How to Identify and Retrieve Duplicate Items within a Pandas DataFrame in Python?

How to get a List of All the Duplicate Items Using Pandas in Python

When working with datasets, it is common to encounter duplicate entries. In this case, you want to identify all duplicate items in your dataset using Pandas.

To achieve this, you can utilize the following approach:

Method 1 (Print All Rows with Duplicate IDs):

<code class="python">import pandas as pd

# Read the CSV data into a DataFrame
df = pd.read_csv("dup.csv")

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

# Create a new DataFrame with only the duplicate values
duplicates = df[ids.isin(ids[ids.duplicated()])]

# Sort the DataFrame by the "ID" column
duplicates.sort_values("ID", inplace=True)

# Print the duplicate values
print(duplicates)</code>

Method 2 (Groupby and Concatenate Duplicate Groups):

This method combines the duplicate groups, resulting in a concise representation of the duplicate items:

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

# Filter the grouped DataFrame to include only groups with more than one row
duplicates = [g for _, g in grouped if len(g) > 1]

# Concatenate the duplicate groups into a new DataFrame
duplicates = pd.concat(duplicates)

# Print the duplicate values
print(duplicates)</code>

Using either Method 1 or Method 2, you can successfully obtain a list of all the duplicate items in your dataset, allowing you to visually inspect them and investigate the discrepancies.

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