https://github.com/fbarffmann/citibike-covid-analysis
Analyzed NYC CitiBike usage during March 2020 to assess the impact of COVID-19 using Python and Tableau. Includes ridership breakdowns, user type trends, and interactive dashboard.
https://github.com/fbarffmann/citibike-covid-analysis
citibike covid19 data-analysis data-visualization exploratory-data-analysis pandas python tableau transportation
Last synced: about 23 hours ago
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Analyzed NYC CitiBike usage during March 2020 to assess the impact of COVID-19 using Python and Tableau. Includes ridership breakdowns, user type trends, and interactive dashboard.
- Host: GitHub
- URL: https://github.com/fbarffmann/citibike-covid-analysis
- Owner: fbarffmann
- Created: 2025-04-01T01:52:32.000Z (24 days ago)
- Default Branch: main
- Last Pushed: 2025-04-13T17:07:14.000Z (11 days ago)
- Last Synced: 2025-04-13T18:24:18.402Z (11 days ago)
- Topics: citibike, covid19, data-analysis, data-visualization, exploratory-data-analysis, pandas, python, tableau, transportation
- Language: Jupyter Notebook
- Homepage: https://public.tableau.com/app/profile/finn.arffmann/viz/CitiBike-Viz/CitiBikeVizStory?publish=yes
- Size: 128 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Citibike Usage Analysis During COVID-19
Analyzed over 800,000 Citibike trips from March 2020 to explore how rider behavior shifted during the early COVID-19 pandemic in New York City. Cleaned raw CSV data, generated visualizations in Tableau, and identified key trends by rider type and location.
## Tools & Technologies Used
- Python
- Pandas
- Tableau
- Data Cleaning & Transformation
- Data Visualization
- Jupyter Notebook
## File Structure
```text
.
├── citibike.ipynb # Python data cleaning & EDA
├── CitiBike-Viz.twb # Tableau workbook for visualization
├── data/202003-citibike-tripdata.csv # Raw trip data
├── 202003-citibike-tripdata_cleaned.csv # Cleaned dataset
```
## Skills Demonstrated
- Cleaning large real-world datasets
- Exploratory Data Analysis (EDA)
- Creating interactive dashboards in Tableau
- Identifying behavioral trends from messy data
- Communicating insights visually
## Key Findings
- Analyzed over 800,000 rides in March 2020.
- COVID-19 drove a shift toward casual riders, increasing their trip volume significantly relative to prior months.
- Popular start stations clustered near parks and residential areas as commuting patterns changed.
- Casual riders took longer, more leisurely rides compared to subscribers.