我有一个包含每天值的数据框(请参见下面的 df)。 我想每周对“预测”字段进行分组，但将星期一作为一周的第一天。
目前我可以通过 pd.TimeGrouper('W') 来做到这一点(见下面的 df_final)但是它从周日开始对一周进行分组(见下面的 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
最佳答案
使用W-MON代替W，检查anchored offsets :
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
关于python - 每周对 python pandas 数据框进行分组(从星期一开始)，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/46562401/