CityBikes Ride Log
Load a month of bike-share ride logs into pandas and answer an operations manager's first questions about the data.
You just joined CityBikes, a city bike-share network. Your manager drops last month's ride log on your desk — rides.csv, 24 rides across six columns. Before Monday's ops review she needs the basics: how big the dataset is, the rides members take for longer trips, and a fare estimate under the new per-minute pricing.
Five steps, ~2 hours. What you'll practice: loading CSVs, inspecting a DataFrame, selecting columns, filtering rows, and NumPy arrays.
You'll practice
Data Analysis in Pythonpandas DataFramesFiltering & SelectionNumPy Arrays
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Load the ride log
40 XPrides.csv is sitting in your workspace. Load it into a pandas DataFrame called rides — the exact variable name matters, every later step builds on it.
Done when: rides is a DataFrame with 24 rows and all 6 columns.
rides.csv — 24 rows, 6 columns
ride_id · start_station · end_station · duration_min (int) · rider_type (member or casual) · distance_km (float)