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href="https://www.python.org/" title="The Python Programming Language">Python</a> » </li> <li><a href="../pep-0000/">PEP Index</a> » </li> <li>PEP 450</li> </ul> <button id="colour-scheme-cycler" onClick="setColourScheme(nextColourScheme())"> <svg aria-hidden="true" class="colour-scheme-icon-when-auto"><use href="#svg-sun-half"></use></svg> <svg aria-hidden="true" class="colour-scheme-icon-when-dark"><use href="#svg-moon"></use></svg> <svg aria-hidden="true" class="colour-scheme-icon-when-light"><use href="#svg-sun"></use></svg> <span class="visually-hidden">Toggle light / dark / auto colour theme</span> </button> </header> <article> <section id="pep-content"> <h1 class="page-title">PEP 450 – Adding A Statistics Module To The Standard Library</h1> <dl class="rfc2822 field-list simple"> <dt class="field-odd">Author<span class="colon">:</span></dt> <dd class="field-odd">Steven D’Aprano <steve at pearwood.info></dd> <dt class="field-even">Status<span class="colon">:</span></dt> <dd class="field-even"><abbr title="Accepted and implementation complete, or no longer active">Final</abbr></dd> <dt class="field-odd">Type<span class="colon">:</span></dt> <dd class="field-odd"><abbr title="Normative PEP with a new feature for Python, implementation change for CPython or interoperability standard for the ecosystem">Standards Track</abbr></dd> <dt class="field-even">Created<span class="colon">:</span></dt> <dd class="field-even">01-Aug-2013</dd> <dt class="field-odd">Python-Version<span class="colon">:</span></dt> <dd class="field-odd">3.4</dd> <dt class="field-even">Post-History<span class="colon">:</span></dt> <dd class="field-even">13-Sep-2013</dd> </dl> <hr class="docutils" /> <section id="contents"> <details><summary>Table of Contents</summary><ul class="simple"> <li><a class="reference internal" href="#abstract">Abstract</a></li> <li><a class="reference internal" href="#rationale">Rationale</a></li> <li><a class="reference internal" href="#comparison-to-other-languages-packages">Comparison To Other Languages/Packages</a><ul> <li><a class="reference internal" href="#r">R</a></li> <li><a class="reference internal" href="#c">C#</a></li> <li><a class="reference internal" href="#ruby">Ruby</a></li> <li><a class="reference internal" href="#php">PHP</a></li> <li><a class="reference internal" href="#delphi">Delphi</a></li> <li><a class="reference internal" href="#gnu-scientific-library">GNU Scientific Library</a></li> </ul> </li> <li><a class="reference internal" href="#design-decisions-of-the-module">Design Decisions Of The Module</a></li> <li><a class="reference internal" href="#api">API</a><ul> <li><a class="reference internal" href="#calculating-mean-median-and-mode">Calculating mean, median and mode</a></li> <li><a class="reference internal" href="#calculating-variance-and-standard-deviation">Calculating variance and standard deviation</a></li> <li><a class="reference internal" href="#other-functions">Other functions</a></li> </ul> </li> <li><a class="reference internal" href="#specification">Specification</a></li> <li><a class="reference internal" href="#what-should-be-the-name-of-the-module">What Should Be The Name Of The Module?</a></li> <li><a class="reference internal" href="#discussion-and-resolved-issues">Discussion And Resolved Issues</a></li> <li><a class="reference internal" href="#frequently-asked-questions">Frequently Asked Questions</a><ul> <li><a class="reference internal" href="#shouldn-t-this-module-spend-time-on-pypi-before-being-considered-for-the-standard-library">Shouldn’t this module spend time on PyPI before being considered for the standard library?</a></li> <li><a class="reference internal" href="#does-the-standard-library-really-need-yet-another-version-of-sum">Does the standard library really need yet another version of <code class="docutils literal notranslate"><span class="pre">sum</span></code>?</a></li> <li><a class="reference internal" href="#will-this-module-be-backported-to-older-versions-of-python">Will this module be backported to older versions of Python?</a></li> <li><a class="reference internal" href="#is-this-supposed-to-replace-numpy">Is this supposed to replace numpy?</a></li> </ul> </li> <li><a class="reference internal" href="#future-work">Future Work</a></li> <li><a class="reference internal" href="#references">References</a></li> <li><a class="reference internal" href="#copyright">Copyright</a></li> </ul> </details></section> <section id="abstract"> <h2><a class="toc-backref" href="#abstract" role="doc-backlink">Abstract</a></h2> <p>This PEP proposes the addition of a module for common statistics functions such as mean, median, variance and standard deviation to the Python standard library. See also <a class="reference external" href="http://bugs.python.org/issue18606">http://bugs.python.org/issue18606</a></p> </section> <section id="rationale"> <h2><a class="toc-backref" href="#rationale" role="doc-backlink">Rationale</a></h2> <p>The proposed statistics module is motivated by the “batteries included” philosophy towards the Python standard library. Raymond Hettinger and other senior developers have requested a quality statistics library that falls somewhere in between high-end statistics libraries and ad hoc code. <a class="footnote-reference brackets" href="#id26" id="id1">[1]</a> Statistical functions such as mean, standard deviation and others are obvious and useful batteries, familiar to any Secondary School student. Even cheap scientific calculators typically include multiple statistical functions such as:</p> <ul class="simple"> <li>mean</li> <li>population and sample variance</li> <li>population and sample standard deviation</li> <li>linear regression</li> <li>correlation coefficient</li> </ul> <p>Graphing calculators aimed at Secondary School students typically include all of the above, plus some or all of:</p> <ul class="simple"> <li>median</li> <li>mode</li> <li>functions for calculating the probability of random variables from the normal, t, chi-squared, and F distributions</li> <li>inference on the mean</li> </ul> <p>and others <a class="footnote-reference brackets" href="#id27" id="id2">[2]</a>. Likewise spreadsheet applications such as Microsoft Excel, LibreOffice and Gnumeric include rich collections of statistical functions <a class="footnote-reference brackets" href="#id28" id="id3">[3]</a>.