scenarios reading large files in Python with pandas - MemoryError on 2GB CSV
I'm trying to read a large CSV file (about 2GB) using pandas in Python 3.10, but I'm running into a `MemoryError`. My machine has 8GB of RAM, and I've tried using the `chunksize` parameter to read the file in smaller parts. However, I'm still working with performance optimization. Here's the code I have so far: ```python import pandas as pd try: for chunk in pd.read_csv('large_file.csv', chunksize=100000): # Process each chunk print(chunk.shape) except MemoryError as e: print(f'Memory behavior occurred: {e}') ``` When I run this code, I get an behavior message: ``` MemoryError: Unable to allocate 2.00 GiB for an array with shape (100000, 15) and data type float64 ``` I've also tried to use `dask` to read the file, thinking it might handle larger datasets better, but I encountered similar memory issues. Here's the Dask code I attempted: ```python import dask.dataframe as dd df = dd.read_csv('large_file.csv') df.compute() ``` This resulted in an even larger memory consumption before crashing. Is there a more efficient way to read or process large CSV files in pandas without hitting memory limits? Any suggestions for optimizing this operation or alternative libraries that could help?