DataFrame.apply() on a large pandas DataFrame in Python 3.9 results in MemoryError
This might be a silly question, but I'm currently working with a large pandas DataFrame (around 5 million rows) in Python 3.9, and I need to apply a custom function across a column. However, when I run the following code, I encounter a `MemoryError`. Here’s the code I'm using: ```python import pandas as pd def custom_function(value): # Simulate some complex computation return value * 2 # Create a large DataFrame for testing large_df = pd.DataFrame({'numbers': range(5_000_000)}) # Attempt to apply the custom function large_df['doubled'] = large_df['numbers'].apply(custom_function) ``` The behavior occurs during the `apply` operation, and the traceback shows this: ``` MemoryError: Unable to allocate array with shape (5000000,) and data type int64 ``` I've tried optimizing the function by simplifying its logic, but the question continues. I also increased the available memory on my machine, but it hasn’t made a difference. Is there a more memory-efficient way to apply a function to such a large DataFrame in pandas? Would switching to a different method like `numpy` or using `dask` for parallel processing help? Any guidance on how to handle this without running into memory issues would be greatly appreciated. Any ideas what could be causing this?