JIYIK CN >

This tutorial teaches how to restore from a MultiIndex to a single index DataFrame in Pandas using Python.

MultiIndexDataFrame, also known as multi-level and hierarchical DataFrame, allows users to have multiple columns that can identify a row while each column index is related to each other through different parent-child relationships in the table.

We can use two methods to convert a multi-level index to a single-level index. We will learn the flexibility and higher efficiency of operating DataFrame in Pandas.

Rename columns to standard columns to convert MultiIndex to single index in Pandas

We have to first create a DataFrame consisting of MultiIndex columns in this method. After that, we can change the name of the columns, i.e. standard columns, so we can get rid of MultiIndex easily and without any errors.

Below is the code for the entire process following this approach from the beginning.

import pandas as pd
import numpy as np

# build an example DataFrame
midx = pd.MultiIndex(
    levels=[["zero", "one"], ["x", "y"]],
    codes=[
        [
            1,
            1,
            0,
        ],
        [
            1,
            0,
            1,
        ],
    ],
)
df = pd.DataFrame(np.random.randn(2, 3), columns=midx)
print(df)

This code will give us the following output.

        one                zero
          y         x         y
0  0.785806 -0.679039  0.513451
1 -0.337862 -0.350690 -1.423253

So, we can observe that a DataFrame has been created with a multi-level index column. To restore this column to a single-level index, we need to rename them in the method below.

df.columns = ["A", "B", "C"]
print(df)

The output of the above code is as follows.

          A         B         C
0  0.785806 -0.679039  0.513451
1 -0.337862 -0.350690 -1.423253

Hierarchical indexes have been removed and only the new names appear, replacing the old names of the columns.

Resetting the levels of columns in Pandas to convert MultiIndex to single index

In this approach, we simply reset the levels of the MultiIndex columns to convert them into single-level columns.

reset_index()Method allows the user to reset the index of a DataFrame and consider the default index again. One or more levels can be removed simultaneously using this method.

We will do this by adding a line to the code snippet we used previously. Let's consider a different example to learn the technique with more clarity and flexibility.

index = pd.MultiIndex.from_tuples(
    [("bird", "falcon"), ("bird", "parrot"), ("mammal", "lion"), ("mammal", "monkey")],
    names=["class", "name"],
)
columns = pd.MultiIndex.from_tuples([("speed", "max"), ("species", "type")])
df = pd.DataFrame(
    [(389.0, "fly"), (24.0, "fly"), (80.5, "run"), (np.nan, "jump")],
    index=index,
    columns=columns,
)
print(df)

The above code will give us the following output.

				speed	species
				max		type
class	name
bird	falcon	389.0	fly
		parrot	24.0	fly
mammal	lion	80.5	run
		monkey	NaN	    jump

We will use reset_index()the method to get the following output.

print(df.reset_index(level="class"))

		 class  speed species
                  max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

reset_index()The method will reset the index of the columns by which the hierarchy levels are diluted and converted into a single-level column DataFrame. The output of the above code will be shown below.

Therefore, in this tutorial, we learned how to convert a MultiIndex column back to a single-level column without errors and easily without confusion.