Skip to content
Related Articles

Related Articles

How to include percentage in pivot table in Pandas?
  • Last Updated : 24 Jan, 2021
GeeksforGeeks - Summer Carnival Banner

Seaborn is an amazing visualization library for statistical graphics plotting in Python. It provides beautiful default styles and color palettes to make statistical plots more attractive. It is built on the top of matplotlib library and also closely integrated into the data structures from pandas.

Pivot table is used to summarize data which includes various statistical concepts. To calculate the percentage of a category in a pivot table we calculate the ratio of category count to the total count. Below are some examples which depict how to include percentage in a pivot table:

Example 1:

In the figure below, the pivot table has been created for the given dataset where the gender percentage has been calculated.



Python3




# importing pandas library
import pandas as pd
  
# creating dataframe
df = pd.DataFrame({'Name': ['John', 'Sammy', 'Stephan', 'Joe', 'Emily', 'Tom'],
                   'Gender': ['Male', 'Female', 'Male',
                              'Female', 'Female', 'Male'],
                   'Age': [45, 6, 4, 36, 12, 43]})
print("Dataset")
print(df)
print("-"*40)
  
# categorizing in age groups
def age_bucket(age):
    if age <= 18:
        return "<18"
    else:
        return ">18"
  
df['Age Group'] = df['Age'].apply(age_bucket)
  
# calculating gender percentage
gender = pd.DataFrame(df.Gender.value_counts(normalize=True)*100).reset_index()
gender.columns = ['Gender', '%Gender']
df = pd.merge(left=df, right=gender, how='inner', on=['Gender'])
  
# creating pivot table
table = pd.pivot_table(df, index=['Gender', '%Gender', 'Age Group'], 
                       values=['Name'], aggfunc={'Name': 'count',})
  
# display table
print("Table")
print(table)

Output:

Example 2:

Here is another example which depicts how to calculate the percentage of a variable to its sum total in a particular column:

Python3




# importing required libraries
import pandas as pd
import matplotlib.pyplot as plt
  
# creating dataframe
df = pd.DataFrame({
    'Name': ['John', 'Emily', 'Smith', 'Joe'],
    'Gender': ['Male', 'Female', 'Male', 'Female'],
    'Salary(in $)': [20, 40, 35, 28]})
  
print("Dataset")
print(df)
print("-"*40)
  
# creating pivot table
table = pd.pivot_table(df, index=['Gender', 'Name'])
  
# calculating percentage
table['% Income'] = (table['Salary(in $)']/table['Salary(in $)'].sum())*100
  
# display table
print("Pivot Table")
print(table)

Output:


Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.

My Personal Notes arrow_drop_up
Recommended Articles
Page :