Get column name where value is something in pandas dataframe

Question

I'm trying to find, at each timestamp, the column name in a dataframe for which the value matches with the one in a timeseries at the same timestamp.

Here is my dataframe:

>>> df                             col5        col4        col3        col2        col1 1979-01-01 00:00:00  1181.220328  912.154923  648.848635  390.986156  138.185861 1979-01-01 06:00:00  1190.724461  920.767974  657.099560  399.395338  147.761352 1979-01-01 12:00:00  1193.414510  918.121482  648.558837  384.632475  126.254342 1979-01-01 18:00:00  1171.670276  897.585930  629.201469  366.652033  109.545607 1979-01-02 00:00:00  1168.892579  900.375126  638.377583  382.584568  132.998706  >>> df.to_dict() {'col4': {<Timestamp: 1979-01-01 06:00:00>: 920.76797370744271, <Timestamp: 1979-01-01 00:00:00>: 912.15492332839756, <Timestamp: 1979-01-01 18:00:00>: 897.58592995700656, <Timestamp: 1979-01-01 12:00:00>: 918.1214819496729}, 'col5': {<Timestamp: 1979-01-01 06:00:00>: 1190.7244605667831, <Timestamp: 1979-01-01 00:00:00>: 1181.2203275146587, <Timestamp: 1979-01-01 18:00:00>: 1171.6702763228691, <Timestamp: 1979-01-01 12:00:00>: 1193.4145103184442}, 'col2': {<Timestamp: 1979-01-01 06:00:00>: 399.39533771666561, <Timestamp: 1979-01-01 00:00:00>: 390.98615646597591, <Timestamp: 1979-01-01 18:00:00>: 366.65203285812231, <Timestamp: 1979-01-01 12:00:00>: 384.63247469269874}, 'col3': {<Timestamp: 1979-01-01 06:00:00>: 657.09956023625466, <Timestamp: 1979-01-01 00:00:00>: 648.84863460462293, <Timestamp: 1979-01-01 18:00:00>: 629.20146872682449, <Timestamp: 1979-01-01 12:00:00>: 648.55883747413225}, 'col1': {<Timestamp: 1979-01-01 06:00:00>: 147.7613518219286, <Timestamp: 1979-01-01 00:00:00>: 138.18586102094068, <Timestamp: 1979-01-01 18:00:00>: 109.54560722575859, <Timestamp: 1979-01-01 12:00:00>: 126.25434189361377}} 

And the time series with values I want to match at each timestamp:

>>> ts 1979-01-01 00:00:00    1181.220328 1979-01-01 06:00:00    657.099560 1979-01-01 12:00:00    126.254342 1979-01-01 18:00:00    109.545607 Freq: 6H  >>> ts.to_dict() {<Timestamp: 1979-01-01 06:00:00>: 657.09956023625466, <Timestamp: 1979-01-01 00:00:00>: 1181.2203275146587, <Timestamp: 1979-01-01 18:00:00>: 109.54560722575859, <Timestamp: 1979-01-01 12:00:00>: 126.25434189361377} 

Then the result would be:

>>> df_result                              value  Column 1979-01-01 00:00:00    1181.220328  col5 1979-01-01 06:00:00    657.099560   col3 1979-01-01 12:00:00    126.254342   col1 1979-01-01 18:00:00    109.545607   col1 

I hope my question is clear enough. Anyone has an idea how to get df_result?

Thanks

Greg

Answer 1

Just wanted to add that for a situation where multiple columns may have the value and you want all the column names in a list, you can do the following (e.g. get all column names with a value = 'x'):

df.apply(lambda row: row[row == 'x'].index, axis=1) 

The idea is that you turn each row into a series (by adding axis=1) where the column names are now turned into the index of the series. You then filter your series with a condition (e.g. row == 'x'), then take the index values (aka column names!).