-b860-a144f532bd69", "colab": { "base_uri": "https://localhost:8080/", "height": 151 @@ -832,7 +832,7 @@ "#Valores distintos de marital-status\n", "df_50k['marital-status'].value_counts()" ], - "execution_count": 143, + "execution_count": 204, "outputs": [ { "output_type": "execute_result", @@ -851,7 +851,7 @@ "metadata": { "tags": [] }, - "execution_count": 143 + "execution_count": 204 } ] }, @@ -860,7 +860,7 @@ "metadata": { "id": "7_Wo0troSfxq", "colab_type": "code", - "outputId": "3e08b64f-e1aa-497e-855f-f5e782d8d3df", + "outputId": "044eda01-f56b-41df-bfe7-9d2522474d06", "colab": { "base_uri": "https://localhost:8080/", "height": 134 @@ -870,7 +870,7 @@ "#Valores distintos de relationship\n", "df_50k['relationship'].value_counts()" ], - "execution_count": 144, + "execution_count": 205, "outputs": [ { "output_type": "execute_result", @@ -888,7 +888,7 @@ "metadata": { "tags": [] }, - "execution_count": 144 + "execution_count": 205 } ] }, @@ -897,7 +897,7 @@ "metadata": { "id": "4R_S4oaG1R6_", "colab_type": "code", - "outputId": "b0a75f1e-95e0-4c0a-b2ec-231eb8b1f53d", + "outputId": "157f1fa0-81c6-46f2-9db2-a734b19736ee", "colab": { "base_uri": "https://localhost:8080/", "height": 286 @@ -907,7 +907,7 @@ "#Valores distintos de occupation\n", "df_50k['occupation'].value_counts()" ], - "execution_count": 145, + "execution_count": 206, "outputs": [ { "output_type": "execute_result", @@ -934,7 +934,7 @@ "metadata": { "tags": [] }, - "execution_count": 145 + "execution_count": 206 } ] }, @@ -943,7 +943,7 @@ "metadata": { "id": "2q5DBeLM1R3m", "colab_type": "code", - "outputId": "ae419da2-0029-42d6-ce39-3bb87d26d275", + "outputId": "e91affba-5142-4188-92e4-c72b8cae06f7", "colab": { "base_uri": "https://localhost:8080/", "height": 134 @@ -953,7 +953,7 @@ "#Valores distintos de relationship\n", "df_50k['relationship'].value_counts()" ], - "execution_count": 146, + "execution_count": 207, "outputs": [ { "output_type": "execute_result", @@ -971,7 +971,7 @@ "metadata": { "tags": [] }, - "execution_count": 146 + "execution_count": 207 } ] }, @@ -980,7 +980,7 @@ "metadata": { "id": "3nlhzegs3PD2", "colab_type": "code", - "outputId": "f7eef8c4-8fb0-43d9-c992-de00eff21a5b", + "outputId": "59e9baea-6347-4708-f4eb-2e335c19dd37", "colab": { "base_uri": "https://localhost:8080/", "height": 118 @@ -990,7 +990,7 @@ "#Valores distintos de race\n", "df_50k['race'].value_counts()" ], - "execution_count": 147, + "execution_count": 208, "outputs": [ { "output_type": "execute_result", @@ -1007,7 +1007,7 @@ "metadata": { "tags": [] }, - "execution_count": 147 + "execution_count": 208 } ] }, @@ -1016,7 +1016,7 @@ "metadata": { "id": "-mgIBM783PAl", "colab_type": "code", - "outputId": "f0f27779-a373-427c-dbf9-267ba11d2582", + "outputId": "a12de272-62aa-48cb-b4f4-b315faa119de", "colab": { "base_uri": "https://localhost:8080/", "height": 67 @@ -1026,7 +1026,7 @@ "#Valores distintos de sex\n", "df_50k['sex'].value_counts()" ], - "execution_count": 148, + "execution_count": 209, "outputs": [ { "output_type": "execute_result", @@ -1040,7 +1040,7 @@ "metadata": { "tags": [] }, - "execution_count": 148 + "execution_count": 209 } ] }, @@ -1049,7 +1049,7 @@ "metadata": { "id": "EjRcmMm73O-2", "colab_type": "code", - "outputId": "977e7f03-8027-4df4-85e2-331d502c7335", + "outputId": "21776725-c081-4b6f-f844-53e5a8a0413f", "colab": { "base_uri": "https://localhost:8080/", "height": 739 @@ -1059,7 +1059,7 @@ "#Valores distintos de native-country\n", "df_50k['native-country'].value_counts()" ], - "execution_count": 149, + "execution_count": 210, "outputs": [ { "output_type": "execute_result", @@ -1113,7 +1113,7 @@ "metadata": { "tags": [] }, - "execution_count": 149 + "execution_count": 210 } ] }, @@ -1122,7 +1122,7 @@ "metadata": { "id": "zkL_oVwT3O73", "colab_type": "code", - "outputId": "e6ab7553-7426-42ed-dd4c-1ea83238f0aa", + "outputId": "f6bfa0a2-564c-4511-96c1-5ee32d32ae13", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -1132,7 +1132,7 @@ "#Valores distintos de flag_50k\n", "df_50k['flag_50k'].value_counts().keys()" ], - "execution_count": 150, + "execution_count": 211, "outputs": [ { "output_type": "execute_result", @@ -1144,7 +1144,7 @@ "metadata": { "tags": [] }, - "execution_count": 150 + "execution_count": 211 } ] }, @@ -1153,7 +1153,7 @@ "metadata": { "id": "2ng16_UcPOCS", "colab_type": "code", - "outputId": "7a171bb4-3fb6-4ed8-c29d-f304530acf09", + "outputId": "a75d33ff-6bbd-4b1c-c129-d8ea6752d125", "colab": { "base_uri": "https://localhost:8080/", "height": 218 @@ -1163,7 +1163,7 @@ "#Valores distintos de hours-per-week\n", "df_50k['hours-per-week'].value_counts()" ], - "execution_count": 151, + "execution_count": 212, "outputs": [ { "output_type": "execute_result", @@ -1186,7 +1186,7 @@ "metadata": { "tags": [] }, - "execution_count": 151 + "execution_count": 212 } ] }, @@ -1221,7 +1221,7 @@ "metadata": { "id": "YUk0xnfaV4yX", "colab_type": "code", - "outputId": "536d095c-6637-43d0-845d-78d71f79aa4c", + "outputId": "9d055fdf-d52d-4e57-f51e-1c024fb7dab9", "colab": { "base_uri": "https://localhost:8080/", "height": 333 @@ -1234,7 +1234,7 @@ "\n", "df_50k.head(5)" ], - "execution_count": 152, + "execution_count": 213, "outputs": [ { "output_type": "execute_result", @@ -1390,7 +1390,7 @@ "metadata": { "tags": [] }, - "execution_count": 152 + "execution_count": 213 } ] }, @@ -1466,7 +1466,7 @@ "metadata": { "id": "Vu2p_1-fDH_3", "colab_type": "code", - "outputId": "4a1a15fc-66d8-4350-9761-651d81c8dbe7", + "outputId": "873d9fb0-b594-4aef-a2bf-133d050d8cb2", "colab": { "base_uri": "https://localhost:8080/", "height": 355 @@ -1482,19 +1482,19 @@ " fmt= '.2f'\n", " )" ], - "execution_count": 154, + "execution_count": 215, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 154 + "execution_count": 215 }, { "output_type": "display_data", @@ -1525,7 +1525,7 @@ "metadata": { "id": "RNLaUYs3CHjQ", "colab_type": "code", - "outputId": "a58648db-0534-4add-c35a-155cd5e8238d", + "outputId": "3ec2f03c-dd46-41c7-ca03-edc6ff0735b6", "colab": { "base_uri": "https://localhost:8080/", "height": 254 @@ -1536,7 +1536,7 @@ "Matrix_Corr = df_50k.corr().where(np.triu(np.ones(df_50k.corr().shape), k=1).astype(np.bool))\n", "Matrix_Corr" ], - "execution_count": 155, + "execution_count": 216, "outputs": [ { "output_type": "execute_result", @@ -1660,7 +1660,7 @@ "metadata": { "tags": [] }, - "execution_count": 155 + "execution_count": 216 } ] }, @@ -1669,7 +1669,7 @@ "metadata": { "id": "-ohsbrRdGmJJ", "colab_type": "code", - "outputId": "bb7b9356-f6a9-4e17-fa3a-be982b9b70f2", + "outputId": "d7bd34c3-b399-41d3-bc8d-fa98dd6d95bc", "colab": { "base_uri": "https://localhost:8080/", "height": 118 @@ -1686,7 +1686,7 @@ "\n", "set_Colunas_Correlacionadas" ], - "execution_count": 156, + "execution_count": 217, "outputs": [ { "output_type": "execute_result", @@ -1703,7 +1703,7 @@ "metadata": { "tags": [] }, - "execution_count": 156 + "execution_count": 217 } ] }, @@ -1712,7 +1712,7 @@ "metadata": { "id": "_0MZj06uIhtG", "colab_type": "code", - "outputId": "95cc6750-f6e6-4652-e44c-8fd84d5d1805", + "outputId": "9ab48c9b-876d-4653-a178-27c5fcc03321", "colab": { "base_uri": "https://localhost:8080/", "height": 681 @@ -1724,19 +1724,19 @@ "mask[np.triu_indices_from(mask)] = 1\n", "sns.heatmap(df_50k.corr().abs(), mask= mask, ax= ax, cmap='RdPu', annot= True, fmt= '.2f', center= 0)" ], - "execution_count": 157, + "execution_count": 218, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 157 + "execution_count": 218 }, { "output_type": "display_data", @@ -1767,7 +1767,7 @@ "metadata": { "id": "BaRNhWV1cFlr", "colab_type": "code", - "outputId": "a49ce253-ad8a-4f7f-d778-36317dba3d66", + "outputId": "583734fc-e2c4-4e00-9ee3-8cd0fcd3e29d", "colab": { "base_uri": "https://localhost:8080/", "height": 284 @@ -1776,7 +1776,7 @@ "source": [ "df_50k.describe()" ], - "execution_count": 185, + "execution_count": 219, "outputs": [ { "output_type": "execute_result", @@ -1812,33 +1812,33 @@ " \n", " \n", " count\n", - " 32537.000000\n", - " 3.253700e+04\n", - " 32537.000000\n", - " 32537.000000\n", - " 32537.000000\n", - " 32537.000000\n", - " 32537.000000\n", + " 32561.000000\n", + " 3.256100e+04\n", + " 32561.000000\n", + " 32561.000000\n", + " 32561.000000\n", + " 32561.000000\n", + " 32561.000000\n", " \n", " \n", " mean\n", - " 38.585549\n", - " 1.897808e+05\n", - " 10.081815\n", - " 1078.443741\n", - " 87.368227\n", - " 40.440329\n", - " 0.240926\n", + " 38.581647\n", + " 1.897784e+05\n", + " 10.080679\n", + " 1077.648844\n", + " 87.303830\n", + " 40.437456\n", + " 0.240810\n", " \n", " \n", " std\n", - " 13.637984\n", - " 1.055565e+05\n", - " 2.571633\n", - " 7387.957424\n", - " 403.101833\n", - " 12.346889\n", - " 0.427652\n", + " 13.640433\n", + " 1.055500e+05\n", + " 2.572720\n", + " 7385.292085\n", + " 402.960219\n", + " 12.347429\n", + " 0.427581\n", " \n", " \n", " min\n", @@ -1873,7 +1873,7 @@ " \n", " 75%\n", " 48.000000\n", - " 2.369930e+05\n", + " 2.370510e+05\n", " 12.000000\n", " 0.000000\n", " 0.000000\n", @@ -1896,13 +1896,13 @@ ], "text/plain": [ " age fnlwgt ... hours-per-week ind_50k\n", - "count 32537.000000 3.253700e+04 ... 32537.000000 32537.000000\n", - "mean 38.585549 1.897808e+05 ... 40.440329 0.240926\n", - "std 13.637984 1.055565e+05 ... 12.346889 0.427652\n", + "count 32561.000000 3.256100e+04 ... 32561.000000 32561.000000\n", + "mean 38.581647 1.897784e+05 ... 40.437456 0.240810\n", + "std 13.640433 1.055500e+05 ... 12.347429 0.427581\n", "min 17.000000 1.228500e+04 ... 1.000000 0.000000\n", "25% 28.000000 1.178270e+05 ... 40.000000 0.000000\n", "50% 37.000000 1.783560e+05 ... 40.000000 0.000000\n", - "75% 48.000000 2.369930e+05 ... 45.000000 0.000000\n", + "75% 48.000000 2.370510e+05 ... 45.000000 0.000000\n", "max 90.000000 1.484705e+06 ... 99.000000 1.000000\n", "\n", "[8 rows x 7 columns]" @@ -1911,7 +1911,7 @@ "metadata": { "tags": [] }, - "execution_count": 185 + "execution_count": 219 } ] }, @@ -1920,7 +1920,7 @@ "metadata": { "id": "KZQOGm7XcrRf", "colab_type": "code", - "outputId": "0de14feb-627f-4f0c-e8f1-6ce9c230a71f", + "outputId": "e83682bd-9aec-4611-b0c1-4853f7d35659", "colab": { "base_uri": "https://localhost:8080/", "height": 166 @@ -1929,7 +1929,7 @@ "source": [ "df_50k.describe(include=[\"O\"])" ], - "execution_count": 186, + "execution_count": 220, "outputs": [ { "output_type": "execute_result", @@ -1967,15 +1967,15 @@ " \n", " \n", " count\n", - " 32537\n", - " 32537\n", - " 32537\n", - " 32537\n", - " 32537\n", - " 32537\n", - " 32537\n", - " 32537\n", - " 32537\n", + " 32561\n", + " 32561\n", + " 32561\n", + " 32561\n", + " 32561\n", + " 32561\n", + " 32561\n", + " 32561\n", + " 32561\n", " \n", " \n", " unique\n", @@ -2003,15 +2003,15 @@ " \n", " \n", " freq\n", - " 22673\n", - " 10494\n", - " 14970\n", - " 4136\n", - " 13187\n", - " 27795\n", - " 21775\n", - " 29153\n", - " 24698\n", + " 22696\n", + " 10501\n", + " 14976\n", + " 4140\n", + " 13193\n", + " 27816\n", + " 21790\n", + " 29170\n", + " 24720\n", " \n", " \n", "\n", @@ -2019,10 +2019,10 @@ ], "text/plain": [ " workclass education marital-status ... sex native-country flag_50k\n", - "count 32537 32537 32537 ... 32537 32537 32537\n", + "count 32561 32561 32561 ... 32561 32561 32561\n", "unique 9 16 7 ... 2 42 2\n", "top Private HS-grad Married-civ-spouse ... Male United-States <=50K\n", - "freq 22673 10494 14970 ... 21775 29153 24698\n", + "freq 22696 10501 14976 ... 21790 29170 24720\n", "\n", "[4 rows x 9 columns]" ] @@ -2030,7 +2030,7 @@ "metadata": { "tags": [] }, - "execution_count": 186 + "execution_count": 220 } ] }, @@ -2043,30 +2043,30 @@ "base_uri": "https://localhost:8080/", "height": 297 }, - "outputId": "7b06937b-6252-4cab-eb3f-c40986e4b2c0" + "outputId": "0760ba89-a98e-4d00-acf3-b715bd9a2b19" }, "source": [ "#Indicador dos 50K por Genero\n", "sns.countplot(x='ind_50k', hue='sex', data=df_50k)" ], - "execution_count": 187, + "execution_count": 221, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 187 + "execution_count": 221 }, { "output_type": "display_data", "data": { - "image/png": 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" ] @@ -2123,7 +2123,7 @@ "metadata": { "id": "fTUWOuBU30or", "colab_type": "code", - "outputId": "4f6a8e79-6530-469d-e7b9-0b90e76a1bb3", + "outputId": "7593ad79-dc79-4515-9ba9-74d02e8cc36c", "colab": { "base_uri": "https://localhost:8080/", "height": 295 @@ -2136,7 +2136,7 @@ "plt.ylabel('Frequencia')\n", "plt.show()" ], - "execution_count": 159, + "execution_count": 223, "outputs": [ { "output_type": "display_data", @@ -2167,7 +2167,7 @@ "metadata": { "id": "OzAQWltWQDVa", "colab_type": "code", - "outputId": "294e4b42-159f-46fe-e11c-220aef8c339d", + "outputId": "2a0e582a-aae8-4c68-a975-d18281326878", "colab": { "base_uri": "https://localhost:8080/", "height": 295 @@ -2180,7 +2180,7 @@ "plt.ylabel('Frequencia')\n", "plt.show()" ], - "execution_count": 160, + "execution_count": 224, "outputs": [ { "output_type": "display_data", @@ -2211,7 +2211,7 @@ "metadata": { "id": "VE6vRQbl8Yb7", "colab_type": "code", - "outputId": "6488f751-4c14-4dc2-cf7f-bc407932fe78", + "outputId": "b405ba0e-1368-4f9c-8d9b-8a5bf92ef718", "colab": { "base_uri": "https://localhost:8080/", "height": 475 @@ -2222,19 +2222,19 @@ "fig, ax = plt.subplots(figsize=(15,7))\n", "df_50k.groupby(['education','race']).count()['age'].unstack().plot(ax=ax, title='Raça vs Escolaridade')" ], - "execution_count": 161, + "execution_count": 225, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 161 + "execution_count": 225 }, { "output_type": "display_data", @@ -2265,7 +2265,7 @@ "metadata": { "id": "lsp3XAlTRd3Y", "colab_type": "code", - "outputId": "a2e1ab0a-bf92-4299-d8cf-6035c1c423b4", + "outputId": "556b4d96-9ac8-4b71-a1e8-07d1ace0768f", "colab": { "base_uri": "https://localhost:8080/", "height": 475 @@ -2276,19 +2276,19 @@ "fig, ax = plt.subplots(figsize=(15,7))\n", "df_50k.groupby(['relationship','ind_50k']).count()['age'].unstack().plot(ax=ax, title='Horas Trabalhadas vs Indicativo do Valor')" ], - "execution_count": 162, + "execution_count": 226, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 162 + "execution_count": 226 }, { "output_type": "display_data", @@ -2352,7 +2352,7 @@ "metadata": { "id": "6mdOurBJvIed", "colab_type": "code", - "outputId": "8e6ff915-bfc9-4f94-c459-11f5af6e6b9f", + "outputId": "a348980a-e768-4f2c-ea28-7cc53b3f3eff", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 @@ -2362,7 +2362,7 @@ "#Lista das linhas duplicadas\n", "df_50k[df_50k.duplicated()]" ], - "execution_count": 175, + "execution_count": 265, "outputs": [ { "output_type": "execute_result", @@ -2402,503 +2402,528 @@ " native-country\n", " flag_50k\n", " ind_50k\n", + " age_class\n", " \n", " \n", " \n", " \n", " 4881\n", " 25\n", - " Private\n", + " private\n", " 308144\n", - " Bachelors\n", + " bachelors\n", " 13\n", - " Never-married\n", - " Craft-repair\n", - " Not-in-family\n", - " White\n", - " Male\n", + " never-married\n", + " craft-repair\n", + " not-in-family\n", + " white\n", + " male\n", " 0\n", " 0\n", " 40\n", - " Mexico\n", - " <=50K\n", + " mexico\n", + " <=50k\n", + " 0\n", " 0\n", " \n", " \n", " 5104\n", " 90\n", - " Private\n", + " private\n", " 52386\n", - " Some-college\n", + " some-college\n", " 10\n", - " Never-married\n", - " Other-service\n", - " Not-in-family\n", - " Asian-Pac-Islander\n", - " Male\n", + " never-married\n", + " other-service\n", + " not-in-family\n", + " asian-pac-islander\n", + " male\n", " 0\n", " 0\n", " 35\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", " 0\n", + " 4\n", " \n", " \n", " 9171\n", " 21\n", - " Private\n", + " private\n", " 250051\n", - " Some-college\n", + " some-college\n", " 10\n", - " Never-married\n", - " Prof-specialty\n", - " Own-child\n", - " White\n", - " Female\n", + " never-married\n", + " prof-specialty\n", + " own-child\n", + " white\n", + " female\n", " 0\n", " 0\n", " 10\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", + " 0\n", " 0\n", " \n", " \n", " 11631\n", " 20\n", - " Private\n", + " private\n", " 107658\n", - " Some-college\n", + " some-college\n", " 10\n", - " Never-married\n", - " Tech-support\n", - " Not-in-family\n", - " White\n", - " Female\n", + " never-married\n", + " tech-support\n", + " not-in-family\n", + " white\n", + " female\n", " 0\n", " 0\n", " 10\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", + " 0\n", " 0\n", " \n", " \n", " 13084\n", " 25\n", - " Private\n", + " private\n", " 195994\n", " 1st-4th\n", " 2\n", - " Never-married\n", - " Priv-house-serv\n", - " Not-in-family\n", - " White\n", - " Female\n", + " never-married\n", + " priv-house-serv\n", + " not-in-family\n", + " white\n", + " female\n", " 0\n", " 0\n", " 40\n", - " Guatemala\n", - " <=50K\n", + " guatemala\n", + " <=50k\n", + " 0\n", " 0\n", " \n", " \n", " 15059\n", " 21\n", - " Private\n", + " private\n", " 243368\n", - " Preschool\n", + " preschool\n", " 1\n", - " Never-married\n", - " Farming-fishing\n", - " Not-in-family\n", - " White\n", - " Male\n", + " never-married\n", + " farming-fishing\n", + " not-in-family\n", + " white\n", + " male\n", " 0\n", " 0\n", " 50\n", - " Mexico\n", - " <=50K\n", + " mexico\n", + " <=50k\n", + " 0\n", " 0\n", " \n", " \n", " 17040\n", " 46\n", - " Private\n", + " private\n", " 173243\n", - " HS-grad\n", + " hs-grad\n", " 9\n", - " Married-civ-spouse\n", - " Craft-repair\n", - " Husband\n", - " White\n", - " Male\n", + " married-civ-spouse\n", + " craft-repair\n", + " husband\n", + " white\n", + " male\n", " 0\n", " 0\n", " 40\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", " 0\n", + " 3\n", " \n", " \n", " 18555\n", " 30\n", - " Private\n", + " private\n", " 144593\n", - " HS-grad\n", + " hs-grad\n", " 9\n", - " Never-married\n", - " Other-service\n", - " Not-in-family\n", - " Black\n", - " Male\n", + " never-married\n", + " other-service\n", + " not-in-family\n", + " black\n", + " male\n", " 0\n", " 0\n", " 40\n", " ?