I’ve got a data frame like this:
DF
ID A B C 00 X0 Y0 PARAMETER_0 01 X1 Y1 PARAMETER_1 02 X2 Y2 PARAMETER_2 03 X3 Y3 PARAMETER_3 04 X4 Y4 PARAMETER_4 05 X5 Y5 PARAMETER_0 06 X6 Y6 PARAMETER_1 07 X7 Y7 PARAMETER_2 08 X8 Y8 PARAMETER_3 09 X9 Y9 PARAMETER_4 10 XX0 YY0 PARAMETER_0 11 XX1 YY1 PARAMETER_1 12 XX2 YY2 PARAMETER_2 13 XX3 YY3 PARAMETER_3 14 XX4 YY4 PARAMETER_4
And I need to split it in multiple data frames by PARAMETER_4 in C column, to get:
DF_1
ID A B C 00 X0 Y0 PARAMETER_0 01 X1 Y1 PARAMETER_1 02 X2 Y2 PARAMETER_2 03 X3 Y3 PARAMETER_3 04 X4 Y4 PARAMETER_4
DF_2
05 X5 Y5 PARAMETER_0 06 X6 Y6 PARAMETER_1 07 X7 Y7 PARAMETER_2 08 X8 Y8 PARAMETER_3 09 X9 Y9 PARAMETER_4
DF_3
10 XX0 YY0 PARAMETER_0 11 XX1 YY1 PARAMETER_1 12 XX2 YY2 PARAMETER_2 13 XX3 YY3 PARAMETER_3 14 XX4 YY4 PARAMETER_4
I cannot find any easy-way function like df.split(axis=0, value='PARAMETER_4')
Any idea about an approach? Thank you in advance!
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Answer
We can use groupby twice here. First we groupby on column C and make a cumcount. Then we groupby on this cumcount to get the seperate dataframes:
dfs = [d for _, d in df.groupby(df.groupby('C').cumcount())]
print(dfs[0], 'n')
print(dfs[1], 'n')
print(dfs[2])
Output
ID A B C
0 0 X0 Y0 PARAMETER_0
1 1 X1 Y1 PARAMETER_1
2 2 X2 Y2 PARAMETER_2
3 3 X3 Y3 PARAMETER_3
4 4 X4 Y4 PARAMETER_4
ID A B C
5 5 X5 Y5 PARAMETER_0
6 6 X6 Y6 PARAMETER_1
7 7 X7 Y7 PARAMETER_2
8 8 X8 Y8 PARAMETER_3
9 9 X9 Y9 PARAMETER_4
ID A B C
10 10 XX0 YY0 PARAMETER_0
11 11 XX1 YY1 PARAMETER_1
12 12 XX2 YY2 PARAMETER_2
13 13 XX3 YY3 PARAMETER_3
14 14 XX4 YY4 PARAMETER_4