Data-Analysis

A collection of pandas practice exercises — five Jupyter notebooks, two standalone scripts and three exported PDFs — working through weather, cars, police-stop, COVID-19, London housing and Udemy course datasets.

This is coursework, not a library or an application. Each file answers a numbered list of questions about one dataset; there is no shared code, no package structure and nothing to install or run as a whole.

Contents

Notebooks

File	Dataset	Contents
Weather data.ipynb	Weather Data Analysis	~138 lines: unique wind speeds, value counts, null handling, renaming columns, .mean(), filtering by weather condition
Cars Data.ipynb	Cars Data Analysis	~40 lines: shape, nulls, value counts, grouping by make
Police Data.ipynb	Police Data Analysis	~63 lines: dropping sparse columns, stop-outcome rates by gender, search-conducted rates, datetime conversion
Covid Data Analysis as of 29Apr2020 (1).ipynb	COVID-19 (snapshot to 29 Apr 2020)	Confirmed/deaths/recovered by region, filtering, seaborn null heatmap
Udemy Courses.ipynb	Udemy course catalogue	The largest of the set (42 cells): paid vs free splits, top/bottom courses by subscriber count, cleaning a price column that mixes numbers with the string "Free", keyword filters for Python/Java courses, published_timestamp → year, and matplotlib/seaborn charts of subscribers by subject, level distribution, price distribution and a price/subscribers/reviews correlation

Scripts

File	Notes
COVID Data.py	The COVID notebook's analysis as a plain script, printing answers to Q1–Qn
London Housing Data Analysis.py	Null handling, to_datetime conversion, deriving a year column, grouping by area
sum_of_array.py	A four-line beginner exercise, unrelated to the rest

PDFs

Census Data Analysis.pdf, Netflix.ipynb - JupyterLab.pdf and Udemy Course… (2) - JupyterLab.pdf are JupyterLab print-outs. The Census and Netflix notebooks themselves are not in this repository — only these PDF exports survive.

The datasets are not included — nothing here runs as-is

Every notebook and script loads its data from a hardcoded absolute Windows path on the original author's machine, for example:

covid = pd.read_csv(r"C:\Users\Harold\Desktop\python\projects\Dataset  Project 4  Covid Data Analysis.csv")
df    = pd.read_csv(r"D:\Documents\python\projects\Udemy.csv", index_col=0)

None of those CSVs are committed. To re-run anything you must source the dataset yourself and edit the path at the top of the file. The saved cell outputs in the notebooks are the only record of the results.

The Dataset Project N … filenames indicate these came from a structured pandas course. TODO: verify which course, so the datasets can be linked and properly credited.

Tech stack

Python 3, pandas, NumPy, matplotlib, seaborn, scikit-learn (in the Udemy notebook only), Jupyter.

python3 -m venv venv && source venv/bin/activate
pip install pandas numpy matplotlib seaborn scikit-learn jupyterlab
jupyter lab

There is no requirements.txt; the list above is inferred from the imports.

A note on the notebook structure

Four of the five notebooks consist of a single code cell holding the entire analysis (up to 138 lines) rather than one cell per step, which makes them awkward to read and to re-execute incrementally. Udemy Courses.ipynb is properly split into 42 cells and is the best example of the set.

Status

Complete as a coursework archive; not maintained. Last commit May 2024. Useful as evidence of pandas familiarity; not reproducible without the source datasets.

Licence

Proprietary software — all rights reserved. Copyright © 2026 Joel Harold Onyango.

This repository is not open source. The full terms are in LICENSE; in summary, you may not copy, redistribute, modify, sublicense, publish, re-host or commercially exploit this software, in whole or in part, without the prior written permission of the copyright holder. Access to this repository does not grant any licence beyond reading it.