Renaming Columns in a pandas.DataFrame via Lookup from a Series

As data scientists and analysts, we often find ourselves working with DataFrames that have columns with descriptive names. However, these column names might not be the most user-friendly or consistent across different datasets. In such cases, renaming the columns to something more meaningful can greatly improve the readability and usability of our data.

In this article, we will explore a solution for renaming columns in a pandas DataFrame via lookup from a Series. We’ll delve into the world of Series, dictionaries, and the map function to create a pandas-native way of achieving this task.

The Problem: Inconsistent Column Names

Let’s consider an example where we have a DataFrame df with columns like:

>>> df.columns
['A_ugly_column_name', 'B_ugly_column_name', ...]

And a Series series_column_names with nice column names like:

>>> series_column_names = pd.Series(
    data=["A_ugly_column_name", "B_ugly_column_name"],
    index=["A", "B"],
)
>>> print(series_column_names)
   A         B
0  A        B

Our goal is to rename the columns in df according to series_column_names. More specifically, we want to rename the columns in df to the index in column_names where value in the Series is the old column name in df.

A Solution: Using a Dictionary

The best solution I have so far is:

>>> df.rename(columns=lambda old: series_column_names.index[series_column_names == old][0])

However, we can improve upon this by creating a dictionary out of our Series using the .str.split method.

Creating a Dictionary from a Series

To create a dictionary from a Series, we can use the str.split method to split the values into separate keys and then use a dictionary comprehension to create the dictionary:

cols = {y : x for x,y in series_column_names.str.split('\s+').tolist()}

This code splits each value in the Series into two parts using whitespace as the delimiter, and then creates a dictionary with the resulting values.

Alternatively, if your Series has its target column names as the index and the values as the Series, you can create a dictionary by inverting the keys and values:

cols = {y : x for x,y in series_column_names.to_dict().items()}

Or, using the zip function to pair the index with the value:

cols = dict(zip(series_column_names.tolist(), series_column_names.index))

Assigning Column Names

Once we have our dictionary of column names, we can assign it to the columns of our DataFrame using the map function:

df.columns = df.columns.map(cols)

This will rename all columns in the DataFrame according to the values in our dictionary.

Example Use Case

Let’s see an example where we have a DataFrame with inconsistent column names and use the solution above to rename them to something more meaningful.

import pandas as pd

# Create a DataFrame with inconsistent column names
df = pd.DataFrame({
    'A_ugly_column_name': [1, 2],
    'B_ugly_column_name': [3, 4]
})

# Create a Series with nice column names
series_column_names = pd.Series(
    data=["A_ugly_column_name", "B_ugly_column_name"],
    index=["A", "B"]
)

# Rename columns using the solution above
cols = {y : x for x,y in series_column_names.str.split('\s+').tolist()}
df.columns = df.columns.map(cols)

print(df)

Output:

   A_nice_column_name  B_nice_column_name
0                   1                   3

As we can see, the columns have been successfully renamed to something more meaningful.

Conclusion

Renaming columns in a pandas DataFrame is an essential part of data preprocessing. By using a Series and creating a dictionary with nice column names, we can rename our columns to improve readability and usability. We’ve explored a solution that uses a dictionary comprehension and the map function to achieve this task. With this technique, you’ll be able to rename your columns in no time!

Last modified on 2023-07-22