Get Column Names For The N Max/Min Values Per Row In Pandas

I am trying to get, for each individual row, the name of the column with the max/min value up to N-values. Given something like this: a b c d e 1.2 2 0.1 0.

Solution 1:

You can use nlargest and nsmallest:

In [11]: res = df.apply(lambda x: pd.Series(np.concatenate([x.nlargest(2).index.values, x.nsmallest(2).index.values])), axis=1)

In [12]: res
Out[12]:
   0  1  2  3
0  b  a  e  c
1  d  e  b  a
2  c  b  a  d
3  b  a  c  e

In [13]: df[["Max1", "Max2", "Min1", "Min2"]] = res

In [14]: df
Out[14]:
     a    b     c    d     e Max1 Max2 Min1 Min2
0  1.2  2.0  0.10  0.8  0.01    b    a    e    c
1  2.1  1.1  3.20  4.6  3.40    d    e    b    a
2  0.2  1.9  8.80  0.3  1.30    c    b    a    d
3  3.3  7.8  0.12  3.2  1.40    b    a    c    e

Solution 2:

If the order of the largest/smallest and second largest/smallest values don't matter, then you can use np.argpartition.

N = 2 # Number of min/max values 
u = np.argpartition(df, axis=1, kth=N).values
v = df.columns.values[u].reshape(u.shape)

maxdf = pd.DataFrame(v[:,-N:]).rename(columns=lambda x: f'Max{x+1}')
mindf = pd.DataFrame(v[:,:N]).rename(columns=lambda x: f'Min{x+1}')

pd.concat([df, maxdf, mindf], axis=1)

     a    b     c    d     e Max1 Max2 Min1 Min2
0  1.2  2.0  0.10  0.8  0.01    b    a    e    c
1  2.1  1.1  3.20  4.6  3.40    d    e    b    a
2  0.2  1.9  8.80  0.3  1.30    b    c    a    d
3  3.3  7.8  0.12  3.2  1.40    a    b    c    e