\n", - " <=50K\n", + " <=50k\n", " 0\n", + " 1\n", " \n", " \n", " 18698\n", " 19\n", - " Private\n", + " private\n", " 97261\n", - " HS-grad\n", + " hs-grad\n", " 9\n", - " Never-married\n", - " Farming-fishing\n", - " Not-in-family\n", - " White\n", - " Male\n", + " never-married\n", + " farming-fishing\n", + " not-in-family\n", + " white\n", + " male\n", " 0\n", " 0\n", " 40\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", + " 0\n", " 0\n", " \n", " \n", " 21318\n", " 19\n", - " Private\n", + " private\n", " 138153\n", - " Some-college\n", + " some-college\n", " 10\n", - " Never-married\n", - " Adm-clerical\n", - " Own-child\n", - " White\n", - " Female\n", + " never-married\n", + " adm-clerical\n", + " own-child\n", + " white\n", + " female\n", " 0\n", " 0\n", " 10\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", + " 0\n", " 0\n", " \n", " \n", " 21490\n", " 19\n", - " Private\n", + " private\n", " 146679\n", - " Some-college\n", + " some-college\n", " 10\n", - " Never-married\n", - " Exec-managerial\n", - " Own-child\n", - " Black\n", - " Male\n", + " never-married\n", + " exec-managerial\n", + " own-child\n", + " black\n", + " male\n", " 0\n", " 0\n", " 30\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", + " 0\n", " 0\n", " \n", " \n", " 21875\n", " 49\n", - " Private\n", + " private\n", " 31267\n", " 7th-8th\n", " 4\n", - " Married-civ-spouse\n", - " Craft-repair\n", - " Husband\n", - " White\n", - " Male\n", + " married-civ-spouse\n", + " craft-repair\n", + " husband\n", + " white\n", + " male\n", " 0\n", " 0\n", " 40\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", " 0\n", + " 3\n", " \n", " \n", " 22300\n", " 25\n", - " Private\n", + " private\n", " 195994\n", " 1st-4th\n", " 2\n", - " Never-married\n", - " Priv-house-serv\n", - " Not-in-family\n", - " White\n", - " Female\n", + " never-married\n", + " priv-house-serv\n", + " not-in-family\n", + " white\n", + " female\n", " 0\n", " 0\n", " 40\n", - " Guatemala\n", - " <=50K\n", + " guatemala\n", + " <=50k\n", + " 0\n", " 0\n", " \n", " \n", " 22367\n", " 44\n", - " Private\n", + " private\n", " 367749\n", - " Bachelors\n", + " bachelors\n", " 13\n", - " Never-married\n", - " Prof-specialty\n", - " Not-in-family\n", - " White\n", - " Female\n", + " never-married\n", + " prof-specialty\n", + " not-in-family\n", + " white\n", + " female\n", " 0\n", " 0\n", " 45\n", - " Mexico\n", - " <=50K\n", + " mexico\n", + " <=50k\n", " 0\n", + " 3\n", " \n", " \n", " 22494\n", " 49\n", - " Self-emp-not-inc\n", + " self-emp-not-inc\n", " 43479\n", - " Some-college\n", + " some-college\n", " 10\n", - " Married-civ-spouse\n", - " Craft-repair\n", - " Husband\n", - " White\n", - " Male\n", + " married-civ-spouse\n", + " craft-repair\n", + " husband\n", + " white\n", + " male\n", " 0\n", " 0\n", " 40\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", " 0\n", + " 3\n", " \n", " \n", " 25872\n", " 23\n", - " Private\n", + " private\n", " 240137\n", " 5th-6th\n", " 3\n", - " Never-married\n", - " Handlers-cleaners\n", - " Not-in-family\n", - " White\n", - " Male\n", + " never-married\n", + " handlers-cleaners\n", + " not-in-family\n", + " white\n", + " male\n", " 0\n", " 0\n", " 55\n", - " Mexico\n", - " <=50K\n", + " mexico\n", + " <=50k\n", + " 0\n", " 0\n", " \n", " \n", " 26313\n", " 28\n", - " Private\n", + " private\n", " 274679\n", - " Masters\n", + " masters\n", " 14\n", - " Never-married\n", - " Prof-specialty\n", - " Not-in-family\n", - " White\n", - " Male\n", + " never-married\n", + " prof-specialty\n", + " not-in-family\n", + " white\n", + " male\n", " 0\n", " 0\n", " 50\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", " 0\n", + " 1\n", " \n", " \n", " 28230\n", " 27\n", - " Private\n", + " private\n", " 255582\n", - " HS-grad\n", + " hs-grad\n", " 9\n", - " Never-married\n", - " Machine-op-inspct\n", - " Not-in-family\n", - " White\n", - " Female\n", + " never-married\n", + " machine-op-inspct\n", + " not-in-family\n", + " white\n", + " female\n", " 0\n", " 0\n", " 40\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", " 0\n", + " 1\n", " \n", " \n", " 28522\n", " 42\n", - " Private\n", + " private\n", " 204235\n", - " Some-college\n", + " some-college\n", " 10\n", - " Married-civ-spouse\n", - " Prof-specialty\n", - " Husband\n", - " White\n", - " Male\n", + " married-civ-spouse\n", + " prof-specialty\n", + " husband\n", + " white\n", + " male\n", " 0\n", " 0\n", " 40\n", - " United-States\n", - " >50K\n", + " united-states\n", + " >50k\n", " 1\n", + " 3\n", " \n", " \n", " 28846\n", " 39\n", - " Private\n", + " private\n", " 30916\n", - " HS-grad\n", + " hs-grad\n", " 9\n", - " Married-civ-spouse\n", - " Craft-repair\n", - " Husband\n", - " White\n", - " Male\n", + " married-civ-spouse\n", + " craft-repair\n", + " husband\n", + " white\n", + " male\n", " 0\n", " 0\n", " 40\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", " 0\n", + " 2\n", " \n", " \n", " 29157\n", " 38\n", - " Private\n", + " private\n", " 207202\n", - " HS-grad\n", + " hs-grad\n", " 9\n", - " Married-civ-spouse\n", - " Machine-op-inspct\n", - " Husband\n", - " White\n", - " Male\n", + " married-civ-spouse\n", + " machine-op-inspct\n", + " husband\n", + " white\n", + " male\n", " 0\n", " 0\n", " 48\n", - " United-States\n", - " >50K\n", + " united-states\n", + " >50k\n", " 1\n", + " 2\n", " \n", " \n", " 30845\n", " 46\n", - " Private\n", + " private\n", " 133616\n", - " Some-college\n", + " some-college\n", " 10\n", - " Divorced\n", - " Adm-clerical\n", - " Unmarried\n", - " White\n", - " Female\n", + " divorced\n", + " adm-clerical\n", + " unmarried\n", + " white\n", + " female\n", " 0\n", " 0\n", " 40\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", " 0\n", + " 3\n", " \n", " \n", " 31993\n", " 19\n", - " Private\n", + " private\n", " 251579\n", - " Some-college\n", + " some-college\n", " 10\n", - " Never-married\n", - " Other-service\n", - " Own-child\n", - " White\n", - " Male\n", + " never-married\n", + " other-service\n", + " own-child\n", + " white\n", + " male\n", " 0\n", " 0\n", " 14\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", + " 0\n", " 0\n", " \n", " \n", " 32404\n", " 35\n", - " Private\n", + " private\n", " 379959\n", - " HS-grad\n", + " hs-grad\n", " 9\n", - " Divorced\n", - " Other-service\n", - " Not-in-family\n", - " White\n", - " Female\n", + " divorced\n", + " other-service\n", + " not-in-family\n", + " white\n", + " female\n", " 0\n", " 0\n", " 40\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", " 0\n", + " 2\n", " \n", " \n", "\n", "" ], "text/plain": [ - " age workclass fnlwgt ... native-country flag_50k ind_50k\n", - "4881 25 Private 308144 ... Mexico <=50K 0\n", - "5104 90 Private 52386 ... United-States <=50K 0\n", - "9171 21 Private 250051 ... United-States <=50K 0\n", - "11631 20 Private 107658 ... United-States <=50K 0\n", - "13084 25 Private 195994 ... Guatemala <=50K 0\n", - "15059 21 Private 243368 ... Mexico <=50K 0\n", - "17040 46 Private 173243 ... United-States <=50K 0\n", - "18555 30 Private 144593 ... ? <=50K 0\n", - "18698 19 Private 97261 ... United-States <=50K 0\n", - "21318 19 Private 138153 ... United-States <=50K 0\n", - "21490 19 Private 146679 ... United-States <=50K 0\n", - "21875 49 Private 31267 ... United-States <=50K 0\n", - "22300 25 Private 195994 ... Guatemala <=50K 0\n", - "22367 44 Private 367749 ... Mexico <=50K 0\n", - "22494 49 Self-emp-not-inc 43479 ... United-States <=50K 0\n", - "25872 23 Private 240137 ... Mexico <=50K 0\n", - "26313 28 Private 274679 ... United-States <=50K 0\n", - "28230 27 Private 255582 ... United-States <=50K 0\n", - "28522 42 Private 204235 ... United-States >50K 1\n", - "28846 39 Private 30916 ... United-States <=50K 0\n", - "29157 38 Private 207202 ... United-States >50K 1\n", - "30845 46 Private 133616 ... United-States <=50K 0\n", - "31993 19 Private 251579 ... United-States <=50K 0\n", - "32404 35 Private 379959 ... United-States <=50K 0\n", + " age workclass fnlwgt ... flag_50k ind_50k age_class\n", + "4881 25 private 308144 ... <=50k 0 0\n", + "5104 90 private 52386 ... <=50k 0 4\n", + "9171 21 private 250051 ... <=50k 0 0\n", + "11631 20 private 107658 ... <=50k 0 0\n", + "13084 25 private 195994 ... <=50k 0 0\n", + "15059 21 private 243368 ... <=50k 0 0\n", + "17040 46 private 173243 ... <=50k 0 3\n", + "18555 30 private 144593 ... <=50k 0 1\n", + "18698 19 private 97261 ... <=50k 0 0\n", + "21318 19 private 138153 ... <=50k 0 0\n", + "21490 19 private 146679 ... <=50k 0 0\n", + "21875 49 private 31267 ... <=50k 0 3\n", + "22300 25 private 195994 ... <=50k 0 0\n", + "22367 44 private 367749 ... <=50k 0 3\n", + "22494 49 self-emp-not-inc 43479 ... <=50k 0 3\n", + "25872 23 private 240137 ... <=50k 0 0\n", + "26313 28 private 274679 ... <=50k 0 1\n", + "28230 27 private 255582 ... <=50k 0 1\n", + "28522 42 private 204235 ... >50k 1 3\n", + "28846 39 private 30916 ... <=50k 0 2\n", + "29157 38 private 207202 ... >50k 1 2\n", + "30845 46 private 133616 ... <=50k 0 3\n", + "31993 19 private 251579 ... <=50k 0 0\n", + "32404 35 private 379959 ... <=50k 0 2\n", "\n", - "[24 rows x 16 columns]" + "[24 rows x 17 columns]" ] }, "metadata": { "tags": [] }, - "execution_count": 175 + "execution_count": 265 } ] }, @@ -2907,7 +2932,7 @@ "metadata": { "id": "7_xocTl-wz3-", "colab_type": "code", - "outputId": "6b363259-90d7-4e6b-d2a0-bb231120f80f", + "outputId": "4f008678-fab6-4e24-8b84-468eb7c87a9f", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -2916,7 +2941,7 @@ "source": [ "print(f'Há {df_50k[df_50k.duplicated()].count()[0]} registos duplicados')" ], - "execution_count": 176, + "execution_count": 266, "outputs": [ { "output_type": "stream", @@ -2932,7 +2957,7 @@ "metadata": { "id": "FESe-vmcxodU", "colab_type": "code", - "outputId": "52954989-a3d0-4862-e368-f97cac22be31", + "outputId": "31bd0447-80f6-4270-c607-2464fc34a051", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -2943,17 +2968,238 @@ "df_50k = df_50k.drop_duplicates()\n", "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " ], - "execution_count": 177, + "execution_count": 267, "outputs": [ { "output_type": "stream", "text": [ - "Ficheiro com 32537 linhas e 16 colunas\n" + "Ficheiro com 32537 linhas e 17 colunas\n" ], "name": "stdout" } ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "3zOh3fPY6mMr", + "colab_type": "text" + }, + "source": [ + "##Transformação" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oOp0l5M-AYtd", + "colab_type": "text" + }, + "source": [ + "> Para as colunas do tipo *string*, eliminar espaços a direta/esquerda e converter as letras para minúsculas" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0YuVlIFN9s2K", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def Transf_Lower(df):\n", + " # Primeira transformação: Aplicar lower() nos nomes das COLUNAS:\n", + " df.columns = [col.lower() for col in df.columns]\n", + "\n", + " # Segunda transformação: Aplicar o método .str.lower() nos valores das COLUNAS object/strings:\n", + " l_ColsObject = df.select_dtypes(include=['object']).columns\n", + " for col in l_ColsObject:\n", + " df[col]= df[col].str.lower().str.strip() " + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "rbI2wQtVAPPr", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 333 + }, + "outputId": "8b898b04-3a6a-4def-a87d-2c6bf6d98ff7" + }, + "source": [ + "Transf_Lower(df_50k)\n", + "df_50k.head(5)" + ], + "execution_count": 271, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countryflag_50kind_50kage_class
039state-gov77516bachelors13never-marriedadm-clericalnot-in-familywhitemale2174040united-states<=50k02
150self-emp-not-inc83311bachelors13married-civ-spouseexec-managerialhusbandwhitemale0013united-states<=50k03
238private215646hs-grad9divorcedhandlers-cleanersnot-in-familywhitemale0040united-states<=50k02
353private23472111th7married-civ-spousehandlers-cleanershusbandblackmale0040united-states<=50k04
428private338409bachelors13married-civ-spouseprof-specialtywifeblackfemale0040cuba<=50k01
\n", + "
