implementing MemoryError when Using `pd.read_csv()` on a Large File with Specific Data Types
This might be a silly question, but Hey everyone, I'm running into an issue that's driving me crazy... I am trying to read a large CSV file (over 5GB) into a Pandas DataFrame using `pd.read_csv()`, but I'm working with a `MemoryError`. The file contains various data types, including integers, floats, and strings. I want to optimize memory usage by specifying the data types for certain columns, but I am unsure how to do this effectively. My current approach looks like this: ```python import pandas as pd # Try to specify data types to save memory dtype_dict = { 'id': 'int32', 'value': 'float32', 'category': 'category' } try: df = pd.read_csv('large_file.csv', dtype=dtype_dict) except MemoryError as e: print(e) ``` However, I still receive a `MemoryError`, and the process crashes. I've also tried using `chunksize` to read the file in parts: ```python chunk_size = 100000 for chunk in pd.read_csv('large_file.csv', dtype=dtype_dict, chunksize=chunk_size): # Process each chunk print(chunk.shape) ``` This approach works, but it's quite slow as I need to concatenate the chunks afterward. Is there a better way to handle this situation? Iād like to load the entire DataFrame while efficiently managing memory usage without running into errors. Any help would be appreciated! My development environment is Windows. I'd really appreciate any guidance on this. My development environment is Linux. What am I doing wrong?