Python Pandas Filter rows of data frame based on function
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Pandas is a powerful data manipulation library for Python, widely used for data analysis and manipulation. One common task in data analysis is filtering rows in a DataFrame based on a specific condition or function. In this tutorial, we'll explore how to filter rows in a Pandas DataFrame using a custom function.
Before we begin, make sure you have Python and Pandas installed on your system. You can install Pandas using the following command:
Let's start by creating a sample DataFrame for demonstration purposes.
This will create a DataFrame with columns 'Name', 'Age', and 'Salary'.
Now, let's create a function that defines the filtering condition. For this example, we'll filter rows where the age is greater than 25.
Next, we'll use the apply function along with our custom function to filter the DataFrame.
In this code, the apply function is used to apply the filter_by_age function to each row of the DataFrame. The result is a new DataFrame containing only the rows that satisfy the filtering condition.
Alternatively, you can use a lambda function for a more concise representation:
This achieves the same result as the previous example.
Filtering rows in a Pandas DataFrame based on a custom function is a common operation in data analysis. By using the apply function, you can easily apply your filtering logic to each row of the DataFrame. This tutorial provides a basic example, and you can customize the filtering function based on your specific requirements.
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