" + ], + "text/plain": [ + " age workclass fnlwgt ... flag_50k ind_50k age_class\n", + "0 39 state-gov 77516 ... <=50k 0 2\n", + "1 50 self-emp-not-inc 83311 ... <=50k 0 3\n", + "2 38 private 215646 ... <=50k 0 2\n", + "3 53 private 234721 ... <=50k 0 4\n", + "4 28 private 338409 ... <=50k 0 1\n", + "\n", + "[5 rows x 17 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 271 + } + ] + }, { "cell_type": "markdown", "metadata": { @@ -2961,7 +3207,77 @@ "colab_type": "text" }, "source": [ - "### Missing Values" + "## Missing Values" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QbfQ4jus77_Y", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# O valor \"?\" aparece nas colunas \"workclass\", \"occupation\" e \"native-country\".\n", + "print(f'{df_50k[df_50k[\"workclass\"].isin([\"?\"])].count()[0] } registos tem a coluna \"workclass\" com Missing Value') \n", + "print(f'{df_50k[df_50k[\"occupation\"].isin([\"?\"])].count()[0] } registos tem a coluna \"occupation\" com Missing Value') \n", + "print(f'{df_50k[df_50k[\"native-country\"].isin([\"?\"])].count()[0] } registos tem a coluna \"native-country\" com Missing Value') \n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XBj6NEZU6-Lk", + "colab_type": "text" + }, + "source": [ + "##Discretizar" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "_G4MKKKs7Cc5", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 386 + }, + "outputId": "f5fae0d2-353a-4756-e0ec-77378be77f24" + }, + "source": [ + "df_50k['age_class'] = pd.qcut(df_50k['age'], 5, labels=False)\n", + "df_50k['age_class'].value_counts()\n", + "sns.catplot(x=\"age\", kind=\"count\", data=df_50k)" + ], + "execution_count": 232, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 232 + }, + { + "output_type": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } ] }, { From dfcd4e0ec40c6bb1f94d77abdb64e17c0b4eb660 Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Thu, 14 Nov 2019 23:07:40 +0000 Subject: [PATCH 30/35] Created using Colaboratory --- dswp_grupo1.ipynb | 708 +++++++++------------------------------------- 1 file changed, 130 insertions(+), 578 deletions(-) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index aedd04324..cf2432c15 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -2352,580 +2352,14 @@ "metadata": { "id": "6mdOurBJvIed", "colab_type": "code", - "outputId": "a348980a-e768-4f2c-ea28-7cc53b3f3eff", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - } - }, - "source": [ - "#Lista das linhas duplicadas\n", - "df_50k[df_50k.duplicated()]" - ], - "execution_count": 265, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countryflag_50kind_50kage_class
488125private308144bachelors13never-marriedcraft-repairnot-in-familywhitemale0040mexico<=50k00
510490private52386some-college10never-marriedother-servicenot-in-familyasian-pac-islandermale0035united-states<=50k04
917121private250051some-college10never-marriedprof-specialtyown-childwhitefemale0010united-states<=50k00
1163120private107658some-college10never-marriedtech-supportnot-in-familywhitefemale0010united-states<=50k00
1308425private1959941st-4th2never-marriedpriv-house-servnot-in-familywhitefemale0040guatemala<=50k00
1505921private243368preschool1never-marriedfarming-fishingnot-in-familywhitemale0050mexico<=50k00
1704046private173243hs-grad9married-civ-spousecraft-repairhusbandwhitemale0040united-states<=50k03
1855530private144593hs-grad9never-marriedother-servicenot-in-familyblackmale0040?<=50k01
1869819private97261hs-grad9never-marriedfarming-fishingnot-in-familywhitemale0040united-states<=50k00
2131819private138153some-college10never-marriedadm-clericalown-childwhitefemale0010united-states<=50k00
2149019private146679some-college10never-marriedexec-managerialown-childblackmale0030united-states<=50k00
2187549private312677th-8th4married-civ-spousecraft-repairhusbandwhitemale0040united-states<=50k03
2230025private1959941st-4th2never-marriedpriv-house-servnot-in-familywhitefemale0040guatemala<=50k00
2236744private367749bachelors13never-marriedprof-specialtynot-in-familywhitefemale0045mexico<=50k03
2249449self-emp-not-inc43479some-college10married-civ-spousecraft-repairhusbandwhitemale0040united-states<=50k03
2587223private2401375th-6th3never-marriedhandlers-cleanersnot-in-familywhitemale0055mexico<=50k00
2631328private274679masters14never-marriedprof-specialtynot-in-familywhitemale0050united-states<=50k01
2823027private255582hs-grad9never-marriedmachine-op-inspctnot-in-familywhitefemale0040united-states<=50k01
2852242private204235some-college10married-civ-spouseprof-specialtyhusbandwhitemale0040united-states>50k13
2884639private30916hs-grad9married-civ-spousecraft-repairhusbandwhitemale0040united-states<=50k02
2915738private207202hs-grad9married-civ-spousemachine-op-inspcthusbandwhitemale0048united-states>50k12
3084546private133616some-college10divorcedadm-clericalunmarriedwhitefemale0040united-states<=50k03
3199319private251579some-college10never-marriedother-serviceown-childwhitemale0014united-states<=50k00
3240435private379959hs-grad9divorcedother-servicenot-in-familywhitefemale0040united-states<=50k02
\n", - "
" - ], - "text/plain": [ - " age workclass fnlwgt ... flag_50k ind_50k age_class\n", - "4881 25 private 308144 ... <=50k 0 0\n", - "5104 90 private 52386 ... <=50k 0 4\n", - "9171 21 private 250051 ... <=50k 0 0\n", - "11631 20 private 107658 ... <=50k 0 0\n", - "13084 25 private 195994 ... <=50k 0 0\n", - "15059 21 private 243368 ... <=50k 0 0\n", - "17040 46 private 173243 ... <=50k 0 3\n", - "18555 30 private 144593 ... <=50k 0 1\n", - "18698 19 private 97261 ... <=50k 0 0\n", - "21318 19 private 138153 ... <=50k 0 0\n", - "21490 19 private 146679 ... <=50k 0 0\n", - "21875 49 private 31267 ... <=50k 0 3\n", - "22300 25 private 195994 ... <=50k 0 0\n", - "22367 44 private 367749 ... <=50k 0 3\n", - "22494 49 self-emp-not-inc 43479 ... <=50k 0 3\n", - "25872 23 private 240137 ... <=50k 0 0\n", - "26313 28 private 274679 ... <=50k 0 1\n", - "28230 27 private 255582 ... <=50k 0 1\n", - "28522 42 private 204235 ... >50k 1 3\n", - "28846 39 private 30916 ... <=50k 0 2\n", - "29157 38 private 207202 ... >50k 1 2\n", - "30845 46 private 133616 ... <=50k 0 3\n", - "31993 19 private 251579 ... <=50k 0 0\n", - "32404 35 private 379959 ... <=50k 0 2\n", - "\n", - "[24 rows x 17 columns]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 265 - } - ] + "colab": {} + }, + "source": [ + "#Lista das linhas duplicadas\n", + "df_50k[df_50k.duplicated()]" + ], + "execution_count": 0, + "outputs": [] }, { "cell_type": "code", @@ -3210,18 +2644,136 @@ "## Missing Values" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "INOfmunhDpDD", + "colab_type": "text" + }, + "source": [ + "**No dataframe, o valor \"?\" será classificado como um *Missing Value*.** \n", + "\n", + "Segue a quantidade de registos que contém MV (e sua porcentagem)" + ] + }, { "cell_type": "code", "metadata": { "id": "QbfQ4jus77_Y", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67 + }, + "outputId": "76ce6cd6-5c58-43fc-e5a9-88b15f578487" }, "source": [ "# O valor \"?\" aparece nas colunas \"workclass\", \"occupation\" e \"native-country\".\n", - "print(f'{df_50k[df_50k[\"workclass\"].isin([\"?\"])].count()[0] } registos tem a coluna \"workclass\" com Missing Value') \n", - "print(f'{df_50k[df_50k[\"occupation\"].isin([\"?\"])].count()[0] } registos tem a coluna \"occupation\" com Missing Value') \n", - "print(f'{df_50k[df_50k[\"native-country\"].isin([\"?\"])].count()[0] } registos tem a coluna \"native-country\" com Missing Value') \n" + "n_mv_workclass = df_50k[df_50k[\"workclass\"].isin([\"?\"])].count()[0]\n", + "n_mv_occupation = df_50k[df_50k[\"occupation\"].isin([\"?\"])].count()[0]\n", + "n_mv_country = df_50k[df_50k[\"native-country\"].isin([\"?\"])].count()[0]\n", + "\n", + "print(f'{n_mv_workclass} registos tem a coluna \"workclass\" com Missing Value ({round((n_mv_workclass*100)/df_50k.shape[0],2)}%)') \n", + "print(f'{n_mv_occupation} registos tem a coluna \"occupation\" com Missing Value ({round((n_mv_occupation*100)/df_50k.shape[0],2)}%)') \n", + "print(f'{n_mv_country} registos tem a coluna \"native-country\" com Missing Value ({round((n_mv_country*100)/df_50k.shape[0],2)}%)') " + ], + "execution_count": 341, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0 registos tem a coluna \"workclass\" com Missing Value (0.0%)\n", + "0 registos tem a coluna \"occupation\" com Missing Value (0.0%)\n", + "0 registos tem a coluna \"native-country\" com Missing Value (0.0%)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2ZbYrNnyEioO", + "colab_type": "text" + }, + "source": [ + "A quantidade de registos que contém \"?\" nas três colunas é muito baixa:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xdFCi4QqDA1S", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "fcb28622-5a51-4e34-bf18-369c2d585afe" + }, + "source": [ + "# Registos onde \"?\" aparece nas 3 colunas\n", + "F1 = df_50k[\"workclass\"].isin([\"?\"]) \n", + "F2 = df_50k[\"occupation\"].isin([\"?\"]) \n", + "F3 = df_50k[\"native-country\"].isin([\"?\"]) \n", + " \n", + "f'{df_50k[F1 & F2 & F3].shape[0]} registos'" + ], + "execution_count": 298, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'27 registos'" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 298 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Is9AbF6MFNQt", + "colab_type": "text" + }, + "source": [ + "> **Quanto aos MVs, opta-se inicialmente por não aplicar qualquer alteração/substituição**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "d8OhJ8VrF7ju", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def Replace_MV(df):\n", + " #Obter as coluanas string\n", + " l_ColsObject = df.select_dtypes(include=['object']).columns\n", + "\n", + " #Substituir \"?\" pel o valor de maior frequencia\n", + " for col in l_ColsObject:\n", + " df[col] = df[col].replace({\"?\" : df_50k[col].mode().iloc[0]})" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "IAtwjEFaIh4p", + "colab_type": "code", + "colab": {} + }, + "source": [ + "#Substituir Missing Value\n", + "#Replace_MV(df_50k)" ], "execution_count": 0, "outputs": [] From 6905b3493c87b5dbf0ed33688d839968a37d15eb Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Thu, 14 Nov 2019 23:37:07 +0000 Subject: [PATCH 31/35] Created using Colaboratory --- dswp_grupo1.ipynb | 407 +++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 381 insertions(+), 26 deletions(-) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index cf2432c15..940384920 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -2326,7 +2326,7 @@ "* **3DP_Feature Engineering**: derivar variaveis\n", "* **3DP_Missing Values Handling**: identificar e tratar os Missing Values\n", "* **3DP_Outliers Handling**: identificar e tratar os Outlier\n", - "* **3DP_Data Transformation**: colocar as variáveis numa mesma escala (RobustScaler?)\n", + "* **3DP_Data Transformation**: colocar as variáveis numa mesma escala (MinMaxScaler?)\n", "\n", "\n", "\n", @@ -2788,88 +2788,403 @@ "##Discretizar" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "Yn3taq3yLUqj", + "colab_type": "text" + }, + "source": [ + "Construir intervalos/classes para variáveis numéricas." + ] + }, { "cell_type": "code", "metadata": { - "id": "_G4MKKKs7Cc5", + "id": "LFPc9boDLf-K", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", - "height": 386 + "height": 67 }, - "outputId": "f5fae0d2-353a-4756-e0ec-77378be77f24" + "outputId": "af3d2b16-9f19-406a-a6c0-b68aad2ae056" }, "source": [ - "df_50k['age_class'] = pd.qcut(df_50k['age'], 5, labels=False)\n", - "df_50k['age_class'].value_counts()\n", - "sns.catplot(x=\"age\", kind=\"count\", data=df_50k)" + "#Colunas numericas\n", + "df_50k.select_dtypes(include=['number']).columns" ], - "execution_count": 232, + "execution_count": 352, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "Index(['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss',\n", + " 'hours-per-week', 'ind_50k', 'age_class'],\n", + " dtype='object')" ] }, "metadata": { "tags": [] }, - "execution_count": 232 + "execution_count": 352 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "_G4MKKKs7Cc5", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 202 }, + "outputId": "71e25da9-17a3-408a-9c3e-14868801c667" + }, + "source": [ + "#Discretizar Age\n", + "df_50k['age_class'] = pd.qcut(df_50k['age'], 10, labels=False)\n", + "df_50k['age_class'].value_counts()" + ], + "execution_count": 353, + "outputs": [ { - "output_type": "display_data", + "output_type": "execute_result", "data": { - "image/png": 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" + ], + "text/plain": [ + "ind_50k 0 1 All\n", + "age_class \n", + "0 7008 177 7185\n", + "1 4896 1068 5964\n", + "2 4639 2121 6760\n", + "3 3794 2375 6169\n", + "4 4361 2098 6459\n", + "All 24698 7839 32537" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 361 + } ] }, { "cell_type": "markdown", "metadata": { - "id": "IJ8WRhPGX6JB", + "id": "h_Nh0ctCX6Gk", "colab_type": "text" }, "source": [ - "#**5MSE - MODEL SELECTION AND EVALUATE**" + "#**4M - MODELING**" ] }, { "cell_type": "markdown", "metadata": { - "id": "66RGztFNaZ-B", + "id": "6CGb0YPFaTP4", "colab_type": "text" }, "source": [ - "> Aplicação das melhores métricas para avaliar o acurácia dos modelos de ML." + "> Aplicar algoritmos (*Supervised* vs *Unsupervised Learning*)." ] }, { @@ -5218,6 +5533,36 @@ } ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "NQ_P9N8URVui", + "colab_type": "text" + }, + "source": [ + "#**5MSE - MODEL SELECTION AND EVALUATE**\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NNTm86ElRgGC", + "colab_type": "text" + }, + "source": [ + "> Aplicação das melhores métricas para avaliar o acurácia dos modelos de ML." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z-WXz1TuRjRN", + "colab_type": "text" + }, + "source": [ + "Não aplicável" + ] + }, { "cell_type": "markdown", "metadata": { @@ -5237,6 +5582,16 @@ "source": [ "> Implementação do Modelo." ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Sc73pOI0RqWu", + "colab_type": "text" + }, + "source": [ + "Não aplicável" + ] } ] } \ No newline at end of file From 4cd016b8fd9cca60fbd106550c8a0fe35c9727ec Mon Sep 17 00:00:00 2001 From: Danielle Pestana Date: Thu, 14 Nov 2019 23:49:50 +0000 Subject: [PATCH 32/35] Created using Colaboratory --- dswp_grupo1.ipynb | 1173 +++++++++++++++++++++++++++++++-------------- 1 file changed, 807 insertions(+), 366 deletions(-) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index 940384920..9ad54c83a 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -116,7 +116,7 @@ "metadata": { "id": "0YTMtMPRbSYD", "colab_type": "code", - "outputId": "fc2ba622-4670-49a5-b931-091b925893be", + "outputId": "20e448c7-9843-4add-e772-dc96ccd67fe9", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -126,7 +126,7 @@ "df_50k = pd.read_csv('https://raw.githubusercontent.com/MathMachado/DSWP/master/Dataframes/adult.data')\n", "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " ], - "execution_count": 193, + "execution_count": 430, "outputs": [ { "output_type": "stream", @@ -142,7 +142,7 @@ "metadata": { "id": "Lxq7B1Vnv1Ys", "colab_type": "code", - "outputId": "5dfb92c9-31f7-4679-f1d0-fe6d2a12465e", + "outputId": "4301db95-5371-448e-89bd-3bfc8d3b51ec", "colab": { "base_uri": "https://localhost:8080/", "height": 296 @@ -152,7 +152,7 @@ "#Detecção de que o ficheiro não tem header\n", "df_50k.head(5)" ], - "execution_count": 194, + "execution_count": 431, "outputs": [ { "output_type": "execute_result", @@ -302,7 +302,7 @@ "metadata": { "tags": [] }, - "execution_count": 194 + "execution_count": 431 } ] }, @@ -311,7 +311,7 @@ "metadata": { "id": "HBKqXT9Bo-BC", "colab_type": "code", - "outputId": "5af62eef-b78d-40ae-ef12-51cba12cbc25", + "outputId": "e26ebaa8-ae4e-47d3-9bff-3836424bf8b7", "colab": { "base_uri": "https://localhost:8080/", "height": 269 @@ -337,7 +337,7 @@ " ]\n", "l_column" ], - "execution_count": 195, + "execution_count": 432, "outputs": [ { "output_type": "execute_result", @@ -363,7 +363,7 @@ "metadata": { "tags": [] }, - "execution_count": 195 + "execution_count": 432 } ] }, @@ -386,7 +386,7 @@ "metadata": { "id": "AjswmEdb4R8X", "colab_type": "code", - "outputId": "bb59ea42-8641-4545-8130-4868f973285e", + "outputId": "0a0d8e07-e6a5-4680-b4d2-b2f752f58c18", "colab": { "base_uri": "https://localhost:8080/", "height": 353 @@ -395,7 +395,7 @@ "source": [ "df_50k.info()" ], - "execution_count": 197, + "execution_count": 434, "outputs": [ { "output_type": "stream", @@ -430,7 +430,7 @@ "metadata": { "id": "CNyS0K4C4dfp", "colab_type": "code", - "outputId": "bc23a584-3023-4fdd-f8c1-042ed6d6e11c", + "outputId": "fcf6a0d9-9a80-4908-a43f-6214ccfbe71b", "colab": { "base_uri": "https://localhost:8080/", "height": 333 @@ -439,7 +439,7 @@ "source": [ "df_50k.head(5)" ], - "execution_count": 198, + "execution_count": 435, "outputs": [ { "output_type": "execute_result", @@ -589,7 +589,7 @@ "metadata": { "tags": [] }, - "execution_count": 198 + "execution_count": 435 } ] }, @@ -598,7 +598,7 @@ "metadata": { "id": "m2p6SOzbo99T", "colab_type": "code", - "outputId": "6a971332-b3ff-4fc5-b262-cfd77692db4f", + "outputId": "26cc9280-a801-4451-ca9b-5f57731e2915", "colab": { "base_uri": "https://localhost:8080/", "height": 101 @@ -608,7 +608,7 @@ "#Colunas do DataFrame\n", "df_50k.columns" ], - "execution_count": 199, + "execution_count": 436, "outputs": [ { "output_type": "execute_result", @@ -624,7 +624,7 @@ "metadata": { "tags": [] }, - "execution_count": 199 + "execution_count": 436 } ] }, @@ -633,7 +633,7 @@ "metadata": { "id": "dMDVRA8eo96a", "colab_type": "code", - "outputId": "9c509a16-eb47-4966-8c1d-c8878ae2b702", + "outputId": "0e57f89a-a0cc-4af2-cfd1-55357db67a8f", "colab": { "base_uri": "https://localhost:8080/", "height": 286 @@ -642,7 +642,7 @@ "source": [ "df_50k.dtypes" ], - "execution_count": 200, + "execution_count": 437, "outputs": [ { "output_type": "execute_result", @@ -669,7 +669,7 @@ "metadata": { "tags": [] }, - "execution_count": 200 + "execution_count": 437 } ] }, @@ -688,7 +688,7 @@ "metadata": { "id": "5SbUk5Srzqok", "colab_type": "code", - "outputId": "d3644a2d-5791-4ffb-adc6-ee22061902a1", + "outputId": "5dab0968-1ea3-4466-ace3-7b3f1e6d81fb", "colab": { "base_uri": "https://localhost:8080/", "height": 185 @@ -698,7 +698,7 @@ "#Valores distintos de WorkClass\n", "df_50k['workclass'].value_counts()" ], - "execution_count": 201, + "execution_count": 438, "outputs": [ { "output_type": "execute_result", @@ -719,7 +719,7 @@ "metadata": { "tags": [] }, - "execution_count": 201 + "execution_count": 438 } ] }, @@ -728,7 +728,7 @@ "metadata": { "id": "VYkfFI451R_m", "colab_type": "code", - "outputId": "679ebc02-3c6d-4160-809e-c68f8373b63f", + "outputId": "14a1a890-1127-41a4-ba59-92417f56ec3c", "colab": { "base_uri": "https://localhost:8080/", "height": 302 @@ -738,7 +738,7 @@ "#Valores distintos de Education\n", "df_50k['education'].value_counts()" ], - "execution_count": 202, + "execution_count": 439, "outputs": [ { "output_type": "execute_result", @@ -766,7 +766,7 @@ "metadata": { "tags": [] }, - "execution_count": 202 + "execution_count": 439 } ] }, @@ -775,7 +775,7 @@ "metadata": { "id": "q67Wyta2XwZx", "colab_type": "code", - "outputId": "2af6ff33-3f27-4cd9-c8ad-63d87f0f40d5", + "outputId": "b280309c-0313-49b7-9ee9-cd3b3653f9e8", "colab": { "base_uri": "https://localhost:8080/", "height": 302 @@ -785,7 +785,7 @@ "#Valores distintos de Education-num\n", "df_50k['education-num'].value_counts()" ], - "execution_count": 203, + "execution_count": 440, "outputs": [ { "output_type": "execute_result", @@ -813,7 +813,7 @@ "metadata": { "tags": [] }, - "execution_count": 203 + "execution_count": 440 } ] }, @@ -822,7 +822,7 @@ "metadata": { "id": "QJly0mWR1R8m", "colab_type": "code", - "outputId": "cd1e8686-c533-4418-b860-a144f532bd69", + "outputId": "06eaab3f-5c9b-4114-a02a-0a65f15acf7d", "colab": { "base_uri": "https://localhost:8080/", "height": 151 @@ -832,7 +832,7 @@ "#Valores distintos de marital-status\n", "df_50k['marital-status'].value_counts()" ], - "execution_count": 204, + "execution_count": 441, "outputs": [ { "output_type": "execute_result", @@ -851,7 +851,7 @@ "metadata": { "tags": [] }, - "execution_count": 204 + "execution_count": 441 } ] }, @@ -860,7 +860,7 @@ "metadata": { "id": "7_Wo0troSfxq", "colab_type": "code", - "outputId": "044eda01-f56b-41df-bfe7-9d2522474d06", + "outputId": "4b1b19c5-d908-4ec1-9ce7-7d9b76eb7d9f", "colab": { "base_uri": "https://localhost:8080/", "height": 134 @@ -870,7 +870,7 @@ "#Valores distintos de relationship\n", "df_50k['relationship'].value_counts()" ], - "execution_count": 205, + "execution_count": 442, "outputs": [ { "output_type": "execute_result", @@ -888,7 +888,7 @@ "metadata": { "tags": [] }, - "execution_count": 205 + "execution_count": 442 } ] }, @@ -897,7 +897,7 @@ "metadata": { "id": "4R_S4oaG1R6_", "colab_type": "code", - "outputId": "157f1fa0-81c6-46f2-9db2-a734b19736ee", + "outputId": "5325afe3-b8ea-4d83-f9d7-df79790a3a09", "colab": { "base_uri": "https://localhost:8080/", "height": 286 @@ -907,7 +907,7 @@ "#Valores distintos de occupation\n", "df_50k['occupation'].value_counts()" ], - "execution_count": 206, + "execution_count": 443, "outputs": [ { "output_type": "execute_result", @@ -934,7 +934,7 @@ "metadata": { "tags": [] }, - "execution_count": 206 + "execution_count": 443 } ] }, @@ -943,7 +943,7 @@ "metadata": { "id": "2q5DBeLM1R3m", "colab_type": "code", - "outputId": "e91affba-5142-4188-92e4-c72b8cae06f7", + "outputId": "487922fb-c883-4bc2-b30e-e6960e1cc305", "colab": { "base_uri": "https://localhost:8080/", "height": 134 @@ -953,7 +953,7 @@ "#Valores distintos de relationship\n", "df_50k['relationship'].value_counts()" ], - "execution_count": 207, + "execution_count": 444, "outputs": [ { "output_type": "execute_result", @@ -971,7 +971,7 @@ "metadata": { "tags": [] }, - "execution_count": 207 + "execution_count": 444 } ] }, @@ -980,7 +980,7 @@ "metadata": { "id": "3nlhzegs3PD2", "colab_type": "code", - "outputId": "59e9baea-6347-4708-f4eb-2e335c19dd37", + "outputId": "fd6f71c9-4ae7-4e48-934a-efb052138af1", "colab": { "base_uri": "https://localhost:8080/", "height": 118 @@ -990,7 +990,7 @@ "#Valores distintos de race\n", "df_50k['race'].value_counts()" ], - "execution_count": 208, + "execution_count": 445, "outputs": [ { "output_type": "execute_result", @@ -1007,7 +1007,7 @@ "metadata": { "tags": [] }, - "execution_count": 208 + "execution_count": 445 } ] }, @@ -1016,7 +1016,7 @@ "metadata": { "id": "-mgIBM783PAl", "colab_type": "code", - "outputId": "a12de272-62aa-48cb-b4f4-b315faa119de", + "outputId": "a68418a4-80df-4edc-d1a4-6e46f6dc116c", "colab": { "base_uri": "https://localhost:8080/", "height": 67 @@ -1026,7 +1026,7 @@ "#Valores distintos de sex\n", "df_50k['sex'].value_counts()" ], - "execution_count": 209, + "execution_count": 446, "outputs": [ { "output_type": "execute_result", @@ -1040,7 +1040,7 @@ "metadata": { "tags": [] }, - "execution_count": 209 + "execution_count": 446 } ] }, @@ -1049,7 +1049,7 @@ "metadata": { "id": "EjRcmMm73O-2", "colab_type": "code", - "outputId": "21776725-c081-4b6f-f844-53e5a8a0413f", + "outputId": "211b30fc-951a-433c-c2ce-113b600e2b61", "colab": { "base_uri": "https://localhost:8080/", "height": 739 @@ -1059,7 +1059,7 @@ "#Valores distintos de native-country\n", "df_50k['native-country'].value_counts()" ], - "execution_count": 210, + "execution_count": 447, "outputs": [ { "output_type": "execute_result", @@ -1113,7 +1113,7 @@ "metadata": { "tags": [] }, - "execution_count": 210 + "execution_count": 447 } ] }, @@ -1122,7 +1122,7 @@ "metadata": { "id": "zkL_oVwT3O73", "colab_type": "code", - "outputId": "f6bfa0a2-564c-4511-96c1-5ee32d32ae13", + "outputId": "370758f3-e01f-4717-f08b-9200c1f6f907", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -1132,7 +1132,7 @@ "#Valores distintos de flag_50k\n", "df_50k['flag_50k'].value_counts().keys()" ], - "execution_count": 211, + "execution_count": 448, "outputs": [ { "output_type": "execute_result", @@ -1144,7 +1144,7 @@ "metadata": { "tags": [] }, - "execution_count": 211 + "execution_count": 448 } ] }, @@ -1153,7 +1153,7 @@ "metadata": { "id": "2ng16_UcPOCS", "colab_type": "code", - "outputId": "a75d33ff-6bbd-4b1c-c129-d8ea6752d125", + "outputId": "42e80ce7-db87-4072-b119-7f855aa9d45a", "colab": { "base_uri": "https://localhost:8080/", "height": 218 @@ -1163,7 +1163,7 @@ "#Valores distintos de hours-per-week\n", "df_50k['hours-per-week'].value_counts()" ], - "execution_count": 212, + "execution_count": 449, "outputs": [ { "output_type": "execute_result", @@ -1186,7 +1186,7 @@ "metadata": { "tags": [] }, - "execution_count": 212 + "execution_count": 449 } ] }, @@ -1221,7 +1221,7 @@ "metadata": { "id": "YUk0xnfaV4yX", "colab_type": "code", - "outputId": "9d055fdf-d52d-4e57-f51e-1c024fb7dab9", + "outputId": "1691a7ac-9566-4db1-ae35-8c9255e367b5", "colab": { "base_uri": "https://localhost:8080/", "height": 333 @@ -1234,7 +1234,7 @@ "\n", "df_50k.head(5)" ], - "execution_count": 213, + "execution_count": 450, "outputs": [ { "output_type": "execute_result", @@ -1390,7 +1390,7 @@ "metadata": { "tags": [] }, - "execution_count": 213 + "execution_count": 450 } ] }, @@ -1466,7 +1466,7 @@ "metadata": { "id": "Vu2p_1-fDH_3", "colab_type": "code", - "outputId": "873d9fb0-b594-4aef-a2bf-133d050d8cb2", + "outputId": "df73b54d-4ade-419b-b728-342222620958", "colab": { "base_uri": "https://localhost:8080/", "height": 355 @@ -1482,19 +1482,19 @@ " fmt= '.2f'\n", " )" ], - "execution_count": 215, + "execution_count": 452, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 215 + "execution_count": 452 }, { "output_type": "display_data", @@ -1525,7 +1525,7 @@ "metadata": { "id": "RNLaUYs3CHjQ", "colab_type": "code", - "outputId": "3ec2f03c-dd46-41c7-ca03-edc6ff0735b6", + "outputId": "32020383-432c-48f8-e37f-366b1dbff742", "colab": { "base_uri": "https://localhost:8080/", "height": 254 @@ -1536,7 +1536,7 @@ "Matrix_Corr = df_50k.corr().where(np.triu(np.ones(df_50k.corr().shape), k=1).astype(np.bool))\n", "Matrix_Corr" ], - "execution_count": 216, + "execution_count": 453, "outputs": [ { "output_type": "execute_result", @@ -1660,7 +1660,7 @@ "metadata": { "tags": [] }, - "execution_count": 216 + "execution_count": 453 } ] }, @@ -1669,7 +1669,7 @@ "metadata": { "id": "-ohsbrRdGmJJ", "colab_type": "code", - "outputId": "d7bd34c3-b399-41d3-bc8d-fa98dd6d95bc", + "outputId": "315af47b-a70f-45c9-e6ce-1a57f2d0077c", "colab": { "base_uri": "https://localhost:8080/", "height": 118 @@ -1686,7 +1686,7 @@ "\n", "set_Colunas_Correlacionadas" ], - "execution_count": 217, + "execution_count": 454, "outputs": [ { "output_type": "execute_result", @@ -1703,7 +1703,7 @@ "metadata": { "tags": [] }, - "execution_count": 217 + "execution_count": 454 } ] }, @@ -1712,7 +1712,7 @@ "metadata": { "id": "_0MZj06uIhtG", "colab_type": "code", - "outputId": "9ab48c9b-876d-4653-a178-27c5fcc03321", + "outputId": "b2933e15-c80f-48b5-fd1c-3d5f02505c88", "colab": { "base_uri": "https://localhost:8080/", "height": 681 @@ -1724,19 +1724,19 @@ "mask[np.triu_indices_from(mask)] = 1\n", "sns.heatmap(df_50k.corr().abs(), mask= mask, ax= ax, cmap='RdPu', annot= True, fmt= '.2f', center= 0)" ], - "execution_count": 218, + "execution_count": 455, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 218 + "execution_count": 455 }, { "output_type": "display_data", @@ -1767,7 +1767,7 @@ "metadata": { "id": "BaRNhWV1cFlr", "colab_type": "code", - "outputId": "583734fc-e2c4-4e00-9ee3-8cd0fcd3e29d", + "outputId": "8bed02a9-8613-46ad-d1b0-74b61757207b", "colab": { "base_uri": "https://localhost:8080/", "height": 284 @@ -1776,7 +1776,7 @@ "source": [ "df_50k.describe()" ], - "execution_count": 219, + "execution_count": 456, "outputs": [ { "output_type": "execute_result", @@ -1911,7 +1911,7 @@ "metadata": { "tags": [] }, - "execution_count": 219 + "execution_count": 456 } ] }, @@ -1920,7 +1920,7 @@ "metadata": { "id": "KZQOGm7XcrRf", "colab_type": "code", - "outputId": "e83682bd-9aec-4611-b0c1-4853f7d35659", + "outputId": "9a6a9d98-eefd-4063-e429-20d1092e3891", "colab": { "base_uri": "https://localhost:8080/", "height": 166 @@ -1929,7 +1929,7 @@ "source": [ "df_50k.describe(include=[\"O\"])" ], - "execution_count": 220, + "execution_count": 457, "outputs": [ { "output_type": "execute_result", @@ -2030,7 +2030,7 @@ "metadata": { "tags": [] }, - "execution_count": 220 + "execution_count": 457 } ] }, @@ -2043,25 +2043,25 @@ "base_uri": "https://localhost:8080/", "height": 297 }, - "outputId": "0760ba89-a98e-4d00-acf3-b715bd9a2b19" + "outputId": "bfc68aa9-5f5f-4852-b2b2-65c934b53d48" }, "source": [ "#Indicador dos 50K por Genero\n", "sns.countplot(x='ind_50k', hue='sex', data=df_50k)" ], - "execution_count": 221, + "execution_count": 458, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 221 + "execution_count": 458 }, { "output_type": "display_data", @@ -2123,7 +2123,7 @@ "metadata": { "id": "fTUWOuBU30or", "colab_type": "code", - "outputId": "7593ad79-dc79-4515-9ba9-74d02e8cc36c", + "outputId": "5c599379-3ced-4358-f274-19aa17c9b843", "colab": { "base_uri": "https://localhost:8080/", "height": 295 @@ -2136,7 +2136,7 @@ "plt.ylabel('Frequencia')\n", "plt.show()" ], - "execution_count": 223, + "execution_count": 460, "outputs": [ { "output_type": "display_data", @@ -2167,7 +2167,7 @@ "metadata": { "id": "OzAQWltWQDVa", "colab_type": "code", - "outputId": "2a0e582a-aae8-4c68-a975-d18281326878", + "outputId": "a746ef59-07f1-40ae-8064-a1ff7a775051", "colab": { "base_uri": "https://localhost:8080/", "height": 295 @@ -2180,7 +2180,7 @@ "plt.ylabel('Frequencia')\n", "plt.show()" ], - "execution_count": 224, + "execution_count": 461, "outputs": [ { "output_type": "display_data", @@ -2211,7 +2211,7 @@ "metadata": { "id": "VE6vRQbl8Yb7", "colab_type": "code", - "outputId": "b405ba0e-1368-4f9c-8d9b-8a5bf92ef718", + "outputId": "10a52837-940c-4a8a-916c-ad824b039e3c", "colab": { "base_uri": "https://localhost:8080/", "height": 475 @@ -2222,19 +2222,19 @@ "fig, ax = plt.subplots(figsize=(15,7))\n", "df_50k.groupby(['education','race']).count()['age'].unstack().plot(ax=ax, title='Raça vs Escolaridade')" ], - "execution_count": 225, + "execution_count": 462, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 225 + "execution_count": 462 }, { "output_type": "display_data", @@ -2265,7 +2265,7 @@ "metadata": { "id": "lsp3XAlTRd3Y", "colab_type": "code", - "outputId": "556b4d96-9ac8-4b71-a1e8-07d1ace0768f", + "outputId": "c05730d1-0ba5-4d18-88f2-555aa4332992", "colab": { "base_uri": "https://localhost:8080/", "height": 475 @@ -2276,19 +2276,19 @@ "fig, ax = plt.subplots(figsize=(15,7))\n", "df_50k.groupby(['relationship','ind_50k']).count()['age'].unstack().plot(ax=ax, title='Horas Trabalhadas vs Indicativo do Valor')" ], - "execution_count": 226, + "execution_count": 463, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 226 + "execution_count": 463 }, { "output_type": "display_data", @@ -2352,21 +2352,562 @@ "metadata": { "id": "6mdOurBJvIed", "colab_type": "code", - "colab": {} + "outputId": "a43bc8dd-1acb-4d88-cd15-90089d121266", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } }, "source": [ "#Lista das linhas duplicadas\n", "df_50k[df_50k.duplicated()]" ], - "execution_count": 0, - "outputs": [] + "execution_count": 464, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countryflag_50kind_50k
488125Private308144Bachelors13Never-marriedCraft-repairNot-in-familyWhiteMale0040Mexico<=50K0
510490Private52386Some-college10Never-marriedOther-serviceNot-in-familyAsian-Pac-IslanderMale0035United-States<=50K0
917121Private250051Some-college10Never-marriedProf-specialtyOwn-childWhiteFemale0010United-States<=50K0
1163120Private107658Some-college10Never-marriedTech-supportNot-in-familyWhiteFemale0010United-States<=50K0
1308425Private1959941st-4th2Never-marriedPriv-house-servNot-in-familyWhiteFemale0040Guatemala<=50K0
1505921Private243368Preschool1Never-marriedFarming-fishingNot-in-familyWhiteMale0050Mexico<=50K0
1704046Private173243HS-grad9Married-civ-spouseCraft-repairHusbandWhiteMale0040United-States<=50K0
1855530Private144593HS-grad9Never-marriedOther-serviceNot-in-familyBlackMale0040?<=50K0
1869819Private97261HS-grad9Never-marriedFarming-fishingNot-in-familyWhiteMale0040United-States<=50K0
2131819Private138153Some-college10Never-marriedAdm-clericalOwn-childWhiteFemale0010United-States<=50K0
2149019Private146679Some-college10Never-marriedExec-managerialOwn-childBlackMale0030United-States<=50K0
