Applying Lambda functions to Pandas Dataframe

Last Updated : 09 Aug, 2024

In Python Pandas, we have the freedom to add different functions whenever needed like lambda function, sort function, etc. We can apply a lambda function to both the columns and rows of the Pandas data frame.

Syntax: lambda arguments: expression
An anonymous function which we can pass in instantly without defining a name or any thing like a full traditional function.

Applying Lambda Functions to Pandas

Below are some methods and ways by which we can apply lambda functions to Pandas:

Dataframe.assign() on a Single Column
Dataframe.assign() on Multiple Columns
Dataframe.apply() on a Single Row
Dataframe.apply() on Multiple Rows
Lambda Function on Multiple Rows and Columns Simultaneously

Dataframe.assign() on a Single Column

In this example, we will apply the lambda function Dataframe.assign() to a single column. The function is applied to the 'Total_Marks' column, and a new column 'Percentage' is formed with its help.

Python

# importing pandas library import pandas as pd   # creating and initializing a list values= [['Rohan',455],['Elvish',250],['Deepak',495],          ['Soni',400],['Radhika',350],['Vansh',450]]   # creating a pandas dataframe df = pd.DataFrame(values,columns=['Name','Total_Marks'])  # Applying lambda function to find  # percentage of 'Total_Marks' column  # using df.assign() df = df.assign(Percentage = lambda x: (x['Total_Marks'] /500 * 100))  # displaying the data frame df

Output:

Dataframe.assign() on Multiple Columns

In this example, we will apply the lambda function Dataframe.assign() to multiple columns. The lambda function is applied to 3 columns i.e., 'Field_1', 'Field_2', and 'Field_3'.

Python

# importing pandas library import pandas as pd  # creating and initializing a nested list values_list = [[15, 2.5, 100], [20, 4.5, 50], [25, 5.2, 80],                [45, 5.8, 48], [40, 6.3, 70], [41, 6.4, 90],                [51, 2.3, 111]]  # creating a pandas dataframe df = pd.DataFrame(values_list, columns=['Field_1', 'Field_2', 'Field_3'])  # Applying lambda function to find # the product of 3 columns using # df.assign() df = df.assign(Product=lambda x: (x['Field_1'] * x['Field_2'] * x['Field_3']))  # printing dataframe df

Output:

Dataframe.apply() on a Single Row

In this example, we will apply the lambda function Dataframe.apply() to single row. The lambda function is applied to a row starting with 'd' and hence square all values corresponding to it.

Python

# importing pandas and numpy libraries import pandas as pd import numpy as np  # creating and initializing a nested list values_list = [[15, 2.5, 100], [20, 4.5, 50], [25, 5.2, 80],                [45, 5.8, 48], [40, 6.3, 70], [41, 6.4, 90],                 [51, 2.3, 111]]  # creating a pandas dataframe df = pd.DataFrame(values_list, columns=['Field_1', 'Field_2', 'Field_3'],                   index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])   # Apply function numpy.square() to square # the values of one row only i.e. row  # with index name 'd' df = df.apply(lambda x: np.square(x) if x.name == 'd' else x, axis=1)   # printing dataframe df

Output:

Dataframe.apply() on Multiple Rows

In this example, we will apply the lambda function to multiple rows using Dataframe.apply(). The lambda function is applied to 3 rows starting with 'a', 'e', and 'g'.

Python

# importing pandas and numpylibraries import pandas as pd import numpy as np  # creating and initializing a nested list values_list = [[15, 2.5, 100], [20, 4.5, 50], [25, 5.2, 80],                [45, 5.8, 48], [40, 6.3, 70], [41, 6.4, 90],                [51, 2.3, 111]]  # creating a pandas dataframe df = pd.DataFrame(values_list, columns=['Field_1', 'Field_2', 'Field_3'],                   index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])   # Apply function numpy.square() to square  # the values of 3 rows only i.e. with row # index name 'a', 'e' and 'g' only df = df.apply(lambda x: np.square(x) if x.name in [               'a', 'e', 'g'] else x, axis=1)  # printing dataframe df

Output:

Lambda Function on Multiple Rows and Columns Simultaneously

In this example, we will apply the lambda function simultaneously to multiple columns and rows using dataframe.assign() and dataframe.apply().

Python

# importing pandas and numpylibraries import pandas as pd import numpy as np  # creating and initializing a nested list values_list = [[1.5, 2.5, 10.0], [2.0, 4.5, 5.0], [2.5, 5.2, 8.0],                [4.5, 5.8, 4.8], [4.0, 6.3, 70], [4.1, 6.4, 9.0],                [5.1, 2.3, 11.1]]  # creating a pandas dataframe df = pd.DataFrame(values_list, columns=['Field_1', 'Field_2', 'Field_3'],                   index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])   # Apply function numpy.square() to square  # the values of 2 rows only i.e. with row # index name 'b' and 'f' only df = df.apply(lambda x: np.square(x) if x.name in ['b', 'f'] else x, axis=1)  # Applying lambda function to find product of 3 columns # i.e 'Field_1', 'Field_2' and 'Field_3' df = df.assign(Product=lambda x: (x['Field_1'] * x['Field_2'] * x['Field_3']))   # printing dataframe df

Output

Lambda Function on Multiple Rows and Columns Simultaneously

Adding New Column to Existing DataFrame in Pandas

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python

Applying Lambda functions to Pandas Dataframe

Applying Lambda Functions to Pandas

Dataframe.assign() on a Single Column

Dataframe.assign() on Multiple Columns

Dataframe.apply() on a Single Row

Dataframe.apply() on Multiple Rows

Lambda Function on Multiple Rows and Columns Simultaneously

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