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Lesson 1.6 – NumPy for Beginners (Agenda)

Total Duration: 3 hours
Environment: Google Colab or VS Code Jupyter

Welcome & Outcomes

  • Introduce the session and goals:
    • Understand what NumPy is and why it matters
    • Create and inspect basic arrays
    • See how NumPy compares to Python lists on performance
    • Learn about array indexing, slicing, and broadcasting
    • Apply universal functions and perform linear algebra operations

Part 1: Performance Benchmark

Notebook: "Part 1: Performance Benchmark" section and code cell.

  • Walk through:
    • import numpy as np
    • Creating a 1,000,000‑element NumPy array and Python list
    • %timeit comparison: NumPy vectorized multiply vs list comprehension
  • Quick learner activity:
    • Learners run the timing cell and briefly describe what they observe

Part 2: The ndarray (N-dimensional array)

Notebook: "Part 2: The ndarray" section.

  • Demonstrate:
    • Creating arrays from Python sequences
    • Array attributes: shape, dtype, ndim
    • Data types and casting using astype
  • [EXERCISE 1: Creation & Casting]
    • Create a 3x4 array of all ones using np.ones()
    • Cast this array to float32
    • Create an array of strings representing numbers and cast to float

Part 3: Arithmetic & Broadcasting

Notebook: "Part 3: Arithmetic & Broadcasting" section.

  • Demonstrate:
    • Element-wise arithmetic operations (addition, multiplication, etc.)
    • Broadcasting: how scalars and arrays of different shapes work together
    • Examples: arr * arr, 1 / arr
  • Discussion:
    • How broadcasting makes code cleaner and more efficient

Part 4: Indexing and Slicing

Notebook: "Part 4: Indexing and Slicing" section.

  • Demonstrate:
    • 1D array indexing (similar to Python lists)
    • 2D array indexing with [row, col] syntax
    • Array slices are views (not copies)
    • Modifying a slice affects the original array
  • [EXERCISE 3: Complex Filtering]
    • Select all scores where the name is NOT 'Bob'
    • Select scores for 'Bob' or 'Will' using the | operator
    • Find all scores less than 80 and set them to 0

Part 6: Universal Functions (ufuncs) and Methods

Notebook: "Part 6: Universal Functions (ufuncs) and Methods" section.

  • Demonstrate:
    • Unary ufuncs: sqrt, exp
    • Binary ufuncs: add, maximum
    • Statistical methods: mean, sum, std
    • Computing statistics along axes (axis=0, axis=1)
  • Discussion:
    • When to use ufuncs vs loops for performance

Part 7: Linear Algebra

Notebook: "Part 7: Linear Algebra" section.

  • Demonstrate:
    • Element-wise multiplication with * vs matrix multiplication
    • Matrix multiplication with .dot() or @ operator
    • Example: multiplying two matrices
  • [EXERCISE 4: Reshaping & Statistics]
    • Create an array of 15 integers using arange(15) and reshape it to (3, 5)
    • Calculate the average value of each row
    • Use np.unique() to find distinct elements
    • Transpose the reshaped array using .T and check the new shape

Wrap-up & Next Steps

  • Review key takeaways:
    • NumPy's performance advantages
    • Creating and manipulating ndarrays
    • Broadcasting and vectorization
    • Linear algebra basics
  • Preview next lesson topics
  • Q&A