12 Minutes to Learn NumPy (Python): Arrays, Reshape, Arange & Linspace

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12 Minutes to Learn NumPy (Python): Arrays, Reshape, Arange & Linspace — Learn the essentials FAST. NumPy array basics, reshape examples, arange vs linspace, random sampling and matrix tips — all in one compact Python crash course.

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What is NumPy? NumPy (Numerical Python) is the foundational Python library for fast numeric arrays and vectorized math — the go-to toolkit for data science, ML, and scientific computing. Learn the core functions you’ll actually use on real projects.
NumPy

In this numpy tutorial you'll get:
• Hands-on NumPy array creation and indexing (np.array, np.arange, np.linspace).
• How and when to use np.reshape to change array shapes (with live examples).
• np.arange vs np.linspace — choose the right range function and avoid common pitfalls.
• Using np.random for sampling, seeds, and test data.
• Quick look at NumPy matrices, broadcasting, and common pitfalls new Python users face.

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🎬 Timestamps:
0:00 | Intro / What is NumPy? (NumPy Python Basics for Beginners)
0:25 | Why Use NumPy in Python (Speed vs Python Lists)
1:20 | Create Arrays in NumPy: np.array, np.arange, np.linspace Explained
3:53 | NumPy Reshape Examples: np.reshape 1D, 2D, and 3D Arrays
6:30 | Indexing and Slicing NumPy Arrays (Beginner Tutorial)
8:30 | NumPy Mathematical Operations with Arrays
9:30 | NumPy Statistical Operations: np.mean, np.std Examples
10:55 | Boolean Indexing with NumPy Arrays
12:25 | Simple NumPy Broadcasting Explained
13:52 | NumPy Random Module: np.random, np.seed, np.randint
15:00 | Array Concatenation in NumPy (Join Multiple Arrays)
16:16 | Next Steps & NumPy Resources

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#numpy #pythontutorial #numpyarrays #learnpython #datascience