Trouble Rearranging Columns with Same Name in Pandas Dataframe in Python

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In this article, we’ll explore an issue many data scientists encounter when working with pandas dataframes: rearranging columns with the same name. We’ll delve into why it’s happening and how to fix it.

Background on DataFrame Columns

A pandas dataframe is a two-dimensional table of data with rows and columns. The columns attribute returns a list of column names. These column names can be used to access specific columns in the dataframe.

When working with large datasets, it’s common for some columns to have duplicate names. This might occur when there are multiple variables with the same name but different units or context.

Why is Duplicate Column Name Causing Issues?

In the given Stack Overflow post, a user is trying to rearrange their dataframe’s columns by using sapie_columns = sapie_df_working.columns.tolist() and then filtering out some columns. However, this approach fails because there are duplicate column names in the dataframe.

When you try to assign new values to these duplicate column names, pandas doesn’t know which one to update. This results in an unexpected number of columns being returned.

sapie_columns = sapie_df_working.columns.tolist()
sapie_columns = [sapie_columns[-1]] + sapie_columns[3:-1]
sapie_df_working = sapie_df_working[sapie_columns]

How to Handle Duplicate Column Names

To avoid this issue, you should use column indices instead of names. You can get the indices using df.columns.tolist().

Let’s look at an example:

import pandas as pd

mock_data = [[11.29, 33.1283, -1.219, -33.11, 930.1, 33.91, 0.1213, 0.134],
             [9.0, 99.101, 9381.0, -940.11, 55.41, -941.1, -1.3913, 1933.1],
             [-192.1, 0.123, 0.1243, 0.213, 751.1, 991.1, -1.333, 9481.1]]

mock_columns = ['a', 'b', 'c', 'a', 'd', 'b', 'g', 'a']

df = pd.DataFrame(columns=mock_columns, data=mock_data)

columns = df.columns.tolist()

filtered_column_indices = [len(columns) - 1] + list(range(3, len(columns) - 1))

df.iloc[:, filtered_column_indices]

Alternative Approach Using `loc` and Column Names

If you need to use column names for filtering, consider using the loc method instead of indexing. The loc method allows label-based selection on the DataFrame.

filtered_columns = df.loc[:, ['a', 'd', 'g']]

This approach is safer when working with dataframes containing duplicate column names.

Conclusion

In this article, we explored why using duplicate column names can cause issues in pandas dataframes. We discussed two alternative approaches to handle this problem: using column indices and loc method for label-based selection. By understanding these techniques, you’ll be able to rearrange columns with the same name efficiently and effectively.

Recommendations

Use column indices when working with large datasets or duplicate column names.
Consider using loc method for label-based selection when working with dataframes containing duplicate column names.

Note: This article was created based on a Stack Overflow question. If you have any further questions, feel free to ask!

Last modified on 2023-07-19