How to Create a Pivot table with multiple indexes from an excel sheet using Pandas in Python?

 The term Pivot Table can be defined as the Pandas function used to create a spreadsheet-style pivot table as a DataFrame. It can be created using the pivot_table() method.

Syntax: pandas.pivot_table(data, index=None)

Parameters:

data : DataFrame
index: column, Grouper, array, or list of the previous

index: It is the feature that allows you to group your data. 

Returns: DataFrame

Note: We can filter the table further by adding the optional parameters. 

Example 1: Link to the CSV File: CSV FILE
We can have a look at the data by running the following program: 

Python3

# importing pandas as pd
import pandas as pd

# Create the dataframe
df=pd.read_csv('GeeksForGeeks.csv')

# Print the dataframe
df

Output:

We know that the index is the feature that allows us to group our data and specifying multiple columns as the indices in pivot function increases the level of details and grouping the data. 
Keeping a single index in the table: 

Python3

# importing pandas as pd
import pandas as pd

# Create the dataframe
df=pd.read_csv('GeeksForGeeks.csv')

# Print the resultant table
print(pd.pivot_table(df,index=["Country"]))

Output:

As we can see that the grouping is done country wise and the numerical data is printed as the average of all the values with regard to the specified index.
Now, Keeping multiple indices in the table: 

Python3

# importing pandas as pd
import pandas as pd

# Create the dataframe
df=pd.read_csv('GeeksForGeeks.csv')

# Print the resultant table
print(pd.pivot_table(df,index=["Country","Salary"]))

Output:

Example 2: Link to the CSV File: CSV FILE

Python3

# importing pandas as pd
import pandas as pd

# Create the dataframe
df=pd.read_csv('GeeksForGeeks_1.csv')

# Print the dataframe
df

Output:

Keeping the number of centuries scored by players and their names as indices, we get: 

Python3

# importing pandas as pd
import pandas as pd

# Create the dataframe
df=pd.read_csv('dataset/new_players.csv')

# Print the resultant table
print(pd.pivot_table(df,index=["century","name"]))

Output: