1.3. NumPy: creating and manipulating numerical data

Authors: Emmanuelle Gouillart, Didrik Pinte, Gaël Varoquaux, and Pauli Virtanen

This chapter gives an overview of NumPy, the core tool for performant numerical computing with Python.

---

• 1.3.1. The NumPy array object
  • 1.3.1.1. What are NumPy and NumPy arrays?
    • NumPy arrays
    • NumPy Reference documentation
    • Import conventions
  • 1.3.1.2. Creating arrays
    • Manual construction of arrays
    • Functions for creating arrays
  • 1.3.1.3. Basic data types
  • 1.3.1.4. Basic visualization
  • 1.3.1.5. Indexing and slicing
  • 1.3.1.6. Copies and views
  • 1.3.1.7. Fancy indexing
    • Using boolean masks
    • Indexing with an array of integers
• 1.3.2. Numerical operations on arrays
  • 1.3.2.1. Elementwise operations
    • Basic operations
    • Other operations
  • 1.3.2.2. Basic reductions
    • Computing sums
    • Other reductions
  • 1.3.2.3. Broadcasting
  • 1.3.2.4. Array shape manipulation
    • Flattening
    • Reshaping
    • Adding a dimension
    • Dimension shuffling
    • Resizing
  • 1.3.2.5. Sorting data
  • 1.3.2.6. Summary
• 1.3.3. More elaborate arrays
  • 1.3.3.1. More data types
    • Casting
    • Different data type sizes
  • 1.3.3.2. Structured data types
  • 1.3.3.3. maskedarray: dealing with (propagation of) missing data
• 1.3.4. Advanced operations
  • 1.3.4.1. Polynomials
    • More polynomials (with more bases)
  • 1.3.4.2. Loading data files
    • Text files
    • Images
    • NumPy’s own format
    • Well-known (& more obscure) file formats
• 1.3.5. Some exercises
  • 1.3.5.1. Array manipulations
  • 1.3.5.2. Picture manipulation: Framing a Face
  • 1.3.5.3. Data statistics
  • 1.3.5.4. Crude integral approximations
  • 1.3.5.5. Mandelbrot set
  • 1.3.5.6. Markov chain
• 1.3.6. Full code examples
  • 1.3.6.1. Full code examples for the numpy chapter