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# Tutorial: NumPy Core
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
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](https://github.com/numpy/numpy/tree/3b377854e8b1a55f15bda6f1166fe9954828231b/numpy/_core)
NumPy Core<sup>[View Repo](https://github.com/numpy/numpy/tree/3b377854e8b1a55f15bda6f1166fe9954828231b/numpy/_core)</sup> 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.
```mermaid
flowchart TD