</p> <p>In contrast, Python currently has no standard way to calculate even the simplest and most obvious statistical functions such as mean. For those who need statistical functions in Python, there are two obvious solutions:</p> <ul class="simple"> <li>install numpy and/or scipy <a class="footnote-reference brackets" href="#id29" id="id4">[4]</a>;</li> <li>or use a Do It Yourself solution.</li> </ul> <p>Numpy is perhaps the most full-featured solution, but it has a few disadvantages:</p> <ul> <li>It may be overkill for many purposes. The documentation for numpy even warns<blockquote> <div>“It can be hard to know what functions are available in numpy. This is not a complete list, but it does cover most of them.”<a class="footnote-reference brackets" href="#id30" id="id5">[5]</a></div></blockquote> <p>and then goes on to list over 270 functions, only a small number of which are related to statistics.</p> </li> <li>Numpy is aimed at those doing heavy numerical work, and may be intimidating to those who don’t have a background in computational mathematics and computer science. For example, <code class="docutils literal notranslate"><span class="pre">numpy.mean</span></code> takes four arguments:<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mean</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> </pre></div> </div> <p>although fortunately for the beginner or casual numpy user, three are optional and <code class="docutils literal notranslate"><span class="pre">numpy.mean</span></code> does the right thing in simple cases:</p> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span> <span class="n">numpy</span><span class="o">.</span><span class="n">mean</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span> <span class="go">2.5</span> </pre></div> </div> </li> <li>For many people, installing numpy may be difficult or impossible. For example, people in corporate environments may have to go through a difficult, time-consuming process before being permitted to install third-party software. For the casual Python user, having to learn about installing third-party packages in order to average a list of numbers is unfortunate.</li> </ul> <p>This leads to option number 2, DIY statistics functions. At first glance, this appears to be an attractive option, due to the apparent simplicity of common statistical functions. For example:</p> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">mean</span><span class="p">(</span><span class="n">data</span><span class="p">):</span> <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="k">def</span><span class="w"> </span><span class="nf">variance</span><span class="p">(</span><span class="n">data</span><span class="p">):</span> <span class="c1"># Use the Computational Formula for Variance.</span> <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="n">ss</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="n">n</span> <span class="k">return</span> <span class="n">ss</span><span class="o">/</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="k">def</span><span class="w"> </span><span class="nf">standard_deviation</span><span class="p">(</span><span class="n">data</span><span class="p">):</span> <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">variance</span><span class="p">(</span><span class="n">data</span><span class="p">))</span> </pre></div> </div> <p>The above appears to be correct with a casual test:</p> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">]</span> <span class="gp">>>> </span><span class="n">variance</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="go">7.5</span> </pre></div> </div> <p>But adding a constant to every data point should not change the variance:</p> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">+</span><span class="mf">1e12</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span> <span class="gp">>>> </span><span class="n">variance</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="go">0.0</span> </pre></div> </div> <p>And variance should <em>never</em> be negative:</p> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">variance</span><span class="p">(</span><span class="n">data</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span> <span class="go">-1239429440.1282566</span> </pre></div> </div> <p>By contrast, the proposed reference implementation gets the exactly correct answer 7.5 for the first two examples, and a reasonably close answer for the third: 6.012. numpy does no better <a class="footnote-reference brackets" href="#id31" id="id6">[6]</a>.</p> <p>Even simple statistical calculations contain traps for the unwary, starting with the Computational Formula itself. Despite the name, it is numerically unstable and can be extremely inaccurate, as can be seen above. It is completely unsuitable for computation by computer <a class="footnote-reference brackets" href="#id32" id="id7">[7]</a>. This problem plagues users of many programming language, not just Python <a class="footnote-reference brackets" href="#id33" id="id8">[8]</a>, as coders reinvent the same numerically inaccurate code over and over again <a class="footnote-reference brackets" href="#id34" id="id9">[9]</a>, or advise others to do so <a class="footnote-reference brackets" href="#id35" id="id10">[10]</a>.