5 Best Ways to Rename Column Names by Index in a Pandas DataFrame Without Using Rename

by

💡 Problem Formulation: When working with Pandas DataFrames, you may frequently encounter the need to rename column names. It’s essential for data clarity and further operations. However, the standard rename() method can sometimes be overkill, especially when you only need to change column names based on their index. This article provides 5 efficient methods to achieve this without using the rename() method. For instance, given a DataFrame with column names ["A", "B", "C"], we might want to rename the second column to "New_Name", resulting in ["A", "New_Name", "C"].

Method 1: Assigning Directly to DataFrame.columns

One straightforward approach is to assign a new list of column names to the DataFrame.columns attribute. This method directly modifies the column labels of the DataFrame. It’s most effective when you are aware of all current column names and positions.

Here’s an example:

import pandas as pd

df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"])
df.columns = ["A", "New_Name", "C"]
print(df)

Output:

   A  New_Name  C
0  1         2  3
1  4         5  6

This code snippet creates a simple DataFrame, then reassigns the column names by directly supplying a modified list of column names to df.columns. It effectively replaces “B” with “New_Name” by keeping the other names intact.

Method 2: Using List Comprehension

If you need to rename just a single column or a few columns by index, using list comprehension offers an elegant and Pythonic solution. This method iterates over the existing columns and replaces the names conditionally based on their index position.

Here’s an example:

df.columns = [“New_Name” if i == 1 else col for i, col in enumerate(df.columns)]
print(df)

Output:

   A  New_Name  C
0  1         2  3
1  4         5  6

This code snippet uses list comprehension to iterate over enumerate(df.columns), which pairs column names with their index. It sets the column name to “New_Name” when the index is 1, otherwise, it keeps the original name.

Method 3: Modifying DataFrame.columns.values

Another option is to access the numpy array that stores the column names via DataFrame.columns.values, and then update the specific index directly. It’s an efficient and less verbose method if you’re only changing a few columns.

Here’s an example:

df.columns.values[1] = "New_Name"
print(df)

Output:

   A  New_Name  C
0  1         2  3
1  4         5  6

This code snippet accesses the underlying numpy array of column names and directly modifies the value at index 1, renaming the second column without affecting the others.

Method 4: Using the pandas.DataFrame.iloc Method

While pandas.DataFrame.iloc is primarily used for selecting data, it can also be utilized to assign column names by using it in conjunction with DataFrame.columns. This yields a flexible method for renaming columns by index.

Here’s an example:

df.columns = df.iloc[:, 1:].rename(columns={df.columns[1]: "New_Name"}).columns
print(df)

Output:

   A  New_Name  C
0  1         2  3
1  4         5  6

This approach is slightly more complex but can be useful when you want to apply a renaming operation that utilizes some logic available in the rename method, without directly using it.

Bonus One-Liner Method 5:

For those who love one-liners, Python allows you to combine list manipulation and assignment in a compact form. Although this is not always recommended for readability, it can be efficient for smaller tasks or quick data manipulations.

Here’s an example:

df = pd.DataFrame([[1, 2, 3]], columns=["A", "B", "C"]); df.columns = ["New_Name" if x=="B" else x for x in df.columns]
print(df)

Output:

   A  New_Name  C
0  1         2  3

This one-liner uses conditional list comprehension to rename the second column to “New_Name” directly within the assignment to df.columns.

Summary/Discussion

  • Method 1: Direct Assignment. Simple and easy to understand. Can be less flexible if you need condition-based renaming.
  • Method 2: List Comprehension. Pythonic and flexible. Good for condition-based renaming. May be less readable for complex conditions.
  • Method 3: Modifying DataFrame.columns.values. Straightforward and suitable for small updates. Not ideal for larger, condition-based changes.
  • Method 4: Using pandas.DataFrame.iloc. Provides some logic-based renaming without using rename(). Less intuitive to read and understand.
  • Method 5: Bonus One-Liner. Compact and quick for small changes. Often not advised for maintainable and readable code.