How to use as_index() function In Python Pandas

Flattening hierarchical index in pandas dataframe using groupby

Pandas provide a function called as_index() which is specified by a boolean value. The as_index() functions groups the dataframe by the specified aggregate function and if as_index() value is False, the resulting dataframe is flattened.

Syntax: pandas.DataFrame.groupby(by, level, axis, as_index)

Parameters:

by – specifies the columns on which the groupby operation has to be performed

level – specifies the index at which the columns has to be grouped

axis – specifies whether to split along rows (0) or columns (1)

as_index – Returns an object with group labels as the index, for aggregated output.

Example:

In this example, We are using the pandas groupby function to group car sales data by quarters and mention the as_index parameter as False and specify the as_index parameter as false ensures that the hierarchical index of the grouped dataframe is flattened.

Python3

# group by cars based on the
# sum of sales on quarter 1 and 2 
# and mention as_index is False
grouped_data = data.groupby(by="cars", as_index=False).agg("sum")
 
# display
print(grouped_data)

Output:

How to flatten a hierarchical index in Pandas

How to flatten a hierarchical index in Pandas DataFrame columns?

In this article, we are going to see the flatten a hierarchical index in Pandas DataFrame columns. Hierarchical Index usually occurs as a result of groupby() aggregation functions. Flatten hierarchical index in Pandas, the aggregated function used will appear in the hierarchical index of the resulting dataframe.