How can I pivot a pandas dataframe (timeseries) with multiple columns at once?

Question

I have a pandas dateframe like the following, with DATETIME as index:

                                  ID      Val1       Val2
DATETIME                                                
2019-01-18 10:35:00                A      482.84387  439.67942
2019-01-18 10:35:00                B       -5.30216   20.22247
2019-01-18 10:40:00                A     -790.63989 -810.00000
2019-01-18 10:40:00                B      257.00000  270.55490
2019-01-18 10:45:00                A       10.54820    5.64659
2019-01-18 10:45:00                B      -85.50000  -89.00000

Note that the DATETIME is repeated for ID s.

My goal is to convert it to something like the following (with column names changed based on ID, if possible):

                                   A_Val1       A_Val2      B_Val1     B_Val2
DATETIME                                                
2019-01-18 10:35:00                482.84387   439.67942      -5.30216   20.22247
2019-01-18 10:40:00               -790.63989  -810.00000     257.00000  270.55490
2019-01-18 10:45:00                 10.54820     5.64659     -85.50000  -89.00000

I used pandas.pivot but it didn't work.

df_2= df_1.pivot(index=df_1.index, columns='ID', values=['Val1', 'Val2'])

error is:

"DatetimeIndex(['2019-01-18 10:35:00', '2019-01-18 10:35:00',\n  ....],\n  dtype='datetime64[ns]', name='DATETIME', freq=None) not in index"

I'm not sure where to go from there. Thanks in advance if you can help.

Answer 1

Use DataFrame.reset_index , pass DATETIME to index parameter and last flatten MultiIndex by f-string s:

df_2= df_1.reset_index().pivot(index='DATETIME', columns='ID', values=['Val1', 'Val2'])
df_2.columns = df_2.columns.map(lambda x: f'{x[1]}_{x[0]}')
print (df_2)
                        A_Val1     B_Val1     A_Val2     B_Val2
DATETIME                                                       
2019-01-18 10:35:00  482.84387   -5.30216  439.67942   20.22247
2019-01-18 10:40:00 -790.63989  257.00000 -810.00000  270.55490
2019-01-18 10:45:00   10.54820  -85.50000    5.64659  -89.00000

Answer 2

Another way using DataFrame.set_index and DataFrame.unstack :

df1 = df.set_index('ID', append=True).unstack()
df1.columns = df1.columns.map(lambda c: f'{c[1]}_{c[0]}')

Result:

# print(df1)
                        A_Val1     B_Val1     A_Val2     B_Val2
DATETIME                                                       
2019-01-18 10:35:00  482.84387   -5.30216  439.67942   20.22247
2019-01-18 10:40:00 -790.63989  257.00000 -810.00000  270.55490
2019-01-18 10:45:00   10.54820  -85.50000    5.64659  -89.00000