[Python] How can I efficiently read a large CSV file in Python without running out of memory?

I'm working with a large CSV file (over 10 million rows) that I need to process in Python. However, when I try to load the entire file into a pandas DataFrame, I run into memory issues.

What are some efficient ways to read and process large CSV files in Python without running out of memory?

I've considered the following approaches:

• Using pandas.read_csv() with chunksize: This allows me to read the file in smaller chunks, but I'm unsure how to effectively process each chunk.
• Using dask.dataframe: I've heard that Dask can handle larger-than-memory datasets. Is it a good alternative?

Using csv.reader: Would this be a more memory-efficient way to read the file line by line?

Could someone provide examples or best practices for handling large CSV files in Python? Any tips on optimizing performance would also be appreciated!

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