How to Efficiently Process Large Datasets in Python Without Crashing Your Application

Discover how to process large amounts of data in Python by breaking it down into manageable chunks. Avoid application crashes and enhance your database writing efficiency with these simple techniques.
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Handling Large Datasets in Python: A Practical Solution

Processing a large volume of data can be daunting, particularly when working with Python libraries such as Pandas, NumPy, or SQLite. A common hurdle faced by developers is the application crashing due to excessive data handled at once. In this guide, we will explore a solution to this problem using a hypothetical scenario based on downloading and writing OHLC (Open, High, Low, Close) data for a significant number of stock symbols.

The Problem: Crashing on Large Data Imports

Consider a scenario where you need to download two years' worth of OHLC data for 10,000 stock symbols. The original approach downloads all data in one go. However, if you attempt to pull the complete dataset, your application crashes, but it works when limited to just 20%.

This discrepancy is common because handling large datasets at once can lead to high memory usage, resulting in crashes.

The Solution: Chunking Your Data

To avoid crashing your application while still processing all the data, a practical solution is to process the data in smaller, manageable chunks. Here's how you can achieve this:

Steps to Implement Chunking

Define Chunk Size: Determine an optimal chunk size that your application can handle without crashing. In our case, we found that processing 2,000 records at a time works effectively.

Calculate Number of Passes: Divide the total number of records by the chunk size to find out how many passes you will need to perform.

Iterate Through Chunks: Loop through the dataset in increments of your chosen chunk size.

Write to Database in Segments: Instead of writing all data in one go, append each chunk to your database iteratively.

Sample Code Implementation

Here’s an example code snippet that utilizes the above steps:

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Conclusion: Embracing Efficient Data Processing

By implementing chunking, you can effectively manage the loading and processing of large datasets without overloading your application. This technique not only prevents crashes but also optimizes your data writing process to the database.

Feel free to adapt this approach to suit your needs, ensuring smooth execution even with significant volumes of data!