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PyBer_Analysis

Project Overview

A Python based ride sharing company requests that an analysis be done on a series of data which contains the companies fare revenue and customer demographics. There request are as follows:

  • A scatter plot which shows the relation between the Average fares ($) vs. the total number of rides (per city).
  • A box and wiskers chart that shows the ride count data for 2019
  • A box and wiskers chart that shows the ride fare data for 2019
  • A box and wiskers chart that shows the driver count data for 2019
  • A pie chart that shows the % of total fares by the city type
  • A pie chart that shows the % of total rides by the city type
  • A pie chart that shows the % of total drivers by the city type

Resources

Data Sources:

  • city_data.csv
  • ride_data.csv

Software:

  • Python 3.7.4
  • Panda
  • Matplotlib

Summary

Fig1.png

Fig2.png

Fig3.png

Fig4.png

Fig5.png

Fig6.png

Fig7.png

Results

  • For the scatter plot we see that Urban rides are more but have lower fares where as Rural rides have much higher fares per ride, but signifcantly less rides.
  • Looking at our box chart, we see that the same results are reflected. Urban have much higher rides, and the fares are much les because of a significant saturation in driver count.
  • The pie charts show that overall the majority of PyBer's business is reliant on Urban as it would be the case being that Urban areas have more people and thus a greater demand for rides.

Challenge Overview

Pyber requested an additional line chart of the total fares by city type from the start of january 2019 to the end of april.

Challenge Summary

The Challenge data chart is comprised of the same types of cities. The data shows that the greatest source of revenue for PyBer is Urban rides. Overall, all three types have similar trends which indicate that the time of year plays a similar impact on ride demand in those three types of areas. The lines do not intersect at any point which means that the distribution of rides remains consistant throughout the year. This would mean that the biggest factor PyBer has to always consider is which areas have the greatest population density in order to secure more revenue. For the indivicual rider. if they wish to maximse their revenue per ride than working in Rural areas is best, but if they wish to get the greatest volume of work than working in Urban areas is best. Suburban areas do have a simialar relation to Rural areas in the sense that you do work less for more, but with slightly less deadzone times where there is no rides being requested.

