Applying Lambda functions to Pandas Dataframe

Adding new column to existing DataFrame in Pandas

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.

Python3

# 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 a Single Column

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’.

Python3

# 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.assign() on Multiple Columns

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.

Python3

# 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 a Single Row

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’.

Python3

# 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:

Dataframe.apply() on Multiple Rows

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().

Python3

# 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

Tags:

#Python pandas-dataFrame #Python Pandas-exercise #Python-pandas #Python #python

Python | Pandas Index.value_counts()

Adding new column to existing DataFrame in Pandas

Applying Lambda functions to Pandas Dataframe

Applying Lambda Functions to Pandas

Dataframe.assign() on a Single Column

Python3

Dataframe.assign() on Multiple Columns

Python3

Dataframe.apply() on a Single Row

Python3

Dataframe.apply() on Multiple Rows

Python3

Lambda Function on Multiple Rows and Columns Simultaneously

Python3

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