How to Handle 'Out of Memory' Errors in Python when Dealing with Large Datasets

The 'Out of Memory' error in Python often occurs when handling large datasets that exceed the available system memory.

This error is common when loading large files into memory or working with extensive data manipulations.

To handle this, start by analyzing your code to identify areas where memory usage can be optimized.

One strategy is to use streaming techniques, such as reading data in chunks instead of loading the entire dataset into memory.

Libraries like pandas and dask provide tools for working with large datasets without consuming excessive memory by processing data in smaller chunks.

If possible, use in-place operations to reduce memory usage.

You can also consider using a database or file-based storage systems like SQLite or Parquet to handle large datasets more efficiently.

To prevent memory overflow, consider setting limits on the size of the datasets processed and use garbage collection mechanisms to clear memory when it's no longer needed.