How to implement guide with reading large csv files using pandas - memoryerror in python 3.10
I'm experimenting with I'm trying to figure out I'm working with a `MemoryError` when trying to read a large CSV file (around 5GB) using Pandas in Python 3.10. My current approach is as follows: ```python import pandas as pd # Attempting to read the CSV file file_path = 'large_file.csv' try: df = pd.read_csv(file_path) except MemoryError as e: print(f'Caught MemoryError: {e}') ``` The behavior message I receive is `MemoryError: Unable to allocate 4.5 GiB for an array with shape (1000000, 20) and data type float64`. I have about 16GB of RAM on my machine, so I expected to be able to handle this size, at least in theory. I’ve tried several things to mitigate the question: 1. Loading the file in chunks by using the `chunksize` parameter: ```python chunk_size = 100000 chunks = [] for chunk in pd.read_csv(file_path, chunksize=chunk_size): chunks.append(chunk) df = pd.concat(chunks, ignore_index=True) ``` But this still leads to a `MemoryError` when concatenating the chunks. 2. Using the `low_memory=False` option as suggested in some forums: ```python df = pd.read_csv(file_path, low_memory=False) ``` Unfortunately, this didn’t help either. 3. I also tried filtering the columns I need while reading the file to reduce memory usage: ```python cols_to_use = ['column1', 'column3', 'column5'] df = pd.read_csv(file_path, usecols=cols_to_use) ``` This still resulted in the same behavior. Is there a best practice for efficiently loading large CSV files into a Pandas DataFrame? I'm also looking to avoid excessive swapping which could slow down my system. Any suggestions or alternative libraries that can handle large datasets more effectively would be greatly appreciated! For reference, this is a production CLI tool. Thanks for any help you can provide!