scenarios processing large CSV files in Pandas: MemoryError on read_csv()
I'm trying to configure Does anyone know how to I've been struggling with this for a few days now and could really use some help... I'm working on a data analysis project that involves processing large CSV files using Pandas (version 1.3.3). When I try to read a CSV file that is around 5GB in size with the following code, I encounter a `MemoryError`: ```python import pandas as pd df = pd.read_csv('large_file.csv') ``` I've already tried adjusting the `chunksize` parameter to read the file in smaller chunks, but I still run into memory issues. Here’s the code I attempted: ```python chunks = pd.read_csv('large_file.csv', chunksize=100000) df = pd.concat(chunks) ``` However, even with chunks, the memory usage still spikes significantly, causing my system to crash. I’ve also considered using `dask`, but I’m not sure if it’s necessary or if I can optimize my current approach. Additionally, I checked my system memory, and I have 16GB RAM, which should be sufficient for processing, but it seems like Pandas is trying to load the entire dataset into memory at once. Is there a way to efficiently read and process large CSV files without running into memory issues? Any suggestions on best practices or specific parameters to use would be greatly appreciated. Any examples would be super helpful. This is part of a larger web app I'm building. Is there a better approach? The stack includes Python and several other technologies. Thanks for your help in advance!