How to efficiently group and aggregate a large DataFrame in Pandas without running into memory issues?
I'm stuck on something that should probably be simple. I'm dealing with I'm relatively new to this, so bear with me. I'm stuck on something that should probably be simple. I've searched everywhere and can't find a clear answer. I'm working with a very large DataFrame (over 10 million rows) in Pandas version 1.5.0, and I'm trying to group by a specific column and then aggregate some values. However, I'm running into memory issues when I try to use `groupby` and `agg` functions together. The DataFrame has multiple columns, and the column I want to group by is a categorical variable, while I want to compute the mean and sum of other numeric columns. My current code looks like this: ```python import pandas as pd df = pd.read_csv('large_dataset.csv') # Attempting to group by 'category' and aggregate 'value1' and 'value2' grouped_df = df.groupby('category').agg({'value1': 'mean', 'value2': 'sum'}) ``` When I run this, I get an `OutOfMemoryError` due to the size of the DataFrame. I've also tried using `df.memory_usage(deep=True)` to check the memory usage, which shows that I am close to my system's limit. I have 16GB of RAM and am currently using a 64-bit Python installation. I've considered chunking the DataFrame using `pd.read_csv(..., chunksize=...)`, but I'm unsure how to aggregate the results from each chunk efficiently. Is there a way to handle this scenario without crashing my system? Any tips on optimizing memory usage or alternative approaches would be greatly appreciated! My development environment is Windows. Any help would be greatly appreciated! For context: I'm using Python on Ubuntu. This is happening in both development and production on Windows 10. Thanks for any help you can provide! I'm on CentOS using the latest version of Python. Is this even possible?