</p> <p>It isn’t just the variance and standard deviation. Even the mean is not quite as straightforward as it might appear. The above implementation seems too simple to have problems, but it does:</p> <ul> <li>The built-in <code class="docutils literal notranslate"><span class="pre">sum</span></code> can lose accuracy when dealing with floats of wildly differing magnitude. Consequently, the above naive <code class="docutils literal notranslate"><span class="pre">mean</span></code> fails this “torture test”:<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">assert</span> <span class="n">mean</span><span class="p">([</span><span class="mf">1e30</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mf">1e30</span><span class="p">])</span> <span class="o">==</span> <span class="mi">1</span> </pre></div> </div> <p>returning 0 instead of 1, a purely computational error of 100%.</p> </li> <li>Using <code class="docutils literal notranslate"><span class="pre">math.fsum</span></code> inside <code class="docutils literal notranslate"><span class="pre">mean</span></code> will make it more accurate with float data, but it also has the side-effect of converting any arguments to float even when unnecessary. E.g. we should expect the mean of a list of Fractions to be a Fraction, not a float.</li> </ul> <p>While the above mean implementation does not fail quite as catastrophically as the naive variance does, a standard library function can do much better than the DIY versions.</p> <p>The example above involves an especially bad set of data, but even for more realistic data sets accuracy is important. The first step in interpreting variation in data (including dealing with ill-conditioned data) is often to standardize it to a series with variance 1 (and often mean 0). This standardization requires accurate computation of the mean and variance of the raw series. Naive computation of mean and variance can lose precision very quickly. Because precision bounds accuracy, it is important to use the most precise algorithms for computing mean and variance that are practical, or the results of standardization are themselves useless.</p> </section> <section id="comparison-to-other-languages-packages"> <h2><a class="toc-backref" href="#comparison-to-other-languages-packages" role="doc-backlink">Comparison To Other Languages/Packages</a></h2> <p>The proposed statistics library is not intended to be a competitor to such third-party libraries as numpy/scipy, or of proprietary full-featured statistics packages aimed at professional statisticians such as Minitab, SAS and Matlab. It is aimed at the level of graphing and scientific calculators.</p> <p>Most programming languages have little or no built-in support for statistics functions. Some exceptions:</p> <section id="r"> <h3><a class="toc-backref" href="#r" role="doc-backlink">R</a></h3> <p>R (and its proprietary cousin, S) is a programming language designed for statistics work. It is extremely popular with statisticians and is extremely feature-rich <a class="footnote-reference brackets" href="#id36" id="id11">[11]</a>.</p> </section> <section id="c"> <h3><a class="toc-backref" href="#c" role="doc-backlink">C#</a></h3> <p>The C# LINQ package includes extension methods to calculate the average of enumerables <a class="footnote-reference brackets" href="#id37" id="id12">[12]</a>.</p> </section> <section id="ruby"> <h3><a class="toc-backref" href="#ruby" role="doc-backlink">Ruby</a></h3> <p>Ruby does not ship with a standard statistics module, despite some apparent demand <a class="footnote-reference brackets" href="#id38" id="id13">[13]</a>. Statsample appears to be a feature-rich third-party library, aiming to compete with R <a class="footnote-reference brackets" href="#id39" id="id14">[14]</a>.</p> </section> <section id="php"> <h3><a class="toc-backref" href="#php" role="doc-backlink">PHP</a></h3> <p>PHP has an extremely feature-rich (although mostly undocumented) set of advanced statistical functions <a class="footnote-reference brackets" href="#id40" id="id15">[15]</a>.</p> </section> <section id="delphi"> <h3><a class="toc-backref" href="#delphi" role="doc-backlink">Delphi</a></h3> <p>Delphi includes standard statistical functions including Mean, Sum, Variance, TotalVariance, MomentSkewKurtosis in its Math library <a class="footnote-reference brackets" href="#id41" id="id16">[16]</a>.</p> </section> <section id="gnu-scientific-library"> <h3><a class="toc-backref" href="#gnu-scientific-library" role="doc-backlink">GNU Scientific Library</a></h3> <p>The GNU Scientific Library includes standard statistical functions, percentiles, median and others <a class="footnote-reference brackets" href="#id42" id="id17">[17]</a>. One innovation I have borrowed from the GSL is to allow the caller to optionally specify the pre-calculated mean of the sample (or an a priori known population mean) when calculating the variance and standard deviation <a class="footnote-reference brackets" href="#id43" id="id18">[18]</a>.</p> </section> </section> <section id="design-decisions-of-the-module"> <h2><a class="toc-backref" href="#design-decisions-of-the-module" role="doc-backlink">Design Decisions Of The Module</a></h2> <p>My intention is to start small and grow the library as needed, rather than try to include everything from the start. Consequently, the current reference implementation includes only a small number of functions: mean, variance, standard deviation, median, mode. (See the reference implementation for a full list.)</p> <p>I have aimed for the following design features:</p> <ul class="simple"> <li>Correctness over speed. It is easier to speed up a correct but slow function than to correct a fast but buggy one.</li> <li>Concentrate on data in sequences, allowing two-passes over the data, rather than potentially compromise on accuracy for the sake of a one-pass algorithm. Functions expect data will be passed as a list or other sequence; if given an iterator, they may internally convert to a list.