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 Index.to_frame() function create a dataFrame from the given index with a column containing the Index. By default, the original Index is reused in the new dataframe. To reinforce a new index for the newly created dataframe, we set the index parameter of the function to be false.
Syntax: Index.to_frame(index=True)
Parameters :
index : Set the index of the returned DataFrame as the original Index.Returns : DataFrame containing the original Index data.
Example #1: Use Index.to_frame() function to convert the index into a dataframe.
# importing pandas as pd import pandas as pd # Creating the index idx = pd.Index(['Alice', 'Bob', 'Rachel', 'Tyler', 'Louis'], name ='Winners') # Print the Index idx |
Output :
Let’s convert the index into a dataframe.
# convert the index into a dataframe idx.to_frame() |
Output :
The function has converted the index into a dataframe. By default the function has created the index of the dataframe using the values of the original Index.
Example #2: Use Index.to_frame() function to convert the index into a dataframe such that the dataframe created uses new index value.
# importing pandas as pd import pandas as pd # Creating the index idx = pd.Index([22, 54, 85, 45, 69, 33]) # Print the Index idx |
Output :
Let’s convert the index into a dataframe.
# convert the index into a dataframe idx.to_frame(index = False) |
Output :
