Скачать или смотреть Efficient Use of Python Concurrent Futures with Pandas DataFrames

Learn how to speed up API calls using `concurrent.futures` in Python for processing large Pandas DataFrames efficiently.
---
This video is based on the question https://stackoverflow.com/q/66324674/ asked by the user 'uniXVanXcel' ( https://stackoverflow.com/u/2582017/ ) and on the answer https://stackoverflow.com/a/66335216/ provided by the user 'Kroim' ( https://stackoverflow.com/u/13936013/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Python Concurrent Futures for Panda Dataframe

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Efficient Use of Python Concurrent Futures with Pandas DataFrames

Handling large datasets efficiently is a common challenge for data scientists and engineers. If you're working with pandas and frequently making API calls, you might find that performance becomes a bottleneck. In this guide, we'll explore how to utilize Python Concurrent Futures to optimize API requests when working with a DataFrame containing thousands of rows. Let's dive into the problem and then break down the solution.

The Problem

Suppose you have a DataFrame with a few thousand rows representing different case IDs and parameters related to an API that you need to validate. Your DataFrame (input_df) might look like this:

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

From this DataFrame, you perform a groupBy(case_id) operation. After grouping, you might see something like:

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

Your next task is to modify the values in the api_param column where stat == 0. For instance, you want to replace data2, data3, and data4 if a subsequent API call confirms that the new data is valid. Here's the step you want to follow:

For each case_id, if stat == 0, send a request to a specific URL (https://example.com/over/there?name=a...[i]).

If the response returns a status of 200, replace the old value with the new one. If not, attempt with dates modified by adding a few data points (e.g., 7 days at a time) until you find a valid response or exhausting the options.

Given that you might have hundreds of cases, each with numerous data points to validate, using Python requests linearly will be painfully slow. That’s where concurrent.futures comes in to amplify your code's efficiency.

The Solution

Here’s how you can structure your code using concurrent.futures to handle the API calls concurrently, thus speeding up the processing significantly. Below is the example solution:

Step 1: Define the API Call Function

This function makes the actual call to your API and checks the response. If the API returns a 404 error, it indicates that the data isn’t valid.

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

Step 2: Implement Concurrency with a ThreadPoolExecutor

The main function uses the ThreadPoolExecutor to handle multiple calls concurrently. The check_api_call function will manage the logic of creating new date values based on the original values and using the API to validate them.

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

Explanation of Code

Future Execution: The ThreadPoolExecutor is used to run task_api concurrently, allowing multiple API calls to be processed simultaneously.

Retry Mechanism: If the initial API response is invalid, you modify the date and retry until you either find a valid date or exhaust the options.

Efficiency Improvement: This approach significantly reduces the waiting time compared to serial API calls, thus speeding up data processing tasks.

Conclusion

By adopting concurrent.futures alongside Pandas DataFrames, you can achieve impressive speed improvements when dealing with large datasets and numerous API calls. This pattern not only keeps your responses timely but also makes your overall data processing pipeline much more efficient.

Implement these strategies in your projects to see how much faster you can validate and modify your dataset. With Python's concurrent capabilities, handling large-scale data processing becomes a manageable task. Happy coding!