</li> <li>Functions should, as much as possible, honour any type of numeric data. E.g. the mean of a list of Decimals should be a Decimal, not a float. When this is not possible, treat float as the “lowest common data type”.</li> <li>Although functions support data sets of floats, Decimals or Fractions, there is no guarantee that <em>mixed</em> data sets will be supported. (But on the other hand, they aren’t explicitly rejected either.)</li> <li>Plenty of documentation, aimed at readers who understand the basic concepts but may not know (for example) which variance they should use (population or sample?). Mathematicians and statisticians have a terrible habit of being inconsistent with both notation and terminology <a class="footnote-reference brackets" href="#id44" id="id19">[19]</a>, and having spent many hours making sense of the contradictory/confusing definitions in use, it is only fair that I do my best to clarify rather than obfuscate the topic.</li> <li>But avoid going into tedious <a class="footnote-reference brackets" href="#id45" id="id20">[20]</a> mathematical detail.</li> </ul> </section> <section id="api"> <h2><a class="toc-backref" href="#api" role="doc-backlink">API</a></h2> <p>The initial version of the library will provide univariate (single variable) statistics functions. The general API will be based on a functional model <code class="docutils literal notranslate"><span class="pre">function(data,</span> <span class="pre">...)</span> <span class="pre">-></span> <span class="pre">result</span></code>, where <code class="docutils literal notranslate"><span class="pre">data</span></code> is a mandatory iterable of (usually) numeric data.</p> <p>The author expects that lists will be the most common data type used, but any iterable type should be acceptable. Where necessary, functions may convert to lists internally. Where possible, functions are expected to conserve the type of the data values, for example, the mean of a list of Decimals should be a Decimal rather than float.</p> <section id="calculating-mean-median-and-mode"> <h3><a class="toc-backref" href="#calculating-mean-median-and-mode" role="doc-backlink">Calculating mean, median and mode</a></h3> <p>The <code class="docutils literal notranslate"><span class="pre">mean</span></code>, <code class="docutils literal notranslate"><span class="pre">median*</span></code> and <code class="docutils literal notranslate"><span class="pre">mode</span></code> functions take a single mandatory argument and return the appropriate statistic, e.g.:</p> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mean</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="go">2.0</span> </pre></div> </div> <p>Functions provided are:</p> <ul class="simple"> <li><dl class="simple"> <dt><code class="docutils literal notranslate"><span class="pre">mean(data)</span></code></dt><dd>arithmetic mean of <em>data</em>.</dd> </dl> </li> <li><dl class="simple"> <dt><code class="docutils literal notranslate"><span class="pre">median(data)</span></code></dt><dd>median (middle value) of <em>data</em>, taking the average of the two middle values when there are an even number of values.</dd> </dl> </li> <li><dl class="simple"> <dt><code class="docutils literal notranslate"><span class="pre">median_high(data)</span></code></dt><dd>high median of <em>data</em>, taking the larger of the two middle values when the number of items is even.</dd> </dl> </li> <li><dl class="simple"> <dt><code class="docutils literal notranslate"><span class="pre">median_low(data)</span></code></dt><dd>low median of <em>data</em>, taking the smaller of the two middle values when the number of items is even.</dd> </dl> </li> <li><dl class="simple"> <dt><code class="docutils literal notranslate"><span class="pre">median_grouped(data,</span> <span class="pre">interval=1)</span></code></dt><dd>50th percentile of grouped <em>data</em>, using interpolation.</dd> </dl> </li> <li><dl class="simple"> <dt><code class="docutils literal notranslate"><span class="pre">mode(data)</span></code></dt><dd>most common <em>data</em> point.</dd> </dl> </li> </ul> <p><code class="docutils literal notranslate"><span class="pre">mode</span></code> is the sole exception to the rule that the data argument must be numeric. It will also accept an iterable of nominal data, such as strings.</p> </section> <section id="calculating-variance-and-standard-deviation"> <h3><a class="toc-backref" href="#calculating-variance-and-standard-deviation" role="doc-backlink">Calculating variance and standard deviation</a></h3> <p>In order to be similar to scientific calculators, the statistics module will include separate functions for population and sample variance and standard deviation. All four functions have similar signatures, with a single mandatory argument, an iterable of numeric data, e.g.:</p> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">variance</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="go">0.5</span> </pre></div> </div> <p>All four functions also accept a second, optional, argument, the mean of the data. This is modelled on a similar API provided by the GNU Scientific Library <a class="footnote-reference brackets" href="#id43" id="id21">[18]</a>. There are three use-cases for using this argument, in no particular order:</p> <ol class="arabic simple"> <li>The value of the mean is known <em>a priori</em>.</li> <li>You have already calculated the mean, and wish to avoid calculating it again.</li> <li>You wish to (ab)use the variance functions to calculate the second moment about some given point other than the mean.</li> </ol> <p>In each case, it is the caller’s responsibility to ensure that given argument is meaningful.</p> <p>Functions provided are:</p> <ul class="simple"> <li><dl class="simple"> <dt><code class="docutils literal notranslate"><span class="pre">variance(data,</span> <span class="pre">xbar=None)</span></code></dt><dd>sample variance of <em>data</em>, optionally using <em>xbar</em> as the sample mean.