implementing reading large CSV files using pandas in Python - OutOfMemoryError
I'm upgrading from an older version and Hey everyone, I'm running into an issue that's driving me crazy. I'm working through a tutorial and I tried several approaches but none seem to work... I'm currently working with an `OutOfMemoryError` while trying to read a large CSV file (size: 5GB) using pandas in Python 3.9. I've tried using the basic `pd.read_csv()` method, but it fails due to memory constraints. Here’s the code I initially used: ```python import pandas as pd df = pd.read_csv('large_file.csv') ``` The behavior I'm working with is: ``` MemoryError: Unable to allocate array with shape (1000000, 50) and data type float64 ``` To mitigate this scenario, I attempted to read the file in chunks by using the `chunksize` parameter: ```python df_chunks = pd.read_csv('large_file.csv', chunksize=100000) for chunk in df_chunks: process(chunk) # Process each chunk ``` While this approach allows me to read the file without running out of memory, I’m struggling with aggregating results from each chunk because I need the entire dataset to perform a final calculation. Is there a more efficient way to handle large CSV files without running into memory issues while still being able to aggregate the results? Also, I’ve considered using Dask, but I’m uncertain about its overhead compared to pandas. Could anyone share best practices or alternative methods for handling large datasets in pandas or recommend whether Dask would be a suitable solution in this case? Thanks in advance! For context: I'm using Python on Ubuntu. Cheers for any assistance! Am I approaching this the right way? The project is a web app built with Python. What are your experiences with this?