runstats 1.2.1

Features

• Pure-Python

• Fully Documented

• 100% Test Coverage

• Numerically Stable

• Statistics summary computes mean, variance, standard deviation, skewness, kurtosis, minimum and maximum.

• Regression summary computes slope, intercept and correlation.

• Developed on Python 2.7

• Tested on CPython 2.6, 2.7, 3.2, 3.3, 3.4 and PyPy 2.5+, PyPy3 2.4+

Quickstart

Installing RunStats is simple with pip:

$ pip install runstats

You can access documentation in the interpreter with Python’s built-in help function:

>>> from runstats import Statistics, Regression
>>> help(Statistics)
>>> help(Regression)

Tutorial

The Python runstats module provides two types for computing running Statistics and Regression. The Regression object leverages Statistics internally for its calculations. Each can be initialized without arguments:

>>> from runstats import Statistics, Regression
>>> stats = Statistics()
>>> regr = Regression()

Statistics objects support three methods for modification. Use push to add values to the summary, clear to reset the summary, and sum to combine Statistics summaries:

>>> for num in range(10):
...     stats.push(num)
>>> stats.mean()
4.5
>>> stats.maximum()
9
>>> stats += stats
>>> stats.mean()
4.5
>>> stats.variance()
8.68421052631579
>>> len(stats)
20
>>> stats.clear()
>>> len(stats)
0
>>> stats.minimum() is None
True

Use the Python built-in len for the number of pushed values. Unfortunately the Python min and max built-ins may not be used for the minimum and maximum as sequences are instead expected. There are instead minimum and maximum methods which are provided for that purpose:

>>> import random
>>> random.seed(0)
>>> for __ in range(1000):
...     stats.push(random.random())
>>> len(stats)
1000
>>> min(stats)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: iteration over non-sequence
>>> stats.minimum()
0.00024069652516689466
>>> stats.maximum()
0.9996851255769114

Statistics summaries provide five measures of a series: mean, variance, standard deviation, skewness and kurtosis:

>>> stats = Statistics([1, 2, 5, 12, 5, 2, 1])
>>> stats.mean()
4.0
>>> stats.variance()
15.33333333333333
>>> stats.stddev()
3.915780041490243
>>> stats.skewness()
1.33122127314735
>>> stats.kurtosis()
0.5496219281663506

All internal calculations use Python’s float type.

Like Statistics, the Regression type supports three methods for modification: push, clear and sum:

>>> regr.clear()
>>> len(regr)
0
>>> for num in range(10):
...     regr.push(num, num + 5)
>>> len(regr)
10
>>> regr.slope()
1.0
>>> more = Regression((num, num + 5) for num in range(10, 20))
>>> total = regr + more
>>> len(total)
20
>>> total.slope()
1.0
>>> total.intercept()
5.0
>>> total.correlation()
1.0

Regression summaries provide three measures of a series of pairs: slope, intercept and correlation. Note that, as a regression, the points need not exactly lie on a line:

>>> regr = Regression([(1.2, 1.9), (3, 5.1), (4.9, 8.1), (7, 11)])
>>> regr.slope()
1.5668320150154176
>>> regr.intercept()
0.21850113956294415
>>> regr.correlation()
0.9983810791694997

Both constructors accept an optional iterable that is consumed and pushed into the summary. Note that you may pass a generator as an iterable and the generator will be entirely consumed.

All internal calculations are based entirely on the C++ code by John Cook as posted in a couple of articles:

• Computing Skewness and Kurtosis in One Pass

• Computing Linear Regression in One Pass

Reference and Indices

• RunStats Documentation

• RunStats API Reference

• RunStats at PyPI

• RunStats at GitHub

• RunStats Issue Tracker

License

Copyright 2015 Grant Jenks

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.