</dd> </dl> </li> <li><dl class="simple"> <dt><code class="docutils literal notranslate"><span class="pre">stdev(data,</span> <span class="pre">xbar=None)</span></code></dt><dd>sample standard deviation of <em>data</em>, optionally using <em>xbar</em> as the sample mean.</dd> </dl> </li> <li><dl class="simple"> <dt><code class="docutils literal notranslate"><span class="pre">pvariance(data,</span> <span class="pre">mu=None)</span></code></dt><dd>population variance of <em>data</em>, optionally using <em>mu</em> as the population mean.</dd> </dl> </li> <li><dl class="simple"> <dt><code class="docutils literal notranslate"><span class="pre">pstdev(data,</span> <span class="pre">mu=None)</span></code></dt><dd>population standard deviation of <em>data</em>, optionally using <em>mu</em> as the population mean.</dd> </dl> </li> </ul> </section> <section id="other-functions"> <h3><a class="toc-backref" href="#other-functions" role="doc-backlink">Other functions</a></h3> <p>There is one other public function:</p> <ul class="simple"> <li><dl class="simple"> <dt><code class="docutils literal notranslate"><span class="pre">sum(data,</span> <span class="pre">start=0)</span></code></dt><dd>high-precision sum of numeric <em>data</em>.</dd> </dl> </li> </ul> </section> </section> <section id="specification"> <h2><a class="toc-backref" href="#specification" role="doc-backlink">Specification</a></h2> <p>As the proposed reference implementation is in pure Python, other Python implementations can easily make use of the module unchanged, or adapt it as they see fit.</p> </section> <section id="what-should-be-the-name-of-the-module"> <h2><a class="toc-backref" href="#what-should-be-the-name-of-the-module" role="doc-backlink">What Should Be The Name Of The Module?</a></h2> <p>This will be a top-level module <code class="docutils literal notranslate"><span class="pre">statistics</span></code>.</p> <p>There was some interest in turning <code class="docutils literal notranslate"><span class="pre">math</span></code> into a package, and making this a sub-module of <code class="docutils literal notranslate"><span class="pre">math</span></code>, but the general consensus eventually agreed on a top-level module. Other potential but rejected names included <code class="docutils literal notranslate"><span class="pre">stats</span></code> (too much risk of confusion with existing <code class="docutils literal notranslate"><span class="pre">stat</span></code> module), and <code class="docutils literal notranslate"><span class="pre">statslib</span></code> (described as “too C-like”).</p> </section> <section id="discussion-and-resolved-issues"> <h2><a class="toc-backref" href="#discussion-and-resolved-issues" role="doc-backlink">Discussion And Resolved Issues</a></h2> <p>This proposal has been previously discussed here <a class="footnote-reference brackets" href="#id46" id="id22">[21]</a>.</p> <p>A number of design issues were resolved during the discussion on Python-Ideas and the initial code review. There was a lot of concern about the addition of yet another <code class="docutils literal notranslate"><span class="pre">sum</span></code> function to the standard library, see the FAQs below for more details. In addition, the initial implementation of <code class="docutils literal notranslate"><span class="pre">sum</span></code> suffered from some rounding issues and other design problems when dealing with Decimals. Oscar Benjamin’s assistance in resolving this was invaluable.</p> <p>Another issue was the handling of data in the form of iterators. The first implementation of variance silently swapped between a one- and two-pass algorithm, depending on whether the data was in the form of an iterator or sequence. This proved to be a design mistake, as the calculated variance could differ slightly depending on the algorithm used, and <code class="docutils literal notranslate"><span class="pre">variance</span></code> etc. were changed to internally generate a list and always use the more accurate two-pass implementation.</p> <p>One controversial design involved the functions to calculate median, which were implemented as attributes on the <code class="docutils literal notranslate"><span class="pre">median</span></code> callable, e.g. <code class="docutils literal notranslate"><span class="pre">median</span></code>, <code class="docutils literal notranslate"><span class="pre">median.low</span></code>, <code class="docutils literal notranslate"><span class="pre">median.high</span></code> etc. Although there is at least one existing use of this style in the standard library, in <code class="docutils literal notranslate"><span class="pre">unittest.mock</span></code>, the code reviewers felt that this was too unusual for the standard library. Consequently, the design has been changed to a more traditional design of separate functions with a pseudo-namespace naming convention, <code class="docutils literal notranslate"><span class="pre">median_low</span></code>, <code class="docutils literal notranslate"><span class="pre">median_high</span></code>, etc.</p> <p>Another issue that was of concern to code reviewers was the existence of a function calculating the sample mode of continuous data, with some people questioning the choice of algorithm, and whether it was a sufficiently common need to be included. So it was dropped from the API, and <code class="docutils literal notranslate"><span class="pre">mode</span></code> now implements only the basic schoolbook algorithm based on counting unique values.