Tutorial-Codebase-Knowledge/docs/NumPy Core/index.md

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Tutorial: NumPy Core

NumPy provides the powerful ndarray object, a multi-dimensional grid optimized for numerical computations on large datasets. It uses dtypes (data type objects) to precisely define the kind of data (like integers or floating-point numbers) stored within an array, ensuring memory efficiency and enabling optimized low-level operations. NumPy also features ufuncs (universal functions), which are functions like add or sin designed to operate element-wise on entire arrays very quickly, leveraging compiled code. Together, these components form the foundation for high-performance scientific computing in Python.

Source Repository: https://github.com/numpy/numpy/tree/3b377854e8b1a55f15bda6f1166fe9954828231b/numpy/_core

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    A1["dtype (Data Type Object)"]
    A2["ufunc (Universal Function)"]
    A3["multiarray Module"]
    A4["umath Module"]
    A5["Numeric Types"]
    A6["Array Printing"]
    A7["__array_function__ Protocol / Overrides"]
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    A2 -- "Operates element-wise on" --> A0
    A3 -- "Provides implementation for" --> A0
    A4 -- "Provides implementation for" --> A2
    A5 -- "Defines scalar types for" --> A1
    A6 -- "Formats for display" --> A0
    A6 -- "Uses for formatting info" --> A1
    A7 -- "Overrides functions from" --> A3
    A7 -- "Overrides functions from" --> A4
    A1 -- "References type hierarchy" --> A5