Apply function to specific selected columns in pandas data frame

Question

I have the following dataframe:

# List of Tuples
matrix = [([22, 23], [34, 35, 65], [23, 29, 31]),
         ([33, 34], [31, 44], [11, 16, 18]),
         ([44, 56, 76], [16, 34, 76], [21, 34]),
         ([55, 34], [32, 35, 38], [22, 24, 26]),
         ([66, 65, 67], [33, 38, 39], [27, 32, 34]),
         ([77, 39, 45], [35, 36, 38], [11, 21, 34])]

# Create a DataFrame object
df = pd.DataFrame(matrix, columns=list('xyz'), index=list('abcdef'))

I'm able to apply my custom function to output start, end items in list like below for all columns:

def fl(x):
    return [x[0], x[len(x)-1]]

df.apply(lambda x : [fl(i) for i in x])

But i want to apply the function to selected columns x & z.

I'm trying like below referring to this link

df.apply(lambda x: fl(x) if x in ['x', 'y'] else x)

and like this:

df[['x', 'y']].apply(fl)

How to get the output with the function applied to only x and z columns with y column unchanged.

Answer 1

Use DataFrame.applymap for elementwise processing, also for last value is possible use [-1] indexing:

def fl(x):
    return [x[0], x[-1]]

df[['x', 'z']] = df[['x', 'z']].applymap(fl) 
print (df)
          x             y         z
a  [22, 23]  [34, 35, 65]  [23, 31]
b  [33, 34]      [31, 44]  [11, 18]
c  [44, 76]  [16, 34, 76]  [21, 34]
d  [55, 34]  [32, 35, 38]  [22, 26]
e  [66, 67]  [33, 38, 39]  [27, 34]
f  [77, 45]  [35, 36, 38]  [11, 34]

Or for solution with DataFrame.apply use zip with mapping tuples to lists and selexting by str:

def fl(x):
    return list(map(list, zip(x.str[0], x.str[-1])))

df[['x', 'z']] = df[['x', 'z']].apply(fl) 
print (df)
          x             y         z
a  [22, 23]  [34, 35, 65]  [23, 31]
b  [33, 34]      [31, 44]  [11, 18]
c  [44, 76]  [16, 34, 76]  [21, 34]
d  [55, 34]  [32, 35, 38]  [22, 26]
e  [66, 67]  [33, 38, 39]  [27, 34]
f  [77, 45]  [35, 36, 38]  [11, 34]