</p> <p>Another significant point of discussion was calculating statistics of <code class="docutils literal notranslate"><span class="pre">timedelta</span></code> objects. Although the statistics module will not directly support <code class="docutils literal notranslate"><span class="pre">timedelta</span></code> objects, it is possible to support this use-case by converting them to numbers first using the <code class="docutils literal notranslate"><span class="pre">timedelta.total_seconds</span></code> method.</p> </section> <section id="frequently-asked-questions"> <h2><a class="toc-backref" href="#frequently-asked-questions" role="doc-backlink">Frequently Asked Questions</a></h2> <section id="shouldn-t-this-module-spend-time-on-pypi-before-being-considered-for-the-standard-library"> <h3><a class="toc-backref" href="#shouldn-t-this-module-spend-time-on-pypi-before-being-considered-for-the-standard-library" role="doc-backlink">Shouldn’t this module spend time on PyPI before being considered for the standard library?</a></h3> <p>Older versions of this module have been available on PyPI <a class="footnote-reference brackets" href="#id47" id="id23">[22]</a> since 2010. Being much simpler than numpy, it does not require many years of external development.</p> </section> <section id="does-the-standard-library-really-need-yet-another-version-of-sum"> <h3><a class="toc-backref" href="#does-the-standard-library-really-need-yet-another-version-of-sum" role="doc-backlink">Does the standard library really need yet another version of <code class="docutils literal notranslate"><span class="pre">sum</span></code>?</a></h3> <p>This proved to be the most controversial part of the reference implementation. In one sense, clearly three sums is two too many. But in another sense, yes. The reasons why the two existing versions are unsuitable are described here <a class="footnote-reference brackets" href="#id48" id="id24">[23]</a> but the short summary is:</p> <ul class="simple"> <li>the built-in sum can lose precision with floats;</li> <li>the built-in sum accepts any non-numeric data type that supports the <code class="docutils literal notranslate"><span class="pre">+</span></code> operator, apart from strings and bytes;</li> <li><code class="docutils literal notranslate"><span class="pre">math.fsum</span></code> is high-precision, but coerces all arguments to float.</li> </ul> <p>There was some interest in “fixing” one or the other of the existing sums. If this occurs before 3.4 feature-freeze, the decision to keep <code class="docutils literal notranslate"><span class="pre">statistics.sum</span></code> can be re-considered.</p> </section> <section id="will-this-module-be-backported-to-older-versions-of-python"> <h3><a class="toc-backref" href="#will-this-module-be-backported-to-older-versions-of-python" role="doc-backlink">Will this module be backported to older versions of Python?</a></h3> <p>The module currently targets 3.3, and I will make it available on PyPI for 3.3 for the foreseeable future. Backporting to older versions of the 3.x series is likely (but not yet decided). Backporting to 2.7 is less likely but not ruled out.</p> </section> <section id="is-this-supposed-to-replace-numpy"> <h3><a class="toc-backref" href="#is-this-supposed-to-replace-numpy" role="doc-backlink">Is this supposed to replace numpy?</a></h3> <p>No. While it is likely to grow over the years (see open issues below) it is not aimed to replace, or even compete directly with, numpy. Numpy is a full-featured numeric library aimed at professionals, the nuclear reactor of numeric libraries in the Python ecosystem. This is just a battery, as in “batteries included”, and is aimed at an intermediate level somewhere between “use numpy” and “roll your own version”.</p> </section> </section> <section id="future-work"> <h2><a class="toc-backref" href="#future-work" role="doc-backlink">Future Work</a></h2> <ul> <li>At this stage, I am unsure of the best API for multivariate statistical functions such as linear regression, correlation coefficient, and covariance. Possible APIs include:<ul> <li>Separate arguments for x and y data:<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">function</span><span class="p">([</span><span class="n">x0</span><span class="p">,</span> <span class="n">x1</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="p">[</span><span class="n">y0</span><span class="p">,</span> <span class="n">y1</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span> </pre></div> </div> </li> <li>A single argument for (x, y) data:<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">function</span><span class="p">([(</span><span class="n">x0</span><span class="p">,</span> <span class="n">y0</span><span class="p">),</span> <span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">y1</span><span class="p">),</span> <span class="o">...</span><span class="p">])</span> </pre></div> </div> <p>This API is preferred by GvR <a class="footnote-reference brackets" href="#id49" id="id25">[24]</a>.</p> </li> <li>Selecting arbitrary columns from a 2D array:<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">function</span><span class="p">([[</span><span class="n">a0</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">y0</span><span class="p">,</span> <span class="n">z0</span><span class="p">],</span> <span class="p">[</span><span class="n">a1</span><span class="p">,</span> <span class="n">x1</span><span class="p">,</span> <span class="n">y1</span><span class="p">,</span> <span class="n">z1</span><span class="p">],</span> <span class="o">...</span><span class="p">],</span> <span class="n">x</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> </pre></div> </div> </li> <li>Some combination of the above.</li> </ul> <p>In the absence of a consensus of preferred API for multivariate stats, I will defer including such multivariate functions until Python 3.5.</p> </li> <li>Likewise, functions for calculating probability of random variables and inference testing (e.g. Student’s t-test) will be deferred until 3.5.