[Python] How do I efficiently parse a large (1GB+) CSV file in Python while handling memory...

I'm working on a Python script that needs to process a very large CSV file, potentially exceeding 1GB in size. My current approach involves loading the entire file into a Pandas DataFrame, but this is leading to MemoryError issues.

I need to perform the following operations on the data:

• Filter rows based on specific conditions in columns 'A' and 'B'.
• Aggregate data by a 'Category' column, summing values in 'Value1' and 'Value2'.
• Write the processed data to a new CSV file.

What are the most efficient and memory-friendly strategies in Python to handle such large CSV files? I'm open to using libraries other than Pandas if they offer better performance for this use case, but a Pandas-centric solution that avoids memory issues would be ideal.

Specifically, I'm looking for advice on:

1. Techniques for processing the file in chunks or iteratively.
2. Best practices for memory optimization when reading and manipulating large datasets.
3. Any specific libraries or patterns that are well-suited for this problem (e.g., csv module, dask, vaex, etc.).I'm working on a Python script that needs to process a very large CSV file, potentially exceeding 1GB in size. My current approach involves loading the entire file into a Pandas DataFrame, but this is leading to MemoryError issues.

I need to perform the following operations on the data:

• Filter rows based on specific conditions in columns 'A' and 'B'.
• Aggregate data by a 'Category' column, summing values in 'Value1' and 'Value2'.
• Write the processed data to a new CSV file.

What are the most efficient and memory-friendly strategies in Python to handle such large CSV files? I'm open to using libraries other than Pandas if they offer better performance for this use case, but a Pandas-centric solution that avoids memory issues would be ideal.

Specifically, I'm looking for advice on:

1. Techniques for processing the file in chunks or iteratively.
2. Best practices for memory optimization when reading and manipulating large datasets.
3. Any specific libraries or patterns that are well-suited for this problem (e.g., csv module, dask, vaex, etc.).

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