How to Find All Duplicate Items in a Pandas DataFrame Using \'isin\' and \'sort_values\'?

Listing All Duplicate Items in a Pandas DataFrame Using 'isin' and 'sort_values'

In this article, we'll address the issue of finding all duplicate items within a list of items possibly containing export errors. Our goal is to retrieve a comprehensive list of these duplicates for manual comparison and troubleshooting.

The 'duplicated' method of pandas returns only the first instance of duplicate values by default. However, using a combination of 'isin' and 'sort_values,' we can display all rows associated with duplicated IDs:

<code class="python"># Import the pandas library
import pandas as pd

# Read the data from the CSV file
df = pd.read_csv('dup.csv')

# Extract the 'ID' column
ids = df['ID']

# Use 'isin' to filter for rows where the 'ID' matches any of the duplicate IDs
df[ids.isin(ids[ids.duplicated()])].sort_values('ID')</code>

This method lists all rows from the DataFrame where the 'ID' column contains any of the IDs flagged as duplicates. The output eliminates duplicate rows, ensuring that each duplicate ID appears only once.

Alternative Method: Grouping by IDs with 'groupby' and 'concat'

An alternative approach involves grouping the DataFrame by 'ID' and then concatenating the groups with more than one row:

<code class="python"># Group the DataFrame by 'ID'
groups = df.groupby('ID')

# Identify groups with more than one row
large_groups = [group for _, group in groups if len(group) > 1]

# Concatenate the large groups
pd.concat(large_groups)</code>

This method retrieves all duplicate items, again excluding duplicates within each duplicate group. By default, the 'concat' function appends the duplicate groups vertically.

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