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

In this article, we will be applying given function to selected rows and column.

For example, we have a dataframe object,

matrix = [(22, 34, 23),
(33, 31, 11),
(44, 16, 21),
(55, 32, 22),
(66, 33, 27),
(77, 35, 11)
]
# Create a DataFrame object
dfObj = pd.DataFrame(matrix, columns=list('xyz'), index=list('abcdef'))
Contents of this dataframe object dgObj are,
Original Dataframe
x    y   z
a 22 34 23
b 33 31 11
c 44 16 21
d 55 32 22
e 66 33 27
f 77 35 11

Now what if we want to apply different functions on all the elements of a single or multiple column or rows. Like,

• Multiply all the values in column â€˜xâ€™ by 2
• Multiply all the values in row â€˜câ€™ by 10
• Add 10 in all the values in column â€˜yâ€™ & â€˜zâ€™

We will use different techniques to see how we can do this.

## Apply a function to a single column in Dataframe

What if we want to square all the values in any of the column for example x,y or z.

We can do such things by applying different methods. We will discuss few methods below:

#### Method 1: Using Dataframe.apply()

We will apply lambda function to all the columns using the above method. And then we will check if column name is whatever we want say x,y or z inside the lambda function. After this, we will square all the values. In this we will be taking z column.

Code:

modDfObj = dfObj.apply(lambda x: np.square(x) if x.name == 'z' else x)
print("Modified Dataframe : Squared the values in column 'z'", modDfObj, sep='\n')
Output:
Modified Dataframe : Squared the values in column 'z'
x y z
a 22 34 529
b 33 31 121
c 44 16 441
d 55 32 484
e 66 33 729
f 77 35 121

#### Method 2: Using [] operator

Using [] operator we will select the column from dataframe and apply numpy.square() method. Later, we will assign it back to the column.

dfObj['z'] = dfObj['z'].apply(np.square)

It will square all the values in column ‘z’.

#### Method 3: Using numpy.square()

dfObj['z'] = np.square(dfObj['z'])

This function will also square all the values in ‘z’.

## Apply a function to a single row in Dataframe

Now, we saw what we have done with the columns. Same thing goes with rows. We will square all the values in row ‘b’. We can use different methods for that.

#### Method 1:UsingÂ Dataframe.apply()

We will apply lambda function to all the rows and will use the above function. We will check the label inside the lambda function and will square the row.

Code:

modDfObj = dfObj.apply(lambda x: np.square(x) if x.name == 'b' else x, axis=1)
print("Modified Dataframe : Squared the values in row 'b'", modDfObj, sep='\n')

Output:

Modified Dataframe : Squared the values in row 'b'
x y z
a 22 34 23
b 1089 961 121
c 44 16 21
d 55 32 22
e 66 33 27
f 77 35 11

#### Method 2 : Using [] Operator

We will do what we have done above. We will select the row from dataframe.loc[] operator and apply numpy.square() method on it. Later, we will assign it back to the row.

dfObj.loc['b'] = dfObj.loc['b'].apply(np.square)

It will square all the values in the row ‘b’.

#### Method 3 : Using numpy.square()

dfObj.loc['b'] = np.square(dfObj.loc['b'])

This will also square the values in row ‘b’.

## Apply a function to a certain columns in Dataframe

We can apply the function in whichever column we want. For instance, squaring the values in ‘x’ and ‘y’.

Code:

modDfObj = dfObj.apply(lambda x: np.square(x) if x.name in ['x', 'y'] else x)
print("Modified Dataframe : Squared the values in column x & y :", modDfObj, sep='\n')
All we have to do is modify the if condition in lambda function and square the values with the name of the variables.

## Apply a function to a certain rows in Dataframe

We can apply the function to specified row. For instance, row ‘b’ and ‘c’.

