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bpo-36324: Apply review comments from Allen Downey (GH-15693)

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Doc/library/statistics.rst
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@ -26,10 +26,10 @@ numeric (:class:`Real`-valued) data.
Unless explicitly noted otherwise, these functions support :class:`int`, Unless explicitly noted otherwise, these functions support :class:`int`,
:class:`float`, :class:`decimal.Decimal` and :class:`fractions.Fraction`. :class:`float`, :class:`decimal.Decimal` and :class:`fractions.Fraction`.
Behaviour with other types (whether in the numeric tower or not) is Behaviour with other types (whether in the numeric tower or not) is
currently unsupported. Mixed types are also undefined and currently unsupported. Collections with a mix of types are also undefined
implementation-dependent. If your input data consists of mixed types, and implementation-dependent. If your input data consists of mixed types,
you may be able to use :func:`map` to ensure a consistent result, e.g. you may be able to use :func:`map` to ensure a consistent result, for
``map(float, input_data)``. example: ``map(float, input_data)``.
Averages and measures of central location Averages and measures of central location
----------------------------------------- -----------------------------------------
@ -102,11 +102,9 @@ However, for reading convenience, most of the examples show sorted sequences.
.. note:: .. note::
The mean is strongly affected by outliers and is not a robust estimator The mean is strongly affected by outliers and is not a robust estimator
for central location: the mean is not necessarily a typical example of the for central location: the mean is not necessarily a typical example of
data points. For more robust, although less efficient, measures of the data points. For more robust measures of central location, see
central location, see :func:`median` and :func:`mode`. (In this case, :func:`median` and :func:`mode`.
"efficient" refers to statistical efficiency rather than computational
efficiency.)
The sample mean gives an unbiased estimate of the true population mean, The sample mean gives an unbiased estimate of the true population mean,
which means that, taken on average over all the possible samples, which means that, taken on average over all the possible samples,
@ -120,9 +118,8 @@ However, for reading convenience, most of the examples show sorted sequences.
Convert *data* to floats and compute the arithmetic mean. Convert *data* to floats and compute the arithmetic mean.
This runs faster than the :func:`mean` function and it always returns a This runs faster than the :func:`mean` function and it always returns a
:class:`float`. The result is highly accurate but not as perfect as :class:`float`. The *data* may be a sequence or iterator. If the input
:func:`mean`. If the input dataset is empty, raises a dataset is empty, raises a :exc:`StatisticsError`.
:exc:`StatisticsError`.
.. doctest:: .. doctest::
@ -136,15 +133,20 @@ However, for reading convenience, most of the examples show sorted sequences.
Convert *data* to floats and compute the geometric mean. Convert *data* to floats and compute the geometric mean.
The geometric mean indicates the central tendency or typical value of the
*data* using the product of the values (as opposed to the arithmetic mean
which uses their sum).
Raises a :exc:`StatisticsError` if the input dataset is empty, Raises a :exc:`StatisticsError` if the input dataset is empty,
if it contains a zero, or if it contains a negative value. if it contains a zero, or if it contains a negative value.
The *data* may be a sequence or iterator.
No special efforts are made to achieve exact results. No special efforts are made to achieve exact results.
(However, this may change in the future.) (However, this may change in the future.)
.. doctest:: .. doctest::
>>> round(geometric_mean([54, 24, 36]), 9) >>> round(geometric_mean([54, 24, 36]), 1)
36.0 36.0
.. versionadded:: 3.8 .. versionadded:: 3.8
@ -174,7 +176,7 @@ However, for reading convenience, most of the examples show sorted sequences.
3.6 3.6
Using the arithmetic mean would give an average of about 5.167, which Using the arithmetic mean would give an average of about 5.167, which
is too high. is well over the aggregate P/E ratio.
:exc:`StatisticsError` is raised if *data* is empty, or any element :exc:`StatisticsError` is raised if *data* is empty, or any element
is less than zero. is less than zero.
@ -312,10 +314,10 @@ However, for reading convenience, most of the examples show sorted sequences.
The mode (when it exists) is the most typical value and serves as a The mode (when it exists) is the most typical value and serves as a
measure of central location. measure of central location.
If there are multiple modes, returns the first one encountered in the *data*. If there are multiple modes with the same frequency, returns the first one
If the smallest or largest of multiple modes is desired instead, use encountered in the *data*. If the smallest or largest of those is
``min(multimode(data))`` or ``max(multimode(data))``. If the input *data* is desired instead, use ``min(multimode(data))`` or ``max(multimode(data))``.
empty, :exc:`StatisticsError` is raised. If the input *data* is empty, :exc:`StatisticsError` is raised.
