I have pandas dataframe in the below mentioned format,
input_df :
gw_mac mac val status
0 AC233FC01403 AC233F264A4C -21 Outwards
1 AC233FC015F6 AC233F264A4C -37 Outwards
2 AC233FC01403 AC233F264A4C -20 Outwards
3 AC233FC015F6 AC233F264A4C -37 Outwards
4 AC233FC01403 AC233F264A4C -29 Outwards
5 AC233FC015F6 AC233F264A4C -39 Outwards
6 AC233FC01403 AC233F264A4C -37 Outwards
7 AC233FC015F6 AC233F264A4C -37 Outwards
8 AC233FC01403 AC233F264A4C -22 Outwards
9 AC233FC015F6 AC233F264A4C -37 Outwards
10 AC233FC015F6 AC233F264A4C -37 Outwards
I need to convert the same like below one,
output_df:
AC233FC01403 AC233FC015F6 mac status
1 -21 -37 AC233F264A4C Outwards
2 -20 -37 AC233F264A4C Outwards
3 -29 -39 AC233F264A4C Outwards
4 -37 -37 AC233F264A4C Outwards
5 -22 -37 AC233F264A4C Outwards
6 0 -37 AC233F264A4C Outwards
Use cumcount
for new counter
column with set_index
, unstack
and reset_index
:
g = df.groupby(['gw_mac','mac','status']).cumcount()
df = (df.set_index([g, 'mac','status','gw_mac'])['val']
.unstack(fill_value=0)
.reset_index(level=[1,2])
.rename_axis(None, axis=1))
print (df)
mac status AC233FC01403 AC233FC015F6
0 AC233F264A4C Outwards -21 -37
1 AC233F264A4C Outwards -20 -37
2 AC233F264A4C Outwards -29 -39
3 AC233F264A4C Outwards -37 -37
4 AC233F264A4C Outwards -22 -37
5 AC233F264A4C Outwards 0 -37
If order of columns is important:
df = df[df.columns[2:].tolist() + df.columns[:2].tolist()]
print (df)
AC233FC01403 AC233FC015F6 mac status
0 -21 -37 AC233F264A4C Outwards
1 -20 -37 AC233F264A4C Outwards
2 -29 -39 AC233F264A4C Outwards
3 -37 -37 AC233F264A4C Outwards
4 -22 -37 AC233F264A4C Outwards
5 0 -37 AC233F264A4C Outwards
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