Apply a function to single or selected columns or rows in Pandas Dataframe

In this article, we will learn different ways to apply a function to single or selected columns or rows in Dataframe. We will use Dataframe/series.apply() method to apply a function.

Apply a function to single row in Pandas Dataframe

Here, we will use different methods to apply a function to single rows by using Pandas dataframe. First lets create a data frame.

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)]

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

print(df)

Output:

   x  y  z
a  1  2  3
b  4  5  6
c  7  8  9

Using Dataframe.apply() and lambda function

Pandas.apply() allow the users to pass a function and apply it on every single value row of the Pandas dataframe. Here, we squared the ‘b_th‘ row.

Python3

# Apply function numpy.square() to lambda
# whose row index is 'b'
df = df.apply(lambda x: np.square(x) if x.name == 'b' else x,
                axis = 1)

# Output
print(df)

Output :

    x   y   z
a   1   2   3
b  16  25  36
c   7   8   9

Using loc[ ] Operator

Pandas DataFrame.loc attribute accesses a group of rows in the given DataFrame to squared the ‘b_th‘ row.

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)]

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

# Apply a function to one row 'b'
# and assign it back to the same row
df.loc['b'] = df.loc['b'].apply(np.square)

print(df)

Output :

      x   y   z
a   1   2   3
b  16  25  36
c   7   8   9

Using numpy.square() method

This mathematical function helps the user to calculate the square value of each element in the array. Here, we are passing b row to make a square of it.

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)]

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

# Apply a function to one row 'b' and
# assign it back to the same row
df.loc['b'] = np.square(df.loc['b'])

# Output
print(df)

Output :

    x   y   z
a   1   2   3
b  16  25  36
c   7   8   9

Apply a function to single column in Pandas Dataframe

Here, we will use different methods to apply a function to single columns by using Pandas Dataframe.

Using Dataframe.apply() and lambda function

Pandas.apply() allow the users to pass a function and apply it on every single value column of the Pandas Dataframe. Here, we squared the ‘z_th‘ column.

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)]

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

# Apply function numpy.square() to lambda
# to find the squares of the values of
# column whose column name is 'z'
new_df = df.apply(lambda x: np.square(x) if x.name == 'z' else x)

# Output
print(new_df)

Output :

   x  y   z
a  1  2   9
b  4  5  36
c  7  8  81

Using loc[ ] Operator

Pandas DataFrame.loc attribute access a group of columns in the given DataFrame to square the ‘z_th‘ column.

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)]

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

# Apply a function to one column 'z'
# and assign it back to the same column
df['z'] = df['z'].apply(np.square)

# Output
print(df)

Output :

   x  y   z
a  1  2   9
b  4  5  36
c  7  8  81

Using numpy.square() method 

This mathematical function helps the user to calculate the square value of each element in the array. Here, we are passing z column to make a square of it.

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)]

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

# Apply a function to one column 'z' and
# assign it back to the same column
df['z'] = np.square(df['z'])

# Output
print(df)

Output:

   x  y   z
a  1  2   9
b  4  5  36
c  7  8  81

Apply function to column and row in the Dataframe

Here. we will see how to apply a function to more than one row and column using df.apply() method.

For Column 

Here, we applied the function to the x, and y columns.

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)]

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

# Apply function numpy.square()
# for square the values of
# two columns 'x' and 'y'
new_df = df.apply(lambda x: np.square(x) if x.name in ['x', 'y'] else x)

# Output
print(new_df)

Output:

    x   y  z
a   1   4  3
b  16  25  6
c  49  64  9

For Row

Here, we applied the function to the b, and c rows.

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)]

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

# Apply function numpy.square() to
# square the values of two rows
# 'b' and 'c'
new_df = df.apply(lambda x: np.square(x) if x.name in ['b', 'c'] else x,
                  axis=1)

# Output
print(new_df)

Output:

    x   y   z
a   1   2   3
b  16  25  36
c  49  64  81