pyber_challenge.png

About

A ridesharing business wants to look at its ride and driver records to figure out its ride vs profit margins. We use Matplotlib via python to create a series of charts.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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PyBer_Analysis
Project Overview
A Python based ride sharing company requests that an analysis be done on a series of data which contains the companies fare revenue and customer demographics. There request are as follows:
  • A scatter plot which shows the relation between the Average fares ($) vs. the total number of rides (per city).
  • A box and wiskers chart that shows the ride count data for 2019
  • A box and wiskers chart that shows the ride fare data for 2019
  • A box and wiskers chart that shows the driver count data for 2019
  • A pie chart that shows the % of total fares by the city type
  • A pie chart that shows the % of total rides by the city type
  • A pie chart that shows the % of total drivers by the city type
Resources
Data Sources:
  • city_data.csv
  • ride_data.csv
Software:
  • Python 3.7.4
  • Panda
  • Matplotlib
Summary
Fig1.png
Fig2.png
Fig3.png
Fig4.png
Fig5.png
Fig6.png
Fig7.png
Results
  • For the scatter plot we see that Urban rides are more but have lower fares where as Rural rides have much higher fares per ride, but signifcantly less rides.
  • Looking at our box chart, we see that the same results are reflected. Urban have much higher rides, and the fares are much les because of a significant saturation in driver count.
  • The pie charts show that overall the majority of PyBer's business is reliant on Urban as it would be the case being that Urban areas have more people and thus a greater demand for rides.
Challenge Overview
Pyber requested an additional line chart of the total fares by city type from the start of january 2019 to the end of april.
Challenge Summary
The Challenge data chart is comprised of the same types of cities. The data shows that the greatest source of revenue for PyBer is Urban rides. Overall, all three types have similar trends which indicate that the time of year plays a similar impact on ride demand in those three types of areas. The lines do not intersect at any point which means that the distribution of rides remains consistant throughout the year. This would mean that the biggest factor PyBer has to always consider is which areas have the greatest population density in order to secure more revenue. For the indivicual rider. if they wish to maximse their revenue per ride than working in Rural areas is best, but if they wish to get the greatest volume of work than working in Urban areas is best. Suburban areas do have a simialar relation to Rural areas in the sense that you do work less for more, but with slightly less deadzone times where there is no rides being requested.
pyber_challenge.png
About
A ridesharing business wants to look at its ride and driver records to figure out its ride vs profit margins. We use Matplotlib via python to create a series of charts. 
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Readme
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1 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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PyBer_Analysis
Project Overview
A Python based ride sharing company requests that an analysis be done on a series of data which contains the companies fare revenue and customer demographics. There request are as follows:
  • A scatter plot which shows the relation between the Average fares ($) vs. the total number of rides (per city).
  • A box and wiskers chart that shows the ride count data for 2019
  • A box and wiskers chart that shows the ride fare data for 2019
  • A box and wiskers chart that shows the driver count data for 2019
  • A pie chart that shows the % of total fares by the city type
  • A pie chart that shows the % of total rides by the city type
  • A pie chart that shows the % of total drivers by the city type
Resources
Data Sources:
  • city_data.csv
  • ride_data.csv
Software:
  • Python 3.7.4
  • Panda
  • Matplotlib
Summary
Fig1.png
Fig2.png
Fig3.png
Fig4.png
Fig5.png
Fig6.png
Fig7.png
Results
  • For the scatter plot we see that Urban rides are more but have lower fares where as Rural rides have much higher fares per ride, but signifcantly less rides.
  • Looking at our box chart, we see that the same results are reflected. Urban have much higher rides, and the fares are much les because of a significant saturation in driver count.
  • The pie charts show that overall the majority of PyBer's business is reliant on Urban as it would be the case being that Urban areas have more people and thus a greater demand for rides.
Challenge Overview
Pyber requested an additional line chart of the total fares by city type from the start of january 2019 to the end of april.
Challenge Summary
The Challenge data chart is comprised of the same types of cities. The data shows that the greatest source of revenue for PyBer is Urban rides. Overall, all three types have similar trends which indicate that the time of year plays a similar impact on ride demand in those three types of areas. The lines do not intersect at any point which means that the distribution of rides remains consistant throughout the year. This would mean that the biggest factor PyBer has to always consider is which areas have the greatest population density in order to secure more revenue. For the indivicual rider. if they wish to maximse their revenue per ride than working in Rural areas is best, but if they wish to get the greatest volume of work than working in Urban areas is best. Suburban areas do have a simialar relation to Rural areas in the sense that you do work less for more, but with slightly less deadzone times where there is no rides being requested.
pyber_challenge.png
About
A ridesharing business wants to look at its ride and driver records to figure out its ride vs profit margins. We use Matplotlib via python to create a series of charts. 
Resources
Readme
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1 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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PyBer_Analysis
Project Overview
A Python based ride sharing company requests that an analysis be done on a series of data which contains the companies fare revenue and customer demographics. There request are as follows:
  • A scatter plot which shows the relation between the Average fares ($) vs. the total number of rides (per city).
  • A box and wiskers chart that shows the ride count data for 2019
  • A box and wiskers chart that shows the ride fare data for 2019
  • A box and wiskers chart that shows the driver count data for 2019
  • A pie chart that shows the % of total fares by the city type
