Metadata-Version: 2.1
Name: runstats
Version: 2.0.0
Summary: Compute statistics and regression in one pass
Home-page: http://www.grantjenks.com/docs/runstats/
Author: Grant Jenks
Author-email: contact@grantjenks.com
License: Apache 2.0
Project-URL: Documentation, http://www.grantjenks.com/docs/runstats/
Project-URL: Funding, http://gum.co/runstats
Project-URL: Source, https://github.com/grantjenks/python-runstats
Project-URL: Tracker, https://github.com/grantjenks/python-runstats/issues
Description: RunStats: Computing Statistics and Regression in One Pass
        =========================================================
        
        `RunStats`_ is an Apache2 licensed Python module for online statistics and
        online regression. Statistics and regression summaries are computed in a single
        pass. Previous values are not recorded in summaries.
        
        Long running systems often generate numbers summarizing performance. It could
        be the latency of a response or the time between requests. It's often useful to
        use these numbers in summary statistics like the arithmetic mean, minimum,
        standard deviation, etc. When many values are generated, computing these
        summaries can be computationally intensive. It may even be infeasible to keep
        every recorded value. In such cases computing online statistics and online
        regression is necessary.
        
        In other cases, you may only have one opportunity to observe all the recorded
        values. Python's generators work exactly this way. Traditional methods for
        calculating the variance and other higher moments requires multiple passes over
        the data. With generators, this is not possible and so computing statistics in
        a single pass is necessary.
        
        There are also scenarios where a user is not interested in a complete summary
        of the entire stream of data but rather wants to observe the current state of
        the system based on the recent past. In these cases exponential statistics are
        used. Instead of weighting all values uniformly in the statistics computation,
        an exponential decay weight is applied to older values. The decay rate is
        configurable and provides a mechanism for balancing recent values with past
        values.
        
        The Python `RunStats`_ module was designed for these cases by providing classes
        for computing online summary statistics and online linear regression in a
        single pass. Summary objects work on sequences which may be larger than memory
        or disk space permit. They may also be efficiently combined together to create
        aggregate summaries.
        
        
        Features
        --------
        
        - Pure-Python
        - Fully Documented
        - 100% Test Coverage
        - Numerically Stable
        - Optional Cython-optimized Extension (5-100 times faster)
        - Statistics summary computes mean, variance, standard deviation, skewness,
          kurtosis, minimum and maximum.
        - Regression summary computes slope, intercept and correlation.
        - Developed on Python 3.9
        - Tested on CPython 3.6, 3.7, 3.8, 3.9
        - Tested on Linux, Mac OS X, and Windows
        - Tested using GitHub Actions
        
        .. image:: https://github.com/grantjenks/python-runstats/workflows/integration/badge.svg
           :target: http://www.grantjenks.com/docs/runstats/
        
        
        Quickstart
        ----------
        
        Installing `RunStats`_ is simple with `pip <http://www.pip-installer.org/>`_::
        
          $ pip install runstats
        
        You can access documentation in the interpreter with Python's built-in help
        function:
        
        .. code-block:: python
        
           >>> import runstats
           >>> help(runstats)                             # doctest: +SKIP
           >>> help(runstats.Statistics)                  # doctest: +SKIP
           >>> help(runstats.Regression)                  # doctest: +SKIP
           >>> help(runstats.ExponentialStatistics)       # doctest: +SKIP
        
        
        Tutorial
        --------
        
        The Python `RunStats`_ module provides three types for computing running
        statistics: Statistics, ExponentialStatistics and Regression.The Regression
        object leverages Statistics internally for its calculations. Each can be
        initialized without arguments:
        
        .. code-block:: python
        
           >>> from runstats import Statistics, Regression, ExponentialStatistics
           >>> stats = Statistics()
           >>> regr = Regression()
           >>> exp_stats = ExponentialStatistics()
        
        Statistics objects support four methods for modification. Use `push` to add
        values to the summary, `clear` to reset the summary, sum to combine Statistics
        summaries and multiply to weight summary Statistics by a scalar.
        
        .. code-block:: python
        
           >>> for num in range(10):
           ...     stats.push(float(num))
           >>> stats.mean()
           4.5
           >>> stats.maximum()
           9.0
           >>> stats += stats
           >>> stats.mean()
           4.5
           >>> stats.variance()
           8.68421052631579
           >>> len(stats)
           20
           >>> stats *= 2
           >>> len(stats)
           40
           >>> stats.clear()
           >>> len(stats)
           0
           >>> stats.minimum()
           nan
        
        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 expected instead. Therefore, there are `minimum` and
        `maximum` methods provided for that purpose:
        
        .. code-block:: python
        
           >>> import random
           >>> random.seed(0)
           >>> for __ in range(1000):
           ...     stats.push(random.random())
           >>> len(stats)
           1000
           >>> min(stats)
           Traceback (most recent call last):
               ...
           TypeError: ...
           >>> stats.minimum()
           0.00024069652516689466
           >>> stats.maximum()
           0.9996851255769114
        
        Statistics summaries provide five measures of a series: mean, variance,
        standard deviation, skewness and kurtosis:
        
        .. code-block:: python
        
           >>> 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 some methods for modification:
        `push`, `clear` and sum:
        
        .. code-block:: python
        
           >>> 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:
        
        .. code-block:: python
        
           >>> 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.
        
