Issues Streaming Large CSV Files with Pandas in Python
I've searched everywhere and can't find a clear answer. I'm trying to process a large CSV file (around 2GB) using Pandas in Python, but I'm running into memory issues. I want to read the file in chunks to avoid loading the entire file into memory at once. However, when I use `pd.read_csv()` with the `chunksize` parameter, I encounter an unexpected behavior. The following code is what I’m using: ```python import pandas as pd chunk_size = 100000 # Number of rows per chunk for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size): # Process the chunk print(f'Processing chunk of {len(chunk)} rows') # Example processing average_value = chunk['some_column'].mean() print(f'Average of some_column: {average_value}') ``` The scenario arises when processing the second chunk; I get a `ValueError: want to mask with array containing NA / NaN values` for the column `some_column` that contains some NaN values. I’ve checked the data and confirmed that `some_column` does contain NaN values, but I thought Pandas would handle it gracefully during mean calculation. To troubleshoot, I tried adding `.dropna()` before the mean calculation, but it still throws the same behavior: ```python average_value = chunk['some_column'].dropna().mean() ``` Not only that, but the performance isn't great either. When running with `chunksize=100000`, it still seems slow, and I wonder if there's a more efficient way to handle this. I’m using Pandas version 1.3.3 and Python 3.8. Can anyone suggest how I can properly handle NaN values while streaming data, and improve the performance for large files like this? This is for a application running on Windows 11. What am I doing wrong?