Code:

modDfObj = dfObj.apply(lambda x: np.square(x) if x.name in ['b', 'c'] else x, axis=1)
print("Modified Dataframe : Squared the values in row b & c :", modDfObj, sep='\n')

The complete code is:

import pandas as pd
import numpy as np
def main():
# List of Tuples
matrix = [(22, 34, 23),
(33, 31, 11),
(44, 16, 21),
(55, 32, 22),
(66, 33, 27),
(77, 35, 11)]
# Create a DataFrame object
dfObj = pd.DataFrame(matrix, columns=list('xyz'), index=list('abcdef'))
print("Original Dataframe", dfObj, sep='\n')
print('********* Apply a function to a single row or column in DataFrame ********')
print('*** Apply a function to a single column *** ')
# Method 1:
# Apply function numpy.square() to square the value one column only i.e. with column name 'z'
modDfObj = dfObj.apply(lambda x: np.square(x) if x.name == 'z' else x)
print("Modified Dataframe : Squared the values in column 'z'", modDfObj, sep='\n')
# Method 2
# Apply a function to one column and assign it back to the column in dataframe
dfObj['z'] = dfObj['z'].apply(np.square)
# Method 3:
# Apply a function to one column and assign it back to the column in dataframe
dfObj['z'] = np.square(dfObj['z']
print('*** Apply a function to a single row *** ')
dfObj = pd.DataFrame(matrix, columns=list('xyz'), index=list('abcdef'))
# Method 1:
# Apply function numpy.square() to square the values of one row only i.e. row with index name 'b'
modDfObj = dfObj.apply(lambda x: np.square(x) if x.name == 'b' else x, axis=1)
print("Modified Dataframe : Squared the values in row 'b'", modDfObj, sep='\n')
# Method 2:
# Apply a function to one row and assign it back to the row in dataframe
dfObj.loc['b'] = dfObj.loc['b'].apply(np.square)
# Method 3:
# Apply a function to one row and assign it back to the column in dataframe
dfObj.loc['b'] = np.square(dfObj.loc['b'])
print('********* Apply a function to certains row or column in DataFrame ********')
dfObj = pd.DataFrame(matrix, columns=list('xyz'), index=list('abcdef'))
print('Apply a function to certain columns only')
# Apply function numpy.square() to square the value 2 column only i.e. with column names 'x' and 'y' only
modDfObj = dfObj.apply(lambda x: np.square(x) if x.name in ['x', 'y'] else x)
print("Modified Dataframe : Squared the values in column x & y :", modDfObj, sep='\n')
print('Apply a function to certain rows only') #
Apply function numpy.square() to square the values of 2 rows only i.e. with row index name 'b' and 'c' only
modDfObj = dfObj.apply(lambda x: np.square(x) if x.name in ['b', 'c'] else x, axis=1)
print("Modified Dataframe : Squared the values in row b & c :", modDfObj, sep='\n')
if __name__ == '__main__':
main()
Output:
Original Dataframe
x y z
a 22 34 23
b 33 31 11
c 44 16 21
d 55 32 22
e 66 33 27
f 77 35 11
********* Apply a function to a single row or column in DataFrame ********
*** Apply a function to a single column ***
Modified Dataframe : Squared the values in column 'z'
x y z
a 22 34 529
b 33 31 121
c 44 16 441
d 55 32 484
e 66 33 729
f 77 35 121
*** Apply a function to a single row ***
Modified Dataframe : Squared the values in row 'b'
x y z
a 22 34 23
b 1089 961 121
c 44 16 21
d 55 32 22
e 66 33 27
f 77 35 11
********* Apply a function to certains row or column in DataFrame ********
Apply a function to certain columns only
Modified Dataframe : Squared the values in column x & y :
x y z
a 484 1156 23
b 1089 961 11
c 1936 256 21
d 3025 1024 22
e 4356 1089 27
f 5929 1225 11
Apply a function to certain rows only
Modified Dataframe : Squared the values in row b & c :
x y z
a 22 34 23
b 1089 961 121
c 1936 256 441
d 55 32 22
e 66 33 27
f 77 35 11