scenarios reading large CSV files in Python using pandas - performance optimization and memory errors
I'm building a feature where I'm trying to configure I've searched everywhere and can't find a clear answer. I'm sure I'm missing something obvious here, but I'm sure I'm missing something obvious here, but I'm trying to read a large CSV file (approximately 1.5 GB) using pandas in Python 3.9, but I keep working with memory errors, specifically `MemoryError: Unable to allocate array with shape (n, m) and data type float64`... I've tried using the `chunksize` parameter in `pd.read_csv`, but it still seems to be hitting memory limits. Here’s what I attempted: ```python import pandas as pd # Attempting to read the CSV file in chunks chunk_size = 50000 # Number of rows per chunk chunks = [] for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size): # Process each chunk here if needed, for example: processed_chunk = chunk.dropna() # Just an example of processing chunks.append(processed_chunk) # Concatenating all processed chunks final_df = pd.concat(chunks, ignore_index=True) ``` This approach still leads to high memory consumption, and I eventually crash my Jupyter notebook. I also tried using `dask` for parallel processing, but it seems to be slower than expected even with `dask.dataframe.read_csv`. Is there a more efficient way to read large CSV files in pandas without overwhelming system memory? Are there specific memory optimizations or file reading techniques I should consider? Any help would be appreciated! Thanks in advance! I'm working on a web app that needs to handle this. How would you solve this? The stack includes Python and several other technologies. I've been using Python for about a year now. Thanks, I really appreciate it!