Pandas - Add Prefix to Column Names - Data Science Parichay

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

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

add prefix to pandas dataframe column names

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

The following is the syntax –

# add prefix to column names
df = df.add_prefix(prefix)

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 prefix 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

# employee data
data = {
    "Name": ["Jim", "Dwight", "Angela", "Tobi"],
    "Age": [26, 28, 27, 32]
}

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

# display the dataframe
df

Output:

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pandas dataframe of employees with "Name" and "Age" columns

Here, we created a dataframe with information about some employees in an office. The dataframe has the following columns – “Name” and “Age”.

Example 1 – Add prefix to column names using `add_prefix()`

Let’s now add the prefix “Employee_” to the column names using the pandas dataframe add_prefix() function.

# add prefix to column names
df = df.add_prefix("Employee_")
# display the dataframe
df

Output:

"Employee_" prefix added to dataframe column names

The column names of the dataframe are now prefixed with “Employee_”.

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

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

# reset column names to original values
df.columns = ["Name", "Age"]
# add prefix to column names
df.columns = ["Employee_"+str(col) for col in df.columns]
# display the dataframe
df

Output:

We get the same result as above.

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

# reset column names to original values
df.columns = ["Name", "Age"]
# add prefix to column names
df.columns = "Employee_" + df.columns
# display the dataframe
df

Output:

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

Summary

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

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

Author

Piyush Raj

Piyush is a data professional passionate about using data to understand things better and make informed decisions. He has experience working as a Data Scientist in the consulting domain and holds an engineering degree from IIT Roorkee. His hobbies include watching cricket, reading, and working on side projects.

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

Examples

Example 1 – Add prefix to column names using add_prefix()

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

Summary

Author

Example 1 – Add prefix to column names using `add_prefix()`