  • A pie chart that shows the % of total rides by the city type
  • A pie chart that shows the % of total drivers by the city type
Resources
Data Sources:
  • city_data.csv
  • ride_data.csv
Software:
  • Python 3.7.4
  • Panda
  • Matplotlib
Summary
Fig1.png
Fig2.png
Fig3.png
Fig4.png
Fig5.png
Fig6.png
Fig7.png
Results
  • For the scatter plot we see that Urban rides are more but have lower fares where as Rural rides have much higher fares per ride, but signifcantly less rides.
  • Looking at our box chart, we see that the same results are reflected. Urban have much higher rides, and the fares are much les because of a significant saturation in driver count.
  • The pie charts show that overall the majority of PyBer's business is reliant on Urban as it would be the case being that Urban areas have more people and thus a greater demand for rides.
Challenge Overview
Pyber requested an additional line chart of the total fares by city type from the start of january 2019 to the end of april.
Challenge Summary
The Challenge data chart is comprised of the same types of cities. The data shows that the greatest source of revenue for PyBer is Urban rides. Overall, all three types have similar trends which indicate that the time of year plays a similar impact on ride demand in those three types of areas. The lines do not intersect at any point which means that the distribution of rides remains consistant throughout the year. This would mean that the biggest factor PyBer has to always consider is which areas have the greatest population density in order to secure more revenue. For the indivicual rider. if they wish to maximse their revenue per ride than working in Rural areas is best, but if they wish to get the greatest volume of work than working in Urban areas is best. Suburban areas do have a simialar relation to Rural areas in the sense that you do work less for more, but with slightly less deadzone times where there is no rides being requested.
pyber_challenge.png
About
A ridesharing business wants to look at its ride and driver records to figure out its ride vs profit margins. We use Matplotlib via python to create a series of charts. 
Resources
Readme
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1 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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PyBer_Analysis
Project Overview
A Python based ride sharing company requests that an analysis be done on a series of data which contains the companies fare revenue and customer demographics. There request are as follows:
  • A scatter plot which shows the relation between the Average fares ($) vs. the total number of rides (per city).
  • A box and wiskers chart that shows the ride count data for 2019
  • A box and wiskers chart that shows the ride fare data for 2019
  • A box and wiskers chart that shows the driver count data for 2019
  • A pie chart that shows the % of total fares by the city type
  • A pie chart that shows the % of total rides by the city type
  • A pie chart that shows the % of total drivers by the city type
Resources
Data Sources:
  • city_data.csv
  • ride_data.csv
Software:
  • Python 3.7.4
  • Panda
  • Matplotlib
Summary
Fig1.png
Fig2.png
Fig3.png
Fig4.png
Fig5.png
Fig6.png
Fig7.png
Results
  • For the scatter plot we see that Urban rides are more but have lower fares where as Rural rides have much higher fares per ride, but signifcantly less rides.
  • Looking at our box chart, we see that the same results are reflected. Urban have much higher rides, and the fares are much les because of a significant saturation in driver count.
  • The pie charts show that overall the majority of PyBer's business is reliant on Urban as it would be the case being that Urban areas have more people and thus a greater demand for rides.
Challenge Overview
Pyber requested an additional line chart of the total fares by city type from the start of january 2019 to the end of april.
Challenge Summary
The Challenge data chart is comprised of the same types of cities. The data shows that the greatest source of revenue for PyBer is Urban rides. Overall, all three types have similar trends which indicate that the time of year plays a similar impact on ride demand in those three types of areas. The lines do not intersect at any point which means that the distribution of rides remains consistant throughout the year. This would mean that the biggest factor PyBer has to always consider is which areas have the greatest population density in order to secure more revenue. For the indivicual rider. if they wish to maximse their revenue per ride than working in Rural areas is best, but if they wish to get the greatest volume of work than working in Urban areas is best. Suburban areas do have a simialar relation to Rural areas in the sense that you do work less for more, but with slightly less deadzone times where there is no rides being requested.
pyber_challenge.png
About
A ridesharing business wants to look at its ride and driver records to figure out its ride vs profit margins. We use Matplotlib via python to create a series of charts. 
Resources
Readme
Activity
Stars
0 stars
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1 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
PyBer_Analysis
Project Overview
A Python based ride sharing company requests that an analysis be done on a series of data which contains the companies fare revenue and customer demographics. There request are as follows:
  • A scatter plot which shows the relation between the Average fares ($) vs. the total number of rides (per city).
  • A box and wiskers chart that shows the ride count data for 2019
  • A box and wiskers chart that shows the ride fare data for 2019
  • A box and wiskers chart that shows the driver count data for 2019
  • A pie chart that shows the % of total fares by the city type
  • A pie chart that shows the % of total rides by the city type
  • A pie chart that shows the % of total drivers by the city type
Resources
Data Sources:
  • city_data.csv
  • ride_data.csv
Software:
  • Python 3.7.4
  • Panda
  • Matplotlib
Summary
Fig1.png
Fig2.png
Fig3.png
Fig4.png
Fig5.png
Fig6.png
Fig7.png
Results
  • For the scatter plot we see that Urban rides are more but have lower fares where as Rural rides have much higher fares per ride, but signifcantly less rides.
  • Looking at our box chart, we see that the same results are reflected. Urban have much higher rides, and the fares are much les because of a significant saturation in driver count.
  • The pie charts show that overall the majority of PyBer's business is reliant on Urban as it would be the case being that Urban areas have more people and thus a greater demand for rides.
Challenge Overview
Pyber requested an additional line chart of the total fares by city type from the start of january 2019 to the end of april.
