Скачать или смотреть How to Filter DataFrame Rows with Pandas Based on Multiple Column Values

Learn how to filter a DataFrame in Python using Pandas to find rows that have values of one or above in at least two columns.
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Filtering DataFrames with Pandas: Finding Rows with Values ≥ 1 in Multiple Columns

Pandas is an incredibly powerful tool for data manipulation and analysis in Python. Whether you're analyzing survey results, financial data, or scientific measurements, you'll often find yourself needing to filter DataFrames to focus on the most relevant data. In this guide, we’ll tackle a common data querying requirement: finding rows within a DataFrame that have values greater than or equal to 1 in at least two columns.

The Problem Statement

Imagine you have a DataFrame that looks like this:

[[See Video to Reveal this Text or Code Snippet]]

Our goal is to create a new DataFrame that includes only the rows where the values in at least two columns are 1 or above.

Expected Result

From the provided DataFrame, the expected result after filtering would be:

[[See Video to Reveal this Text or Code Snippet]]

The Solution: Step-by-Step Guide

Let’s break down how to achieve this using Pandas. The complete code to filter the DataFrame will be:

[[See Video to Reveal this Text or Code Snippet]]

Step 1: Create a Boolean DataFrame

First, we need to create a boolean DataFrame where each element is True if its corresponding value is greater than or equal to 1, and False otherwise. This is done using the ge() function:

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The output will look something like this:

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Step 2: Summing the Boolean Values Across Columns

Next, we need to sum the boolean values for each row. This will give us a count of how many columns have values >= 1. We achieve this by specifying the axis parameter:

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This will return a Series that looks like this:

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Step 3: Filtering Rows with Two or More True Values

Since we are interested in rows where the sum is greater than or equal to 2, we apply another condition:

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This provides us with the following boolean Series:

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Indexing the Original DataFrame

Finally, we can filter our original DataFrame using the boolean Series created. By using .loc[], we can retrieve only the rows of interest:

[[See Video to Reveal this Text or Code Snippet]]

And this will give you our desired DataFrame:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Filtering DataFrames based on multiple column conditions is a frequent task when working with Pandas. By following the steps outlined above, you can easily extract the data that meets your specific criteria. Remember, understanding how to manipulate these structures effectively will greatly enhance your data analysis capabilities. Happy coding!