</li> <li>There is considerable interest in including one-pass functions that can calculate multiple statistics from data in iterator form, without having to convert to a list. The experimental <code class="docutils literal notranslate"><span class="pre">stats</span></code> package on PyPI includes co-routine versions of statistics functions. Including these will be deferred to 3.5.</li> </ul> </section> <section id="references"> <h2><a class="toc-backref" href="#references" role="doc-backlink">References</a></h2> <aside class="footnote-list brackets"> <aside class="footnote brackets" id="id26" role="doc-footnote"> <dt class="label" id="id26">[<a href="#id1">1</a>]</dt> <dd><a class="reference external" href="https://mail.python.org/pipermail/python-dev/2010-October/104721.html">https://mail.python.org/pipermail/python-dev/2010-October/104721.html</a></aside> <aside class="footnote brackets" id="id27" role="doc-footnote"> <dt class="label" id="id27">[<a href="#id2">2</a>]</dt> <dd><a class="reference external" href="http://support.casio.com/pdf/004/CP330PLUSver310_Soft_E.pdf">http://support.casio.com/pdf/004/CP330PLUSver310_Soft_E.pdf</a></aside> <aside class="footnote brackets" id="id28" role="doc-footnote"> <dt class="label" id="id28">[<a href="#id3">3</a>]</dt> <dd>Gnumeric:: <a class="reference external" href="https://projects.gnome.org/gnumeric/functions.shtml">https://projects.gnome.org/gnumeric/functions.shtml</a><p>LibreOffice: <a class="reference external" href="https://help.libreoffice.org/Calc/Statistical_Functions_Part_One">https://help.libreoffice.org/Calc/Statistical_Functions_Part_One</a> <a class="reference external" href="https://help.libreoffice.org/Calc/Statistical_Functions_Part_Two">https://help.libreoffice.org/Calc/Statistical_Functions_Part_Two</a> <a class="reference external" href="https://help.libreoffice.org/Calc/Statistical_Functions_Part_Three">https://help.libreoffice.org/Calc/Statistical_Functions_Part_Three</a> <a class="reference external" href="https://help.libreoffice.org/Calc/Statistical_Functions_Part_Four">https://help.libreoffice.org/Calc/Statistical_Functions_Part_Four</a> <a class="reference external" href="https://help.libreoffice.org/Calc/Statistical_Functions_Part_Five">https://help.libreoffice.org/Calc/Statistical_Functions_Part_Five</a></p> </aside> <aside class="footnote brackets" id="id29" role="doc-footnote"> <dt class="label" id="id29">[<a href="#id4">4</a>]</dt> <dd>Scipy: <a class="reference external" href="http://scipy-central.org/">http://scipy-central.org/</a> Numpy: <a class="reference external" href="http://www.numpy.org/">http://www.numpy.org/</a></aside> <aside class="footnote brackets" id="id30" role="doc-footnote"> <dt class="label" id="id30">[<a href="#id5">5</a>]</dt> <dd><a class="reference external" href="http://wiki.scipy.org/Numpy_Functions_by_Category">http://wiki.scipy.org/Numpy_Functions_by_Category</a></aside> <aside class="footnote brackets" id="id31" role="doc-footnote"> <dt class="label" id="id31">[<a href="#id6">6</a>]</dt> <dd>Tested with numpy 1.6.1 and Python 2.7.</aside> <aside class="footnote brackets" id="id32" role="doc-footnote"> <dt class="label" id="id32">[<a href="#id7">7</a>]</dt> <dd><a class="reference external" href="http://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/">http://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-standard-deviation/</a></aside> <aside class="footnote brackets" id="id33" role="doc-footnote"> <dt class="label" id="id33">[<a href="#id8">8</a>]</dt> <dd><a class="reference external" href="http://rosettacode.org/wiki/Standard_deviation">http://rosettacode.org/wiki/Standard_deviation</a></aside> <aside class="footnote brackets" id="id34" role="doc-footnote"> <dt class="label" id="id34">[<a href="#id9">9</a>]</dt> <dd><a class="reference external" href="https://bitbucket.org/larsyencken/simplestats/src/c42e048a6625/src/basic.py">https://bitbucket.org/larsyencken/simplestats/src/c42e048a6625/src/basic.py</a></aside> <aside class="footnote brackets" id="id35" role="doc-footnote"> <dt class="label" id="id35">[<a href="#id10">10</a>]</dt> <dd><a class="reference external" href="http://stackoverflow.com/questions/2341340/calculate-mean-and-variance-with-one-iteration">http://stackoverflow.com/questions/2341340/calculate-mean-and-variance-with-one-iteration</a></aside> <aside class="footnote brackets" id="id36" role="doc-footnote"> <dt class="label" id="id36">[<a href="#id11">11</a>]</dt> <dd><a class="reference external" href="http://www.r-project.org/">http://www.r-project.org/</a></aside> <aside class="footnote brackets" id="id37" role="doc-footnote"> <dt class="label" id="id37">[<a href="#id12">12</a>]</dt> <dd><a class="reference external" href="http://msdn.microsoft.com/en-us/library/system.linq.enumerable.average.aspx">http://msdn.microsoft.com/en-us/library/system.linq.enumerable.average.aspx</a></aside> <aside class="footnote brackets" id="id38" role="doc-footnote"> <dt class="label" id="id38">[<a href="#id13">13</a>]</dt> <dd><a class="reference external" href="https://www.bcg.wisc.edu/webteam/support/ruby/standard_deviation">https://www.bcg.wisc.edu/webteam/support/ruby/standard_deviation</a></aside> <aside class="footnote brackets" id="id39" role="doc-footnote"> <dt class="label" id="id39">[<a href="#id14">14</a>]</dt> <dd><a class="reference external" href="http://ruby-statsample.rubyforge.org/">http://ruby-statsample.rubyforge.org/</a></aside> <aside class="footnote brackets" id="id40" role="doc-footnote"> <dt class="label" id="id40">[<a href="#id15">15</a>]</dt> <dd><a class="reference external" href="http://www.php.net/manual/en/ref.stats.php">http://www.php.net/manual/en/ref.stats.php</a></aside> <aside class="footnote brackets" id="id41" role="doc-footnote"> <dt class="label" id="id41">[<a href="#id16">16</a>]</dt> <dd><a class="reference external" href="http://www.ayton.id.au/gary/it/Delphi/D_maths.htm#Delphi%20Statistical%20functions">http://www.ayton.id.au/gary/it/Delphi/D_maths.htm#Delphi%20Statistical%20functions</a>.