Removing Duplicate Rows from DataFrames in Pandas: A Step-by-Step Guide

Introduction

Pandas is a powerful library used for data manipulation and analysis in Python. One of the common tasks when working with dataframes is to remove duplicate rows based on certain criteria. In this article, we will explore how to achieve this using the merge function, query, and drop functions.

Understanding DataFrames

Before diving into the solution, it’s essential to understand what a DataFrame is in Pandas. A DataFrame is a two-dimensional table of data with rows and columns, similar to an Excel spreadsheet or a SQL database table. Each column represents a variable, while each row represents an observation.

DataFrames can be created from various sources such as CSV files, Excel spreadsheets, SQL databases, and more. Once created, DataFrames can be manipulated using various functions such as filtering, sorting, grouping, merging, and much more.

Merging DataFrames

One of the ways to remove duplicate rows is by merging two DataFrames based on common columns. The merge function allows us to join two DataFrames based on one or more columns. We can specify the type of join using the how parameter (left, right, inner, outer).

Let’s consider an example:

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'id': [1, 2, 3],
    'name': ['John', 'Mary', 'David']
})

df2 = pd.DataFrame({
    'id': [1, 2, 4],
    'age': [25, 30, 35]
})

In this example, we have two DataFrames df1 and df2. We want to merge these DataFrames based on the id column.

# Merge df1 and df2 based on the id column
merged_df = pd.merge(df1, df2, on='id')

The resulting merged_df will contain all columns from both df1 and df2, with duplicate rows removed.

Filtering DataFrames

Another way to remove duplicate rows is by filtering out rows based on certain conditions using the query function. The query function allows us to specify a SQL-like query string that filters the DataFrame.

Let’s consider an example:

# Filter df1 to only include rows where id is 2 or 3
filtered_df = df1.query('id in [2, 3]')

The resulting filtered_df will contain only the rows from df1 where id is either 2 or 3.

Removing Duplicate Rows

Now that we have filtered out duplicate rows using the merge and query functions, we can remove the helper column created by the merge function using the drop function.

# Drop the _merge column from merged_df
final_df = merged_df.drop('_merge', axis=1)

The resulting final_df will be the final DataFrame with duplicate rows removed.

Conclusion

In this article, we explored how to remove duplicate rows from DataFrames in Pandas using the merge, query, and drop functions. We learned about the different types of joins that can be performed using the merge function and how to filter out rows using the query function.

By combining these techniques, you can efficiently remove duplicate rows from your DataFrames and improve the quality of your data analysis results.

Tips and Variations

To remove duplicate rows based on multiple columns, use the on parameter in the merge function.
To remove duplicate rows based on a specific column, use the query function with a SQL-like query string.
To remove duplicate rows from a DataFrame while preserving the original index, use the drop_duplicates function.
To remove duplicate rows from a DataFrame while ignoring the inplace=True parameter, use the drop_duplicates function with the keep='first'" parameter.

Example Use Cases

Removing duplicate rows based on multiple columns:

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'id': [1, 2, 3],
    'name': ['John', 'Mary', 'David'],
    'age': [25, 30, 35]
})

df2 = pd.DataFrame({
    'id': [1, 2, 4],
    'name': ['Jane', 'Mary', 'Emily'],
    'age': [25, 30, 35]
})

# Merge df1 and df2 based on the id column
merged_df = pd.merge(df1, df2, on='id')

# Remove duplicate rows based on multiple columns (id and name)
final_df = merged_df.drop_duplicates(['id', 'name'])

Removing duplicate rows from a DataFrame while preserving the original index:

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'index': [1, 2, 3],
    'value': [10, 20, 30]
})

# Remove duplicate rows based on the value column
final_df = df.drop_duplicates('value', keep='first')

Removing duplicate rows from a DataFrame while ignoring the inplace=True parameter:

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'index': [1, 2, 3],
    'value': [10, 20, 30]
})

# Remove duplicate rows based on the value column
final_df = df.drop_duplicates('value', keep='first')

Last modified on 2023-06-29