给定特定值，计算两个日期之间的天数

Question

I have a dataframe df1 , and I want to calculate the days between two dates given three conditions and create a new column DiffDays with the difference in days.

1) When Yes is 1

2) When values in Value are non-zero

3) Must be UserId specific (perhaps with groupby() )

df1 = pd.DataFrame({'Date':['02.01.2017', '03.01.2017', '04.01.2017', '05.01.2017', '01.01.2017', '02.01.2017', '03.01.2017'],
                   'UserId':[1,1,1,1,2,2,2],
                   'Value':[0,0,0,100,0,1000,0],
                   'Yes':[1,0,0,0,1,0,0]})

For example, when Yes is 1, calculate the dates between when Value is non-zero, which is 05.01.2017 and when Yes is 1, which is 02.01.2017. The result is three days for UserId in row 3.

Expected outcome:

        Date    UserId  Value   Yes  DiffDays
0   02.01.2017  1        0.0    1    0
1   03.01.2017  1        0.0    0.0  0
2   04.01.2017  1        0.0    0.0  0
3   05.01.2017  1        100    0.0  3
4   01.01.2017  2        0.0    1    0
5   02.01.2017  2        1000   0.0  1
6   03.01.2017  2        0.0    0.0  0

I couldn't find anything on Stackoverflow about this, and not sure how to start.

Answer 1

def dayDiff(groupby):
    if (not (groupby.Yes == 1).any()) or (not (groupby.Value > 0).any()):
        return np.zeros(groupby.Date.count())

    min_date = groupby[groupby.Yes == 1].Date.iloc[0]    
    max_date = groupby[groupby.Value > 0].Date.iloc[0]
    delta = max_date - min_date
    return np.where(groupby.Value > 0 , delta.days, 0)


df1.Date = pd.to_datetime(df1.Date, dayfirst=True)
DateDiff = df1.groupby('UserId').apply(dayDiff).explode().rename('DateDiff').reset_index(drop=True)
pd.concat([df1, DateDiff], axis=1)

Returns:

Date    UserId  Value     Yes       DateDiff
0   2017-01-02  1   0      1          0
1   2017-01-03  1   0      0          0
2   2017-01-04  1   0      0          0
3   2017-01-05  1   100    0          3
4   2017-01-01  2   0      1          0
5   2017-01-02  2   1000   0          1
6   2017-01-03  2   0      0          0

Although this answers your question, the date diff logic is hard to follow, especially when it comes to the placement of the DateDiff values.

Update

pd.Series.explode() was only introduced in pandas version 0.25 , for those using previous versions:

df1.Date = pd.to_datetime(df1.Date, dayfirst=True)
DateDiff = (df1
            .groupby('UserId')
            .apply(dayDiff)
            .to_frame()
            .explode(0)
            .reset_index(drop=True)
            .rename(columns={0: 'DateDiff'}))
pd.concat([df1, DateDiff], axis=1)

This will yield the same results.