Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas MultiIndex.to_frame() function create a DataFrame with the levels of the MultiIndex as columns.
Syntax: MultiIndex.to_frame(index=True)
Parameters :
index : Set the index of the returned DataFrame as the original MultiIndex.Returns : DataFrame : a DataFrame containing the original MultiIndex data.
Example #1: Use MultiIndex.to_frame() function to construct a dataframe using the MultiIndex levels as the column and index.
# importing pandas as pd import pandas as pd # Create the MultiIndex midx = pd.MultiIndex.from_tuples([(10, 'Ten'), (10, 'Twenty'), (20, 'Ten'), (20, 'Twenty')], names =['Num', 'Char']) # Print the MultiIndex print(midx) |
Output :
Now let’s construct the dataframe from the MultiIndex.
# Construct the DataFrame midx.to_frame(index = True) |
Output :
As we can see in the output, the function has constructed the Dataframe using the MultiIndex. Notice the index of the dataframe is constructed using the levels of the MultiIndex.
Example #2: Use MultiIndex.to_frame() function to construct a DataFrame using the MultiIndex. Do not use the MultiIndex levels to construct the index of the Dataframe.
# importing pandas as pd import pandas as pd # Create the MultiIndex midx = pd.MultiIndex.from_tuples([(10, 'Ten'), (10, 'Twenty'), (20, 'Ten'), (20, 'Twenty')], names =['Num', 'Char']) # Print the MultiIndex print(midx) |
Output :
Now let’s create a dataframe using the midx MultiIndex.
# Create Dataframe with new index values. midx.to_frame(index = False) |
Output :
As we can see in the output, the function has returned a DataFrame having different index value.
