Handling Duplicates In Dataframes Using Pandas

Introduction

Data duplication is a common problem while handling large datasets. These duplicates can greatly impact data analysis and predictions if not handled properly. In this blog, we'll discuss a technique called De-duplication using a powerful Python library called Pandas for handling duplicates. As promised, we'll provide working code snippets in Python.

Installation

First, we need to have the Pandas library installed. If you already have it, you can skip this step. If not, you can install it using pip.

pip install pandas

Create a DataFrame with Duplicates

Let's start by creating a DataFrame with duplicate values. A DataFrame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns in Pandas.

import pandas as pd

# Create dataframe
data = {'Name': ['John', 'Anna', 'John', 'Anna', 'Ben', 'John'],
        'Age': [20, 25, 20, 25, 30, 20],
        'Score': [85, 90, 85, 90, 95, 85]}
df = pd.DataFrame(data)

print(df)

Duplicate Detection

Now we'll detect duplicates. Pandas dataframe.duplicated() function returns a Boolean Series denoting duplicate rows.

# Detect duplicate rows
duplicates = df.duplicated()

print(duplicates)

Remove Duplicates

We can remove duplicates using the drop_duplicates() function. This function returns a DataFrame with the duplicates removed.

# Remove duplicates
df = df.drop_duplicates()

print(df)

Conclusion

We have successfully detected and removed duplicates from the DataFrame using the Pandas library in Python. This is a powerful tool to have handy when cleaning up large datasets in data science.

Always remember, garbage in, garbage out. So always clean your data before doing any data analysis or predictions.