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Pandas – Add Suffix to Column Names

In this tutorial, we will look at how to add a suffix to the column names in a pandas dataframe with the help of some examples.

How to add a suffix to each column name of a pandas dataframe?

add suffix to all column names of pandas dataframe

You can use the built-in pandas dataframe add_suffix() function to add a suffix to the column names of a dataframe. Pass the suffix string as an argument.

The following is the syntax –

# add suffix to column names
df = df.add_suffix(suffix)

It returns the dataframe (or series if applied on a pandas series) with the updated labels.

Alternatively, you can use a list comprehension to create a list of new column names with the suffix added.

Examples

Let’s now look at some examples of using the above methods.

First, we will create a dataframe that we will be using throughout this tutorial.

import pandas as pd

# sales data
data = {
    "Apple": [10, 12, 13, 9, 15],
    "Mango": [12, 11, 19, 18, 13]
}

# create pandas dataframe
df = pd.DataFrame(data)

# display the dataframe
df

Output:

fruit sales dataframe

Here, we created a dataframe with information about the day-level sale volume of some fruits at a local grocery store. The dataframe has the following columns – “Apple” and “Mango” which represent the sale of the respective fruit in kilograms.

Example 1 – Add suffix to column names using add_suffix()

Let’s now add the suffix “_Kg” to the column names using the pandas dataframe add_suffix() function.

# add suffix to column names
df = df.add_suffix("_Kg")
# display the dataframe
df

Output:

dataframe with suffix "_Kg" added to column names

The column names of the dataframe are now suffixed with “_Kg”.

Example 2 – Add suffix to column names using a list comprehension

Alternatively, you can use a list comprehension to create a new list of column names with the suffix added.

For example, let’s take the same use-case from the above example. We will reset the column names to their original values and then add the suffix to column names using a list comprehension.

# reset column names to original values
df.columns = ["Apple", "Mange"]
# add suffix to column names
df.columns = [str(col)+"_Kg" for col in df.columns]
# display the dataframe
df

Output:

dataframe with suffix "_Kg" added to column names

We get the same result as above.

You can further simplify this step by directly adding the suffix to df.columns.

# reset column names to original values
df.columns = ["Apple", "Mango"]
# add suffix to column names
df.columns = df.columns + "_Kg"
# display the dataframe
df

Output:

dataframe with suffix "_Kg" added to column names

We get the same result as above. The suffix “_Kg” is added to each column name in the dataframe.

Summary

In this tutorial, we looked at how to add a suffix to each column name of a pandas dataframe. The following are the key takeaways.

  • You can use the pandas dataframe built-in add_suffix() function to add a suffix to the column names.
  • Alternatively, you can use a list comprehension to modify the column names with the added suffix.

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Author

  • Piyush

    Piyush is a data scientist passionate about using data to understand things better and make informed decisions. In the past, he's worked as a Data Scientist for ZS and holds an engineering degree from IIT Roorkee. His hobbies include watching cricket, reading, and working on side projects.