How to efficiently handle large CSV files in Python 3.9 without running out of memory?
I'm not sure how to approach I need help solving I've looked through the documentation and I'm still confused about I've searched everywhere and can't find a clear answer... I'm working on a project where I need to process a large CSV file (about 2 GB in size) using Python 3.9. I've been using the `pandas` library to read the file, but I'm running into performance optimization and even some memory errors while loading the entire dataset into a DataFrame. Specifically, I'm working with a `MemoryError` when trying to execute `df = pd.read_csv('large_file.csv')`. To mitigate this, I tried using the `chunksize` parameter to read the CSV in smaller chunks, like so: ```python import pandas as pd chunk_size = 100000 chunks = [] for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size): chunks.append(chunk) # Concatenate chunks into one DataFrame full_df = pd.concat(chunks, ignore_index=True) ``` However, this approach still consumes too much memory, and the concatenation at the end seems to be the bottleneck, consuming even more resources. I also considered using `dask`, but I'm not fully sure how to implement it effectively. Can someone suggest best practices for processing large CSV files in a memory-efficient way? Are there alternative libraries or methods that can guide to achieve this without running into memory issues? I would be open to any suggestions that align with `pandas` or even exploring `dask` if it can provide a important performance boost. Thank you! For context: I'm using Python on Ubuntu. How would you solve this? For context: I'm using Python on macOS. This is my first time working with Python LTS. Could this be a known issue? I've been using Python for about a year now. Thanks in advance!