Скачать или смотреть How to Save Output of a Loop in a Pandas DataFrame Outside the Loop

This guide provides a step-by-step guide on saving the output of a for loop in a Pandas DataFrame, ensuring efficient data management and manipulation in Python.
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This video is based on the question https://stackoverflow.com/q/72163736/ asked by the user 'Noorulain Islam' ( https://stackoverflow.com/u/18551983/ ) and on the answer https://stackoverflow.com/a/72163999/ provided by the user 'Tomer Mantzuri' ( https://stackoverflow.com/u/19013332/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Introduction

When working with data in Python, we often encounter situations where we need to manipulate a DataFrame in a for loop and save the results. A common challenge is how to store the outputs from each iteration of the loop in a separate DataFrame defined outside the loop. In this guide, we will explore an effective method to achieve that using the Pandas library.

Problem Statement

You may have a DataFrame that contains essential data, and you wish to extract subsets of that data iteratively in a loop. However, the output for each iteration needs to be saved to a new DataFrame that was declared outside the loop. Here’s an example scenario:

You have a DataFrame created from a dictionary and want to loop through it to extract specific rows and columns. You want to compile all the outputs into a single DataFrame.

Example Scenario

Given the following code snippet:

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

The challenge is saving the output of df3 for each iteration into df4.

Solution

To solve this challenge, we will utilize the pd.concat() function, which allows us to concatenate (combine) DataFrames along a particular axis. By using pd.concat(), we can efficiently merge the output of our loop into the df4 DataFrame.

Step-by-Step Guide

Let’s break down the solution into simple steps:

Step 1: Import Pandas and Create the Initial DataFrame

First, we need to import the Pandas library and set up our initial DataFrame:

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

Step 2: Initialize an Empty DataFrame for Results

Create an empty DataFrame df4 to hold the results from each iteration:

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

Step 3: Loop Through the DataFrame and Extract Data

In the for loop, extract the desired rows and columns, and append the result to df4 using pd.concat():

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

Step 4: Print the Final DataFrame

After the loop, print df4 to see the compiled results from all iterations:

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

Complete Code Example

Here’s the complete corrected code:

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

Conclusion

Managing data with Pandas requires effective strategies, especially when it involves loops and iterations. By utilizing the pd.concat() function, we can seamlessly collect our outputs into a single DataFrame outside the loop. This method enhances data organization and improves code readability. Happy coding!