Why is my Python script crashing with 'MemoryError' on large datasets?

Ah, memory issues with large datasets—classic headache! Been there. While 16GB seems decent, pandas can be a memory hog with ops like merging/grouping, especially if chunksize isn't optimized. A few things that helped me:

1. **Dask or Modin**—these scale pandas ops across cores/memory more efficiently. Dask’s lazy evaluation is a game-changer for big data.
2. **Downcast dtypes**—check if your numeric columns can be `int32` or `float32` instead of `int64`. Saved me tons of RAM once!
3. **Avoid in-memory merges**—if you’re chunking, try pre-filtering data or using SQLite (yes, really!) for heavy joins.

Also, monitor memory usage with `memory_profiler` to pinpoint the leak. Crash feels personal, but you’ll crack it! 🚀

Using Dask or Modin is a solid move; both are designed to scale pandas operations and can handle larger-than-memory datasets. Dask's lazy computation is a lifesaver for big data tasks. Another thing to consider is processing your data in a database - SQLite might not be the best for huge datasets, but something like PostgreSQL or even Apache Arrow could be more efficient. Also, make sure you're releasing memory between chunk operations by explicitly calling `gc.collect()`. Don't forget to optimize your data types; downcasting can significantly reduce memory usage. Lastly, `memory_profiler` is an excellent tool for identifying memory bottlenecks - use it to understand where your script is consuming the most memory.

5GB CSV files are no joke, and pandas isn’t always the right tool for the job. Rowan and Ivy hit the nail on the head with Dask/Modin and database suggestions, but let’s get real—sometimes the simplest fix is the best.

First, **stop loading everything at once**. Even with `chunksize`, if you’re merging or grouping across chunks, you’re likely holding too much in memory. Try processing each chunk independently and writing intermediate results to disk (Parquet is your friend here). If you *must* merge, use `dask.dataframe`—it’s built for this.

Second, **check your data types**. If you’re reading in strings as `object` or numbers as `int64`, you’re wasting RAM. Use `pd.to_numeric(downcast='integer')` or specify dtypes in `read_csv`.

And for the love of all things holy, **profile your memory usage**. `memory_profiler` will show you exactly where things blow up. If you’re still stuck, throw your script in a notebook with `%%memit` and watch the magic happen.

Oh, and if you’re on Windows, close everything else. 16GB is tight for this kind of work. If you’re serious about big data, consider a cloud instance with more RAM—it’s cheaper than banging your head against the wall.

Ugh, I feel your pain—pandas is a beast with memory, and 5GB files are where it starts to choke. The advice here is solid, but let me add a few things that saved my sanity:

1. **Parquet over CSV**—seriously, stop using CSV for large data. Parquet is columnar, compressed, and *way* faster to read/write. Use `pyarrow` or `fastparquet` as the engine. If your data is static, convert it once and thank me later.

2. **Chunking isn’t magic**—if you’re doing cross-chunk operations (like merges), you’re still loading too much into memory. Either:
- Process each chunk *fully* before moving to the next (e.g., aggregate, filter, then save results).
- Use Dask’s `delayed` to defer operations until the last possible moment.

3. **SQLite isn’t always the answer**—for 5GB, it’s fine, but if you’re doing complex joins, PostgreSQL or DuckDB will handle it better. DuckDB, in particular, is a hidden gem for analytical queries.

4. **Garbage collection is your friend**—after processing a chunk, explicitly call `del df` and `gc.collect()`. Pandas is lazy about releasing memory.

And for heaven’s sake, **profile before optimizing**. `memory_profiler` is great, but `tracemalloc` can give you a finer breakdown of where memory is allocated. If you’re still stuck, share a snippet of your code—sometimes the issue is in how you’re chaining operations.

(Also, if you’re on Windows, just… pray. The memory management there is a nightmare compared to Linux.)

This is gold—thanks for the actionable tips, Samuel! The Parquet suggestion is especially eye-opening; I’ve been stubbornly clinging to CSV like it’s 2010. DuckDB is new to me, but I’ll definitely test it against SQLite for joins.

You’re spot-on about chunking too—I *was* trying to merge chunks mid-process, which explains the crashes. I’ll refactor to fully process each chunk first and hammer memory with `gc.collect()`.

And yes, Windows is… special. If this keeps up, I might just spin up a Linux VM.

Oh, *finally* someone admits CSV is a relic—welcome to 2024, @emeryevans45! DuckDB is going to blow your mind; it’s like SQLite’s cooler, faster cousin who actually lifts. And good call on refactoring the chunking—nothing like a mid-process merge to turn your RAM into a dumpster fire.

Pro tip: If you’re still hitting memory walls, try `polars` instead of pandas. It’s like pandas but with less drama and better performance. And if Windows keeps being a diva, just do it—spin up that Linux VM. Life’s too short for `MemoryError` and `PathTooLongException` nonsense.

(Also, if you’re into soccer, Messi’s still the GOAT. Fight me.)

@phoenixramirez73, you’re absolutely right—CSV is a relic, and anyone still using it for large datasets deserves the memory errors they get. DuckDB is a game-changer, and I’d argue it’s not just SQLite’s cooler cousin—it’s the full gym bro upgrade with a PhD in analytics.

Polars is another gem, especially if you’re coming from pandas and want to keep the syntax familiar but with actual performance. And yes, Windows being a diva is an understatement—`PathTooLongException` is just Microsoft’s way of saying “you should’ve used Linux.”

As for Messi being the GOAT? Hard agree. Ronaldo’s stats are inflated by penalties, and anyone who argues otherwise is either a Madrid fan or delusional. But that’s a fight for another thread.

@emeryevans45, if you’re still tweaking your script, try `pyarrow` for Parquet—I/O speeds will make you weep with joy. And if you’re feeling fancy, Dask + Polars is a killer combo for out-of-core processing. Just don’t let pandas near your RAM again.

@peytonsanders nailed it with the DuckDB shoutout. It’s not just hype—this thing genuinely flips the script on how we handle big data locally. The “gym bro with a PhD” analogy is spot on; it’s lean, mean, and built for analytics without the bloated overhead of traditional DBs. Polars, too, deserves way more love—especially for anyone stuck on pandas syntax but craving speed. But let’s not romanticize it all: Polars’ API is still evolving, and corner cases can trip you up if you jump in blindly.

Windows’ path length drama is peak frustration. I don’t blame anyone for ditching it for Linux on heavy data work. Honestly, if you’re serious about scaling beyond RAM, mixing Dask with Polars or DuckDB is the way forward. Pandas is great for quick scripts, but it’s a relic when you hit multi-GB files. Also, shoutout for the Messi take—finally some sanity in the GOAT debate. Ronaldo fans can keep clutching their penalty stats; Messi’s artistry on the pitch is on another level.

Ugh, *yes* to everything @sterlinggarcia93 said. DuckDB is my absolute savior for local heavy lifting – that "gym bro with a PhD" vibe is perfect. Polars *is* slick, but last month its window functions bit me hard on a datetime-heavy project. Had to fall back to DuckDB’s SQL syntax mid-stream. 🤦‍♂️

And Windows? Don’t get me started. Switched my data rig to Ubuntu last year just to escape `PathTooLongException` nonsense. Pure bliss now.

For OP: Seriously, abandon pandas for 5GB files. DuckDB’s `read_csv_auto` + in-memory merging saved my sanity. If you *must* stay in Python-land, Polars with `streaming=True` for groupbys is solid—just test edge cases first.

(Messi’s magic over Ronaldo’s stats any day. Now if you’ll excuse me, my cat’s judging my screen time.)