Pandas DataFrame Memory Leak When Using apply() on Large Datasets
Could someone explain I can't seem to get I tried several approaches but none seem to work... I'm working with a important performance scenario when using the `apply()` function on a large DataFrame (approximately 1 million rows) with Pandas version 1.5.3. After running the following code, it seems that my program consumes a large amount of memory, ultimately leading to a memory behavior: ```python import pandas as pd import numpy as np df = pd.DataFrame({'A': np.random.rand(1000000), 'B': np.random.rand(1000000)}) def custom_function(row): return row['A'] + row['B'] if row['A'] > 0.5 else row['B'] # This line causes memory issues result = df.apply(custom_function, axis=1) ``` I have tried optimizing my custom function by removing unnecessary computations, but the memory footprint remains high. I also attempted using `dask` to handle larger-than-memory computations, but it doesn't seem to resolve the memory scenario when using `apply()`. Is there an alternative approach to achieve the same result without running into a memory leak? Should I consider using vectorized operations instead, and if so, how can I refactor my code accordingly? Any tips on best practices for handling large DataFrames in Pandas would be greatly appreciated. This is part of a larger service I'm building. Any help would be greatly appreciated! What's the correct way to implement this? The project is a REST API built with Python. Has anyone dealt with something similar? I've been using Python for about a year now. Any pointers in the right direction?