I’ve got a data frame like this:
DF
JavaScript
x
17
17
1
ID A B C2
00 X0 Y0 PARAMETER_03
01 X1 Y1 PARAMETER_14
02 X2 Y2 PARAMETER_25
03 X3 Y3 PARAMETER_36
04 X4 Y4 PARAMETER_47
05 X5 Y5 PARAMETER_08
06 X6 Y6 PARAMETER_19
07 X7 Y7 PARAMETER_210
08 X8 Y8 PARAMETER_311
09 X9 Y9 PARAMETER_412
10 XX0 YY0 PARAMETER_013
11 XX1 YY1 PARAMETER_114
12 XX2 YY2 PARAMETER_215
13 XX3 YY3 PARAMETER_316
14 XX4 YY4 PARAMETER_417
And I need to split it in multiple data frames by PARAMETER_4 in C column, to get:
DF_1
JavaScript
1
7
1
ID A B C2
00 X0 Y0 PARAMETER_03
01 X1 Y1 PARAMETER_14
02 X2 Y2 PARAMETER_25
03 X3 Y3 PARAMETER_36
04 X4 Y4 PARAMETER_47
DF_2
JavaScript
1
6
1
05 X5 Y5 PARAMETER_02
06 X6 Y6 PARAMETER_13
07 X7 Y7 PARAMETER_24
08 X8 Y8 PARAMETER_35
09 X9 Y9 PARAMETER_46
DF_3
JavaScript
1
6
1
10 XX0 YY0 PARAMETER_02
11 XX1 YY1 PARAMETER_13
12 XX2 YY2 PARAMETER_24
13 XX3 YY3 PARAMETER_35
14 XX4 YY4 PARAMETER_46
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:
JavaScript
1
6
1
dfs = [d for _, d in df.groupby(df.groupby('C').cumcount())]2
3
print(dfs[0], 'n')4
print(dfs[1], 'n')5
print(dfs[2])6
Output
JavaScript
1
21
21
1
ID A B C2
0 0 X0 Y0 PARAMETER_03
1 1 X1 Y1 PARAMETER_14
2 2 X2 Y2 PARAMETER_25
3 3 X3 Y3 PARAMETER_36
4 4 X4 Y4 PARAMETER_4 7
8
ID A B C9
5 5 X5 Y5 PARAMETER_010
6 6 X6 Y6 PARAMETER_111
7 7 X7 Y7 PARAMETER_212
8 8 X8 Y8 PARAMETER_313
9 9 X9 Y9 PARAMETER_4 14
15
ID A B C16
10 10 XX0 YY0 PARAMETER_017
11 11 XX1 YY1 PARAMETER_118
12 12 XX2 YY2 PARAMETER_219
13 13 XX3 YY3 PARAMETER_320
14 14 XX4 YY4 PARAMETER_421