Apply a function to each row or column in Dataframe using pandas.apply()

Applying a function to a single or selected columns/rows in one go is a better way. For this we use the pandas apply function. There are different ways to apply a function to each row or column in DataFrame. We will learn about various ways in this post.

Creating DataFrame

Before seeing different ways to apply a function to a row/column. Let’s create a small dataframe and see that. 

Python3

# import pandas and numpy library
import pandas as pd
import numpy as np

# list of tuples
matrix = [(1,2,3,4),
          (5,6,7,8,),
          (9,10,11,12),
          (13,14,15,16)]

# Create a Dataframe object
df = pd.DataFrame(matrix, columns = list('abcd'))

# Output
print(df)

Output :

    a   b   c   d
0   1   2   3   4
1   5   6   7   8
2   9  10  11  12
3  13  14  15  16

Method 1: Applying lambda function to each row/column

When working with Pandas, you can use lambda functions with the apply() method to perform operations on rows or columns of a DataFrame. This is especially useful for simple or one-time calculations.

Example 1: For Column 

Python3

# Applying a lambda function to each
# column which will add 10 to the value
new_df = df.apply(lambda x : x + 10)

# Output
print(new_df)

Output :

    a   b   c   d
0  11  12  13  14
1  15  16  17  18
2  19  20  21  22
3  23  24  25  26

Example 2: For Row 

Python3

# Applying a lambda function to each
# row which will add 5 to the value
new_df = df.apply(lambda x: x + 5, axis = 1)

# Output
print(new_df)

Output :

    a   b   c   d
0   6   7   8   9
1  10  11  12  13
2  14  15  16  17
3  18  19  20  21

Method 2: Applying user defined function to each row/column

Performing a custom operation on the elements within rows or columns of a DataFrame requires the application of a user-defined function.

Example 1: For Column  

Python3

# function to returns x*x
def squareData(x):
    return x * x

# Applying a user defined function to
# each column that will square the given
# value
new_df = df.apply(squareData)

# Output
print(new_df)

Output :

     a    b    c    d
0    1    4    9   16
1   25   36   49   64
2   81  100  121  144
3  169  196  225  256

Example 2: For Row  

Python3

# function to returns x*x
def squareData(x):
    return x * x

# Applying a user defined function
# to each row that will square the given value
new_df = df.apply(squareData, axis = 1)

# Output
print(new_df)

Output :

      a    b    c    d
0    1    4    9   16
1   25   36   49   64
2   81  100  121  144
3  169  196  225  256

In the above examples, we saw how a user defined function is applied to each row and column. We can also apply user defined functions which take two arguments. 

Example 1: For Column 

Python3

# function to returns x+y
def addData(x, y):
    return x + y

# Applying a user defined function to each
# column which will add value in each
# column by given number
new_df = df.apply(addData, args = [1])

# Output
print(new_df)

Output:

    a   b   c   d
0   2   3   4   5
1   6   7   8   9
2  10  11  12  13
3  14  15  16  17

Example 2: For Row 

Python3

# function to returns x+y
def addData(x, y):
    return x + y

# Applying a user defined function to each
# row which will add value in each row by
# given number
new_df = df.apply(addData, axis = 1,
                    args = [3])

# Output
print(new_df)

Output :

    a   b   c   d
0   4   5   6   7
1   8   9  10  11
2  12  13  14  15
3  16  17  18  19

Method 3: Applying numpy function to each row/column

Let’s use apply function to apply numpy function to row/column.

Example 1: For Column 

Python3

# Applying a numpy function to each
# column by squaring each value
new_df = df.apply(np.square)

# Output
print(new_df)

Output :

    a    b    c    d
0    1    4    9   16
1   25   36   49   64
2   81  100  121  144
3  169  196  225  256

Example 2: For Row 

Python3

# Apply a numpy function to each row
# to find square root of each value
new_df = df.apply(np.sqrt, axis = 1)

# Output
print(new_df)

Output :

          a         b         c         d
0  1.000000  1.414214  1.732051  2.000000
1  2.236068  2.449490  2.645751  2.828427
2  3.000000  3.162278  3.316625  3.464102
3  3.605551  3.741657  3.872983  4.000000

Method 4: Applying a Reducing function to each row/column 

A Reducing function will take a row or column as a series and returns either a series of the same size as that of the input row/column or it will return a single variable depending upon the function we use.

Example 1: For Column 

Python3

# Applying a numpy function to get the sum
# of all values in each column
new_df = df.apply(np.sum)

# Output
print(new_df)

Output :

a    28
b    32
c    36
d    40
dtype: int64

Example 2: For Row

Python3

# Applying a numpy function to get t
# he sum of all values in each row
new_df = df.apply(np.sum, axis = 1)

# Output
print(new_df)

Output :

0    10
1    26
2    42
3    58
dtype: int64