How to flatten a hierarchical index in columns?

While creating structured grouped data in pandas, we use the hierarchical indexing method. These hierarchical indexes are useful for complex data queries. Here, we are going to learn how to flatten these hierarchical indices in columns. Pandas allows us to achieve this task by using the reset_index() method.

pandas.DataFrame.reset_index() method

This method is used to rearrange or reset the hierarchical indices that occurred due to groupby aggregated functions.

Syntax:

DataFrame.reset_index(
    level=None, 
    drop=False, 
    inplace=False, 
    col_level=0, 
    col_fill=''
    )

Parameter(s):

It takes a parameter called level, if defined then removes the particular levels from the indices.
It takes another parameter called drop, which is defined to set the flatten index to the default integer index.
Also, it takes a parameter called inplace which modifies the original DataFrame without creating a copy.

To work with pandas, we need to import pandas package first, below is the syntax:

import pandas as pd

# Importing pandas package import pandas as pd # Creating a Dictionary dict = { 'Name':['Amit','Bhairav','Chirag','Divyansh','Esha'], 'Age':[20,20,19,21,18], 'ID_No':[30,40,50,60,70] } # Creating a DataFrame df = pd.DataFrame(dict) # Display DataFrame print("DataFrame:\n",df,"\n") # Using groupby on Name based on sum of age result = df.groupby(by='Name').agg(sum) # Display Result print("Grouped data\n",result,"\n") # Reset index reset = result.reset_index() # Display reset print("Reset indices\n",reset)

How to flatten a hierarchical index in columns?

pandas.DataFrame.reset_index() method

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