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Dec 16th, 2017
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  1. # When you're sure of the format, it's much quicker to explicitly convert your dates than use `parse_dates`
  2. # Makes sense; was just surprised by the time difference.
  3. import pandas as pd
  4. from datetime import datetime
  5. to_datetime = lambda d: datetime.strptime(d, '%m/%d/%Y %H:%M')
  6.  
  7. %time trips = pd.read_csv('data/divvy/Divvy_Trips_2013.csv', parse_dates=['starttime', 'stoptime'])
  8. # CPU times: user 1min 29s, sys: 331 ms, total: 1min 29s
  9. # Wall time: 1min 30s
  10.  
  11. %time trips = pd.read_csv('data/divvy/Divvy_Trips_2013.csv', converters={'starttime': to_datetime, 'stoptime': to_datetime})
  12. # CPU times: user 17.6 s, sys: 269 ms, total: 17.9 s
  13. # Wall time: 17.9 s
  14.  
  15. # $ wc -l divvy/Divvy_Trips_2013.csv
  16. # 759789 divvy/Divvy_Trips_2013.csv
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