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  • API reference
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  • Installing
  • User Guide
  • API reference
  • Building from source
  • Development
  • Release notes
  • GitHub
  • Twitter

Section Navigation

User guide

  • Special functions (scipy.special)
  • Integration (scipy.integrate)
  • Optimization (scipy.optimize)
  • Interpolation (scipy.interpolate)
  • Fourier Transforms (scipy.fft)
  • Signal Processing (scipy.signal)
  • Linear Algebra (scipy.linalg)
  • Sparse Arrays (scipy.sparse)
  • Sparse eigenvalue problems with ARPACK
  • Compressed Sparse Graph Routines (scipy.sparse.csgraph)
  • Spatial data structures and algorithms (scipy.spatial)
  • Statistics (scipy.stats)
    • Probability distributions
    • Sample statistics and hypothesis tests
    • Universal Non-Uniform Random Number Sampling in SciPy
    • Kernel density estimation
    • Multiscale Graph Correlation (MGC)
    • Quasi-Monte Carlo
  • Multidimensional image processing (scipy.ndimage)
  • File IO (scipy.io)
  • SciPy User Guide
  • Statistics...

Statistics (scipy.stats)#

In this tutorial, we discuss many, but certainly not all, features of scipy.stats. The intention here is to provide a user with a working knowledge of this package. We refer to the reference manual for further details.

Note: This documentation is work in progress.

  • Probability distributions
    • Continuous Statistical Distributions
    • Discrete Statistical Distributions
    • Getting help
    • Common methods
    • Random number generation
    • Shifting and scaling
    • Shape parameters
    • Freezing a distribution
    • Broadcasting
    • Specific points for discrete distributions
    • Fitting distributions
    • Performance issues and cautionary remarks
    • Remaining issues
    • Building specific distributions
  • Sample statistics and hypothesis tests
    • Analysing one sample
    • Comparing two samples
    • Resampling and Monte Carlo Methods
  • Universal Non-Uniform Random Number Sampling in SciPy
    • Introduction
    • Basic concepts of the Interface
    • Generators in scipy.stats.sampling
    • References
  • Kernel density estimation
    • Univariate estimation
    • Multivariate estimation
  • Multiscale Graph Correlation (MGC)
  • Quasi-Monte Carlo
    • Calculate the discrepancy
    • Using a QMC engine
    • Making a QMC engine, i.e., subclassing QMCEngine
    • Guidelines on using QMC

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Probability distributions

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