将列表中的日期与数据框中的日期范围进行比较

Question

I'm having difficulty figuring out a way to count the occurrences of holidays between datetime ranges in a dataframe. The holidays are in a list while the datetime ranges are in the dataframe as shown below: (note that this is a subset of a very large data set)

df = pd.DataFrame({'Date': ['2018-12-19 18:47','2019-01-01 06:11','2019-01-12 10:05','2019-02-17 14:22','2019-03-08 16:17','2019-03-25 17:35','2019-02-14 17:35'],
              'End Date': ['2018-12-28 18:47','2019-01-05 06:11','2019-01-16 10:05','2019-02-19 14:22','2019-03-12 16:17','2019-03-26 17:35','2019-05-27 17:35']})

df['Date'] = pd.to_datetime(df['Date'])
df['End Date'] = pd.to_datetime(df['End Date'])

Holidays = [date(2018,12,24),date(2018,12,25),date(2019,1,1),date(2019,1,21),date(2019,2,18),date(2019,3,8),date(2019,5,27)]

I've been able to find a way that determine whether or not a Holiday is within the datetime ranges, but not get an actual count.

Is there a way to alter the code below to gather the count rather than boolean values?

This is what I've tried so far:

df['Holidays'] = [any([(z>=x)&(z<=y) for z in Holidays]) for x , y in zip(df['Date'].dt.date,df['End Date'].dt.date)]

The result I'm looking for is as follows:

result = pd.DataFrame({'Date': ['2018-12-19 18:47','2019-01-01 06:11','2019-01-12 10:05','2019-02-17 14:22','2019-03-08 16:17','2019-03-25 17:35','2019-02-14 17:35'],
                   'End Date': ['2018-12-28 18:47','2019-01-05 06:11','2019-01-16 10:05','2019-02-19 14:22','2019-03-12 16:17','2019-03-26 17:35','2019-05-27 17:35'],
                   'Holidays': [2,1,0,1,1,0,3]})

Answer 1

We can make a function that checks this condition and then apply it row-wise.

def fn(series):
    return sum([series.iloc[0] <= h <= series.iloc[1] for h in Holidays])

df.assign(Holidays=df.apply(fn, axis=1))

                 Date            End Date  Holidays
0 2018-12-19 18:47:00 2018-12-28 18:47:00         2
1 2019-01-01 06:11:00 2019-01-05 06:11:00         0
2 2019-01-12 10:05:00 2019-01-16 10:05:00         0
3 2019-02-17 14:22:00 2019-02-19 14:22:00         1
4 2019-03-08 16:17:00 2019-03-12 16:17:00         0
5 2019-03-25 17:35:00 2019-03-26 17:35:00         0
6 2019-02-14 17:35:00 2019-05-27 17:35:00         3

Your desired output is incorrect because the Holidays list has no hours for any of the date timestamps. To get the output that you posted we will have to round down to the day.

def fn(series):
    return sum([series.iloc[0].floor('d') <= h <= series.iloc[1].floor('d') for h in Holidays])

df.assign(Holidays=df.apply(fn, axis=1))

               Date          End Date  Holidays
0  2018-12-19 18:47  2018-12-28 18:47         2
1  2019-01-01 06:11  2019-01-05 06:11         1
2  2019-01-12 10:05  2019-01-16 10:05         0
3  2019-02-17 14:22  2019-02-19 14:22         1
4  2019-03-08 16:17  2019-03-12 16:17         1
5  2019-03-25 17:35  2019-03-26 17:35         0
6  2019-02-14 17:35  2019-05-27 17:35         3