How to use reset_index() function In Python Pandas
Pandas provide a function called reset_index() to flatten the hierarchical index created due to the groupby aggregation function in Python.
Syntax: pandas.DataFrame.reset_index(level, drop, inplace)
Parameters:
- level – removes only the specified levels from the index
- drop – resets the index to the default integer index
- inplace – modifies the dataframe object permanently without creating a copy.
Example:
In this example, We used the pandas groupby function to group car sales data by quarters and reset_index() pandas function to flatten the hierarchical indexed columns of the grouped dataframe.
Python3
# import the python pandas packageimport pandas as pd# create a sample dataframedata = pd.DataFrame({"cars": ["bmw", "bmw", "benz", "benz"], "sale_q1 in Cr": [20, 22, 24, 26], 'sale_q2 in Cr': [11, 13, 15, 17]}, columns=["cars", "sale_q1 in Cr", 'sale_q2 in Cr'])# group by cars based on the sum# of sales on quarter 1 and 2grouped_data = data.groupby(by="cars").agg("sum")print(grouped_data)# use reset_index to flattened# the hierarchical dataframe.flat_data = grouped_data.reset_index()print(flat_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.
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