Greg Reda gjreda

gjreda / gist:7433f5f70299610d9b6b

Last active April 11, 2023 16:23

pandas' read_csv parse_dates vs explicit date conversion

	# When you're sure of the format, it's much quicker to explicitly convert your dates than use `parse_dates`
	# Makes sense; was just surprised by the time difference.
	import pandas as pd
	from datetime import datetime
	to_datetime = lambda d: datetime.strptime(d, '%m/%d/%Y %H:%M')

	%time trips = pd.read_csv('data/divvy/Divvy_Trips_2013.csv', parse_dates=['starttime', 'stoptime'])
	# CPU times: user 1min 29s, sys: 331 ms, total: 1min 29s
	# Wall time: 1min 30s

gjreda / gist:8611946

Created January 25, 2014 04:37

Weird numpy/pandas groupby behavior when using min() on a np.datetime64 field.

	# OSX 10.7.5
	# python 2.7.5
	# pandas 0.13.0
	# numpy 1.8.0

	import pandas as pd
	import numpy as np
	from StringIO import StringIO

	d = """row1,'2013-10-01'

gjreda / espn-cbb.py

Created October 26, 2013 22:24

Grabs college basketball play-by-play data for a given date range. Example usage: python espn.cbb.py 2013-01-01 2013-01-07

	from bs4 import BeautifulSoup
	from urllib2 import urlopen
	from datetime import datetime, timedelta
	from time import sleep
	import sys
	import csv

	# CONSTANTS
	ESPN_URL = "http://scores.espn.go.com"