Python Pandas - Create a DataFrame with the levels of the MultiIndex as columns but avoid setting the index of the returned DataFrame

To create a DataFrame with the levels of the MultiIndex as columns, use the multiIndex.to_frame(). The index parameter is set False to avoid setting the index of the returned DataFrame

At first, import the required libraries −

import pandas as pd

MultiIndex is a multi-level, or hierarchical, index object for pandas objects. Create arrays −

arrays = [[1, 2, 3, 4], ['John', 'Tim', 'Jacob', 'Chris']]

The "names" parameter sets the names for each of the index levels. The from_arrays() is used to create a MultiIndex −

multiIndex = pd.MultiIndex.from_arrays(arrays, names=('ranks', 'student'))

Create a DataFrame with the levels of the MultiIndex as columns using to_frame(). Use the "index" parameter and set it to "False" to avoid setting the index of the returned DataFrame −

dataFrame = multiIndex.to_frame(index=False)

Example

Following is the code −

import pandas as pd

# MultiIndex is a multi-level, or hierarchical, index object for pandas objects
# Create arrays
arrays = [[1, 2, 3, 4], ['John', 'Tim', 'Jacob', 'Chris']]

# The "names" parameter sets the names for each of the index levels
# The from_arrays() is used to create a MultiIndex
multiIndex = pd.MultiIndex.from_arrays(arrays, names=('ranks', 'student'))

# display the MultiIndex
print("The Multi-index...\n",multiIndex)

# get the levels in MultiIndex
print("\nThe levels in Multi-index...\n",multiIndex.levels)

# Create a DataFrame with the levels of the MultiIndex as columns using to_frame()
# Use the "index" parameter and set it to "False" to avoid setting the index of the returned #DataFrame
dataFrame = multiIndex.to_frame(index=False)

# Return the DataFrame
print("\nThe DataFrame...\n",dataFrame)

Output

This will produce the following output −

The Multi-index...
MultiIndex([(1, 'John'),
            (2, 'Tim'),
            (3, 'Jacob'),
            (4, 'Chris')],
            names=['ranks', 'student'])

The levels in Multi-index...
   [[1, 2, 3, 4], ['Chris', 'Jacob', 'John', 'Tim']]

The DataFrame...
   ranks   student
0      1      John
1      2       Tim
2      3     Jacob
3      4     Chris