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docs/NumPy Core/01_ndarray__n_dimensional_array_.md

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+---
+layout: default
+title: "ndarray (N-dimensional array)"
+parent: "NumPy Core"
+nav_order: 1
+---
+
 # Chapter 1: ndarray (N-dimensional array)
 
 Welcome to the NumPy Core tutorial! If you're interested in how NumPy works under the hood, you're in the right place. NumPy is the foundation for scientific computing in Python, and its core strength comes from a special object called the `ndarray`.

docs/NumPy Core/02_dtype__data_type_object_.md

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+---
+layout: default
+title: "dtype (data type object)"
+parent: "NumPy Core"
+nav_order: 2
+---
+
 # Chapter 2: dtype (Data Type Object)
 
 In [Chapter 1: ndarray (N-dimensional array)](01_ndarray__n_dimensional_array_.md), we learned that NumPy's `ndarray` is a powerful grid designed to hold items **of the same type**. This "same type" requirement is fundamental to NumPy's speed and efficiency. But how does NumPy know *what kind* of data it's storing? That's where the `dtype` comes in!

docs/NumPy Core/03_ufunc__universal_function_.md

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+---
+layout: default
+title: "ufunc (universal function)"
+parent: "NumPy Core"
+nav_order: 3
+---
+
 # Chapter 3: ufunc (Universal Function)
 
 Welcome back! In [Chapter 1: ndarray (N-dimensional array)](01_ndarray__n_dimensional_array_.md), we met the `ndarray`, NumPy's powerful container for numerical data. In [Chapter 2: dtype (Data Type Object)](02_dtype__data_type_object_.md), we learned how `dtype`s specify the exact *kind* of data stored within those arrays.

docs/NumPy Core/04_numeric_types___numerictypes__.md

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+---
+layout: default
+title: "Numeric Types (numerictypes)"
+parent: "NumPy Core"
+nav_order: 4
+---
+
 # Chapter 4: Numeric Types (`numerictypes`)
 
 Hello again! In [Chapter 3: ufunc (Universal Function)](03_ufunc__universal_function_.md), we saw how NumPy uses universal functions (`ufuncs`) to perform fast calculations on arrays. We learned that these `ufuncs` operate element by element and can handle different data types using optimized C loops.

docs/NumPy Core/05_array_printing___arrayprint__.md

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+---
+layout: default
+title: "Array Printing (arrayprint)"
+parent: "NumPy Core"
+nav_order: 5
+---
+
 # Chapter 5: Array Printing (`arrayprint`)
 
 In the previous chapter, [Chapter 4: Numeric Types (`numerictypes`)](04_numeric_types___numerictypes__.md), we explored the different kinds of data NumPy can store in its arrays, like `int32`, `float64`, and more. Now that we know about the arrays ([`ndarray`](01_ndarray__n_dimensional_array_.md)), their data types ([`dtype`](02_dtype__data_type_object_.md)), the functions that operate on them ([`ufunc`](03_ufunc__universal_function_.md)), and the specific number types (`numerictypes`), a practical question arises: How do we actually *look* at these arrays, especially if they are very large?

docs/NumPy Core/06_multiarray_module.md

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+---
+layout: default
+title: "Multiarray Module"
+parent: "NumPy Core"
+nav_order: 6
+---
+
 # Chapter 6: multiarray Module
 
 Welcome back! In [Chapter 5: Array Printing (`arrayprint`)](05_array_printing___arrayprint__.md), we saw how NumPy takes complex arrays and presents them in a readable format. We've now covered the array container ([`ndarray`](01_ndarray__n_dimensional_array_.md)), its data types ([`dtype`](02_dtype__data_type_object_.md)), the functions that compute on them ([`ufunc`](03_ufunc__universal_function_.md)), the catalog of types ([`numerictypes`](04_numeric_types___numerictypes__.md)), and how arrays are displayed ([`arrayprint`](05_array_printing___arrayprint__.md)).

docs/NumPy Core/07_umath_module.md

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+---
+layout: default
+title: "Umath Module"
+parent: "NumPy Core"
+nav_order: 7
+---
+
 # Chapter 7: umath Module
 
 Welcome to Chapter 7! In [Chapter 6: multiarray Module](06_multiarray_module.md), we explored the core C engine that defines the `ndarray` object and handles fundamental operations like creating arrays and accessing elements. We saw that the actual power comes from C code.

docs/NumPy Core/08___array_function___protocol___overrides___overrides__.md

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+---
+layout: default
+title: "__array_function__ Protocol (overrides)"
+parent: "NumPy Core"
+nav_order: 8
+---
+
 # Chapter 8: __array_function__ Protocol / Overrides (`overrides`)
 
 Welcome to the final chapter of our NumPy Core exploration! In [Chapter 7: umath Module](07_umath_module.md), we learned how NumPy implements its fast, element-wise mathematical functions (`ufuncs`) using optimized C code. We've seen the core components: the `ndarray` container, `dtype` descriptions, `ufunc` operations, numeric types, printing, and the C modules (`multiarray`, `umath`) that power them.