2187549Private312677th-8th4Married-civ-spouseCraft-repairHusbandWhiteMale0040United-States<=50K0
2230025Private1959941st-4th2Never-marriedPriv-house-servNot-in-familyWhiteFemale0040Guatemala<=50K0
2236744Private367749Bachelors13Never-marriedProf-specialtyNot-in-familyWhiteFemale0045Mexico<=50K0
2249449Self-emp-not-inc43479Some-college10Married-civ-spouseCraft-repairHusbandWhiteMale0040United-States<=50K0
2587223Private2401375th-6th3Never-marriedHandlers-cleanersNot-in-familyWhiteMale0055Mexico<=50K0
2631328Private274679Masters14Never-marriedProf-specialtyNot-in-familyWhiteMale0050United-States<=50K0
2823027Private255582HS-grad9Never-marriedMachine-op-inspctNot-in-familyWhiteFemale0040United-States<=50K0
2852242Private204235Some-college10Married-civ-spouseProf-specialtyHusbandWhiteMale0040United-States>50K1
2884639Private30916HS-grad9Married-civ-spouseCraft-repairHusbandWhiteMale0040United-States<=50K0
2915738Private207202HS-grad9Married-civ-spouseMachine-op-inspctHusbandWhiteMale0048United-States>50K1
3084546Private133616Some-college10DivorcedAdm-clericalUnmarriedWhiteFemale0040United-States<=50K0
3199319Private251579Some-college10Never-marriedOther-serviceOwn-childWhiteMale0014United-States<=50K0
3240435Private379959HS-grad9DivorcedOther-serviceNot-in-familyWhiteFemale0040United-States<=50K0
\n", + "
" + ], + "text/plain": [ + " age workclass fnlwgt ... native-country flag_50k ind_50k\n", + "4881 25 Private 308144 ... Mexico <=50K 0\n", + "5104 90 Private 52386 ... United-States <=50K 0\n", + "9171 21 Private 250051 ... United-States <=50K 0\n", + "11631 20 Private 107658 ... United-States <=50K 0\n", + "13084 25 Private 195994 ... Guatemala <=50K 0\n", + "15059 21 Private 243368 ... Mexico <=50K 0\n", + "17040 46 Private 173243 ... United-States <=50K 0\n", + "18555 30 Private 144593 ... ? <=50K 0\n", + "18698 19 Private 97261 ... United-States <=50K 0\n", + "21318 19 Private 138153 ... United-States <=50K 0\n", + "21490 19 Private 146679 ... United-States <=50K 0\n", + "21875 49 Private 31267 ... United-States <=50K 0\n", + "22300 25 Private 195994 ... Guatemala <=50K 0\n", + "22367 44 Private 367749 ... Mexico <=50K 0\n", + "22494 49 Self-emp-not-inc 43479 ... United-States <=50K 0\n", + "25872 23 Private 240137 ... Mexico <=50K 0\n", + "26313 28 Private 274679 ... United-States <=50K 0\n", + "28230 27 Private 255582 ... United-States <=50K 0\n", + "28522 42 Private 204235 ... United-States >50K 1\n", + "28846 39 Private 30916 ... United-States <=50K 0\n", + "29157 38 Private 207202 ... United-States >50K 1\n", + "30845 46 Private 133616 ... United-States <=50K 0\n", + "31993 19 Private 251579 ... United-States <=50K 0\n", + "32404 35 Private 379959 ... United-States <=50K 0\n", + "\n", + "[24 rows x 16 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 464 + } + ] }, { "cell_type": "code", "metadata": { "id": "7_xocTl-wz3-", "colab_type": "code", - "outputId": "4f008678-fab6-4e24-8b84-468eb7c87a9f", + "outputId": "a17209d6-8278-4c5d-e48c-3d16f696fa80", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -2375,7 +2916,7 @@ "source": [ "print(f'Há {df_50k[df_50k.duplicated()].count()[0]} registos duplicados')" ], - "execution_count": 266, + "execution_count": 465, "outputs": [ { "output_type": "stream", @@ -2391,7 +2932,7 @@ "metadata": { "id": "FESe-vmcxodU", "colab_type": "code", - "outputId": "31bd0447-80f6-4270-c607-2464fc34a051", + "outputId": "3cd3304b-100a-4bc8-918c-463fbec5f1fa", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -2402,12 +2943,12 @@ "df_50k = df_50k.drop_duplicates()\n", "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " ], - "execution_count": 267, + "execution_count": 466, "outputs": [ { "output_type": "stream", "text": [ - "Ficheiro com 32537 linhas e 17 colunas\n" + "Ficheiro com 32537 linhas e 16 colunas\n" ], "name": "stdout" } @@ -2462,13 +3003,13 @@ "base_uri": "https://localhost:8080/", "height": 333 }, - "outputId": "8b898b04-3a6a-4def-a87d-2c6bf6d98ff7" + "outputId": "8802e09a-1651-4304-a30d-51a8b9e63599" }, "source": [ "Transf_Lower(df_50k)\n", "df_50k.head(5)" ], - "execution_count": 271, + "execution_count": 468, "outputs": [ { "output_type": "execute_result", @@ -2508,7 +3049,6 @@ " native-country\n", " flag_50k\n", " ind_50k\n", - " age_class\n", " \n", " \n", " \n", @@ -2530,7 +3070,6 @@ " united-states\n", " <=50k\n", " 0\n", - " 2\n", " \n", " \n", " 1\n", @@ -2550,7 +3089,6 @@ " united-states\n", " <=50k\n", " 0\n", - " 3\n", " \n", " \n", " 2\n", @@ -2570,7 +3108,6 @@ " united-states\n", " <=50k\n", " 0\n", - " 2\n", " \n", " \n", " 3\n", @@ -2590,7 +3127,6 @@ " united-states\n", " <=50k\n", " 0\n", - " 4\n", " \n", " \n", " 4\n", @@ -2610,27 +3146,26 @@ " cuba\n", " <=50k\n", " 0\n", - " 1\n", " \n", " \n", "\n", "" ], "text/plain": [ - " age workclass fnlwgt ... flag_50k ind_50k age_class\n", - "0 39 state-gov 77516 ... <=50k 0 2\n", - "1 50 self-emp-not-inc 83311 ... <=50k 0 3\n", - "2 38 private 215646 ... <=50k 0 2\n", - "3 53 private 234721 ... <=50k 0 4\n", - "4 28 private 338409 ... <=50k 0 1\n", + " age workclass fnlwgt ... native-country flag_50k ind_50k\n", + "0 39 state-gov 77516 ... united-states <=50k 0\n", + "1 50 self-emp-not-inc 83311 ... united-states <=50k 0\n", + "2 38 private 215646 ... united-states <=50k 0\n", + "3 53 private 234721 ... united-states <=50k 0\n", + "4 28 private 338409 ... cuba <=50k 0\n", "\n", - "[5 rows x 17 columns]" + "[5 rows x 16 columns]" ] }, "metadata": { "tags": [] }, - "execution_count": 271 + "execution_count": 468 } ] }, @@ -2665,7 +3200,7 @@ "base_uri": "https://localhost:8080/", "height": 67 }, - "outputId": "76ce6cd6-5c58-43fc-e5a9-88b15f578487" + "outputId": "ec450cde-2784-4640-fb35-8eee0e1ee54d" }, "source": [ "# O valor \"?\" aparece nas colunas \"workclass\", \"occupation\" e \"native-country\".\n", @@ -2677,14 +3212,14 @@ "print(f'{n_mv_occupation} registos tem a coluna \"occupation\" com Missing Value ({round((n_mv_occupation*100)/df_50k.shape[0],2)}%)') \n", "print(f'{n_mv_country} registos tem a coluna \"native-country\" com Missing Value ({round((n_mv_country*100)/df_50k.shape[0],2)}%)') " ], - "execution_count": 341, + "execution_count": 469, "outputs": [ { "output_type": "stream", "text": [ - "0 registos tem a coluna \"workclass\" com Missing Value (0.0%)\n", - "0 registos tem a coluna \"occupation\" com Missing Value (0.0%)\n", - "0 registos tem a coluna \"native-country\" com Missing Value (0.0%)\n" + "1836 registos tem a coluna \"workclass\" com Missing Value (5.64%)\n", + "1843 registos tem a coluna \"occupation\" com Missing Value (5.66%)\n", + "582 registos tem a coluna \"native-country\" com Missing Value (1.79%)\n" ], "name": "stdout" } @@ -2709,7 +3244,7 @@ "base_uri": "https://localhost:8080/", "height": 34 }, - "outputId": "fcb28622-5a51-4e34-bf18-369c2d585afe" + "outputId": "20ffa997-969b-4429-e51d-addfc813e741" }, "source": [ "# Registos onde \"?\" aparece nas 3 colunas\n", @@ -2719,7 +3254,7 @@ " \n", "f'{df_50k[F1 & F2 & F3].shape[0]} registos'" ], - "execution_count": 298, + "execution_count": 470, "outputs": [ { "output_type": "execute_result", @@ -2731,7 +3266,7 @@ "metadata": { "tags": [] }, - "execution_count": 298 + "execution_count": 470 } ] }, @@ -2785,7 +3320,7 @@ "colab_type": "text" }, "source": [ - "##Discretizar" + "##Discretizar (???)" ] }, { @@ -2807,27 +3342,27 @@ "base_uri": "https://localhost:8080/", "height": 67 }, - "outputId": "af3d2b16-9f19-406a-a6c0-b68aad2ae056" + "outputId": "2d6490b7-f613-4435-f298-574ccd32e2be" }, "source": [ "#Colunas numericas\n", "df_50k.select_dtypes(include=['number']).columns" ], - "execution_count": 352, + "execution_count": 473, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Index(['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss',\n", - " 'hours-per-week', 'ind_50k', 'age_class'],\n", + " 'hours-per-week', 'ind_50k'],\n", " dtype='object')" ] }, "metadata": { "tags": [] }, - "execution_count": 352 + "execution_count": 473 } ] }, @@ -2840,14 +3375,14 @@ "base_uri": "https://localhost:8080/", "height": 202 }, - "outputId": "71e25da9-17a3-408a-9c3e-14868801c667" + "outputId": "dd649ef2-ef8e-4828-90e9-39d2f6d8a266" }, "source": [ "#Discretizar Age\n", "df_50k['age_class'] = pd.qcut(df_50k['age'], 10, labels=False)\n", "df_50k['age_class'].value_counts()" ], - "execution_count": 353, + "execution_count": 474, "outputs": [ { "output_type": "execute_result", @@ -2863,13 +3398,13 @@ "6 3006\n", "9 2998\n", "3 2591\n", - "Name: age_classe, dtype: int64" + "Name: age_class, dtype: int64" ] }, "metadata": { "tags": [] }, - "execution_count": 353 + "execution_count": 474 } ] }, @@ -2882,12 +3417,12 @@ "base_uri": "https://localhost:8080/", "height": 333 }, - "outputId": "37b31cd9-95ec-4d8c-edb3-ec16926858c5" + "outputId": "948ac930-6c69-4eb6-d221-4a5f3c07791e" }, "source": [ "df_50k.head(5)" ], - "execution_count": 362, + "execution_count": 475, "outputs": [ { "output_type": "execute_result", @@ -2928,7 +3463,6 @@ " flag_50k\n", " ind_50k\n", " age_class\n", - " age_classe\n", " \n", " \n", " \n", @@ -2950,7 +3484,6 @@ " united-states\n", " <=50k\n", " 0\n", - " 2\n", " 5\n", " \n", " \n", @@ -2971,7 +3504,6 @@ " united-states\n", " <=50k\n", " 0\n", - " 3\n", " 7\n", " \n", " \n", @@ -2992,7 +3524,6 @@ " united-states\n", " <=50k\n", " 0\n", - " 2\n", " 5\n", " \n", " \n", @@ -3013,7 +3544,6 @@ " united-states\n", " <=50k\n", " 0\n", - " 4\n", " 8\n", " \n", " \n", @@ -3034,7 +3564,6 @@ " cuba\n", " <=50k\n", " 0\n", - " 1\n", " 2\n", " \n", " \n", @@ -3042,20 +3571,20 @@ "" ], "text/plain": [ - " age workclass fnlwgt ... ind_50k age_class age_classe\n", - "0 39 state-gov 77516 ... 0 2 5\n", - "1 50 self-emp-not-inc 83311 ... 0 3 7\n", - "2 38 private 215646 ... 0 2 5\n", - "3 53 private 234721 ... 0 4 8\n", - "4 28 private 338409 ... 0 1 2\n", + " age workclass fnlwgt ... flag_50k ind_50k age_class\n", + "0 39 state-gov 77516 ... <=50k 0 5\n", + "1 50 self-emp-not-inc 83311 ... <=50k 0 7\n", + "2 38 private 215646 ... <=50k 0 5\n", + "3 53 private 234721 ... <=50k 0 8\n", + "4 28 private 338409 ... <=50k 0 2\n", "\n", - "[5 rows x 18 columns]" + "[5 rows x 17 columns]" ] }, "metadata": { "tags": [] }, - "execution_count": 362 + "execution_count": 475 } ] }, @@ -3064,108 +3593,14 @@ "metadata": { "id": "RBWitqcsPgMw", "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 254 - }, - "outputId": "44e4779d-0e84-468a-9cb1-3c35649f8e55" + "colab": {} }, "source": [ - "pd.crosstab(df_50k['age_class'], df_50k['ind_50k'], margins=True)" + "#Pra que?\n", + "#pd.crosstab(df_50k['age_class'], df_50k['ind_50k'], margins=True)" ], - "execution_count": 361, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "ind_50k 0 1 All\n", - "age_class \n", - "0 7008 177 7185\n", - "1 4896 1068 5964\n", - "2 4639 2121 6760\n", - "3 3794 2375 6169\n", - "4 4361 2098 6459\n", - "All 24698 7839 32537" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 361 - } - ] + "execution_count": 0, + "outputs": [] }, { "cell_type": "markdown", @@ -3389,16 +3824,16 @@ "metadata": { "id": "tMH_RurjvpKN", "colab_type": "code", - "outputId": "8c847dfa-df0c-466d-8985-7ee08a9a7585", + "outputId": "86d6489a-6540-48ce-cfc9-0a0061468daa", "colab": { "base_uri": "https://localhost:8080/", - "height": 343 + "height": 333 } }, "source": [ "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n", "\n", - "copyDataframe = df_50k\n", + "copyDataframe = df_50k.copy()\n", "copyDataframe.head()\n", "\n", "copyDataframe['sex'].unique()\n", @@ -3426,7 +3861,7 @@ "\n", "copyDataframe.head()" ], - "execution_count": 0, + "execution_count": 480, "outputs": [ { "output_type": "execute_result", @@ -3466,52 +3901,54 @@ " native-country\n", " flag_50k\n", " ind_50k\n", + " age_class\n", " sex_le\n", " race_le\n", " marital_status_le\n", " relationship_le\n", " flag_50k_le\n", - " Female\n", - " Male\n", - " Amer-Indian-Eskimo\n", - " Asian-Pac-Islander\n", - " Black\n", - " Other\n", - " White\n", - " Divorced\n", - " Married-AF-spouse\n", - " Married-civ-spouse\n", - " Married-spouse-absent\n", - " Never-married\n", - " Separated\n", - " Widowed\n", - " Husband\n", - " Not-in-family\n", - " Other-relative\n", - " Own-child\n", - " Unmarried\n", - " Wife\n", + " female\n", + " male\n", + " amer-indian-eskimo\n", + " asian-pac-islander\n", + " black\n", + " other\n", + " white\n", + " divorced\n", + " married-af-spouse\n", + " married-civ-spouse\n", + " married-spouse-absent\n", + " never-married\n", + " separated\n", + " widowed\n", + " husband\n", + " not-in-family\n", + " other-relative\n", + " own-child\n", + " unmarried\n", + " wife\n", " \n", " \n", " \n", " \n", " 0\n", " 39\n", - " State-gov\n", + " state-gov\n", " 77516\n", - " Bachelors\n", + " bachelors\n", " 13\n", - " Never-married\n", - " Adm-clerical\n", - " Not-in-family\n", - " White\n", - " Male\n", + " never-married\n", + " adm-clerical\n", + " not-in-family\n", + " white\n", + " male\n", " 2174\n", " 0\n", " 40\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", " 0\n", + " 5\n", " 1\n", " 4\n", " 4\n", @@ -3541,21 +3978,22 @@ " \n", " 1\n", " 50\n", - " Self-emp-not-inc\n", + " self-emp-not-inc\n", " 83311\n", - " Bachelors\n", + " bachelors\n", " 13\n", - " Married-civ-spouse\n", - " Exec-managerial\n", - " Husband\n", - " White\n", - " Male\n", + " married-civ-spouse\n", + " exec-managerial\n", + " husband\n", + " white\n", + " male\n", " 0\n", " 0\n", " 13\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", " 0\n", + " 7\n", " 1\n", " 4\n", " 2\n", @@ -3585,21 +4023,22 @@ " \n", " 2\n", " 38\n", - " Private\n", + " private\n", " 215646\n", - " HS-grad\n", + " hs-grad\n", " 9\n", - " Divorced\n", - " Handlers-cleaners\n", - " Not-in-family\n", - " White\n", - " Male\n", + " divorced\n", + " handlers-cleaners\n", + " not-in-family\n", + " white\n", + " male\n", " 0\n", " 0\n", " 40\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", " 0\n", + " 5\n", " 1\n", " 4\n", " 0\n", @@ -3629,21 +4068,22 @@ " \n", " 3\n", " 53\n", - " Private\n", + " private\n", " 234721\n", " 11th\n", " 7\n", - " Married-civ-spouse\n", - " Handlers-cleaners\n", - " Husband\n", - " Black\n", - " Male\n", + " married-civ-spouse\n", + " handlers-cleaners\n", + " husband\n", + " black\n", + " male\n", " 0\n", " 0\n", " 40\n", - " United-States\n", - " <=50K\n", + " united-states\n", + " <=50k\n", " 0\n", + " 8\n", " 1\n", " 2\n", " 2\n", @@ -3673,21 +4113,22 @@ " \n", " 4\n", " 28\n", - " Private\n", + " private\n", " 338409\n", - " Bachelors\n", + " bachelors\n", " 13\n", - " Married-civ-spouse\n", - " Prof-specialty\n", - " Wife\n", - " Black\n", - " Female\n", + " married-civ-spouse\n", + " prof-specialty\n", + " wife\n", + " black\n", + " female\n", " 0\n", " 0\n", " 40\n", - " Cuba\n", - " <=50K\n", + " cuba\n", + " <=50k\n", " 0\n", + " 2\n", " 0\n", " 2\n", " 2\n", @@ -3719,20 +4160,20 @@ "" ], "text/plain": [ - " age workclass fnlwgt ... Own-child Unmarried Wife\n", - "0 39 State-gov 77516 ... 0 0 0\n", - "1 50 Self-emp-not-inc 83311 ... 0 0 0\n", - "2 38 Private 215646 ... 0 0 0\n", - "3 53 Private 234721 ... 0 0 0\n", - "4 28 Private 338409 ... 0 0 1\n", + " age workclass fnlwgt ... own-child unmarried wife\n", + "0 39 state-gov 77516 ... 0 0 0\n", + "1 50 self-emp-not-inc 83311 ... 0 0 0\n", + "2 38 private 215646 ... 0 0 0\n", + "3 53 private 234721 ... 0 0 0\n", + "4 28 private 338409 ... 