Python | Pandas dataframe.add_prefix()

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. Dataframe.add_prefix() function can be used with both series as well as dataframes.

For Series, the row labels are prefixed.
For DataFrame, the column labels are prefixed.

Syntax:  DataFrame.add_prefix(prefix)

Parameters:
prefix : string

Returns: with_prefix: type of caller

For link to CSV file Used in Code, click here Example #1: Prefix col_ in each columns in the dataframe

Python3 1==

# importing pandas as pd
import pandas as pd

# Making data frame from the csv file
df = pd.read_csv("nba.csv")

# Printing the first 10 rows of the
# dataframe for visualization
df[:10]

Python3 1==

# Using add_prefix() function 
# to add 'col_' in each column label
df = df.add_prefix('col_')

# Print the dataframe
df

Output:

Example #2: Using add_prefix() with Series in pandas add_prefix() alters the row index labels in the case of series.

Python3 1==

# importing pandas as pd
import pandas as pd

# Creating a Series 
df = pd.Series([1, 2, 3, 4, 5, 10, 11, 21, 4])

# This will prefix 'Row_' in 
# each row of the series
df = df.add_prefix('Row_')

# Print the Series
df

Output:

Python | Pandas dataframe.add_prefix()

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