``mode`` assumes discrete data, and returns a single value. This is the ``mode`` assumes discrete data, and returns a single value. This is the
standard treatment of the mode as commonly taught in schools: standard treatment of the mode as commonly taught in schools:
@ -325,8 +327,8 @@ However, for reading convenience, most of the examples show sorted sequences.
>>> mode([1, 1, 2, 3, 3, 3, 3, 4]) >>> mode([1, 1, 2, 3, 3, 3, 3, 4])
3 3
The mode is unique in that it is the only statistic which also applies The mode is unique in that it is the only statistic in this package that
to nominal (non-numeric) data: also applies to nominal (non-numeric) data:
.. doctest:: .. doctest::
@ -368,15 +370,16 @@ However, for reading convenience, most of the examples show sorted sequences.
.. function:: pvariance(data, mu=None) .. function:: pvariance(data, mu=None)
Return the population variance of *data*, a non-empty iterable of real-valued Return the population variance of *data*, a non-empty sequence or iterator
numbers. Variance, or second moment about the mean, is a measure of the of real-valued numbers. Variance, or second moment about the mean, is a
variability (spread or dispersion) of data. A large variance indicates that measure of the variability (spread or dispersion) of data. A large
the data is spread out; a small variance indicates it is clustered closely variance indicates that the data is spread out; a small variance indicates
around the mean. it is clustered closely around the mean.
If the optional second argument *mu* is given, it should be the mean of If the optional second argument *mu* is given, it is typically the mean of
*data*. If it is missing or ``None`` (the default), the mean is the *data*. It can also be used to compute the second moment around a
automatically calculated. point that is not the mean. If it is missing or ``None`` (the default),
the arithmetic mean is automatically calculated.
Use this function to calculate the variance from the entire population. To Use this function to calculate the variance from the entire population. To
estimate the variance from a sample, the :func:`variance` function is usually estimate the variance from a sample, the :func:`variance` function is usually
@ -401,10 +404,6 @@ However, for reading convenience, most of the examples show sorted sequences.
>>> pvariance(data, mu) >>> pvariance(data, mu)
1.25 1.25
This function does not attempt to verify that you have passed the actual mean
as *mu*. Using arbitrary values for *mu* may lead to invalid or impossible
results.
Decimals and Fractions are supported: Decimals and Fractions are supported:
.. doctest:: .. doctest::
@ -423,11 +422,11 @@ However, for reading convenience, most of the examples show sorted sequences.
σ². When called on a sample instead, this is the biased sample variance σ². When called on a sample instead, this is the biased sample variance
s², also known as variance with N degrees of freedom. s², also known as variance with N degrees of freedom.
If you somehow know the true population mean μ, you may use this function If you somehow know the true population mean μ, you may use this
to calculate the variance of a sample, giving the known population mean as function to calculate the variance of a sample, giving the known
the second argument. Provided the data points are representative population mean as the second argument. Provided the data points are a
(e.g. independent and identically distributed), the result will be an random sample of the population, the result will be an unbiased estimate
unbiased estimate of the population variance. of the population variance.
.. function:: stdev(data, xbar=None) .. function:: stdev(data, xbar=None)
@ -502,19 +501,19 @@ However, for reading convenience, most of the examples show sorted sequences.
:func:`pvariance` function as the *mu* parameter to get the variance of a :func:`pvariance` function as the *mu* parameter to get the variance of a
sample. sample.
.. function:: quantiles(dist, *, n=4, method='exclusive') .. function:: quantiles(data, *, n=4, method='exclusive')
Divide *dist* into *n* continuous intervals with equal probability. Divide *data* into *n* continuous intervals with equal probability.
Returns a list of ``n - 1`` cut points separating the intervals. Returns a list of ``n - 1`` cut points separating the intervals.
Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles. Set Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles. Set
*n* to 100 for percentiles which gives the 99 cuts points that separate *n* to 100 for percentiles which gives the 99 cuts points that separate
*dist* in to 100 equal sized groups. Raises :exc:`StatisticsError` if *n* *data* in to 100 equal sized groups. Raises :exc:`StatisticsError` if *n*
is not least 1. is not least 1.
The *dist* can be any iterable containing sample data or it can be an The *data* can be any iterable containing sample data or it can be an
instance of a class that defines an :meth:`~inv_cdf` method. For meaningful instance of a class that defines an :meth:`~inv_cdf` method. For meaningful
results, the number of data points in *dist* should be larger than *n*. results, the number of data points in *data* should be larger than *n*.
Raises :exc:`StatisticsError` if there are not at least two data points. Raises :exc:`StatisticsError` if there are not at least two data points.
For sample data, the cut points are linearly interpolated from the For sample data, the cut points are linearly interpolated from the
@ -523,7 +522,7 @@ However, for reading convenience, most of the examples show sorted sequences.
cut-point will evaluate to ``104``. cut-point will evaluate to ``104``.
