想要将df.Month扩展为Weeks，并将数量平均分配给Weeks。每周从星期一开始。
df
Country Item Month Qty
-------------------------------------------
0 New Zealand Apple 2017-10-31 100
1 Puerto Rico Banana 2017-11-30 200
2 France Apple 2017-10-31 400
...
所需的输出是：
Country Item Week Qty
-------------------------------------------
0 New Zealand Apple 2017-10-01 20
1 New Zealand Apple 2017-10-08 20
2 New Zealand Apple 2017-10-15 20
3 New Zealand Apple 2017-10-22 20
4 New Zealand Apple 2017-10-29 20
5 Puerto Rico Banana 2017-11-05 50
6 Puerto Rico Banana 2017-11-12 50
7 Puerto Rico Banana 2017-11-19 50
8 Puerto Rico Banana 2017-11-26 50
9 France Apple 2017-10-01 80
10 France Apple 2017-10-08 80
...
使用以下方法制作了数周的数据框：
mondays = pd.Series(pd.date_range(first_day, last_day, freq='W-Mon'))weeks = pd.DataFrame({'Week':mondays})2周
Week
----------
0 2017-10-01
1 2017-10-08
2 2017-10-15
3 2017-10-22
4 2017-10-29
5 2017-11-05
6 2017-11-12
7 2017-11-19
8 2017-11-26
...
这是问题的扩展：Distribute month's quantity equally into weeks。
最佳答案
您可以使用：
mondays = pd.Series(pd.date_range('2017-10-01', '2017-11-26 ', freq='W-Mon'))
weeks = pd.DataFrame({'Week':mondays})
#month period for merge
df['Month'] = pd.to_datetime(df['Month']).dt.to_period('m')
weeks['Week'] = pd.to_datetime(weeks['Week'])
#month period for merge
weeks['Month'] = weeks['Week'].dt.to_period('m')
#merge by Month
df = pd.merge(df, weeks, on='Month')
#divide by map by Series created by count
df['Qty'] = df['Qty'].div(df['Month'].map(weeks['Month'].value_counts()))
df = df.drop('Month', 1)
print (df)
Country Item Qty Week
0 New Zealand Apple 20.000000 2017-10-02
1 New Zealand Apple 20.000000 2017-10-09
2 New Zealand Apple 20.000000 2017-10-16
3 New Zealand Apple 20.000000 2017-10-23
4 New Zealand Apple 20.000000 2017-10-30
5 France Apple 80.000000 2017-10-02
6 France Apple 80.000000 2017-10-09
7 France Apple 80.000000 2017-10-16
8 France Apple 80.000000 2017-10-23
9 France Apple 80.000000 2017-10-30
10 Puerto Rico Banana 66.666667 2017-11-06
11 Puerto Rico Banana 66.666667 2017-11-13
12 Puerto Rico Banana 66.666667 2017-11-20