[FIXED] Selecting column with smallest value in pandas dataframe

Issue

I have a pandas dataframe with this structure:

df_test

V       |  A    | B   |  C      |  D
-10     |  nan  | nan |  nan    |  nan  
-9.9    |  10   | 1   |  -2200  |  100
-9.8    |  11   | 2   |  -2211  |  1

I would like to add a new column minimum_difference containing the column name with the smallest absolute value for that specific row (ignoring V), like this:

V       |  A    | B   |  C      |  D    | minimum_difference
-10     |  nan  | nan |  nan    |  nan  | nan
-9.9    |  10   | 1   |  -2200  |  100  | B
-9.8    |  11   | 2   |  -2211  |  1    | D

Solution

You can use df.drop to skip the V column, and df.abs to convert the dataframe's values to their absolute equivalents, and use df.idxmin with axis=1 to find the index (which will be a column name) of the smallest (absolute) value:

df_test['minimum_difference'] = df_test.drop('V', axis=1).abs().idxmin(axis=1)

Output:

>>> df
      V     A    B       C      D minimum_difference
0 -10.0   NaN  NaN     NaN    NaN                NaN
1  -9.9  10.0  1.0 -2200.0  100.0                  B
2  -9.8  11.0  2.0 -2211.0    1.0                  D

Answered By - richardec