The *method* for computing quantiles can be varied depending on The *method* for computing quantiles can be varied depending on
whether the data in *dist* includes or excludes the lowest and whether the data in *data* includes or excludes the lowest and
highest possible values from the population. highest possible values from the population.
The default *method* is "exclusive" and is used for data sampled from The default *method* is "exclusive" and is used for data sampled from
@ -535,14 +534,14 @@ However, for reading convenience, most of the examples show sorted sequences.
Setting the *method* to "inclusive" is used for describing population Setting the *method* to "inclusive" is used for describing population
data or for samples that are known to include the most extreme values data or for samples that are known to include the most extreme values
from the population. The minimum value in *dist* is treated as the 0th from the population. The minimum value in *data* is treated as the 0th
percentile and the maximum value is treated as the 100th percentile. percentile and the maximum value is treated as the 100th percentile.
The portion of the population falling below the *i-th* of *m* sorted The portion of the population falling below the *i-th* of *m* sorted
data points is computed as ``(i - 1) / (m - 1)``. Given 11 sample data points is computed as ``(i - 1) / (m - 1)``. Given 11 sample
values, the method sorts them and assigns the following percentiles: values, the method sorts them and assigns the following percentiles:
0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%. 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%.
If *dist* is an instance of a class that defines an If *data* is an instance of a class that defines an
:meth:`~inv_cdf` method, setting *method* has no effect. :meth:`~inv_cdf` method, setting *method* has no effect.
.. doctest:: .. doctest::
@ -580,7 +579,7 @@ A single exception is defined:
:class:`NormalDist` is a tool for creating and manipulating normal :class:`NormalDist` is a tool for creating and manipulating normal
distributions of a `random variable distributions of a `random variable
<http://www.stat.yale.edu/Courses/1997-98/101/ranvar.htm>`_. It is a <http://www.stat.yale.edu/Courses/1997-98/101/ranvar.htm>`_. It is a
composite class that treats the mean and standard deviation of data class that treats the mean and standard deviation of data
measurements as a single entity. measurements as a single entity.
Normal distributions arise from the `Central Limit Theorem Normal distributions arise from the `Central Limit Theorem
@ -616,13 +615,14 @@ of applications in statistics.
.. classmethod:: NormalDist.from_samples(data) .. classmethod:: NormalDist.from_samples(data)
Makes a normal distribution instance computed from sample data. The Makes a normal distribution instance with *mu* and *sigma* parameters
*data* can be any :term:`iterable` and should consist of values that estimated from the *data* using :func:`fmean` and :func:`stdev`.
can be converted to type :class:`float`.
If *data* does not contain at least two elements, raises The *data* can be any :term:`iterable` and should consist of values
:exc:`StatisticsError` because it takes at least one point to estimate that can be converted to type :class:`float`. If *data* does not
a central value and at least two points to estimate dispersion. contain at least two elements, raises :exc:`StatisticsError` because it
takes at least one point to estimate a central value and at least two
points to estimate dispersion.
.. method:: NormalDist.samples(n, *, seed=None) .. method:: NormalDist.samples(n, *, seed=None)
@ -636,10 +636,10 @@ of applications in statistics.
.. method:: NormalDist.pdf(x) .. method:: NormalDist.pdf(x)
Using a `probability density function (pdf) Using a `probability density function (pdf)
<https://en.wikipedia.org/wiki/Probability_density_function>`_, <https://en.wikipedia.org/wiki/Probability_density_function>`_, compute
compute the relative likelihood that a random variable *X* will be near the relative likelihood that a random variable *X* will be near the
the given value *x*. Mathematically, it is the ratio ``P(x <= X < given value *x*. Mathematically, it is the limit of the ratio ``P(x <=
x+dx) / dx``. X < x+dx) / dx`` as *dx* approaches zero.
The relative likelihood is computed as the probability of a sample The relative likelihood is computed as the probability of a sample
occurring in a narrow range divided by the width of the range (hence occurring in a narrow range divided by the width of the range (hence
@ -667,8 +667,10 @@ of applications in statistics.
.. method:: NormalDist.overlap(other) .. method:: NormalDist.overlap(other)
Returns a value between 0.0 and 1.0 giving the overlapping area for Measures the agreement between two normal probability distributions.
the two probability density functions. Returns a value between 0.0 and 1.0 giving `the overlapping area for
the two probability density functions
<https://www.rasch.org/rmt/rmt101r.htm>`_.
