How to Identify All Duplicate Rows in a Pandas DataFrame?

How Do I Get a List of All the Duplicate Items Using Pandas in Python?

Problem:

Your Pandas DataFrame contains duplicate rows, but using the duplicated() method only returns the first duplicate instance. You desire a comprehensive list of all occurrences of duplicated rows for manual comparison.

Solution 1: Isolate Rows with Duplicate IDs

1. Import Pandas as pd.
2. Read your data into a DataFrame df.
3. Extract the ID column into a separate Series ids.
4. Filter df based on whether the ID value matches any of the duplicate IDs in ids[ids.duplicated()]:

<code class="python">df[ids.isin(ids[ids.duplicated()])].sort_values("ID")</code>

While this method effectively retrieves all duplicate rows, it creates duplicate ID rows in the output.

Solution 2: Group by ID and Filter for Duplicates

1. Use groupby("ID") on df to group rows by their ID values.
2. Filter the resulting groups to retain only those with more than one row:

<code class="python">pd.concat(g for _, g in df.groupby("ID") if len(g) > 1)</code>

This approach yields a streamlined output without redundant ID rows.

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