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

1
17
2/6/2013 5:04:03 PM

Accepted Answer

Here is one, perhaps inelegant, way to do it:

df_result = pd.DataFrame(ts, columns=['value'])

Set up a function which grabs the column name which contains the value (from ts):

def get_col_name(row):    
    b = (df.ix[row.name] == row['value'])
    return b.index[b.argmax()]

for each row, test which elements equal the value, and extract column name of a True.

And apply it (row-wise):

In [3]: df_result.apply(get_col_name, axis=1)
Out[3]: 
1979-01-01 00:00:00    col5
1979-01-01 06:00:00    col3
1979-01-01 12:00:00    col1
1979-01-01 18:00:00    col1

i.e. use df_result['Column'] = df_result.apply(get_col_name, axis=1).

.

Note: there is quite a lot going on in get_col_name so perhaps it warrants some further explanation:

In [4]: row = df_result.irow(0) # an example row to pass to get_col_name

In [5]: row
Out[5]: 
value    1181.220328
Name: 1979-01-01 00:00:00

In [6]: row.name # use to get rows of df
Out[6]: <Timestamp: 1979-01-01 00:00:00>

In [7]: df.ix[row.name]
Out[7]: 
col5    1181.220328
col4     912.154923
col3     648.848635
col2     390.986156
col1     138.185861
Name: 1979-01-01 00:00:00

In [8]: b = (df.ix[row.name] == row['value'])
        #checks whether each elements equal row['value'] = 1181.220328  

In [9]: b
Out[9]: 
col5     True
col4    False
col3    False
col2    False
col1    False
Name: 1979-01-01 00:00:00

In [10]: b.argmax() # index of a True value
Out[10]: 0

In [11]: b.index[b.argmax()] # the index value (column name)
Out[11]: 'col5'

It might be there is more efficient way to do this...

11
2/6/2013 6:53:17 PM

Following on from Andy's detailed answer, the solution to selecting the column name of the highest value per row can be simplified to a single line:

df['column'] = df.apply(lambda x: df.columns[x.argmax()], axis = 1)

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