# simplestatistics 0.3.0

Simple statistical functions implemented in readable Python.

simple-statistics for Python.

simplestatistics is compatible with Python 2 & 3. ### 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)
-0.3461277235039042
```

## 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

If you want coverage reports, you need to have `coverage <https://pypi.python.org/pypi/coverage>`__ installed:

```pip install coverage
nosetests --with-coverage --cover-package=simplestatistics --with-doctest
```

Otherwise, to just run the tests:

```nosetests --with-doctest
```

## 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])
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])

### 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])
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)

## 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

File Type Py Version Uploaded on Size
Source 2016-12-04 27KB