{"id":27646173,"url":"https://github.com/fbarffmann/citibike-covid-analysis","last_synced_at":"2026-04-12T18:57:43.412Z","repository":{"id":287747837,"uuid":"958312836","full_name":"fbarffmann/citibike-covid-analysis","owner":"fbarffmann","description":"Analyzed NYC CitiBike usage during March 2020 to assess the impact of COVID-19 using Python and Tableau. 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Cleaned raw CSV data, generated visualizations in Tableau, and identified key trends by rider type and location.\n\n## Tools \u0026 Technologies Used\n\n- Python\n- Pandas\n- Tableau\n- Data Cleaning \u0026 Transformation\n- Data Visualization\n- Jupyter Notebook\n\n## File Structure\n\n```text\n.\n├── citibike.ipynb                      # Python data cleaning \u0026 EDA\n├── CitiBike-Viz.twb                    # Tableau workbook for visualization\n├── data/202003-citibike-tripdata.csv   # Raw trip data\n├── 202003-citibike-tripdata_cleaned.csv # Cleaned dataset\n```\n\n## Skills Demonstrated\n\n- Cleaning large real-world datasets\n- Exploratory Data Analysis (EDA)\n- Creating interactive dashboards in Tableau\n- Identifying behavioral trends from messy data\n- Communicating insights visually\n\n## Key Findings\n\n- Analyzed over 800,000 rides in March 2020.\n- COVID-19 drove a shift toward casual riders, increasing their trip volume significantly relative to prior months.\n- Popular start stations clustered near parks and residential areas as commuting patterns changed.\n- Casual riders took longer, more leisurely rides compared to subscribers.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffbarffmann%2Fcitibike-covid-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffbarffmann%2Fcitibike-covid-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffbarffmann%2Fcitibike-covid-analysis/lists"}