Methods to Rename Column Names
In Pandas, there are primarily two ways to rename columns:
- Using the rename() function: With Pandas, we can easily rename columns using the rename() function. We can add a dictionary to the columns option to rename columns that have index numbers. The present column names should serve as the dictionary’s keys, while the new names should serve as the values.
- Using List Comprehension : Another strategy is to create new column names depending on the index numbers by using list comprehension. This approach comes in particularly useful when managing a lot of columns.
Before we get started, let’s go over some fundamental notions about this topic.
Column Index
In a data frame, a column index has two main functions:
- Labeling: Just like row labels for rows, it gives each column in the DataFrame a distinct identity. This makes it simple for users to identify and make explicit references to different columns.
- Location: It indicates where a column is located inside the DataFrame. Instead of depending on names that could be confusing, this enables accessing particular columns based on their index position.
Why Rename Columns?
The names of your columns are important identifiers for the various attributes in your dataset. Sometimes, they might be too lengthy or complex, making it challenging to work with them. Renaming columns helps simplify data processing and makes your code easier to read.
Rename column name with an index number of the CSV file in Pandas
In this blog post, we will learn how to rename the column name with an index number of the CSV file in Pandas.
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