Error while reading large files with Pandas: MemoryError on DataFrame creation
I need help solving I'm currently working on a project where I need to read a large CSV file (around 5GB) using Pandas (version 1.3.3). When I attempt to load the file into a DataFrame using `pd.read_csv()`, I keep encountering a `MemoryError`. I've tried various methods to optimize memory usage, but none have worked so far. Here’s the code snippet I’m using: ```python import pandas as pd # Attempting to read a large CSV file try: df = pd.read_csv('large_file.csv') except MemoryError as e: print('MemoryError:', e) ``` I’ve also tried using the `chunksize` parameter, which allows me to load the data in smaller chunks, but this complicates the subsequent data processing steps I need to perform. ```python chunks = pd.read_csv('large_file.csv', chunksize=100000) for chunk in chunks: # Process each chunk pass ``` While chunking works, it’s not ideal as I need to perform operations that require accessing the entire DataFrame. I also attempted using `dtypes` to specify data types upfront to reduce memory usage, but it still results in the same error: ```python column_types = { 'column1': 'int32', 'column2': 'float32', ... } df = pd.read_csv('large_file.csv', dtype=column_types) ``` Additionally, I’ve considered using Dask or other libraries that are intended for larger-than-memory datasets. However, I’m unsure if this is the best route or if there are optimizations within Pandas itself that I might be missing. Can anyone suggest the best approach to handle this situation effectively? This is part of a larger API I'm building. What's the best practice here? I've been using Python for about a year now. I'd really appreciate any guidance on this.