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Beyond NumPy: PyArrow’s Rising Role in Modern Data Science
But why can it not replace NumPy
NumPy has long been the cornerstone of data processing in Python, offering robust capabilities for handling arrays and matrices.
However, as modern datasets grow exponentially in scale and complexity, driven by the surge of AI and big data, NumPy begins to show its age.
In response, PyArrow, which is the Python bindings of Apache Arrow, emerges as a high-performance, cross-language alternative designed for today’s demanding data workflows.
It offers enhanced computing speed, better memory efficiency, support for complex data types, zero-copy data sharing, and so on.
Some people even said it would replace NumPy completely in the foreseeable future.
Will it?
Let’s have a deeper look through this article.
Key New Ideas of PyArrow
It’s meaningless to just implement NumPy again. There must be something new from PyArrow that doesn’t exist in NumPy.
So firstly, let’s see what essential new ideas the PyArrow brings.