Challenge Summary
The Challenge data chart is comprised of the same types of cities. The data shows that the greatest source of revenue for PyBer is Urban rides. Overall, all three types have similar trends which indicate that the time of year plays a similar impact on ride demand in those three types of areas. The lines do not intersect at any point which means that the distribution of rides remains consistant throughout the year. This would mean that the biggest factor PyBer has to always consider is which areas have the greatest population density in order to secure more revenue. For the indivicual rider. if they wish to maximse their revenue per ride than working in Rural areas is best, but if they wish to get the greatest volume of work than working in Urban areas is best. Suburban areas do have a simialar relation to Rural areas in the sense that you do work less for more, but with slightly less deadzone times where there is no rides being requested.
pyber_challenge.png
About
A ridesharing business wants to look at its ride and driver records to figure out its ride vs profit margins. We use Matplotlib via python to create a series of charts. 
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
PyBer_Analysis
Project Overview
A Python based ride sharing company requests that an analysis be done on a series of data which contains the companies fare revenue and customer demographics. There request are as follows:
  • A scatter plot which shows the relation between the Average fares ($) vs. the total number of rides (per city).
  • A box and wiskers chart that shows the ride count data for 2019
  • A box and wiskers chart that shows the ride fare data for 2019
  • A box and wiskers chart that shows the driver count data for 2019
  • A pie chart that shows the % of total fares by the city type
  • A pie chart that shows the % of total rides by the city type
  • A pie chart that shows the % of total drivers by the city type
Resources
Data Sources:
  • city_data.csv
  • ride_data.csv
Software:
  • Python 3.7.4
  • Panda
  • Matplotlib
Summary
Fig1.png
Fig2.png
Fig3.png
Fig4.png
Fig5.png
Fig6.png
Fig7.png
Results
  • For the scatter plot we see that Urban rides are more but have lower fares where as Rural rides have much higher fares per ride, but signifcantly less rides.
  • Looking at our box chart, we see that the same results are reflected. Urban have much higher rides, and the fares are much les because of a significant saturation in driver count.
  • The pie charts show that overall the majority of PyBer's business is reliant on Urban as it would be the case being that Urban areas have more people and thus a greater demand for rides.
Challenge Overview
Pyber requested an additional line chart of the total fares by city type from the start of january 2019 to the end of april.
Challenge Summary
The Challenge data chart is comprised of the same types of cities. The data shows that the greatest source of revenue for PyBer is Urban rides. Overall, all three types have similar trends which indicate that the time of year plays a similar impact on ride demand in those three types of areas. The lines do not intersect at any point which means that the distribution of rides remains consistant throughout the year. This would mean that the biggest factor PyBer has to always consider is which areas have the greatest population density in order to secure more revenue. For the indivicual rider. if they wish to maximse their revenue per ride than working in Rural areas is best, but if they wish to get the greatest volume of work than working in Urban areas is best. Suburban areas do have a simialar relation to Rural areas in the sense that you do work less for more, but with slightly less deadzone times where there is no rides being requested.
pyber_challenge.png
About
A ridesharing business wants to look at its ride and driver records to figure out its ride vs profit margins. We use Matplotlib via python to create a series of charts. 
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PyBer_Analysis
Project Overview
A Python based ride sharing company requests that an analysis be done on a series of data which contains the companies fare revenue and customer demographics. There request are as follows:
  • A scatter plot which shows the relation between the Average fares ($) vs. the total number of rides (per city).
  • A box and wiskers chart that shows the ride count data for 2019
  • A box and wiskers chart that shows the ride fare data for 2019
  • A box and wiskers chart that shows the driver count data for 2019
  • A pie chart that shows the % of total fares by the city type
  • A pie chart that shows the % of total rides by the city type
  • A pie chart that shows the % of total drivers by the city type
Resources
Data Sources:
  • city_data.csv
  • ride_data.csv
Software:
  • Python 3.7.4
  • Panda
  • Matplotlib
Summary
Fig1.png
Fig2.png
Fig3.png
Fig4.png
Fig5.png
Fig6.png
Fig7.png
Results
  • For the scatter plot we see that Urban rides are more but have lower fares where as Rural rides have much higher fares per ride, but signifcantly less rides.
  • Looking at our box chart, we see that the same results are reflected. Urban have much higher rides, and the fares are much les because of a significant saturation in driver count.
  • The pie charts show that overall the majority of PyBer's business is reliant on Urban as it would be the case being that Urban areas have more people and thus a greater demand for rides.
Challenge Overview
Pyber requested an additional line chart of the total fares by city type from the start of january 2019 to the end of april.
Challenge Summary
The Challenge data chart is comprised of the same types of cities. The data shows that the greatest source of revenue for PyBer is Urban rides. Overall, all three types have similar trends which indicate that the time of year plays a similar impact on ride demand in those three types of areas. The lines do not intersect at any point which means that the distribution of rides remains consistant throughout the year. This would mean that the biggest factor PyBer has to always consider is which areas have the greatest population density in order to secure more revenue. For the indivicual rider. if they wish to maximse their revenue per ride than working in Rural areas is best, but if they wish to get the greatest volume of work than working in Urban areas is best. Suburban areas do have a simialar relation to Rural areas in the sense that you do work less for more, but with slightly less deadzone times where there is no rides being requested.
pyber_challenge.png
About
A ridesharing business wants to look at its ride and driver records to figure out its ride vs profit margins. We use Matplotlib via python to create a series of charts. 
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages