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:
