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

.
├── 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.