Customizing Column Names When Reading Excel Files with Pandas

Understanding Pandas DataFrame Reading and Column Renaming

When working with data from various sources, including Excel files, pandas is often used to read and manipulate the data. One common issue users encounter when reading Excel files with a header row is that the column names are automatically renamed to date-time formats, such as “2021-01-01” or “01/02/23”. This can be inconvenient for analysis and visualization.

Why Does Pandas Rename Columns?

Pandas automatically renames columns from their original format to a more standardized format when reading Excel files. This is done to improve data type consistency and to facilitate easier comparison across different datasets.

How to Prevent Column Renaming in Pandas

Fortunately, pandas provides several ways to prevent column renaming or to customize the naming process. In this article, we’ll explore one possible workaround using a combination of existing pandas functionality and some basic data manipulation techniques.

Reading Excel Files with Customized Column Names

One common approach is to read the Excel file into a pandas DataFrame without automatically renaming the columns. Instead, you can specify the names of the columns when reading the file.

df = pd.read_excel('test.xlsx', engine='openpyxl', usecols='A:D')

In this example, usecols is used to specify that only columns A through D should be read from the Excel file. By specifying custom column names, you can avoid pandas automatically renaming the columns.

However, this approach might not always work as expected, especially if your data has complex formatting or relationships between columns. Therefore, we’ll explore an alternative solution using existing pandas functionality to customize the naming process.

Workaround: Transposing DataFrames and Using String Formatting

One possible workaround involves transposing a DataFrame with datetime values, converting the index to a string format using strftime(), and then transposing again to restore the original column names. Here’s a step-by-step guide:

Removing the UserID Column and Transposing Datetime Columns

First, read the Excel file into a pandas DataFrame and remove the UserID column.

# Read the Excel file
df = pd.read_excel('test.xlsx', engine='openpyxl')

# Remove the UserID column
uid = df['UserID']
df.drop(columns='UserID', axis=1, inplace=True)

Next, transpose the DataFrame to bring the datetime columns as indices. This will allow us to access and format these values programmatically.

# Transpose the DataFrame
df = df.T

Converting Index to String Format

Now, we’ll use strftime() to convert the index (datetime values) to a string format that meets our requirements.

# Convert the datetime index to a string format
df.index = pd.to_datetime(df.index).strftime('%b-%y')

In this example, %b is used for abbreviated month names and %y represents the year in two digits. You can modify this format to suit your specific needs.

Restoring Original Column Names

Finally, transpose the DataFrame again to restore the original column names.

# Transpose back to the original orientation
df = df.T

Adding UserID Column Back

To complete the workaround, we’ll add the UserID column back at the beginning of the DataFrame using insert().

# Add UserID column back to the start of the DataFrame
df.insert(loc=0, column='UserID', value=uid)
print(df)

Full Workaround Example

Here’s the complete workaround:

# Import necessary libraries
import pandas as pd

# Read Excel file
df = pd.read_excel('test.xlsx', engine='openpyxl')

# Remove UserID column
uid = df['UserID']
df.drop(columns='UserID', axis=1, inplace=True)

# Transpose to bring datetime columns as indices
df = df.T

# Convert datetime index to string format
df.index = pd.to_datetime(df.index).strftime('%b-%y')

# Transpose back to original orientation
df = df.T

# Add UserID column back to the start of the DataFrame
df.insert(loc=0, column='UserID', value=uid)

print(df)

Conclusion

In this article, we explored a workaround for reading Excel files with header rows that automatically rename columns to date-time formats. By transposing the DataFrame, converting the index to a string format using strftime(), and restoring the original column names, you can achieve your desired output.

Keep in mind that this approach might not be the most elegant solution and may have limitations depending on the complexity of your data. However, it provides a practical workaround for common use cases where pandas automatic column renaming is inconvenient.

Remember to always explore existing pandas functionality before resorting to workarounds like these. With practice and experience, you’ll become proficient in leveraging pandas’ features to tackle even the most challenging data analysis tasks.

Last modified on 2025-03-02