0 0 1\n", "\n", - "[5 rows x 41 columns]" + "[5 rows x 42 columns]" ] }, "metadata": { "tags": [] }, - "execution_count": 38 + "execution_count": 480 } ] }, @@ -3761,17 +4202,17 @@ "metadata": { "id": "S3YDfY4nA-bw", "colab_type": "code", - "outputId": "83b6ea98-6660-4745-a979-beb94e54b053", + "outputId": "db7c0f9b-0528-411d-e55f-0b6cf0237c14", "colab": { "base_uri": "https://localhost:8080/", - "height": 287 + "height": 274 } }, "source": [ "X_train = pd.read_csv('https://raw.githubusercontent.com/massaoHigaskino/DSWP/lectures/project/Execution/adult_50k/datascience_adult_50k_v_t1_adult_data_dataset.csv', index_col= 'id');\n", "X_train.head()" ], - "execution_count": 0, + "execution_count": 481, "outputs": [ { "output_type": "execute_result", @@ -4403,7 +4844,7 @@ "metadata": { "tags": [] }, - "execution_count": 40 + "execution_count": 481 } ] }, @@ -4412,17 +4853,17 @@ "metadata": { "id": "jWmSQdveC-NP", "colab_type": "code", - "outputId": "bf9e485f-e926-4183-f0ce-2ddd19052e6f", + "outputId": "114b7d69-35e5-4374-93a6-5c0ff0d04611", "colab": { "base_uri": "https://localhost:8080/", - "height": 238 + "height": 225 } }, "source": [ "y_train = pd.read_csv('https://raw.githubusercontent.com/massaoHigaskino/DSWP/lectures/project/Execution/adult_50k/datascience_adult_50k_v_t1_adult_data_target.csv', index_col= 'id');\n", "y_train.head()" ], - "execution_count": 0, + "execution_count": 482, "outputs": [ { "output_type": "execute_result", @@ -4491,7 +4932,7 @@ "metadata": { "tags": [] }, - "execution_count": 39 + "execution_count": 482 } ] }, @@ -4500,17 +4941,17 @@ "metadata": { "id": "NlRs-MXBC-4N", "colab_type": "code", - "outputId": "d57aa85d-f948-4b89-8093-d3b91d50e03d", + "outputId": "b91c294d-f4b3-4613-e607-20bb149982c5", "colab": { "base_uri": "https://localhost:8080/", - "height": 287 + "height": 274 } }, "source": [ "X_test = pd.read_csv('https://raw.githubusercontent.com/massaoHigaskino/DSWP/lectures/project/Execution/adult_50k/datascience_adult_50k_v_t1_adult_test_dataset.csv', index_col= 'id');\n", "X_test.head()" ], - "execution_count": 0, + "execution_count": 483, "outputs": [ { "output_type": "execute_result", @@ -5142,7 +5583,7 @@ "metadata": { "tags": [] }, - "execution_count": 38 + "execution_count": 483 } ] }, @@ -5151,17 +5592,17 @@ "metadata": { "id": "ft_PmPkeC_LD", "colab_type": "code", - "outputId": "3dc4e61c-5f56-4d9b-ca58-24a5495ac495", + "outputId": "c78ccc93-49ea-4be2-9fef-6f19595f50b9", "colab": { "base_uri": "https://localhost:8080/", - "height": 238 + "height": 225 } }, "source": [ "y_test = pd.read_csv('https://raw.githubusercontent.com/massaoHigaskino/DSWP/lectures/project/Execution/adult_50k/datascience_adult_50k_v_t1_adult_test_target.csv', index_col= 'id');\n", "y_test.head()" ], - "execution_count": 0, + "execution_count": 484, "outputs": [ { "output_type": "execute_result", @@ -5230,7 +5671,7 @@ "metadata": { "tags": [] }, - "execution_count": 49 + "execution_count": 484 } ] }, @@ -5249,10 +5690,10 @@ "metadata": { "id": "6NuSOifMN_rk", "colab_type": "code", - "outputId": "57c72fdb-9092-4ba0-f121-b81e76a36c45", + "outputId": "16f9a6c8-0ced-4cca-95dd-fdde45d96c60", "colab": { "base_uri": "https://localhost:8080/", - "height": 121 + "height": 118 } }, "source": [ @@ -5268,7 +5709,7 @@ "# Treina...\n", "Model_DT.fit(X_train, y_train)" ], - "execution_count": 0, + "execution_count": 485, "outputs": [ { "output_type": "execute_result", @@ -5285,7 +5726,7 @@ "metadata": { "tags": [] }, - "execution_count": 53 + "execution_count": 485 } ] }, @@ -5294,10 +5735,10 @@ "metadata": { "id": "RsiADVY2OKgY", "colab_type": "code", - "outputId": "4d8fafdf-ad77-4e1e-bc91-7866374957ec", + "outputId": "70ada26a-c63c-42b0-88bb-44270e41c6d6", "colab": { "base_uri": "https://localhost:8080/", - "height": 52 + "height": 50 } }, "source": [ @@ -5306,7 +5747,7 @@ "print(f'Média das Acurácias calculadas pelo CV....: {100*round(a_Scores_CV.mean(),4)}')\n", "print(f'std médio das Acurácias calculadas pelo CV: {100*round(a_Scores_CV.std(),4)}')" ], - "execution_count": 0, + "execution_count": 486, "outputs": [ { "output_type": "stream", @@ -5323,16 +5764,16 @@ "metadata": { "id": "CBw5nAhlOUWT", "colab_type": "code", - "outputId": "7c8d7ea0-6717-4e1b-fc6b-aa01102d21da", + "outputId": "c6b261f1-2da3-4a6b-fa84-81cbd0576897", "colab": { "base_uri": "https://localhost:8080/", - "height": 52 + "height": 50 } }, "source": [ "print(f'Acurácias: {a_Scores_CV}')" ], - "execution_count": 0, + "execution_count": 487, "outputs": [ { "output_type": "stream", @@ -5349,7 +5790,7 @@ "metadata": { "id": "gvKT9gZaOZXO", "colab_type": "code", - "outputId": "f8a97f50-8a2a-4c4b-a11b-c29f2449045f", + "outputId": "272d575e-5ff7-4e51-eee5-7ce7ce460379", "colab": { "base_uri": "https://localhost:8080/", "height": 538 @@ -5365,7 +5806,7 @@ "cf_categories = ['Zero', 'One']\n", "make_confusion_matrix(cf_matrix, group_names= cf_labels, categories= cf_categories)" ], - "execution_count": 0, + "execution_count": 488, "outputs": [ { "output_type": "display_data", @@ -5396,10 +5837,10 @@ "metadata": { "id": "DM6ficlv_9oB", "colab_type": "code", - "outputId": "8796857a-49fb-4b3a-cf7f-2686d535040b", + "outputId": "49034208-4483-4b31-cec0-2561990584d6", "colab": { "base_uri": "https://localhost:8080/", - "height": 139 + "height": 134 } }, "source": [ @@ -5409,7 +5850,7 @@ "# Treina...\n", "Model_RF.fit(X_train, y_train)" ], - "execution_count": 0, + "execution_count": 489, "outputs": [ { "output_type": "execute_result", @@ -5427,7 +5868,7 @@ "metadata": { "tags": [] }, - "execution_count": 41 + "execution_count": 489 } ] }, @@ -5436,10 +5877,10 @@ "metadata": { "id": "DYeWL0k6IVl9", "colab_type": "code", - "outputId": "9f4b5a05-1325-4edc-bb08-8a1db18a6d0a", + "outputId": "0cc9c8a7-4446-49d4-8626-63dd146ec0d5", "colab": { "base_uri": "https://localhost:8080/", - "height": 52 + "height": 50 } }, "source": [ @@ -5448,7 +5889,7 @@ "print(f'Média das Acurácias calculadas pelo CV....: {100*round(a_Scores_CV.mean(),4)}')\n", "print(f'std médio das Acurácias calculadas pelo CV: {100*round(a_Scores_CV.std(),4)}')" ], - "execution_count": 0, + "execution_count": 490, "outputs": [ { "output_type": "stream", @@ -5475,16 +5916,16 @@ "metadata": { "id": "NrZdl0A8JUTU", "colab_type": "code", - "outputId": "b65a44d6-0dca-4cb8-f7d1-c1e69e7e3e84", + "outputId": "42577cdf-dfb8-4679-b59d-8fd5a35f3402", "colab": { "base_uri": "https://localhost:8080/", - "height": 52 + "height": 50 } }, "source": [ "print(f'Acurácias: {a_Scores_CV}')" ], - "execution_count": 0, + "execution_count": 491, "outputs": [ { "output_type": "stream", @@ -5501,7 +5942,7 @@ "metadata": { "id": "q42UUhYbJVdE", "colab_type": "code", - "outputId": "f0c5f632-a8a0-46ee-9f04-16b1db300106", + "outputId": "63910153-bf5b-4fab-c89e-19efcf8a41d0", "colab": { "base_uri": "https://localhost:8080/", "height": 538 @@ -5517,7 +5958,7 @@ "cf_categories = ['Zero', 'One']\n", "make_confusion_matrix(cf_matrix, group_names= cf_labels, categories= cf_categories)" ], - "execution_count": 0, + "execution_count": 492, "outputs": [ { "output_type": "display_data", From fe5823010d28ac87676e4cc2fa52f8748c3c5e8e Mon Sep 17 00:00:00 2001 From: Fagner Candido Date: Fri, 15 Nov 2019 13:41:10 +0000 Subject: [PATCH 33/35] Created using Colaboratory --- dswp_grupo1.ipynb | 505 ++++++++++++++++++++++++++++------------------ 1 file changed, 314 insertions(+), 191 deletions(-) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index 9ad54c83a..4c9fcf004 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -23,7 +23,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { @@ -116,7 +116,7 @@ "metadata": { "id": "0YTMtMPRbSYD", "colab_type": "code", - "outputId": "20e448c7-9843-4add-e772-dc96ccd67fe9", + "outputId": "1cddfd01-1514-464c-ac48-eeec5cfdfed8", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -126,7 +126,7 @@ "df_50k = pd.read_csv('https://raw.githubusercontent.com/MathMachado/DSWP/master/Dataframes/adult.data')\n", "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " ], - "execution_count": 430, + "execution_count": 55, "outputs": [ { "output_type": "stream", @@ -152,7 +152,7 @@ "#Detecção de que o ficheiro não tem header\n", "df_50k.head(5)" ], - "execution_count": 431, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -311,10 +311,10 @@ "metadata": { "id": "HBKqXT9Bo-BC", "colab_type": "code", - "outputId": "e26ebaa8-ae4e-47d3-9bff-3836424bf8b7", + "outputId": "ca2f4329-26eb-4bbf-df6a-f41e16f4ed5d", "colab": { "base_uri": "https://localhost:8080/", - "height": 269 + "height": 272 } }, "source": [ @@ -337,7 +337,7 @@ " ]\n", "l_column" ], - "execution_count": 432, + "execution_count": 59, "outputs": [ { "output_type": "execute_result", @@ -363,7 +363,7 @@ "metadata": { "tags": [] }, - "execution_count": 432 + "execution_count": 59 } ] }, @@ -386,16 +386,16 @@ "metadata": { "id": "AjswmEdb4R8X", "colab_type": "code", - "outputId": "0a0d8e07-e6a5-4680-b4d2-b2f752f58c18", + "outputId": "947f5f4f-167d-4dcf-b80b-9e21586d8eea", "colab": { "base_uri": "https://localhost:8080/", - "height": 353 + "height": 357 } }, "source": [ "df_50k.info()" ], - "execution_count": 434, + "execution_count": 61, "outputs": [ { "output_type": "stream", @@ -430,16 +430,16 @@ "metadata": { "id": "CNyS0K4C4dfp", "colab_type": "code", - "outputId": "fcf6a0d9-9a80-4908-a43f-6214ccfbe71b", + "outputId": "221a24c5-3335-4a8c-a21f-1b7064c05676", "colab": { "base_uri": "https://localhost:8080/", - "height": 333 + "height": 204 } }, "source": [ "df_50k.head(5)" ], - "execution_count": 435, + "execution_count": 62, "outputs": [ { "output_type": "execute_result", @@ -589,7 +589,7 @@ "metadata": { "tags": [] }, - "execution_count": 435 + "execution_count": 62 } ] }, @@ -598,17 +598,17 @@ "metadata": { "id": "m2p6SOzbo99T", "colab_type": "code", - "outputId": "26cc9280-a801-4451-ca9b-5f57731e2915", + "outputId": "5b44778e-ac5f-4dad-89b1-3ca14bbe19dc", "colab": { "base_uri": "https://localhost:8080/", - "height": 101 + "height": 102 } }, "source": [ "#Colunas do DataFrame\n", "df_50k.columns" ], - "execution_count": 436, + "execution_count": 63, "outputs": [ { "output_type": "execute_result", @@ -624,7 +624,7 @@ "metadata": { "tags": [] }, - "execution_count": 436 + "execution_count": 63 } ] }, @@ -633,16 +633,16 @@ "metadata": { "id": "dMDVRA8eo96a", "colab_type": "code", - "outputId": "0e57f89a-a0cc-4af2-cfd1-55357db67a8f", + "outputId": "79c632c5-0406-482d-c889-472e23b5f37d", "colab": { "base_uri": "https://localhost:8080/", - "height": 286 + "height": 289 } }, "source": [ "df_50k.dtypes" ], - "execution_count": 437, + "execution_count": 64, "outputs": [ { "output_type": "execute_result", @@ -669,7 +669,7 @@ "metadata": { "tags": [] }, - "execution_count": 437 + "execution_count": 64 } ] }, @@ -688,17 +688,17 @@ "metadata": { "id": "5SbUk5Srzqok", "colab_type": "code", - "outputId": "5dab0968-1ea3-4466-ace3-7b3f1e6d81fb", + "outputId": "5d190990-c6d6-4d14-f7eb-62826c356b0b", "colab": { "base_uri": "https://localhost:8080/", - "height": 185 + "height": 187 } }, "source": [ "#Valores distintos de WorkClass\n", "df_50k['workclass'].value_counts()" ], - "execution_count": 438, + "execution_count": 65, "outputs": [ { "output_type": "execute_result", @@ -719,7 +719,7 @@ "metadata": { "tags": [] }, - "execution_count": 438 + "execution_count": 65 } ] }, @@ -728,17 +728,17 @@ "metadata": { "id": "VYkfFI451R_m", "colab_type": "code", - "outputId": "14a1a890-1127-41a4-ba59-92417f56ec3c", + "outputId": "aaa2a3bf-09a3-4712-e849-030d097f7a50", "colab": { "base_uri": "https://localhost:8080/", - "height": 302 + "height": 306 } }, "source": [ "#Valores distintos de Education\n", "df_50k['education'].value_counts()" ], - "execution_count": 439, + "execution_count": 66, "outputs": [ { "output_type": "execute_result", @@ -766,7 +766,7 @@ "metadata": { "tags": [] }, - "execution_count": 439 + "execution_count": 66 } ] }, @@ -775,17 +775,17 @@ "metadata": { "id": "q67Wyta2XwZx", "colab_type": "code", - "outputId": "b280309c-0313-49b7-9ee9-cd3b3653f9e8", + "outputId": "9bd3649a-1e64-409a-bf83-5afe2175ef22", "colab": { "base_uri": "https://localhost:8080/", - "height": 302 + "height": 306 } }, "source": [ "#Valores distintos de Education-num\n", "df_50k['education-num'].value_counts()" ], - "execution_count": 440, + "execution_count": 67, "outputs": [ { "output_type": "execute_result", @@ -813,7 +813,7 @@ "metadata": { "tags": [] }, - "execution_count": 440 + "execution_count": 67 } ] }, @@ -822,17 +822,17 @@ "metadata": { "id": "QJly0mWR1R8m", "colab_type": "code", - "outputId": "06eaab3f-5c9b-4114-a02a-0a65f15acf7d", + "outputId": "e87bdfde-bb64-4b6f-f460-9235d930260b", "colab": { "base_uri": "https://localhost:8080/", - "height": 151 + "height": 153 } }, "source": [ "#Valores distintos de marital-status\n", "df_50k['marital-status'].value_counts()" ], - "execution_count": 441, + "execution_count": 68, "outputs": [ { "output_type": "execute_result", @@ -851,7 +851,7 @@ "metadata": { "tags": [] }, - "execution_count": 441 + "execution_count": 68 } ] }, @@ -860,17 +860,17 @@ "metadata": { "id": "7_Wo0troSfxq", "colab_type": "code", - "outputId": "4b1b19c5-d908-4ec1-9ce7-7d9b76eb7d9f", + "outputId": "78fb8942-1388-4a8e-d683-607895ca50d4", "colab": { "base_uri": "https://localhost:8080/", - "height": 134 + "height": 136 } }, "source": [ "#Valores distintos de relationship\n", "df_50k['relationship'].value_counts()" ], - "execution_count": 442, + "execution_count": 69, "outputs": [ { "output_type": "execute_result", @@ -888,7 +888,7 @@ "metadata": { "tags": [] }, - "execution_count": 442 + "execution_count": 69 } ] }, @@ -897,17 +897,17 @@ "metadata": { "id": "4R_S4oaG1R6_", "colab_type": "code", - "outputId": "5325afe3-b8ea-4d83-f9d7-df79790a3a09", + "outputId": "d80933a6-99f7-4218-b993-7951ebd6377c", "colab": { "base_uri": "https://localhost:8080/", - "height": 286 + "height": 289 } }, "source": [ "#Valores distintos de occupation\n", "df_50k['occupation'].value_counts()" ], - "execution_count": 443, + "execution_count": 70, "outputs": [ { "output_type": "execute_result", @@ -934,7 +934,7 @@ "metadata": { "tags": [] }, - "execution_count": 443 + "execution_count": 70 } ] }, @@ -943,17 +943,17 @@ "metadata": { "id": "2q5DBeLM1R3m", "colab_type": "code", - "outputId": "487922fb-c883-4bc2-b30e-e6960e1cc305", + "outputId": "f63ae7ad-85ed-4888-96e8-7993fa22f64f", "colab": { "base_uri": "https://localhost:8080/", - "height": 134 + "height": 136 } }, "source": [ "#Valores distintos de relationship\n", "df_50k['relationship'].value_counts()" ], - "execution_count": 444, + "execution_count": 71, "outputs": [ { "output_type": "execute_result", @@ -971,7 +971,7 @@ "metadata": { "tags": [] }, - "execution_count": 444 + "execution_count": 71 } ] }, @@ -980,17 +980,17 @@ "metadata": { "id": "3nlhzegs3PD2", "colab_type": "code", - "outputId": "fd6f71c9-4ae7-4e48-934a-efb052138af1", + "outputId": "0e272821-3e29-4654-b038-1b0749cb1f3f", "colab": { "base_uri": "https://localhost:8080/", - "height": 118 + "height": 119 } }, "source": [ "#Valores distintos de race\n", "df_50k['race'].value_counts()" ], - "execution_count": 445, + "execution_count": 72, "outputs": [ { "output_type": "execute_result", @@ -1007,7 +1007,7 @@ "metadata": { "tags": [] }, - "execution_count": 445 + "execution_count": 72 } ] }, @@ -1016,17 +1016,17 @@ "metadata": { "id": "-mgIBM783PAl", "colab_type": "code", - "outputId": "a68418a4-80df-4edc-d1a4-6e46f6dc116c", + "outputId": "10b1ddcf-a3aa-4c6a-e253-7b08726a2f53", "colab": { "base_uri": "https://localhost:8080/", - "height": 67 + "height": 68 } }, "source": [ "#Valores distintos de sex\n", "df_50k['sex'].value_counts()" ], - "execution_count": 446, + "execution_count": 73, "outputs": [ { "output_type": "execute_result", @@ -1040,7 +1040,7 @@ "metadata": { "tags": [] }, - "execution_count": 446 + "execution_count": 73 } ] }, @@ -1049,17 +1049,17 @@ "metadata": { "id": "EjRcmMm73O-2", "colab_type": "code", - "outputId": "211b30fc-951a-433c-c2ce-113b600e2b61", + "outputId": "d49b6c22-60ef-4214-d737-b2377d2921f4", "colab": { "base_uri": "https://localhost:8080/", - "height": 739 + "height": 748 } }, "source": [ "#Valores distintos de native-country\n", "df_50k['native-country'].value_counts()" ], - "execution_count": 447, + "execution_count": 74, "outputs": [ { "output_type": "execute_result", @@ -1092,19 +1092,19 @@ " Portugal 37\n", " Nicaragua 34\n", " Peru 31\n", - " France 29\n", " Greece 29\n", + " France 29\n", " Ecuador 28\n", " Ireland 24\n", " Hong 20\n", " Cambodia 19\n", " Trinadad&Tobago 19\n", - " Thailand 18\n", " Laos 18\n", + " Thailand 18\n", " Yugoslavia 16\n", " Outlying-US(Guam-USVI-etc) 14\n", - " Honduras 13\n", " Hungary 13\n", + " Honduras 13\n", " Scotland 12\n", " Holand-Netherlands 1\n", "Name: native-country, dtype: int64" @@ -1113,7 +1113,7 @@ "metadata": { "tags": [] }, - "execution_count": 447 + "execution_count": 74 } ] }, @@ -1122,7 +1122,7 @@ "metadata": { "id": "zkL_oVwT3O73", "colab_type": "code", - "outputId": "370758f3-e01f-4717-f08b-9200c1f6f907", + "outputId": "4ed63792-a014-4070-9dcf-1ee08253b2ab", "colab": { "base_uri": "https://localhost:8080/", "height": 34 @@ -1132,7 +1132,7 @@ "#Valores distintos de flag_50k\n", "df_50k['flag_50k'].value_counts().keys()" ], - "execution_count": 448, + "execution_count": 75, "outputs": [ { "output_type": "execute_result", @@ -1144,7 +1144,7 @@ "metadata": { "tags": [] }, - "execution_count": 448 + "execution_count": 75 } ] }, @@ -1153,17 +1153,17 @@ "metadata": { "id": "2ng16_UcPOCS", "colab_type": "code", - "outputId": "42e80ce7-db87-4072-b119-7f855aa9d45a", + "outputId": "ec6aefcf-9f39-43ef-bf6b-27cfb8926a94", "colab": { "base_uri": "https://localhost:8080/", - "height": 218 + "height": 221 } }, "source": [ "#Valores distintos de hours-per-week\n", "df_50k['hours-per-week'].value_counts()" ], - "execution_count": 449, + "execution_count": 76, "outputs": [ { "output_type": "execute_result", @@ -1186,7 +1186,7 @@ "metadata": { "tags": [] }, - "execution_count": 449 + "execution_count": 76 } ] }, @@ -1221,10 +1221,10 @@ "metadata": { "id": "YUk0xnfaV4yX", "colab_type": "code", - "outputId": "1691a7ac-9566-4db1-ae35-8c9255e367b5", + "outputId": "a8c9aa39-6dce-4c06-88a2-1bc3ec9ddbd3", "colab": { "base_uri": "https://localhost:8080/", - "height": 333 + "height": 204 } }, "source": [ @@ -1234,7 +1234,7 @@ "\n", "df_50k.head(5)" ], - "execution_count": 450, + "execution_count": 77, "outputs": [ { "output_type": "execute_result", @@ -1390,7 +1390,7 @@ "metadata": { "tags": [] }, - "execution_count": 450 + "execution_count": 77 } ] }, @@ -1466,7 +1466,7 @@ "metadata": { "id": "Vu2p_1-fDH_3", "colab_type": "code", - "outputId": "df73b54d-4ade-419b-b728-342222620958", + "outputId": "67361cfb-e9b8-47d8-b71e-d17160d24a4d", "colab": { "base_uri": "https://localhost:8080/", "height": 355 @@ -1482,19 +1482,19 @@ " fmt= '.2f'\n", " )" ], - "execution_count": 452, + "execution_count": 79, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 452 + "execution_count": 79 }, { "output_type": "display_data", @@ -1525,10 +1525,10 @@ "metadata": { "id": "RNLaUYs3CHjQ", "colab_type": "code", - "outputId": "32020383-432c-48f8-e37f-366b1dbff742", + "outputId": "9b5b9759-f66d-406d-8388-999f9c8f4d21", "colab": { "base_uri": "https://localhost:8080/", - "height": 254 + "height": 266 } }, "source": [ @@ -1536,7 +1536,7 @@ "Matrix_Corr = df_50k.corr().where(np.triu(np.ones(df_50k.corr().shape), k=1).astype(np.bool))\n", "Matrix_Corr" ], - "execution_count": 453, + "execution_count": 80, "outputs": [ { "output_type": "execute_result", @@ -1660,7 +1660,7 @@ "metadata": { "tags": [] }, - "execution_count": 453 + "execution_count": 80 } ] }, @@ -1669,10 +1669,10 @@ "metadata": { "id": "-ohsbrRdGmJJ", "colab_type": "code", - "outputId": "315af47b-a70f-45c9-e6ce-1a57f2d0077c", + "outputId": "8e15c65e-0014-44a0-80d2-696bfcf39c0f", "colab": { "base_uri": "https://localhost:8080/", - "height": 118 + "height": 119 } }, "source": [ @@ -1686,7 +1686,7 @@ "\n", "set_Colunas_Correlacionadas" ], - "execution_count": 454, + "execution_count": 81, "outputs": [ { "output_type": "execute_result", @@ -1703,7 +1703,7 @@ "metadata": { "tags": [] }, - "execution_count": 454 + "execution_count": 81 } ] }, @@ -1712,7 +1712,7 @@ "metadata": { "id": "_0MZj06uIhtG", "colab_type": "code", - "outputId": "b2933e15-c80f-48b5-fd1c-3d5f02505c88", + "outputId": "a5ee9042-6610-433a-8a75-0ec806e5f2a1", "colab": { "base_uri": "https://localhost:8080/", "height": 681 @@ -1724,19 +1724,19 @@ "mask[np.triu_indices_from(mask)] = 1\n", "sns.heatmap(df_50k.corr().abs(), mask= mask, ax= ax, cmap='RdPu', annot= True, fmt= '.2f', center= 0)" ], - "execution_count": 455, + "execution_count": 82, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 455 + "execution_count": 82 }, { "output_type": "display_data", @@ -1767,16 +1767,16 @@ "metadata": { "id": "BaRNhWV1cFlr", "colab_type": "code", - "outputId": "8bed02a9-8613-46ad-d1b0-74b61757207b", + "outputId": "32c8db3d-136a-438d-f514-0bccf4f07d3a", "colab": { "base_uri": "https://localhost:8080/", - "height": 284 + "height": 297 } }, "source": [ "df_50k.describe()" ], - "execution_count": 456, + "execution_count": 83, "outputs": [ { "output_type": "execute_result", @@ -1911,7 +1911,7 @@ "metadata": { "tags": [] }, - "execution_count": 456 + "execution_count": 83 } ] }, @@ -1920,16 +1920,16 @@ "metadata": { "id": "KZQOGm7XcrRf", "colab_type": "code", - "outputId": "9a6a9d98-eefd-4063-e429-20d1092e3891", + "outputId": "84e73272-ff07-4849-90b9-0b9c63a4cff1", "colab": { "base_uri": "https://localhost:8080/", - "height": 166 + "height": 173 } }, "source": [ "df_50k.describe(include=[\"O\"])" ], - "execution_count": 457, + "execution_count": 84, "outputs": [ { "output_type": "execute_result", @@ -2030,7 +2030,7 @@ "metadata": { "tags": [] }, - "execution_count": 457 + "execution_count": 84 } ] }, @@ -2039,29 +2039,29 @@ "metadata": { "id": "3hL2PFjl3V6q", "colab_type": "code", + "outputId": "79825c1e-4e9e-40b9-f17d-8a5076e792ff", "colab": { "base_uri": "https://localhost:8080/", "height": 297 - }, - "outputId": "bfc68aa9-5f5f-4852-b2b2-65c934b53d48" + } }, "source": [ "#Indicador dos 50K por Genero\n", "sns.countplot(x='ind_50k', hue='sex', data=df_50k)" ], - "execution_count": 458, + "execution_count": 85, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": { "tags": [] }, - "execution_count": 458 + "execution_count": 85 }, { "output_type": "display_data", @@ -2123,7 +2123,7 @@ "metadata": { "id": "fTUWOuBU30or", "colab_type": "code", - "outputId": "5c599379-3ced-4358-f274-19aa17c9b843", + "outputId": "e40e009e-58b0-4e3a-d052-a439be6b3884", "colab": { "base_uri": "https://localhost:8080/", "height": 295 @@ -2136,7 +2136,7 @@ "plt.ylabel('Frequencia')\n", "plt.show()" ], - "execution_count": 460, + "execution_count": 87, "outputs": [ { "output_type": "display_data", @@ -2167,7 +2167,7 @@ "metadata": { "id": "OzAQWltWQDVa", "colab_type": "code", - "outputId": "a746ef59-07f1-40ae-8064-a1ff7a775051", + "outputId": "3226a843-78ac-4497-9367-5c96a5d567f4", "colab": { "base_uri": "https://localhost:8080/", "height": 295 @@ -2180,7 +2180,7 @@ "plt.ylabel('Frequencia')\n", "plt.show()" ], - "execution_count": 461, + "execution_count": 88, "outputs": [ { "output_type": "display_data", @@ -2222,7 +2222,7 @@ "fig, ax = plt.subplots(figsize=(15,7))\n", "df_50k.groupby(['education','race']).count()['age'].unstack().plot(ax=ax, title='Raça vs Escolaridade')" ], - "execution_count": 462, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -2276,7 +2276,7 @@ "fig, ax = plt.subplots(figsize=(15,7))\n", "df_50k.groupby(['relationship','ind_50k']).count()['age'].unstack().plot(ax=ax, title='Horas Trabalhadas vs Indicativo do Valor')" ], - "execution_count": 463, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -2362,7 +2362,7 @@ "#Lista das linhas duplicadas\n", "df_50k[df_50k.duplicated()]" ], - "execution_count": 464, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -2916,7 +2916,7 @@ "source": [ "print(f'Há {df_50k[df_50k.duplicated()].count()[0]} registos duplicados')" ], - "execution_count": 465, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -2943,7 +2943,7 @@ "df_50k = df_50k.drop_duplicates()\n", "print(f'Ficheiro com {df_50k.shape[0]} linhas e {df_50k.shape[1]} colunas') " ], - "execution_count": 466, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -2999,17 +2999,17 @@ "metadata": { "id": "rbI2wQtVAPPr", "colab_type": "code", + "outputId": "c085273b-ca6a-495f-f534-2525f3436a9f", "colab": { "base_uri": "https://localhost:8080/", - "height": 333 - }, - "outputId": "8802e09a-1651-4304-a30d-51a8b9e63599" + "height": 204 + } }, "source": [ "Transf_Lower(df_50k)\n", "df_50k.head(5)" ], - "execution_count": 468, + "execution_count": 90, "outputs": [ { "output_type": "execute_result", @@ -3165,7 +3165,7 @@ "metadata": { "tags": [] }, - "execution_count": 468 + "execution_count": 90 } ] }, @@ -3196,11 +3196,11 @@ "metadata": { "id": "QbfQ4jus77_Y", "colab_type": "code", + "outputId": "100174b2-6b0a-46fd-b94f-967e8811d250", "colab": { "base_uri": "https://localhost:8080/", - "height": 67 - }, - "outputId": "ec450cde-2784-4640-fb35-8eee0e1ee54d" + "height": 68 + } }, "source": [ "# O valor \"?\" aparece nas colunas \"workclass\", \"occupation\" e \"native-country\".\n", @@ -3212,14 +3212,14 @@ "print(f'{n_mv_occupation} registos tem a coluna \"occupation\" com Missing Value ({round((n_mv_occupation*100)/df_50k.shape[0],2)}%)') \n", "print(f'{n_mv_country} registos tem a coluna \"native-country\" com Missing Value ({round((n_mv_country*100)/df_50k.shape[0],2)}%)') " ], - "execution_count": 469, + "execution_count": 91, "outputs": [ { "output_type": "stream", "text": [ "1836 registos tem a coluna \"workclass\" com Missing Value (5.64%)\n", "1843 registos tem a coluna \"occupation\" com Missing Value (5.66%)\n", - "582 registos tem a coluna \"native-country\" com Missing Value (1.79%)\n" + "583 registos tem a coluna \"native-country\" com Missing Value (1.79%)\n" ], "name": "stdout" } @@ -3240,11 +3240,11 @@ "metadata": { "id": "xdFCi4QqDA1S", "colab_type": "code", + "outputId": "7af0f382-c300-48c7-98fe-858be17279a5", "colab": { "base_uri": "https://localhost:8080/", "height": 34 - }, - "outputId": "20ffa997-969b-4429-e51d-addfc813e741" + } }, "source": [ "# Registos onde \"?\" aparece nas 3 colunas\n", @@ -3254,7 +3254,7 @@ " \n", "f'{df_50k[F1 & F2 & F3].shape[0]} registos'" ], - "execution_count": 470, + "execution_count": 92, "outputs": [ { "output_type": "execute_result", @@ -3266,7 +3266,7 @@ "metadata": { "tags": [] }, - "execution_count": 470 + "execution_count": 92 } ] }, @@ -3338,17 +3338,17 @@ "metadata": { "id": "LFPc9boDLf-K", "colab_type": "code", + "outputId": "cd7d007d-edb5-482b-c64d-2202ba1af30a", "colab": { "base_uri": "https://localhost:8080/", - "height": 67 - }, - "outputId": "2d6490b7-f613-4435-f298-574ccd32e2be" + "height": 68 + } }, "source": [ "#Colunas numericas\n", "df_50k.select_dtypes(include=['number']).columns" ], - "execution_count": 473, + "execution_count": 95, "outputs": [ { "output_type": "execute_result", @@ -3362,7 +3362,7 @@ "metadata": { "tags": [] }, - "execution_count": 473 + "execution_count": 95 } ] }, @@ -3371,32 +3371,32 @@ "metadata": { "id": "_G4MKKKs7Cc5", "colab_type": "code", + "outputId": "1f2823fe-fb89-40dd-805d-d037f6b44143", "colab": { "base_uri": "https://localhost:8080/", - "height": 202 - }, - "outputId": "dd649ef2-ef8e-4828-90e9-39d2f6d8a266" + "height": 204 + } }, "source": [ "#Discretizar Age\n", "df_50k['age_class'] = pd.qcut(df_50k['age'], 10, labels=False)\n", "df_50k['age_class'].value_counts()" ], - "execution_count": 474, + "execution_count": 96, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "0 3888\n", - "4 3517\n", + "0 3895\n", + "4 3518\n", "8 3461\n", - "2 3373\n", - "1 3297\n", - "5 3243\n", - "7 3163\n", - "6 3006\n", - "9 2998\n", + "2 3376\n", + "1 3301\n", + "5 3245\n", + "7 3167\n", + "6 3008\n", + "9 2999\n", "3 2591\n", "Name: age_class, dtype: int64" ] @@ -3404,7 +3404,7 @@ "metadata": { "tags": [] }, - "execution_count": 474 + "execution_count": 96 } ] }, @@ -3413,16 +3413,16 @@ "metadata": { "id": "FC2Mw64mOlN0", "colab_type": "code", + "outputId": "61a4a993-acb1-4cfc-e2e0-d32d45cc3478", "colab": { "base_uri": "https://localhost:8080/", - "height": 333 - }, - "outputId": "948ac930-6c69-4eb6-d221-4a5f3c07791e" + "height": 204 + } }, "source": [ "df_50k.head(5)" ], - "execution_count": 475, + "execution_count": 97, "outputs": [ { "output_type": "execute_result", @@ -3584,7 +3584,7 @@ "metadata": { "tags": [] }, - "execution_count": 475 + "execution_count": 97 } ] }, @@ -3637,7 +3637,11 @@ "metadata": { "id": "d0SAY78uAtVl", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 71 + }, + "outputId": "64a6c1aa-6468-48db-83ce-633e0a8e64bf" }, "source": [ "import numpy as np\n", @@ -3669,8 +3673,17 @@ "import warnings\n", "warnings.filterwarnings(\"ignore\")" ], - "execution_count": 0, - "outputs": [] + "execution_count": 98, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n", + " \"(https://pypi.org/project/six/).\", DeprecationWarning)\n" + ], + "name": "stderr" + } + ] }, { "cell_type": "markdown", @@ -3824,10 +3837,10 @@ "metadata": { "id": "tMH_RurjvpKN", "colab_type": "code", - "outputId": "86d6489a-6540-48ce-cfc9-0a0061468daa", + "outputId": "cccb7717-d1d8-48fd-895a-ae4353c8750b", "colab": { "base_uri": "https://localhost:8080/", - "height": 333 + "height": 343 } }, "source": [ @@ -3861,7 +3874,7 @@ "\n", "copyDataframe.head()" ], - "execution_count": 480, + "execution_count": 101, "outputs": [ { "output_type": "execute_result", @@ -4173,7 +4186,7 @@ "metadata": { "tags": [] }, - "execution_count": 480 + "execution_count": 101 } ] }, @@ -4202,17 +4215,17 @@ "metadata": { "id": "S3YDfY4nA-bw", "colab_type": "code", - "outputId": "db7c0f9b-0528-411d-e55f-0b6cf0237c14", + "outputId": "5563b885-4c31-4c0e-c7b4-243471084d1e", "colab": { "base_uri": "https://localhost:8080/", - "height": 274 + "height": 284 } }, "source": [ "X_train = pd.read_csv('https://raw.githubusercontent.com/massaoHigaskino/DSWP/lectures/project/Execution/adult_50k/datascience_adult_50k_v_t1_adult_data_dataset.csv', index_col= 'id');\n", "X_train.head()" ], - "execution_count": 481, + "execution_count": 155, "outputs": [ { "output_type": "execute_result", @@ -4844,7 +4857,7 @@ "metadata": { "tags": [] }, - "execution_count": 481 + "execution_count": 155 } ] }, @@ -4853,17 +4866,17 @@ "metadata": { "id": "jWmSQdveC-NP", "colab_type": "code", - "outputId": "114b7d69-35e5-4374-93a6-5c0ff0d04611", + "outputId": "588f0c4f-3c60-400e-c985-9c5bfe59c141", "colab": { "base_uri": "https://localhost:8080/", - "height": 225 + "height": 235 } }, "source": [ "y_train = pd.read_csv('https://raw.githubusercontent.com/massaoHigaskino/DSWP/lectures/project/Execution/adult_50k/datascience_adult_50k_v_t1_adult_data_target.csv', index_col= 'id');\n", "y_train.head()" ], - "execution_count": 482, + "execution_count": 156, "outputs": [ { "output_type": "execute_result", @@ -4932,7 +4945,7 @@ "metadata": { "tags": [] }, - "execution_count": 482 + "execution_count": 156 } ] }, @@ -4941,17 +4954,17 @@ "metadata": { "id": "NlRs-MXBC-4N", "colab_type": "code", - "outputId": "b91c294d-f4b3-4613-e607-20bb149982c5", + "outputId": "b79dee37-a8e7-428b-c090-6b66d60fc085", "colab": { "base_uri": "https://localhost:8080/", - "height": 274 + "height": 284 } }, "source": [ "X_test = pd.read_csv('https://raw.githubusercontent.com/massaoHigaskino/DSWP/lectures/project/Execution/adult_50k/datascience_adult_50k_v_t1_adult_test_dataset.csv', index_col= 'id');\n", "X_test.head()" ], - "execution_count": 483, + "execution_count": 157, "outputs": [ { "output_type": "execute_result", @@ -5583,7 +5596,7 @@ "metadata": { "tags": [] }, - "execution_count": 483 + "execution_count": 157 } ] }, @@ -5592,17 +5605,17 @@ "metadata": { "id": "ft_PmPkeC_LD", "colab_type": "code", - "outputId": "c78ccc93-49ea-4be2-9fef-6f19595f50b9", + "outputId": "fc22278c-79d1-4655-87ed-b8a31ed2bdea", "colab": { "base_uri": "https://localhost:8080/", - "height": 225 + "height": 235 } }, "source": [ "y_test = pd.read_csv('https://raw.githubusercontent.com/massaoHigaskino/DSWP/lectures/project/Execution/adult_50k/datascience_adult_50k_v_t1_adult_test_target.csv', index_col= 'id');\n", "y_test.head()" ], - "execution_count": 484, + "execution_count": 158, "outputs": [ { "output_type": "execute_result", @@ -5671,7 +5684,7 @@ "metadata": { "tags": [] }, - "execution_count": 484 + "execution_count": 158 } ] }, @@ -5685,15 +5698,125 @@ "##Decision Tree" ] }, + { + "cell_type": "code", + "metadata": { + "id": "lE0WvRvxUJry", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 606 + }, + "outputId": "b08b8a64-775a-426a-d623-1856245cb2c8" + }, + "source": [ + "class ProcessDecisionTree:\n", + " def __init__(self,\n", + " iSeed,\n", + " iCV,\n", + " X_train,\n", + " y_train,\n", + " y_test,\n", + " X_test, \n", + " make_confusion_matrix,\n", + " criterion='gini',\n", + " splitter='best',\n", + " max_depth=None,\n", + " min_samples_split=2,\n", + " min_samples_leaf=1,\n", + " min_impurity_decrease=0.0,\n", + " min_impurity_split=None,\n", + " presort=False,\n", + " class_weight=None,\n", + " max_leaf_nodes=None,\n", + " max_features=None,\n", + " min_weight_fraction_leaf=0.0):\n", + " self.criterion=criterion\n", + " self.splitter=splitter\n", + " self.max_depth=max_depth\n", + " self.min_samples_split=min_samples_split\n", + " self.min_samples_leaf=min_samples_leaf\n", + " self.min_impurity_decrease=min_impurity_decrease\n", + " self.min_impurity_split=min_impurity_split\n", + " self.presort=presort\n", + " self.class_weight=class_weight\n", + " self.max_leaf_nodes=max_leaf_nodes\n", + " self.max_features=max_features\n", + " self.min_weight_fraction_leaf=min_weight_fraction_leaf\n", + " self.random_state=iSeed\n", + " self.cv=iCV\n", + " self.X_train=X_train\n", + " self.y_train=y_train\n", + " self.y_test=y_test\n", + " self.X_test=X_test\n", + " self.make_confusion_matrix=make_confusion_matrix\n", + "\n", + " def start(self):\n", + " self.Model_DT = DecisionTreeClassifier(criterion=self.criterion, \n", + " splitter=self.splitter, \n", + " max_depth=self.max_depth, \n", + " min_samples_split=self.min_samples_split, \n", + " min_samples_leaf=self.min_samples_leaf, \n", + " min_weight_fraction_leaf=self.min_weight_fraction_leaf, \n", + " max_features=self.max_features, \n", + " random_state=self.random_state, \n", + " max_leaf_nodes=self.max_leaf_nodes, \n", + " min_impurity_decrease=self.min_impurity_decrease, \n", + " min_impurity_split=self.min_impurity_split, \n", + " class_weight=self.class_weight, \n", + " presort=self.presort)\n", + " self.Model_DT.fit(self.X_train, self.y_train)\n", + " self.a_Scores_CV = cross_val_score(self.Model_DT, self.X_train, self.y_train, cv=self.cv)\n", + " print(f'Média das Acurácias calculadas pelo CV....: {100*round(self.a_Scores_CV.mean(),4)}')\n", + " print(f'std médio das Acurácias calculadas pelo CV: {100*round(self.a_Scores_CV.std(),4)}')\n", + " print(f'Acurácias: {self.a_Scores_CV}')\n", + "\n", + " self.y_pred = self.Model_DT.predict(self.X_test)\n", + "\n", + " #Confusion Matrix\n", + " self.cf_matrix = confusion_matrix(self.y_test, self.y_pred)\n", + " self.cf_labels = ['True Neg','False Pos','False Neg','True Pos']\n", + " self.cf_categories = ['Zero', 'One']\n", + " self.make_confusion_matrix(self.cf_matrix, group_names= self.cf_labels, categories= self.cf_categories)\n", + "\n", + "decisionTree = ProcessDecisionTree(i_Seed, i_CV, X_train, y_train, y_test, X_test, make_confusion_matrix)\n", + "decisionTree.start()\n" + ], + "execution_count": 172, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Média das Acurácias calculadas pelo CV....: 81.67\n", + "std médio das Acurácias calculadas pelo CV: 0.84\n", + "Acurácias: [0.80810562 0.81449631 0.81695332 0.80804668 0.82371007 0.81234644\n", + " 0.8215602 0.82463145 0.83261671 0.8046683 ]\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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fZWbtA/8cUtHhi8zsV2CV/7mGmb0Z9IAiIiJp4HXga+fc2UANYBnQC5jpnKsM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\nkc9Qx8fHc0uTq3h7yKeUKFk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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, { "cell_type": "code", "metadata": { "id": "6NuSOifMN_rk", "colab_type": "code", - "outputId": "16f9a6c8-0ced-4cca-95dd-fdde45d96c60", + "outputId": "3b481dd3-c15d-478b-e97b-43378c3b8e8f", "colab": { "base_uri": "https://localhost:8080/", - "height": 118 + "height": 119 } }, "source": [ @@ -5709,7 +5832,7 @@ "# Treina...\n", "Model_DT.fit(X_train, y_train)" ], - "execution_count": 485, + "execution_count": 111, "outputs": [ { "output_type": "execute_result", @@ -5726,7 +5849,7 @@ "metadata": { "tags": [] }, - "execution_count": 485 + "execution_count": 111 } ] }, @@ -5735,10 +5858,10 @@ "metadata": { "id": "RsiADVY2OKgY", "colab_type": "code", - "outputId": "70ada26a-c63c-42b0-88bb-44270e41c6d6", + "outputId": "204d2b98-833d-477c-eb94-38bb2bc18717", "colab": { "base_uri": "https://localhost:8080/", - "height": 50 + "height": 51 } }, "source": [ @@ -5747,7 +5870,7 @@ "print(f'Média das Acurácias calculadas pelo CV....: {100*round(a_Scores_CV.mean(),4)}')\n", "print(f'std médio das Acurácias calculadas pelo CV: {100*round(a_Scores_CV.std(),4)}')" ], - "execution_count": 486, + "execution_count": 112, "outputs": [ { "output_type": "stream", @@ -5764,16 +5887,16 @@ "metadata": { "id": "CBw5nAhlOUWT", "colab_type": "code", - "outputId": "c6b261f1-2da3-4a6b-fa84-81cbd0576897", + "outputId": "7e777df8-71ef-4f3b-e9f2-f05c3371b6b2", "colab": { "base_uri": "https://localhost:8080/", - "height": 50 + "height": 51 } }, "source": [ "print(f'Acurácias: {a_Scores_CV}')" ], - "execution_count": 487, + "execution_count": 113, "outputs": [ { "output_type": "stream", @@ -5790,7 +5913,7 @@ "metadata": { "id": "gvKT9gZaOZXO", "colab_type": "code", - "outputId": "272d575e-5ff7-4e51-eee5-7ce7ce460379", + "outputId": "b658e9e5-8e69-4630-df29-52c9ee8110df", "colab": { "base_uri": "https://localhost:8080/", "height": 538 @@ -5806,7 +5929,7 @@ "cf_categories = ['Zero', 'One']\n", "make_confusion_matrix(cf_matrix, group_names= cf_labels, categories= cf_categories)" ], - "execution_count": 488, + "execution_count": 114, "outputs": [ { "output_type": "display_data", @@ -5850,7 +5973,7 @@ "# Treina...\n", "Model_RF.fit(X_train, y_train)" ], - "execution_count": 489, + "execution_count": 0, "outputs": [ { "output_type": "execute_result", @@ -5889,7 +6012,7 @@ "print(f'Média das Acurácias calculadas pelo CV....: {100*round(a_Scores_CV.mean(),4)}')\n", "print(f'std médio das Acurácias calculadas pelo CV: {100*round(a_Scores_CV.std(),4)}')" ], - "execution_count": 490, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -5925,7 +6048,7 @@ "source": [ "print(f'Acurácias: {a_Scores_CV}')" ], - "execution_count": 491, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -5958,7 +6081,7 @@ "cf_categories = ['Zero', 'One']\n", "make_confusion_matrix(cf_matrix, group_names= cf_labels, categories= cf_categories)" ], - "execution_count": 492, + "execution_count": 0, "outputs": [ { "output_type": "display_data", From 60a5f71beda7b91d9190feea27b1ea94cbbe98a6 Mon Sep 17 00:00:00 2001 From: Fagner Candido Date: Fri, 15 Nov 2019 13:59:19 +0000 Subject: [PATCH 34/35] Created using Colaboratory --- dswp_grupo1.ipynb | 77 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 77 insertions(+) diff --git a/dswp_grupo1.ipynb b/dswp_grupo1.ipynb index 4c9fcf004..72fa8758f 100644 --- a/dswp_grupo1.ipynb +++ b/dswp_grupo1.ipynb @@ -5955,6 +5955,83 @@ "##Random Forest" ] }, + { + "cell_type": "code", + "metadata": { + "id": "4ysbE6UsTO2m", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 606 + }, + "outputId": "2130eb96-624e-455e-c8db-82c2019b82d0" + }, + "source": [ + "class ProcessRandomForest:\n", + " def __init__(self, iSeed,\n", + " iCV, X_train, \n", + " y_train, y_test,\n", + " X_test, n_estimators=100, \n", + " min_samples_split= 2, max_features=\"auto\"):\n", + " self.n_estimators = n_estimators\n", + " self.min_samples_split = min_samples_split\n", + " self.max_features = max_features\n", + " self.iSeed = iSeed\n", + " self.iCV = iCV\n", + " self.X_train = X_train\n", + " self.y_train = y_train\n", + " self.y_test = y_test\n", + " self.X_test = X_test\n", + "\n", + " def start(self):\n", + " self.Model_RF= RandomForestClassifier(n_estimators=self.n_estimators, min_samples_split=self.min_samples_split, max_features=self.max_features, random_state= self.iSeed)\n", + " self.Model_RF.fit(self.X_train, self.y_train)\n", + "\n", + " # Cross-Validation com 10 folds\n", + " self.a_Scores_CV = cross_val_score(self.Model_RF, self.X_train, self.y_train, cv= self.iCV)\n", + " print(f'Média das Acurácias calculadas pelo CV....: {100*round(self.a_Scores_CV.mean(),4)}')\n", + " print(f'std médio das Acurácias calculadas pelo CV: {100*round(self.a_Scores_CV.std(),4)}')\n", + "\n", + " print(f'Acurácias: {self.a_Scores_CV}')\n", + " \n", + " # Faz predições...\n", + " self.y_pred = self.Model_RF.predict(self.X_test)\n", + "\n", + " # Confusion Matrix\n", + " self.cf_matrix = confusion_matrix(self.y_test, self.y_pred)\n", + " self.cf_labels = ['True Neg','False Pos','False Neg','True Pos']\n", + " self.cf_categories = ['Zero', 'One']\n", + " make_confusion_matrix(self.cf_matrix, group_names = self.cf_labels, categories = self.cf_categories)\n", + "\n", + "processRandomForest = ProcessRandomForest(i_Seed, i_CV, X_train, y_train, y_test, X_test)\n", + "processRandomForest.start()" + ], + "execution_count": 185, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Média das Acurácias calculadas pelo CV....: 85.61999999999999\n", + "std médio das Acurácias calculadas pelo CV: 0.51\n", + "Acurácias: [0.84801965 0.86179361 0.85841523 0.84674447 0.85995086 0.85227273\n", + " 0.85780098 0.85718673 0.86210074 0.85810811]\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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rzawJ4MwsArgDWBlkZyIi8t+SSxcg1QC2AW+b2RnAL8BdQHnn3Ca/z2agvP++\nErA+1fob/LaM2o9ZZpLpbcA9QFVgC3CO3yYiIie47BjmNbNeZrYw1avXIbuNBBoCrzrnzgL28v9D\nugA45xxHeIpfVsvMs3m3Al1y4FhERERwzo0GRh+hywZgQ6pnx0/BK6ZbzKyCc26TP4y71V8eDVRJ\ntX5lvy0aaHpI+9dBjvmoxdTM3iCd6u6cO/QvBREROcHkxiivc26zma03s1OccyuAFsCf/qsHMNL/\nd5q/ynSgr5lNxLvYKNYvuLOBx1NddHQp8ECQY8rMnOkXqd4XAK4k7RiziIhITrsDeM/M8gFrgRvx\npi4nm1lPYB3Q2e87E7gcWA3E+X1xzsWY2aPAAr/fMOdcTJCDMW9Y+RhW8L4o/Hvn3HkZ9dn+b2KO\njVOLZJfYuITcPgSRLFGzXMFsy48DZ67M8t/3Iy+vk+ceBRHkcYI1+P8rpERE5ASmB7x7MjNnupP/\nnzMNATEcctWUiIjIieyIxdS8G4jOwLviCSDZHeu4sIiI/GfpOfeeIyZ0v3DOdM4l+S8VUhERkUNk\nZs70VzM7yzm3ONuPRkRE8pSQoilwhGJqZpHOuUTgLGCBma3Be8qE4YXWhhmtKyIiciI5UjL9Ge9x\nTe1y6FhERCSPUTD1HKmYGoBzbk0OHYuIiOQx+nJwz5GKaVkzuyejhc65Z7PheERERPKcIxXTCKAI\nfkIVERE5lC5A8hypmG5yzg3LsSMRERHJo446ZyoiIpIRBVPPkYppixw7ChERyZN0AZInwycgBf0a\nGhERkRNNkG+NERERAcA0Iwjo23NERETCpmQqIiKBac7Uo2IqIiKBqZh6NMwrIiISJiVTEREJzHSj\nKaBkKiIiEjYlUxERCUxzph4lUxERkTApmYqISGCaMvWomIqISGD6CjaPhnlFRETCpGQqIiKB6QIk\nj5KpiIhImJRMRUQkME2ZelRMRUQksJC+gg3QMK+IiEjYlExFRCQwDfN6lExFRETCpGQqIiKB6dYY\nj4qpiIgEpicgeTTMKyIiEiYlUxERCUzB1KNkKiIiEiYlUxERCUxzph4lUxERkTApmYqISGAKph4V\nUxERCUzDmx79HERERMKkZCoiIoGZxnkBJVMREZGwKZmKiEhgyqUeFVMREQlM95l6NMwrIiISJiVT\nEREJTLnUo2QqIiISJiVTEREJTFOmHhVTEREJTPeZejTMKyIiEiYlUxERCUyJzKOfg4iISJiUTEVE\nJDDNmXqUTEVERMKkZCoiIoEpl3pUTEVEJDAN83o0zCsiIhImJVMREQlMicyjn4OIiEiYlExFRCQw\nzZl6VExFRCQwlVKPhnlFRFtZRjwAACAASURBVETCpGQqIiKBaZTXo2QqIiISJiVTEREJLKRZU0DF\nVEREwqBhXo+GeUVERMKkZCoiIoGZhnkBJVMREZGwKZmKiEhgmjP1KJmKiEhgISzLX5llZhFmttjM\nZvifa5jZfDNbbWaTzCyf357f/7zaX1491TYe8NtXmFmr4D8HERGRvOkuYFmqz08AzznnagE7gZ5+\ne09gp9/+nN8PMzsN6ALUA1oDr5hZRJADUTEVEZHAzLL+lbn9WmXgCuBN/7MBzYEpfpexQAf/fXv/\nM/7yFn7/9sBE59x+59xfwGqgSZCfg4qpiIgcV8ysl5ktTPXqlU6354H7gGT/c2lgl3Mu0f+8Aajk\nv68ErAfwl8f6/VPa01nnmOgCJBERCSw7LkByzo0GRme8T2sDbHXO/WJmTbP+CI6diqmIiOQ15wPt\nzOxyoABQDHgBKGFmkX76rAxE+/2jgSrABjOLBIoDO1K1H5R6nWOiYpqNLvzf6Zxcq3bK55HPvESF\niumPIGzaGM2Afrfz7uRpYe2zb68biIuL4613JwOw7M8/ePn5pxk1+p2wtiuyO3YXg/p5o207Y3YQ\nCoUoXqIkAM+Nfo+oqKgs2c/ihfMYPvheTqpQkYSEBJpdejldut+SJduWrJcbD21wzj0APADgJ9N7\nnXPdzOwDoCMwEegBHPyFOt3//JO//EvnnDOz6cD7ZvYsUBGoDfwc5JhUTLNR/vz5GTthao7vd1fM\nDn764TvOPf/CHN+3/HcVK16CUW97f6S9+9arFCxYiKu79kjTxzmHc45QKLzLMRqc1ZiHRjxPfFwc\nfW7oRJPzLuLkWqeEtU3JHqHj6z7T+4GJZvYYsBgY47ePAcab2WogBu8KXpxzS81sMvAnkAj0cc4l\nBdmximkO27QxmmFDBrIvPh6Ae+5/kNPPOCtNn7VrVvP4Iw+SkJCAS3YMf+p5qlStxuyZn/DBxHdJ\nSEigXv0G9B84hIiIw6/i7tr9JsaNef2wYpqUlMSrLz3H4l9+JuFAAld17kqHqzuTnJzMs088xi8L\nfqbcSScRGRlJm3ZX0qxl4Fuu5ASyccM/PDKwHzXrnMKalSsY9vQo+t7YmQ8++x6Ab76YxeKF8+k3\ncCg7Y3bw8jPD2bplM6GQ0fuu+6lbr0GG2y5YqBA165zKpugNVKpSjVFPP8bqlcuJjIik150DOP3M\nRvy1ZhXPjxxKUmIiycmOISOeo0LFyjl1+pLLnHNfA1/779eSztW4zrl9QKcM1h8ODA/3OFRMs9H+\n/fvp0fUqACpWrMyIZ16kZMlSPP/Km+TPn5/1/6xj6KABKUOyB3384SQ6dbmeVpe3ISHhAMlJyfz9\n1xrmfv4Zr415l8ioKJ4eMYzPP5vBZW3aH7bf+g3O4NuvvuCXBfMpVLhwSvuMaR9SpEgRxoyfzIED\nB+h903U0Oec8VixbyqZNG3lvynR2xuygW8d2tGl3Zfb+cOQ/ZcM/f9F/8KPUqVuPpMTEDPu99sIT\ndLz2BurWa8CWTdE8fP+dvDruwwz7x+6MYeWyP+jRqy/Tp0wgKiofr46dwrq/VvPQgDt4c8J0Pv1o\nMld16cHFLVqRcOAAzrnsOEXJgJ7N61ExzUbpDfMmJiby7JPDWbViOaGIEOvXrTtsvfqnn8HYt0az\nbetmLm5+CVWqVmPhz/NYvuxPena/BvAKdclSpTPc9w09b2XsmNe57c57Utp+nvcja1at5Ku5nwOw\n999/Wf/POpb8uojmLVsRCoUoXaYsZzUOdJuVnMAqVKpMnbr1jtrv14Xzif7n75TP/+7Zzf79+8if\nv0Cafr8tXkjfm64hZCG69riFylWrs/S3xSnDytVq1KJ0mbJs2vAPp9Y/g4nj3mDr5o2cf3ELKlau\nmqXnJpIZKqY5bNL74yhVqjRjJ04lOTmZ5uc1PKzPpZe14bT6Dfjp+2+5987e3DdoKM7BZW3ac9sd\nd2dqP42anMPoV19i6e9LUtqcc9w9YBBnn3dBmr4//fBteCclJ7wCBQqmvLdQiNTh8MCB/SnvnXOZ\nuljp4JxpZrRo3YZT6zfg55++Y8i9feg38GFOP7PRsZ2ABKZn83r00IYc9u+/eyhdpiyhUIjZMz8h\nKenwue7oDeupVLkKnbpex4UXN2f16pU0bnI2X8/9nJ0xOwDvysrNmzYecV89et7K+2PfSvl89rnn\n89GUSSQmJADwz7q/iY+Po8EZDfl67hySk5OJ2bGdxb8EuphNBIBQKESRokWJXr+O5ORkfvruq5Rl\nZzY+mxlTJ6Z8XrNqeaa3W6/BWXw9ZyYA//y9lpgd26hQuSqbNm6gYuWqdOjUjSbnXcjfa1Zl3cnI\nUVk2/F9epGSaw67q1JUHB/Rj1qfTOPu8CyhYsOBhfb6cM4tZMz8hMjKS0qXL0P2mWyhWvAS33H4n\n/frcgkt2REZGcs/AwZxUoWKG+zrvgosoUbJUyue2HTqyaeNGbuzWCYejRImSjHzmJZq2uISFC+bR\nrWM7yp10EqfUPY3CRYpmy/nLieHG3v0Y0v92ipcsRa1TTiXhgPcHXJ+7H2DUM48zZ+Z0kpKSaNCw\nMX3uGZSpbbbr2JWXnnqM23p0JDIikv4PPkZUVBRfz/mMb774jMjISEqVKUu3G3tn56mJpMuyY7J+\n+7+JugIgj4mL20uhQoWJ3bWLm7t34bW3xlO6TNncPqxcFRuXkNuHIJIlapYrmG1x79uVMVn++/6i\nOqXyXDxVMhUA7uvXhz17dpOYkMANN996whdSEZFjoWIqAHpCkogEklfnOLOaiulx5PFHBvPDd99Q\nslSplMcKjnn9ZaZ/NIUSJb3Htt3apx/nXXARsbt28eB9/Vj+5x9c1rYD/e8fnLKd5cuWMnzog+zf\nv49zz7+IfgMewHTJneSQbVs288zwweyMicEMWre7mg6dujHuzZeZ993XhEJG8ZKluGfQMEqXKcdP\n333F+DdfIRQyQhGR3HrnAOo18B5ksnXLJl544hG2b90CGMOeeonyFQJ9qYdkE/1q8WjO9Djy66KF\nFCxYiEeHPpCmmBYsWIhru9+Ypm98fBwrly9j7ZrVrF2zKk0xvbn7NfQbMIh69Rtw75296djlOj1a\nMADNmQYTs30bMTu2U+uUU4mL28udPbvy0OPPUaZceQoVLgLAtCnv88/fa7nj3sHEx8VRoGBBzIy/\nVq9kxND7GP3exwDcf0dPrul+Mw3/dy7xcXFYyNLchiOZk51zpt+v2pnlv+8vqF0yz5Vo3RpzHDmz\nYWOKFS+eqb4FCxbijLMakS9fvjTt27dtY++/e6l/+hmYGa2vaMd3X8/NjsMVSVepMmWpdcqpABQq\nVJiq1U9m+/atKYUUYF98fMrwYMFChVJGTvbti095/89fa0hKSqLh/85N6adCevyxbHjlRRrmzQM+\nnPw+sz6dTt3T6tH37gEUK5Zxwd22bQvlypdP+Vy2/Els27o1Jw5T5DBbNkWzZuVy6p52OgBjR7/E\n3NkzKFy4CCNfeCOl34/ffsk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