每周(从星期一开始)python pandas dataframe组

Question

我有一个具有每天值的数据框（请参见下面的DF）。我想每周对"预测"字段进行分组，但周一是一周的第一天。

目前，我可以通过PD.TimeGrouper（'W'）（请参阅下面的DF_FINAL），但在周日开始的一周（请参阅下面的DF_Final）

import pandas as pd
data = [("W1","G1",1234,pd.to_datetime("2015-07-1"),8),
        ("W1","G1",1234,pd.to_datetime("2015-07-30"),2),
        ("W1","G1",1234,pd.to_datetime("2015-07-15"),2),
        ("W1","G1",1234,pd.to_datetime("2015-07-2"),4),
        ("W1","G2",2345,pd.to_datetime("2015-07-5"),5),
        ("W1","G2",2345,pd.to_datetime("2015-07-7"),1),
        ("W1","G2",2345,pd.to_datetime("2015-07-9"),1),
        ("W1","G2",2345,pd.to_datetime("2015-07-11"),3)]
labels = ["Site","Type","Product","Date","Forecast"]
df = pd.DataFrame(data,columns=labels).set_index(["Site","Type","Product","Date"])
df

                              Forecast
Site Type Product Date                
W1   G1   1234    2015-07-01         8
                  2015-07-30         2
                  2015-07-15         2
                  2015-07-02         4
     G2   2345    2015-07-05         5
                  2015-07-07         1
                  2015-07-09         1
                  2015-07-11         3

df_final = (df
     .reset_index()
     .set_index("Date")
     .groupby(["Site","Product",pd.TimeGrouper('W')])["Forecast"].sum()
     .astype(int)
     .reset_index())
df_final["DayOfWeek"] = df_final["Date"].dt.dayofweek
df_final
  Site  Product       Date  Forecast  DayOfWeek
0   W1     1234 2015-07-05        12          6
1   W1     1234 2015-07-19         2          6
2   W1     1234 2015-08-02         2          6
3   W1     2345 2015-07-05         5          6
4   W1     2345 2015-07-12         5          6

Answer 1

使用W-MON代替W，检查锚定偏移：

df_final = (df
     .reset_index()
     .set_index("Date")
     .groupby(["Site","Product",pd.Grouper(freq='W-MON')])["Forecast"].sum()
     .astype(int)
     .reset_index())
df_final["DayOfWeek"] = df_final["Date"].dt.dayofweek
print (df_final)
  Site  Product       Date  Forecast  DayOfWeek
0   W1     1234 2015-07-06        12          0
1   W1     1234 2015-07-20         2          0
2   W1     1234 2015-08-03         2          0
3   W1     2345 2015-07-06         5          0
4   W1     2345 2015-07-13         5          0

Answer 2

我对此问题有三种解决方案，如下所述。首先，我应该指出，前接受的答案是不正确的。这是原因：

# let's create an example df of length 9, 2020-03-08 is a Sunday
s = pd.DataFrame({'dt':pd.date_range('2020-03-08', periods=9, freq='D'),
                  'counts':0})
> s

	dt	counts
0	2020-03-08 00:00:00	0
1	2020-03-09 00:00:00	0
2	2020-03-10 00:00:00	0
3	2020-03-11 00:00:00	0
4	2020-03-12 00:00:00	0
5	2020-03-13 00:00:00	0
6	2020-03-14 00:00:00	0
7	2020-03-15 00:00:00	0
8	2020-03-16 00:00:00	0