Modul Numerik dan Matematika

Modul-modul yang diuraikan dalam bab ini menyediakan fungsi-fungsi dan tipe data yang berkaitan dengan angka dan matematika. Modul numbers mendefinisikan hirarki abstrak tipe numerik. Modul math dan cmath berisi berbagai fungsi matematika untuk angka-angka pecahan floating-point dan kompleks. Modul decimal mendukung representasi tepat angka desimal, menggunakan presisi aritmatika yang berubah-ubah arbitrary.

Modul-modul berikut didokumentasikan dalam bab ini:

• numbers --- Numeric abstract base classes
  • The numeric tower
  • Notes for type implementers
    • Adding More Numeric ABCs
    • Implementing the arithmetic operations
• math --- Mathematical functions
  • Number-theoretic and representation functions
  • Power and logarithmic functions
  • Trigonometric functions
  • Angular conversion
  • Hyperbolic functions
  • Special functions
  • Konstanta
• cmath --- Mathematical functions for complex numbers
  • Conversions to and from polar coordinates
  • Power and logarithmic functions
  • Trigonometric functions
  • Hyperbolic functions
  • Classification functions
  • Konstanta
• decimal --- Decimal fixed-point and floating-point arithmetic
  • Quick-start Tutorial
  • Decimal objects
    • Logical operands
  • Context objects
  • Konstanta
  • Rounding modes
  • Signals
  • Floating-Point Notes
    • Mitigating round-off error with increased precision
    • Special values
  • Working with threads
  • Recipes
  • Decimal FAQ
• fractions --- Rational numbers
• random --- Generate pseudo-random numbers
  • Bookkeeping functions
  • Functions for bytes
  • Functions for integers
  • Functions for sequences
  • Discrete distributions
  • Real-valued distributions
  • Alternative Generator
  • Notes on Reproducibility
  • Contoh-contoh
  • Recipes
• statistics --- Mathematical statistics functions
  • Averages and measures of central location
  • Measures of spread
  • Statistics for relations between two inputs
  • Function details
  • Pengecualian
  • NormalDist objects
  • Examples and Recipes
    • Classic probability problems
    • Monte Carlo inputs for simulations
    • Approximating binomial distributions
    • Naive bayesian classifier
    • Kernel density estimation