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!
You can create helper array
withcompare PARAMETER_4
, swap values by indexing and cumulative sum for dictionary of DataFrame
s:
s = pd.factorize(df['C'].eq('PARAMETER_4').iloc[::-1].cumsum().sort_index())[0] + 1
print (s)
[1 1 1 1 1 2 2 2 2 2 3 3 3 3 3]
dfs = dict(tuple(df.groupby(s)))
print (dfs[1])
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
What you need is possible, but not recommended :
s = df['C'].eq('PARAMETER_4').iloc[::-1].cumsum()
for i, g in df.groupby(s):
globals()[f'DF_{i}'] = g
print (DF_1)
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
Another idea is check column c
and grouping by counter Series created by GroupBy.cumcount
:
s = df.groupby('C').cumcount() + 1
dfs = dict(tuple(df.groupby(s)))
print (dfs[1])
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
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
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