How to Remove Duplicate Columns in a Pandas Dataframe?

Remove Duplicate Columns in a Pandas Dataframe

When dealing with dataframes that contain duplicate columns, it becomes necessary to eliminate these redundancies for effective data analysis. This article provides a comprehensive solution to remove duplicate columns in Pandas, addressing all aspects of the issue.

Duplicated Column Names

To remove columns based solely on duplicate names, a straightforward solution is:

<code class="python">df = df.loc[:,~df.columns.duplicated()].copy()</code>

This line checks for duplicate column names and retains only those that are unique.

Duplicated Column Values

If the goal is to remove columns based on duplicate values, a different approach is required without transposing the dataframe:

<code class="python">df = df.loc[:,~df.apply(lambda x: x.duplicated(),axis=1).all()].copy()</code>

This method checks for duplicated values within each column and eliminates columns where all values are duplicates.

Duplicated Indexes

To remove duplicated indexes, follow a similar approach:

<code class="python">df = df.loc[~df.index.duplicated(),:].copy()</code>

Additional Notes

• The provided solutions assume the dataframe is already loaded into a variable named df.
• ~df.columns.duplicated() and ~df.index.duplicated() return boolean arrays indicating which columns or indexes are duplicates.
• The all() function in ~df.apply(lambda x: x.duplicated(),axis=1).all() checks if all values within a column are duplicates.
• The .copy() method is used to create a new dataframe with the modifications, avoiding any issues with modifying the existing dataframe.

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