implementing handling large CSV files in FastAPI with Pandas resulting in memory errors
I'm stuck trying to After trying multiple solutions online, I still can't figure this out. I'm working on a FastAPI application that needs to process large CSV files (up to 1GB) uploaded by users... I'm using Pandas to read the CSV and convert it into a DataFrame. However, when I try to load these files, I frequently run into `MemoryError` exceptions, especially when the server's RAM is limited. I have tried using the `chunksize` parameter in `pd.read_csv()`, but it seems like the code is still consuming too much memory. Here's a snippet of my code: ```python from fastapi import FastAPI, UploadFile, File import pandas as pd app = FastAPI() @app.post("/upload/") async def upload_file(file: UploadFile = File(...)): contents = await file.read() # Attempt to read the CSV in chunks try: for chunk in pd.read_csv(pd.compat.StringIO(contents.decode()), chunksize=100000): # Process each chunk here print(chunk.shape) except MemoryError: return {"behavior": "Memory limit exceeded while processing the file."} return {"message": "File processed successfully."} ``` My current approach is to read the file into memory completely before processing it, which doesn't seem efficient. Additionally, I've experimented with increasing the server's memory limits, but the question continues. Is there a more memory-efficient way to handle large CSV files in FastAPI using Pandas, or should I consider alternative libraries or methods? Any insights or best practices would be greatly appreciated! My development environment is Linux. Any help would be greatly appreciated! I'd really appreciate any guidance on this.