simplestatistics

simplestatistics is compatible with Python 3.

Version 0.4.0 was the last version to not use Python 3 specific features. Going forward, simplestatistics will adopt Python 3 features (e.g., type hints).

Installation

Install the current PyPI release:

pip install simplestatistics

Or install the development version from GitHub:

pip install git+https://github.com/sheriferson/simplestatistics

Usage

>>> import simplestatistics as ss
>>> ss.mean([1, 2, 3])
2.0
>>> ss.t_test([1, 2, 2.4, 3, 0.9], 2)
-0.3461277235039039

Documentation

You can read the documentation online.

Or you can generate it yourself:

Inside simplestatistics/.

make html

Documentation will be generated in _build/html/.

Tests

To run all doctests and see test coverage:

pip install -r requirements.txt
pytest simplestatistics --doctest-modules --cov=simplestatistics

The code adheres to PEP8 guidelines except for the following checkers:

invalid-name
len-as-condition
superfluous-parens
unidiomatic-typecheck

To lint the code, make sure you have [pylint] installed (pip install pylint), cd into the simplestatistics/statistics directory, then run:

pylint -d 'invalid-name, len-as-condition, superfluous-parens, unidiomatic-typecheck' *.py

Functions and examples

Descriptive statistics

Function	Example
Min	`min([-3, 0, 3])`
Max	`max([1, 2, 3])`
Sum	`sum([1, 2, 3.5])`
Quantiles	`quantile([3, 6, 7, 8, 8, 9, 10, 13, 15, 16, 20], [0.25, 0.75])`
Product	`product([1.25, 2.75], [2.5, 3.40])`

Measures of central tendency

Function	Example
Mean	`mean([1, 2, 3])`
Median	`median([10, 2, -5, -1])`
Mode	`mode([2, 1, 3, 2, 1])`
Geometric mean	`geometric_mean([1, 10])`
Harmonic mean	`harmonic_mean([1, 2, 4])`
Root mean square	`root_mean_square([1, -1, 1, -1])`
Add to mean	`add_to_mean(40, 4, (10, 12))`
Skewness	`skew([1, 2, 5])`
Kurtosis	`kurtosis([1, 2, 3, 4, 5])`

Measures of dispersion

Function	Example
Sample and population variance	`variance([1, 2, 3], sample = True)`
Sample and population Standard deviation	`standard_deviation([1, 2, 3], sample = True)`
Sample and population Coefficient of variation	`coefficient_of_variation([1, 2, 3], sample = True)`
Interquartile range	`interquartile_range([1, 3, 5, 7])`
Sum of Nth power deviations	`sum_nth_power_deviations([-1, 0, 2, 4], 3)`
Sample and population Standard scores (z-scores)	`z_scores([-2, -1, 0, 1, 2], sample = True)`

Linear regression

Function	Example
Simple linear regression	`linear_regression([1, 2, 3, 4, 5], [4, 4.5, 5.5, 5.3, 6])`
Linear regression line function generator	`linear_regression_line([.5, 9.5])([1, 2, 3])`

Similarity

Function	Example
Correlation	`correlate([2, 1, 0, -1, -2, -3, -4, -5], [0, 1, 1, 2, 3, 2, 4, 5])`
Covariance	`covariance([1,2,3,4,5,6], [6,5,4,3,2,1])`

Distributions

Function	Example
Factorial	`factorial(20)` or `factorial([1, 5, 20])`
Choose	`choose(5, 3)`
Normal distribution	`normal(4, 8, 2)` or `normal([1, 4], 8, 2)`
Binomial distribution	`binomial(4, 12, 0.2)` or `binomial([3,4,5], 12, 0.5)`
Bernoulli distribution	`bernoulli(0.25)`
Poisson distribution	`poisson(3, [0, 1, 2, 3])`
Gamma function	`gamma_function([1, 2, 3, 4, 5])`
Beta distribution	`beta([.1, .2, .3], 5, 2)`
One-sample t-test	`t_test([1, 2, 3, 4, 5, 6], 3.385)`
Chi Squared Distribution Table	`chi_squared_dist_table(k = 10, p = .01)`

Classifiers

Function	Example
Naive Bayesian classifier	See documentation for examples of how to train and classify.
Perceptron	See documentation for examples of how to train and classify.

Errors

Function	Example
Gauss error function	`error_function(1)`

Hyperbolic functions

Function	Example
sinh	`sinh(2)`
cosh	`cosh(2.5)`
tanh	`tanh(.2)`

Spirit and rules

Everything should be implemented in raw, organic, locally sourced Python.
Use libraries only if you have to and only when unrelated to the math/statistics. For example, from functools import reduce to make reduce available for those using python3. That's okay, because it's about making Python work and not about making the stats easier.
It's okay to use operators and functions if they correspond to regular calculator buttons. For example, all calculators have a built-in square root function, so there is no need to implement that ourselves, we can use math.sqrt(). Anything beyond that, like mean, median, we have to write ourselves.

Pull requests are welcome!

Contributors

Jim Anderson (jhowardanderson)
Lidiane Taquehara (lidimayra)
Pierre-Selim (PierreSelim)
Tom MacWright (tmcw)

sheriferson / simplestatistics

readme