In Python scientific computing, how do I stream data?

In Python scientific computing, streaming data allows you to process large datasets in real time without loading the entire dataset into memory. Libraries like `pandas`, `NumPy`, and `Dask` offer tools to handle such scenarios efficiently.

Streaming data can be particularly beneficial when dealing with large files or live data feeds. For instance, using generators or libraries like `pyarrow` can help in reading and writing data in chunks. Here's an example of how to implement streaming data using a generator:

def read_large_file(file_path): with open(file_path, 'r') as file: for line in file: yield line.strip() for line in read_large_file('large_data.txt'): process(line)

Python scientific computing streaming data pandas NumPy Dask data processing generators

Related Questions

How do I avoid rehashing overhead with std::set in multithreaded code?

How do I find elements with custom comparators with std::set for embedded targets?

How do I erase elements while iterating with std::set for embedded targets?

How do I provide stable iteration order with std::unordered_map for large datasets?

How do I reserve capacity ahead of time with std::unordered_map for large datasets?

How do I erase elements while iterating with std::unordered_map in multithreaded code?

How do I provide stable iteration order with std::map for embedded targets?

How do I provide stable iteration order with std::map in multithreaded code?

How do I avoid rehashing overhead with std::map in performance-sensitive code?

How do I merge two containers efficiently with std::map for embedded targets?