Identifying Duplicate Entries in Pandas DataFrames

Introduction

Working with data often involves identifying and handling duplicate entries. In Python, the Pandas library provides efficient methods to detect duplicates within DataFrame structures. This tutorial explains how to use Pandas to find all instances of duplicate items based on one or more columns. Understanding this concept is crucial for tasks such as data cleaning and preprocessing.

Understanding Duplicates in DataFrames

In a DataFrame, a duplicate entry refers to rows that have identical values in specified columns. Identifying duplicates is essential when you want to ensure the uniqueness of your dataset or analyze repeated occurrences.

Pandas offers several methods to identify duplicates, including:

• duplicated(): Returns a Boolean Series indicating whether each row is a duplicate.
• drop_duplicates(): Removes duplicate rows from the DataFrame.

These functions are configurable to specify which columns to consider when identifying duplicates and how many occurrences of duplicates should be kept or marked.

Using the duplicated() Method

The duplicated() method is versatile, allowing you to specify a subset of columns to check for duplication. By default, it marks all duplicates as True, except for their first occurrence.

Syntax

DataFrame.duplicated(subset=None, keep='first')

• subset: List of column names to consider for identifying duplicates.
• keep: Controls which duplicates to mark:
  • 'first': Marks duplicates as True except for the first occurrence (default).
  • 'last': Marks duplicates as True except for the last occurrence.
  • False: Marks all duplicate occurrences as True.

Example

Let’s consider a DataFrame with some duplicate entries based on an ‘ID’ column:

import pandas as pd

# Sample data
data = {
    "ID": ["1536D", "F15D", "8096", "A036", "8944", "1004E", 
           "11795", "30D7", "3AE2", "B0FE", "127A1", "161FF", 
           "A036", "475B", "151A3", "CA62", "D31B", "20F5", 
           "8096", "14E48", "177F8", "553E", "12D5F", "C6DC", 
           "11795", "17B43", "A036"],
    "ENROLLMENT_DATE": ["12-Feb-12", "18-May-12", "8-Aug-12", "1-Apr-12",
                        "19-Feb-12", "8-Jun-12", "3-Jul-12", "11-Nov-12",
                        "21-Feb-12", "17-Feb-12", "11-Dec-11", "20-Feb-12",
                        "30-Nov-11", "25-Sep-12", "7-Mar-12", "", 
                        "18-Dec-11", "8-Jul-12", "19-Dec-11", "",
                        "20-Aug-12", "11-Oct-12", "18-Jul-12", "",
                        "27-Feb-12", "11-Aug-12", "11-Aug-12"]
}

df = pd.DataFrame(data)

# Find all duplicates for the 'ID' column
duplicates = df[df.duplicated(subset=['ID'], keep=False)]
print(duplicates)

This code will output all rows with duplicate ‘ID’ values, including every occurrence.

Using groupby() and Concatenation

Another approach to identify duplicate entries is by using the groupby() function along with concatenation. This method groups data based on specified columns and extracts groups containing duplicates:

Example

# Group by 'ID' column and filter out groups with more than one entry
duplicates = pd.concat([group for _, group in df.groupby('ID') if len(group) > 1])
print(duplicates)

This code will give you all the duplicate rows, grouped by their ‘ID’.

Conclusion

Identifying duplicates is a common task when working with data. By using Pandas’ duplicated() method or leveraging groupby(), you can efficiently find and manage duplicate entries in your datasets. These techniques are instrumental for ensuring data integrity and preparing your data for analysis.