chore: import upstream snapshot with attribution

2026-07-13 12:48:47 +08:00
commit 85f20078e8
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+---
+layout: default
+title: "NumPy Core"
+nav_order: 16
+has_children: true
+---
+
+# Tutorial: NumPy Core
+
+> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+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
+    A0["ndarray (N-dimensional array)"]
+    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"]
+    A0 -- "Has data type" --> A1
+    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
+```