You are on the right track with chunksize, but there are more efficient ways to handle large CSV files in Python. Here are some optimized approaches:

1. Use Pandas with chunksize (Optimized) Your approach is valid, but you can speed it up by processing only necessary columns and using usecols and dtype to reduce memory usage: import pandas as pd

chunksize = 100000
dtype = {'column1': 'int32', 'column2': 'float32'} # Specify dtypes to save memory

for chunk in pd.read_csv('large_file.csv', chunksize=chunksize, usecols=['column1', 'column2'], dtype=dtype):
# Process chunk
print(chunk.head())
Which method should you choose?

Use chunksize if you prefer native Pandas. Use Dask if you need parallel processing. Use Modin if you want a drop-in replacement for Pandas with better speed. Use Vaex or PyArrow for low-memory operations. Hope this helps! 🚀