Statistics for Humans

Install

From PyPi

pip install verysimplestats

From GitHub (with pip)

pip install git+https://github.com/PierreSelim/verysimplestats.git

Purity

Scientific code, requires correctness. Functional programming guarantees part of the correctness thanks to purity. The important part is not being able to represents values that do not exist. We chose to raise ValueError when input data do not permit computations (instead of using None)

Tests

The doctest can be launched with:

nosetests --with-doctest --with-coverage --cover-package=verysimplestats

Examples

Mean value

>>> import verysimplestats as stats
>>> stats.mean([1, 2, 3, 4, 5])
3.0

Median value

>>> import verysimplestats as stats
>>> stats.median([5, 2, 6, 4, 1, 3])
3.5

Linear regression

>>> import verysimplestats as stats
>>> lm = stats.linear_regression([1, 2, 3], [1, 3, 4.5])
>>> lm
LinearRegression([1, 2, 3], [1, 3, 4.5])
>>> (lm.slope, lm.intercept, lm.rsquared)
(1.7499999999999984, -0.6666666666666634, 0.9932432432432422)
>>> [round(e, 4) for e in lm.residuals]
[-0.0833, 0.1667, -0.0833]

Variance is computed only for list of length greater or equal to 2

>>> import verysimplestats as stats
>>> stats.variance([1])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "verysimplestats\statistics.py", line 79, in variance
    raise ValueError(msg.format(x=x))
ValueError: Variance only exists for list with at least 2 elements [1]

Supported functions

Functions	Examples
mean(x: list) -> float	mean([1, 2, 3, 4, 5])
median(x: list) -> float	median([5, 2, 6, 4, 1, 3])
variance(x: list) -> float	variance([1, 2, 3, 4, 5])
standard_deviation(x: list) -> float	standard_deviation([1, 2, 3, 4, 5])
covariance(x: list, y: list) -> float	covariance([1, 2, 3, 4, 5], [1, 2, 3, 4, 5])
correlation(x: list, y: list) -> float	correlation([1, 2, 3, 4, 5], [1, 2, 3, 4, 5])
rsquared(x: list, y: list) -> float	rsquared([1, 2, 3], [1, 3, 4.5])
ordinary_least_square(x: list, y: list) -> (float, float)	ordinary_least_square([1, 2, 3], [1, 3, 4.5])
linear_forecast(slope: float, intercept: float, value: float) -> float	linear_forecast(2, -1, 3)
residuals(slope: float, intercept: float, x: float, y: float) -> float	residuals(1.75, -0.667, [1, 2, 3], [1, 3, 4.5])
linear_regression(x: list, y: list) -> LinearRegression	linear_regression([1, 2, 3], [1, 3, 4.5])

License (MIT)

The MIT License (MIT)

Copyright (c) 2016 Pierre-Selim

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE