implementing Memory Leak in Python 3.9 Using Pandas and NumPy for Large Datasets
I'm working through a tutorial and I'm stuck trying to I'm not sure how to approach I'm maintaining legacy code that I'm currently working with a dataset that contains millions of rows, and I'm working with a persistent memory leak scenario while using Python 3.9 with Pandas and NumPy. My script processes the data in chunks to avoid loading everything into memory at once, but I still notice that the memory usage keeps growing and eventually crashes my application. I've tried using `del` to delete unused variables and calling `gc.collect()` to force garbage collection, but the memory never seems to fully recover. Here’s a simplified version of my code: ```python import pandas as pd import numpy as np gc.collect() chunksize = 100000 for chunk in pd.read_csv('large_dataset.csv', chunksize=chunksize): # Perform some operations processed_chunk = chunk.apply(lambda x: np.log(x + 1)) # Attempting to clear memory del chunk gc.collect() # Final operations final_result = pd.concat(processed_chunks) ``` Despite trying to delete the `chunk` variable and calling `gc.collect()`, the memory usage in my system continues to increase until it exhausts available memory. I monitored the memory using `memory_profiler`, which confirmed that memory is retained by objects in the `processed_chunks`. I suspect that the `apply` function or the way I concatenate the chunks might be contributing to this leak. How can I effectively manage memory in this situation, or are there better practices to handle large datasets in Pandas while minimizing memory usage? For context: I'm using Python on macOS. The stack includes Python and several other technologies. What am I doing wrong? I'm working with Python in a Docker container on CentOS. Thanks in advance! I'm developing on Ubuntu 20.04 with Python. Thanks for any help you can provide!