nsp_lesson1

Introduction to NumPy and Matplotlib

Ch2-6 NumPy Arrays

• More efficient
  • Use for large scale arrays / matrices
• Convert between lists
• tolist() and np.array()
• .shape check the dimensions
• .dtype check the data type of the elements
• np.zeros() creates an array filled with 0s of the set shape
• np.arange(n) creates an array from 0 to n-1
• np.linspace(s, e, num=g) creates an array with elements, from s to e linearly separated with gap size g
• +, -, +=, and -= work elementwise
• Boolean indexing
  • arr > n Maps the array to booleans, compares them elementwise
  • arr[arr > n] keeps all elements that satisfy the condition
• Array stacking
  • np.vstack() - stack on vertical stack
  • np.hstack() - stack on horizontal stack
  • np.dstack() - stack on depth stack
• Array concatenation
  • Appends on the same axis, unlike stacking
• Array splitting
  • Can specify splitting at what positions
  • np.vsplit() - splits on vertical axis
  • np.hsplit() - splits on horizontal axis
  • np.dsplit() - splits on depth axis
• Repeating arrays
  • np.repeat() repeats items elementwise
    • Flattens array if no axis is specified
• Nonzero searching
  • np.nonzero() returns the indices in arrays
• Unique filtering
  • np.unique() returns the unique elements
    • Makes a set
• Sorting
  • arr.sort() is a mutator
  • np.argsort returns the sorted indices

Ch7 MISC NumPy

• Saving and loading
  • Will save as .npz
  • Can also save as txt by .savetxt() and .loadtxt()
• Random
  • Seeding
    • rg = default_rng(seed)
  • Use rg for functions
    • .random(), .integers()
• Print options
  • np.set_printoptions(threshold=x)
    • Prints more items up to x