Uber Trip Analysis (2014 Datasets)
I welcome anyone to copy, manipulate or use this analysis. You can view all of the project files in my Uber GitHub Repository
All of my code for this project has been embedded:
Orginal Code (Jupyter Notebooks)
Only graphs have been included within this presentation, but all of the corresponding tables can be found within the embedded code.
The location of the original datasets can be found within the Credits of this project.
Contents
- About
- Aim of Project
- 1) Busiest Days (red histogram)
- 2) Sorting From Least to Most Busy Days (linegraph)
- 3) Busiest Days of the Week (green histogram)
- 4) Busiest Hours of Day and Night (blue histogram)
- 5) Overall Most Popular Time and Day (heatmap)
- 6) Most Popular Areas (map of Manhattan)
- Orginal Code (Jupyter Notebooks)
- Conclusion
- Credits
About
This project analyses multiple sets of data (from the year 2014) containing the location of Uber vehicles at different times and dates. These datasets include useful information such as the longitude and latitude of each vehicle when picking up a customer.
The Python libraries used within this project are as follows:
- pandas
- matplotlib.pyplot
- seaborn
Aim of Project
Using Python with Jupiter notebooks, this project is aimed at extracting and comparing graphical information to ultimately determine an answer to the following questions:
- What are the most popular days and times for people riding with Uber?
- Where are the majority of Uber customers being picked up from?
1) Busiest Days (red histogram)
The following histograms:
Concise analysis of results:
April 2014:
May 2014:
June 2014:
July 2014:
August 2014:
September 2014:
Summary 1:
-
2) Sorting From Least to Most Busy Days (linegraph)
The following linegraphs:
Concise analysis of results:
April 2014:
May 2014:
June 2014:
July 2014:
August 2014:
September 2014:
Summary 2:
3) Busiest Days of the Week (green histogram)
The following weekday histograms:
Concise analysis of results:
April 2014:
May 2014:
June 2014:
July 2014:
August 2014:
September 2014:
Summary 3:
4) Busiest Hours of Day and Night (blue histogram)
The following hour histograms:
Concise analysis of results:
April 2014:
May 2014:
June 2014:
July 2014:
August 2014:
September 2014:
Summary 4:
5) Overall Most Popular Time and Day (heatmap)
The following heatmaps:
Concise analysis of results:
April 2014:
May 2014:
June 2014:
July 2014:
August 2014:
September 2014:
Summary 5:
6) Most Popular Areas (map of Manhattan)
The following location maps:
Concise analysis of results:
April 2014:
May 2014:
June 2014:
July 2014:
August 2014:
September 2014:
Summary 6:
Orginal Code (Jupyter Notebooks)
NOTE - The written code follows the same structure for each month; only minor changes were made to the code for each month of data, and therefore the notebooks are all very similar.
April 2014:
May 2014:
June 2014:
July 2014:
August 2014:
September 2014:
I welcome anyone to copy, manipulate or use this analysis. You can view all of the project files in my Uber GitHub Repository
Conclusion
Credits
All of the original datasets were collected from the following site:
https://github.com/fivethirtyeight/uber-tlc-foil-response/tree/master/uber-trip-data
The following YouTube video was used as a guide and inspiration for this project:
https://www.youtube.com/watch?v=Q73ADVZCqSU&list=PLyPUZJrW8mpacz46h_xROWjENH5u5GmEL&index=8&t=200s