Working with dates and times in Pandas
summary
Live coding in a Jupyter notebook shows reading taxi.csv with pd.read_csv and using parse_dates to convert pickup/dropoff strings into 64-bit timestamps, extracting components via the dt accessor, and subtracting timestamps to create a trip_time timedelta column. The dataset has 10,000 rows, yields 84 trips under one minute and one 12.5‑hour outlier. Indexing by timestamp enables label slicing and resample('1H') aggregations (mean, count) to summarize trips hourly.
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chapters (10)
- 0:00Dates and times in Pandas
- 2:22Timestamps and time deltas
- 6:33Loading datetime data
- 11:24String dates to datetime
- 14:11Extracting datetime components
- 18:30Trip durations with time deltas
- 21:38Datetime indexes
- 24:47Slicing time series
- 25:29Resampling time series
- 27:32Q&A
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