pandas 计算两个系列之间的工作日
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Counting the business days between two series
提问by brian_the_bungler
Is there a better way than bdate_range() to measure business days between two columns of dates via pandas?
有没有比 bdate_range() 更好的方法来通过 Pandas 测量两列日期之间的工作日？
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:
输出：
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!
谢谢！
回答by Antonbass
brian_the_bungler was onto the most efficient way of doing this using numpy's busday_count:
brian_the_bungler 是使用 numpy 的 busday_count 最有效的方法：
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
在我的机器上，这在你的测试用例上快了 300 倍，在更大的日期数组上快了 1000 倍
