This project involved extracting NYC Citi Bike datasets from Citi Bike and using Tableau to visualize and determine answers to company leadership questions. The data was extracted from the website, cleaned and transformed in a Jupyter Notebook using Python and Pandas, and pushed out to a combined csv to then load into Tableau. Each of the visualizations created contain highly interactive features for users to explore the differing trends related to short-term customers and annual subscribers, peak hours and days of the week, and the breakdown of users' gender.
View this project on my Tableau Story.
A report of findings is located in Analysis.md.
Some of the questions answered include:
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How many trips have been recorded total during the chosen period?
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By what percentage has total ridership grown?
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How has the proportion of short-term customers and annual subscribers changed?
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What are the peak hours in which bikes are used during summer months?
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What are the peak hours in which bikes are used during winter months?
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What are the top 10 stations in the city for starting a journey/ ending a journey?
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What are the bottom 10 stations in the city for starting a journey/ ending a journey?
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What is the gender breakdown of active participants?
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How does the average trip duration change by age?
Datasets Downloaded: (3 months of Summer 2019 & 3 months of Winter 2019-2020)
- 201906-citibike-tripdata.csv
- 201907-citibike-tripdata.csv
- 201908-citibike-tripdata.csv
- 201912-citibike-tripdata.csv
- 202001-citibike-tripdata.csv
- 202002-citibike-tripdata.csv
Note: The datasets used are not included in this folder due to storage space, but can be downloaded from the website directly, if you would like to replicate this project for your own analysis.
Glimpse of Visualizations:
