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 :
