Скачать или смотреть How to Use Pandas to Remove Duplicated Rows Based on Multiple Criteria

Learn how to efficiently remove duplicated rows from a dataframe in `Pandas` using multiple conditions, ensuring your data analysis remains clean and accurate.
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Managing Duplicated Rows in Pandas: A Guide

When working with large datasets in Python, particularly using the Pandas library, you may encounter situations where you need to remove duplicated rows based on specific criteria. This task might seem daunting at first, especially when multiple conditions are at play. In this guide, we'll walk through a real-world example to illustrate how to achieve this with ease.

The Problem Statement

Imagine you have a dataframe containing various records, each with an ID, COD1, COD2, GRADE, and DATA. Your goal is to remove rows based on two conditions:

For each unique ID, check if COD1 equals COD2.

Also, check if the GRADE is either 'A' or 'C'.

If both conditions are met for any row with the same ID, you will remove those rows entirely from the dataframe. Let's dive into how to implement this using Pandas.

Sample DataFrame

To illustrate, we will start with the following dataframe:

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This data contains entries with some duplicated values across different columns.

The Strategy to Remove Duplicates

We will use logical conditions and group operations in Pandas to filter out the unwanted rows. Here's how to do it step by step:

1. Define the Conditions

First, we need to define our conditions using boolean indexing. We'll check if COD1 is equal to COD2 and if GRADE is among the specified grades ('A' or 'C').

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2. Group By ID and Determine Rows to Keep

Next, we will apply a grouping operation to the dataframe according to ID and use the transform method with any to identify if any of the rows meet the defined conditions within that group.

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3. Review the Updated DataFrame

After performing the operations, you should have a new dataframe that excludes the unwanted rows. The expected output will look like this:

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This should output:

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Conclusion

By leveraging group operations and boolean indexing in Pandas, you can efficiently cleanse your dataframe by removing rows that match certain criteria. This not only helps maintain the accuracy of your data analysis but also enhances the overall quality of your datasets. Remember, correctly identifying and removing duplicates is a crucial step in ensuring valid results in any data-driven project.

Now, you’re equipped to handle complex filtering tasks in Pandas with confidence! Happy coding!