I am really struggling with something that sounded trivial when I first looked into it. I have asked a similar question here problem with pandas efficiency when working with dates where I was more wondering about the efficiency about one piece of code. I figure I could make a new post as I think that finding out a way to modify a dataframe as such could be helpful to more people than just me.
我需要修改一个数据框，以使其包含自当前日期起的所有日期。
例：
Id Supplier Avg_NetAmountSpent Date Quantity NetAmount
0 206433 BESNOWED 6593.0 2020-05-08 91 10181
1 206433 BESNOWED 6593.0 2020-05-06 1076 6069
2 206434 LENTIVECTOR 7335.0 2020-05-08 91 10181
3 206434 LENTIVECTOR 7335.0 2020-05-06 1076 6069
我想更改数据框，使其看起来像这样：
Id Supplier Avg_NetAmountSpent Date Quantity NetAmount
0 206433 BESNOWED 6593.0 2020-05-06 91 10181
1 206433 BESNOWED 0 2020-05-07 0 0
2 206433 BESNOWED 6593.0 2020-05-08 91 10181
3 206433 BESNOWED 0 2020-05-09 0 0
4 206433 BESNOWED 0 2020-05-10 0 0
5 206433 BESNOWED 0 2020-05-11 0 0
6 206434 LENTIVECTOR 7335.0 2020-05-06 1076 6069
7 206434 LENTIVECTOR 0 2020-05-07 0 0
8 206434 LENTIVECTOR 7335.0 2020-05-08 91 10181
9 206434 LENTIVECTOR 0 2020-05-09 0 0
10 206434 LENTIVECTOR 0 2020-05-10 0 0
11 206434 LENTIVECTOR 0 2020-05-11 0 0
我已经尝试过这样做，但这效率很低，采用上面的格式会更容易：
today1 = pd.to_datetime('today').normalize()
frequency1 = '1D'
Nbin1 = (today1 - data_initial['Date'].min()) // pd.Timedelta(frequency1) + 1# Number of bins
bins1 = [today1 - n * pd.Timedelta(frequency1) for n in range(Nbin1, -1, -1)]
data11 = data_initial.groupby(['Id', pd.cut(data_initial['Date'], bins=bins1)]).sum().reset_index()
我想知道是否有一种有效的方法来实现这一目标？
