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Bikeshare Insights: Summer in the Windy City

This dataset contains information on Divvy Bikes, a bikeshare program that provides residents and visitors of Chicago with a convenient way to explore the city.

The workspace is set up with one CSV file containing bikeshare activities at the peak of the summer-July 2023. Columns include ride ID, bike type, start and end times, station names and IDs, location coordinates, and member type. Feel free to make this workspace yours by adding and removing cells, or editing any of the existing cells.

Source: Divvy Bikes

📊 Visualization ideas

  • Bar chart: Display the number of times each bike type is used to identify the most and least used bikes.
  • Grouped bar chart: Compare bike usage by member type (member vs. casual) to see if it affects bike choice.
  • Heatmap: Vividly illustrate the popularity of bikes at different times during the day and week.

You can query the pre-loaded CSV files using SQL directly. Here’s a sample query:

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DataFrameas
df
variable
SELECT rideable_type, start_station_name
FROM '202307-divvy-tripdata.parquet'
LIMIT 10
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import pandas as pd
divvy_jan2023 = pd.read_parquet("202307-divvy-tripdata.parquet")
divvy_jan2023.head()
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