Pandas DataFrame MemoryError When Using concat() on Large DataFrames with NaN Values
Hey everyone, I'm running into an issue that's driving me crazy. I've looked through the documentation and I'm still confused about I'm working with a `MemoryError` when trying to concatenate two large DataFrames that contain a important number of NaN values. I'm using `pandas` version 1.3.5 and have two DataFrames, `df1` and `df2`, that I'm attempting to concatenate along the rows. Both DataFrames are around 5 million rows each, and when I attempt the following code: ```python import pandas as pd df1 = pd.DataFrame({'A': range(5000000), 'B': [None] * 5000000}) df2 = pd.DataFrame({'A': range(5000000, 10000000), 'B': [None] * 5000000}) result = pd.concat([df1, df2], ignore_index=True) ``` I get the following behavior message: ``` MemoryError: Unable to allocate 37.2 MiB for an array with shape (10000000,) and data type object ``` I have tried increasing my system's virtual memory but it hasn't helped. I've also looked into using `pd.concat()` with the `sort=False` parameter since I read that it could improve performance, but the MemoryError continues. Is there a more efficient way to handle concatenation of large DataFrames with many NaN values? Should I consider using a different approach like Dask or chunking the DataFrames before concatenating? Any advice on how to mitigate this scenario would be greatly appreciated. My development environment is Ubuntu. Thanks in advance!