Pivot Tables in Pandas

Last Updated : 11 Dec, 2020

In this article, we will see the Pivot Tables in Pandas. Let’s discuss some concepts:

Pandas : Pandas is an open-source library that is built on top of the NumPy library. It is a Python package that offers various data structures and operations for manipulating numerical data and time series. It is mainly popular for importing and analyzing data much easier. Pandas is fast and it has high-performance & productivity for users.

Pivot Tables: A pivot table is a table of statistics that summarizes the data of a more extensive table (such as from a database, spreadsheet, or business intelligence program). This summary might include sums, averages, or other statistics, which the pivot table groups together in a meaningful way.

Steps Needed

Import Library (Pandas)
Import / Load / Create data.
Use Pandas.pivot_table() method with different variants.

Here, we will discuss some variants of pivot table over the dataframe shown below :

Python3

# import packages 
import pandas as pd 
  
# create data 
df = pd.DataFrame({'ID': {0: 23, 1: 43, 2: 12,  
                          3: 13, 4: 67, 5: 89, 
                          6: 90, 7: 56, 8: 34}, 
                     
                 'Name': {0: 'Ram', 1: 'Deep', 2: 'Yash', 
                          3: 'Aman', 4: 'Arjun', 5: 'Aditya', 
                          6: 'Akash', 7: 'Chalsea', 
                          8: 'Divya'}, 
                     
                 'Marks': {0: 89, 1: 97, 2: 45, 
                           3: 78, 4: 56, 5: 76, 
                           6: 81, 7: 87, 8: 100}, 
                     
                 'Grade': {0: 'B', 1: 'A', 2: 'F', 
                           3: 'C', 4: 'E', 5: 'C', 
                           6: 'B', 7: 'B', 8: 'A'}}) 
  
# view data 
display(df) 

Output:

Example 1: Simple use of pivot_table() method.

Python3

# import packages 
import pandas as pd 
  
# create data 
df = pd.DataFrame({'ID': {0: 23, 1: 43, 2: 12,  
                          3: 13, 4: 67, 5: 89, 
                          6: 90, 7: 56, 8: 34}, 
                     
                 'Name': {0: 'Ram', 1: 'Deep', 2: 'Yash', 
                          3: 'Aman', 4: 'Arjun', 5: 'Aditya', 
                          6: 'Akash', 7: 'Chalsea', 
                          8: 'Divya'}, 
                     
                 'Marks': {0: 89, 1: 97, 2: 45, 
                           3: 78, 4: 56, 5: 76, 
                           6: 81, 7: 87, 8: 100}, 
                     
                 'Grade': {0: 'B', 1: 'A', 2: 'F', 
                           3: 'C', 4: 'E', 5: 'C', 
                           6: 'B', 7: 'B', 8: 'A'}}) 
  
# simple use pivot_table() method 
print(pd.pivot_table(df, index = ["ID"]))

Output :

Example 2: Pivot table with multiple columns indexes.

Python3

# import packages 
import pandas as pd 
  
# create data 
df = pd.DataFrame({'ID': {0: 23, 1: 43, 2: 12,  
                          3: 13, 4: 67, 5: 89, 
                          6: 90, 7: 56, 8: 34}, 
                     
                 'Name': {0: 'Ram', 1: 'Deep', 2: 'Yash', 
                          3: 'Aman', 4: 'Arjun', 5: 'Aditya', 
                          6: 'Akash', 7: 'Chalsea', 
                          8: 'Divya'}, 
                     
                 'Marks': {0: 89, 1: 97, 2: 45, 
                           3: 78, 4: 56, 5: 76, 
                           6: 81, 7: 87, 8: 100}, 
                     
                 'Grade': {0: 'B', 1: 'A', 2: 'F', 
                           3: 'C', 4: 'E', 5: 'C', 
                           6: 'B', 7: 'B', 8: 'A'}}) 
# multiple columns with  
# pivot_table() method 
display(pd.pivot_table(df,  
                     index = ["ID", "Name"])) 

Output :

Example 3: Pivot table with an aggregate function.

Python3

# import packages 
import pandas as pd 
import numpy as np 
  
# create data 
df = pd.DataFrame({'ID': {0: 23, 1: 43, 2: 12, 
                          3: 13, 4: 67, 5: 89,  
                          6: 90, 7: 56, 8: 34}, 
                     
                   'Name': {0: 'Ram', 1: 'Deep', 
                            2: 'Yash', 3: 'Aman', 
                            4: 'Arjun', 5: 'Aditya', 
                            6: 'Akash',7: 'Chalsea', 
                            8: 'Divya'}, 
  
                   'Marks': {0: 89, 1: 97, 2: 45,  
                             3: 78, 4: 56, 5: 76, 
                             6: 81, 7: 87, 8: 100}, 
  
                   'Grade': {0: 'B', 1: 'A', 2: 'F', 3: 'C', 
                             4: 'E', 5: 'C', 6: 'B', 7: 'B', 
                             8: 'A'}}) 
  
# Pivot Table with mean  
# aggregate function on marks 
display(pd.pivot_table(df, 
                     index = ["Grade"], 
                     values = ["Marks"], 
                     aggfunc = np.mean)) 

Output :

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