Pandas Groupby的两列日期之间的工作日

Question

I have a Dataframe in Pandas with a letter and two dates as columns. I would like to calculate the difference between the two date columns for the previous row using shift(1) provided that the Letter value is the same (using a groupby ). The complex part is I would like to calculate business days, not just elapsed days. The best way I have found to do that is using a numpy.busday_count , which takes two lists as an argument. I am essentially trying to use .apply to make each row it's own list. Not sure if this is the best way to do it, but running into some problems, which are ambiguous.

import pandas as pd
from datetime import datetime
import numpy as np

# create dataframe
df = pd.DataFrame(data=[['A', datetime(2016,01,07), datetime(2016,01,09)],
                        ['A', datetime(2016,03,01), datetime(2016,03,8)],
                        ['B', datetime(2016,05,01), datetime(2016,05,10)],
                        ['B', datetime(2016,06,05), datetime(2016,06,07)]],
                   columns=['Letter', 'First Day', 'Last Day'])

# convert to dates since pandas reads them in as time series
df['First Day'] = df['First Day'].apply(lambda x: x.to_datetime().date())
df['Last Day'] = df['Last Day'].apply(lambda x: x.to_datetime().date())

df['Gap'] = (df.groupby('Letter')
                         .apply(
                                lambda x: (
                                            np.busday_count(x['First Day'].shift(1).tolist(),
                                                            x['Last Day'].shift(1).tolist())))
                         .reset_index(drop=True))
print df

I get the following error on the lambda function. I'm not sure what object it's having problems with as the two passed arguments should be dates:

ValueError: Could not convert object to NumPy datetime

Desired Output:

  Letter   First Day    Last Day   Gap
0      A  2016-01-07  2016-01-09  NAN
1      A  2016-03-01  2016-03-08  1
2      B  2016-05-01  2016-05-10  NAN
3      B  2016-06-05  2016-06-07  7

Answer 1

The following should work - first removing the leading zeros from the date digits):

df = pd.DataFrame(data=[['A', datetime(2016, 1, 7), datetime(2016, 1, 9)],
                        ['A', datetime(2016, 3, 1), datetime(2016, 3, 8)],
                        ['B', datetime(2016, 5, 1), datetime(2016, 5, 10)],
                        ['B', datetime(2016, 6, 5), datetime(2016, 6, 7)]],
                  columns=['Letter', 'First Day', 'Last Day'])

df['Gap'] = df.groupby('Letter')
              .apply(
               lambda x: 
                   pd.DataFrame(
                       np.busday_count(x['First Day'].tolist(), x['Last Day'].tolist())).shift())
                  .reset_index(drop=True)

  Letter  First Day   Last Day  Gap
0      A 2016-01-07 2016-01-09  NaN
1      A 2016-03-01 2016-03-08  2.0
2      B 2016-05-01 2016-05-10  NaN
3      B 2016-06-05 2016-06-07  6.0

I don't think you need the .date() conversion.