        The ExponentialStatistics are constructed by providing a decay rate, initial
        mean, and initial variance. The decay rate has default 0.9 and must be between
        0 and 1. The initial mean and variance default to zero.
        
        .. code-block:: python
        
           >>> exp_stats = ExponentialStatistics()
           >>> exp_stats.decay
           0.9
           >>> exp_stats.mean()
           0.0
           >>> exp_stats.variance()
           0.0
        
        The decay rate is the weight by which the current statistics are discounted
        by. Consequently, (1 - decay) is the weight of the new value. Like the `Statistics` class,
        there are four methods for modification: `push`, `clear`, sum and
        multiply.
        
        .. code-block:: python
        
           >>> for num in range(10):
           ...     exp_stats.push(num)
           >>> exp_stats.mean()
           3.486784400999999
           >>> exp_stats.variance()
           11.593430921943071
           >>> exp_stats.stddev()
           3.4049127627507683
        
        The decay of the exponential statistics can also be changed. The value must be
        between 0 and 1.
        
        .. code-block:: python
        
           >>> exp_stats.decay
           0.9
           >>> exp_stats.decay = 0.5
           >>> exp_stats.decay
           0.5
           >>> exp_stats.decay = 10
           Traceback (most recent call last):
             ...
           ValueError: decay must be between 0 and 1
        
        The clear method allows to optionally set a new mean, new variance and new
        decay. If none are provided mean and variance reset to zero, while the decay is
        not changed.
        
        .. code-block:: python
        
           >>> exp_stats.clear()
           >>> exp_stats.decay
           0.5
           >>> exp_stats.mean()
           0.0
           >>> exp_stats.variance()
           0.0
        
        Combining `ExponentialStatistics` is done by adding them together. The mean and
        variance are simply added to create a new object. To weight each
        `ExponentialStatistics`, multiply them by a constant factor. If two
        `ExponentialStatistics` are added then the leftmost decay is used for the new
        object. The `len` method is not supported.
        
        .. code-block:: python
        
           >>> alpha_stats = ExponentialStatistics(iterable=range(10))
           >>> beta_stats = ExponentialStatistics(decay=0.1)
           >>> for num in range(10):
           ...     beta_stats.push(num)
           >>> exp_stats = beta_stats * 0.5 + alpha_stats * 0.5
           >>> exp_stats.decay
           0.1
           >>> exp_stats.mean()
           6.187836645
        
        All internal calculations of the Statistics and Regression classes 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`_
        
        .. _`Computing Skewness and Kurtosis in One Pass`: http://www.johndcook.com/blog/skewness_kurtosis/
        .. _`Computing Linear Regression in One Pass`: http://www.johndcook.com/blog/running_regression/
        
        The ExponentialStatistics implementation is based on:
        
        * Finch, 2009, Incremental Calculation of Weighted Mean and Variance
        
        The pure-Python version of `RunStats`_ is directly available if preferred.
        
        .. code-block:: python
        
           >>> import runstats.core   # Pure-Python
           >>> runstats.core.Statistics
           <class 'runstats.core.Statistics'>
        
        When importing from `runstats` the Cython-optimized version `_core` is
        preferred and the `core` version is used as fallback. Micro-benchmarking
        Statistics and Regression by calling `push` repeatedly shows the
        Cython-optimized extension as 20-40 times faster than the pure-Python
        extension.
        
        .. _`RunStats`: http://www.grantjenks.com/docs/runstats/
        
        
        Reference and Indices
        ---------------------
        
        * `RunStats Documentation`_
        * `RunStats API Reference`_
        * `RunStats at PyPI`_
        * `RunStats at GitHub`_
        * `RunStats Issue Tracker`_
        
        .. _`RunStats Documentation`: http://www.grantjenks.com/docs/runstats/
        .. _`RunStats API Reference`: http://www.grantjenks.com/docs/runstats/api.html
        .. _`RunStats at PyPI`: https://pypi.python.org/pypi/runstats/
        .. _`RunStats at GitHub`: https://github.com/grantjenks/python-runstats/
        .. _`RunStats Issue Tracker`: https://github.com/grantjenks/python-runstats/issues/
        
        
        License
        -------
        
        Copyright 2013-2021 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.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: Implementation :: CPython
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
