How to Rename Columns in Pandas DataFrame?

What is Pandas DataFrame?

Pandas, a Python package, shines as a flexible data manipulation and analysis tool. One of its useful features is the ability to rename columns in Pandas in a DataFrame easily. Column names are more than simply labels; they give context to the data they contain, making renaming a necessary skill.

Consider a DataFrame to be a table with rows and columns. Assume you're working with a DataFrame containing sales data, and the column names need to be clarified or might be improved. This is where renaming columns comes into play.

The technique is incredibly simple with Pandas. You may use the rename() method to give the present name of the column and the intended new name. You may also rename numerous columns by passing a dictionary that maps current names to new ones.

Here's a quick example:

Output:

Remember, the inplace=True parameter modifies the DataFrame in place without the need to reassign it.

How to Rename Columns in Pandas DataFrame?

Have you ever wanted to modify the name of a column in your Pandas DataFrame? Column renaming is important, whether cleaning up data or making it more intelligible. Pandas, thankfully, make this procedure straightforward and efficient. In this post, we'll show you how to rename a column in Pandas.

Step 1: Import Pandas

First, ensure that Pandas is installed by running import pandas as pd.

Step 2: Load Your Data

Load your dataset with pd.read_csv() or any appropriate method.

Step 3: Identify the Column

Choose the column that you wish to rename. Assume you wish to change a column "old_name" to "new_name."

Step 4: Rename the Column

Use the rename() method on your DataFrame. Specify the dictionary parameter columns as follows:

The inplace=True argument ensures the change is applied directly to your DataFrame.

Step 5: Check the Result

Display the first few rows of your DataFrame to confirm the modification using df.head().

Step 6: Save the Changes

If you're satisfied with the changes, save the modified DataFrame using methods like to_csv().

Column renaming may be a chore, but it greatly enhances the readability and usefulness of your data. Pandas make the procedure simple, allowing you to modify your data effectively. So, whether you're a data newbie or a seasoned analyst, you can easily rename Pandas columns.

Method 1: Using rename() Function

The rename() function allows you to rename columns in Pandas by specifying a dictionary mapping old names to new ones. It's a versatile method that enables targeted renaming.

Output:

Method 2: By Assigning a List of New Column Names

This technique generates a list of new column names and immediately assigns them to the DataFrame's .columns property. This is very useful if you wish to rename all columns at once.

Output:

Method 3: Rename Column Names Using DataFrame set_axis() Function

The set_axis() method allows you to rename a DataFrame's row or column index. You may rename the columns while listing new column names while retaining the DataFrame's structure.

Output:

Method 4: Rename Column Names Using DataFrame add_prefix() and add_suffix() Functions

The add_prefix() and add_suffix() allow you to prepend or append text to existing column names. These functions are efficient when adding a common identifier to multiple columns.

Output:

Method 5: Replace Specific Texts of Column Names Using Dataframe.columns.str.replace Function

This technique may be used to rename columns in Pandas by substituting certain substrings. You may do accurate substitutions in column names by using the str.replace method on the columns property.

Output:

About Pandas DataFrame rename() Method

If you've ever wished you could easily change your data columns, the Pandas DataFrame rename() function is the way to go. This handy method allows you to rename columns in Pandas with a few lines of code. Whether you're changing column names for clarity or compatibility, this solution saves you the time and effort of manually manipulating data.

Definition and Application

The Pandas library's rename() method is a feature-rich utility that allows you to modify the names of one or more columns in a DataFrame. It's the same as giving your data columns a new identification while keeping the underlying data the same. This is especially useful when importing data from many sources with varying naming patterns or working on collaborative projects with naming preferences.

Syntax

The syntax of the rename() method is refreshingly straightforward:

Here, df is your DataFrame, old_name is the current column name you want to change, and new_name is the desired new name. The inplace=True parameter ensures that the changes are applied directly to your DataFrame without needing to reassign it to a new variable.

Parameters

The rename() method offers you a few essential parameters to fine-tune your column renaming process:

• columns: This parameter is the heart of the method, taking a dictionary where keys are the existing column names, and values are the new names you want to assign.
• inplace: When set to True, it modifies the DataFrame in place, saving the memory and improving the performance. If set to False (default), the original DataFrame remains unchanged, and the method returns a new DataFrame with the modifications.
• level: This parameter allows you to specify which level you want to rename if you're working with multi-level column indexes.