scenarios reading large CSV file with Pandas - MemoryError in Python 3.9
I'm migrating some code and I've searched everywhere and can't find a clear answer... I've searched everywhere and can't find a clear answer. I'm trying to read a large CSV file (about 1.5GB) using Pandas in Python 3.9, but I keep running into a `MemoryError`. My current approach is straightforward: ```python import pandas as pd # Attempting to read the CSV file try: df = pd.read_csv('large_file.csv') except MemoryError as e: print(f'MemoryError: {e}') ``` My system has 8GB of RAM, and I expected Pandas to handle this size without crashing. I also tried using the `chunksize` parameter like this: ```python df_chunks = pd.read_csv('large_file.csv', chunksize=100000) for chunk in df_chunks: process(chunk) # Assuming process is a function I've defined ``` This way, I'm only loading a portion of the data at a time, but I still encounter memory issues when I try to process the chunks. The function `process` attempts to concatenate the chunks into a single DataFrame, which seems to be the point of failure. The behavior I receive is `ValueError: want to concatenate object of type 'NoneType'; only 'DataFrame' objects are valid`. I suspect that some of the chunks are empty or that I'm not handling them correctly. I've also considered trying Dask for handling larger-than-memory datasets, but I'd like to understand what I'm doing wrong here first. Any advice on how to efficiently read and process large CSV files with Pandas without running into memory issues would be greatly appreciated. For context: I'm using Python on Linux. Thanks in advance! This is part of a larger API I'm building. Any ideas what could be causing this? Is this even possible?