</aside> <aside class="footnote brackets" id="id42" role="doc-footnote"> <dt class="label" id="id42">[<a href="#id17">17</a>]</dt> <dd><a class="reference external" href="http://www.gnu.org/software/gsl/manual/html_node/Statistics.html">http://www.gnu.org/software/gsl/manual/html_node/Statistics.html</a></aside> <aside class="footnote brackets" id="id43" role="doc-footnote"> <dt class="label" id="id43">[18]<em> (<a href='#id18'>1</a>, <a href='#id21'>2</a>) </em></dt> <dd><a class="reference external" href="http://www.gnu.org/software/gsl/manual/html_node/Mean-and-standard-deviation-and-variance.html">http://www.gnu.org/software/gsl/manual/html_node/Mean-and-standard-deviation-and-variance.html</a></aside> <aside class="footnote brackets" id="id44" role="doc-footnote"> <dt class="label" id="id44">[<a href="#id19">19</a>]</dt> <dd><a class="reference external" href="http://mathworld.wolfram.com/Skewness.html">http://mathworld.wolfram.com/Skewness.html</a></aside> <aside class="footnote brackets" id="id45" role="doc-footnote"> <dt class="label" id="id45">[<a href="#id20">20</a>]</dt> <dd>At least, tedious to those who don’t like this sort of thing.</aside> <aside class="footnote brackets" id="id46" role="doc-footnote"> <dt class="label" id="id46">[<a href="#id22">21</a>]</dt> <dd><a class="reference external" href="https://mail.python.org/pipermail/python-ideas/2011-September/011524.html">https://mail.python.org/pipermail/python-ideas/2011-September/011524.html</a></aside> <aside class="footnote brackets" id="id47" role="doc-footnote"> <dt class="label" id="id47">[<a href="#id23">22</a>]</dt> <dd><a class="reference external" href="https://pypi.python.org/pypi/stats/">https://pypi.python.org/pypi/stats/</a></aside> <aside class="footnote brackets" id="id48" role="doc-footnote"> <dt class="label" id="id48">[<a href="#id24">23</a>]</dt> <dd><a class="reference external" href="https://mail.python.org/pipermail/python-ideas/2013-August/022630.html">https://mail.python.org/pipermail/python-ideas/2013-August/022630.html</a></aside> <aside class="footnote brackets" id="id49" role="doc-footnote"> <dt class="label" id="id49">[<a href="#id25">24</a>]</dt> <dd><a class="reference external" href="https://mail.python.org/pipermail/python-dev/2013-September/128429.html">https://mail.python.org/pipermail/python-dev/2013-September/128429.html</a></aside> </aside> </section> <section id="copyright"> <h2><a class="toc-backref" href="#copyright" role="doc-backlink">Copyright</a></h2> <p>This document has been placed in the public domain.</p> </section> </section> <hr class="docutils" /> <p>Source: <a class="reference external" href="https://github.com/python/peps/blob/main/peps/pep-0450.rst">https://github.com/python/peps/blob/main/peps/pep-0450.rst</a></p> <p>Last modified: <a class="reference external" href="https://github.com/python/peps/commits/main/peps/pep-0450.rst">2025-02-01 08:59:27 GMT</a></p> </article> <nav id="pep-sidebar"> <h2>Contents</h2> <ul> <li><a class="reference internal" href="#abstract">Abstract</a></li> <li><a class="reference internal" href="#rationale">Rationale</a></li> <li><a class="reference internal" href="#comparison-to-other-languages-packages">Comparison To Other Languages/Packages</a><ul> <li><a class="reference internal" href="#r">R</a></li> <li><a class="reference internal" href="#c">C#</a></li> <li><a class="reference internal" href="#ruby">Ruby</a></li> <li><a class="reference internal" href="#php">PHP</a></li> <li><a class="reference internal" href="#delphi">Delphi</a></li> <li><a class="reference internal" href="#gnu-scientific-library">GNU Scientific Library</a></li> </ul> </li> <li><a class="reference internal" href="#design-decisions-of-the-module">Design Decisions Of The Module</a></li> <li><a class="reference internal" href="#api">API</a><ul> <li><a class="reference internal" href="#calculating-mean-median-and-mode">Calculating mean, median and mode</a></li> <li><a class="reference internal" href="#calculating-variance-and-standard-deviation">Calculating variance and standard deviation</a></li> <li><a class="reference internal" href="#other-functions">Other functions</a></li> </ul> </li> <li><a class="reference internal" href="#specification">Specification</a></li> <li><a class="reference internal" href="#what-should-be-the-name-of-the-module">What Should Be The Name Of The Module?</a></li> <li><a class="reference internal" href="#discussion-and-resolved-issues">Discussion And Resolved Issues</a></li> <li><a class="reference internal" href="#frequently-asked-questions">Frequently Asked Questions</a><ul> <li><a class="reference internal" href="#shouldn-t-this-module-spend-time-on-pypi-before-being-considered-for-the-standard-library">Shouldn’t this module spend time on PyPI before being considered for the standard library?</a></li> <li><a class="reference internal" href="#does-the-standard-library-really-need-yet-another-version-of-sum">Does the standard library really need yet another version of <code class="docutils literal notranslate"><span class="pre">sum</span></code>?</a></li> <li><a class="reference internal" href="#will-this-module-be-backported-to-older-versions-of-python">Will this module be backported to older versions of Python?</a></li> <li><a class="reference internal" href="#is-this-supposed-to-replace-numpy">Is this supposed to replace numpy?</a></li> </ul> </li> <li><a class="reference internal" href="#future-work">Future Work</a></li> <li><a class="reference internal" href="#references">References</a></li> <li><a class="reference internal" href="#copyright">Copyright</a></li> </ul> <br> <a id="source" href="https://github.com/python/peps/blob/main/peps/pep-0450.rst">Page Source (GitHub)</a> </nav> </section> <script 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