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
# 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] |
# 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.
# 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:
