Counting the business days between two series
pandas, python
Solution
brian_the_bungler was onto the most efficient way of doing this using numpy's busday_count:
import numpy as np
A = [d.date() for d in df['A']]
B = [d.date() for d in df['B']]
df['DIFF'] = np.busday_count(A, B)
print df
On my machine this is 300x faster on your test case, and 1000s of times faster on much larger arrays of dates
Problem
Is there a better way than bdate_range() to measure business days between two columns of dates via pandas? ``` df = pd.DataFrame({ 'A' : ['1/1/2013', '2/2/2013', '3/3/2013'], 'B': ['1/12/2013', '4/4/2013', '3/3/2013']}) print df df['A'] = pd.to_datetime(df['A']) df['B'] = pd.to_datetime(df['B']) f = lambda x: len(pd.bdate_range(x['A'], x['B'])) df['DIFF'] = df.apply(f, axis=1) print df ``` With output of: ``` A B 0 1/1/2013 1/12/2013 1 2/2/2013 4/4/2013 2 3/3/2013 3/3/2013 A B DIFF 0 2013-01-01 00:00:00 2013-01-12 00:00:00 9 1 2013-02-02 00:00:00 2013-04-04 00:00:00 44 2 2013-03-03 00:00:00 2013-03-03 00:00:00 0 ``` Thanks!