Applying Lambda functions to Pandas Dataframe

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.

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'.

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.

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'.

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().

Output