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.

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• 1.3.1. The Numpy array object
  • 1.3.1.1. What are Numpy and Numpy arrays?
    • 1.3.1.1.1. Numpy arrays
    • 1.3.1.1.2. Numpy Reference documentation
    • 1.3.1.1.3. Import conventions
  • 1.3.1.2. Creating arrays
    • 1.3.1.2.1. Manual construction of arrays
    • 1.3.1.2.2. 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
    • 1.3.1.7.1. Using boolean masks
    • 1.3.1.7.2. Indexing with an array of integers
• 1.3.2. Numerical operations on arrays
  • 1.3.2.1. Elementwise operations
    • 1.3.2.1.1. Basic operations
    • 1.3.2.1.2. Other operations
  • 1.3.2.2. Basic reductions
    • 1.3.2.2.1. Computing sums
    • 1.3.2.2.2. Other reductions
  • 1.3.2.3. Broadcasting
  • 1.3.2.4. Array shape manipulation
    • 1.3.2.4.1. Flattening
    • 1.3.2.4.2. Reshaping
    • 1.3.2.4.3. Adding a dimension
    • 1.3.2.4.4. Dimension shuffling
    • 1.3.2.4.5. 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
    • 1.3.3.1.1. Casting
    • 1.3.3.1.2. 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
    • 1.3.4.1.1. More polynomials (with more bases)
  • 1.3.4.2. Loading data files
    • 1.3.4.2.1. Text files
    • 1.3.4.2.2. Images
    • 1.3.4.2.3. Numpy’s own format
    • 1.3.4.2.4. 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