Instances of :class:`NormalDist` support addition, subtraction, Instances of :class:`NormalDist` support addition, subtraction,
multiplication and division by a constant. These operations multiplication and division by a constant. These operations
@ -740,12 +742,11 @@ Carlo simulation <https://en.wikipedia.org/wiki/Monte_Carlo_method>`_:
... return (3*x + 7*x*y - 5*y) / (11 * z) ... return (3*x + 7*x*y - 5*y) / (11 * z)
... ...
>>> n = 100_000 >>> n = 100_000
>>> seed = 86753099035768 >>> X = NormalDist(10, 2.5).samples(n, seed=3652260728)
>>> X = NormalDist(10, 2.5).samples(n, seed=seed) >>> Y = NormalDist(15, 1.75).samples(n, seed=4582495471)
>>> Y = NormalDist(15, 1.75).samples(n, seed=seed) >>> Z = NormalDist(50, 1.25).samples(n, seed=6582483453)
>>> Z = NormalDist(50, 1.25).samples(n, seed=seed) >>> quantiles(map(model, X, Y, Z)) # doctest: +SKIP
>>> NormalDist.from_samples(map(model, X, Y, Z)) # doctest: +SKIP [1.4591308524824727, 1.8035946855390597, 2.175091447274739]
NormalDist(mu=1.8661894803304777, sigma=0.65238717376862)
Normal distributions commonly arise in machine learning problems. Normal distributions commonly arise in machine learning problems.

30
Lib/statistics.py
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@ -322,7 +322,6 @@ def fmean(data):
"""Convert data to floats and compute the arithmetic mean. """Convert data to floats and compute the arithmetic mean.
This runs faster than the mean() function and it always returns a float. This runs faster than the mean() function and it always returns a float.
The result is highly accurate but not as perfect as mean().
If the input dataset is empty, it raises a StatisticsError. If the input dataset is empty, it raises a StatisticsError.
>>> fmean([3.5, 4.0, 5.25]) >>> fmean([3.5, 4.0, 5.25])
@ -546,7 +545,8 @@ def mode(data):
>>> mode(["red", "blue", "blue", "red", "green", "red", "red"]) >>> mode(["red", "blue", "blue", "red", "green", "red", "red"])
'red' 'red'
If there are multiple modes, return the first one encountered. If there are multiple modes with same frequency, return the first one
encountered:
>>> mode(['red', 'red', 'green', 'blue', 'blue']) >>> mode(['red', 'red', 'green', 'blue', 'blue'])
'red' 'red'
@ -615,28 +615,28 @@ def multimode(data):
# position is that fewer options make for easier choices and that # position is that fewer options make for easier choices and that
# external packages can be used for anything more advanced. # external packages can be used for anything more advanced.
def quantiles(dist, /, *, n=4, method='exclusive'): def quantiles(data, /, *, n=4, method='exclusive'):
"""Divide *dist* into *n* continuous intervals with equal probability. """Divide *data* into *n* continuous intervals with equal probability.
Returns a list of (n - 1) cut points separating the intervals. Returns a list of (n - 1) cut points separating the intervals.
Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles. Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles.
Set *n* to 100 for percentiles which gives the 99 cuts points that Set *n* to 100 for percentiles which gives the 99 cuts points that
separate *dist* in to 100 equal sized groups. separate *data* in to 100 equal sized groups.
The *dist* can be any iterable containing sample data or it can be The *data* can be any iterable containing sample data or it can be
an instance of a class that defines an inv_cdf() method. For sample an instance of a class that defines an inv_cdf() method. For sample
data, the cut points are linearly interpolated between data points. data, the cut points are linearly interpolated between data points.
If *method* is set to *inclusive*, *dist* is treated as population If *method* is set to *inclusive*, *data* is treated as population
data. The minimum value is treated as the 0th percentile and the data. The minimum value is treated as the 0th percentile and the
maximum value is treated as the 100th percentile. maximum value is treated as the 100th percentile.
""" """
if n < 1: if n < 1:
raise StatisticsError('n must be at least 1') raise StatisticsError('n must be at least 1')
if hasattr(dist, 'inv_cdf'): if hasattr(data, 'inv_cdf'):
return [dist.inv_cdf(i / n) for i in range(1, n)] return [data.inv_cdf(i / n) for i in range(1, n)]
data = sorted(dist) data = sorted(data)
ld = len(data) ld = len(data)
if ld < 2: if ld < 2:
raise StatisticsError('must have at least two data points') raise StatisticsError('must have at least two data points')
@ -745,7 +745,7 @@ def variance(data, xbar=None):
def pvariance(data, mu=None): def pvariance(data, mu=None):
"""Return the population variance of ``data``. """Return the population variance of ``data``.
data should be an iterable of Real-valued numbers, with at least one data should be a sequence or iterator of Real-valued numbers, with at least one
value. The optional argument mu, if given, should be the mean of value. The optional argument mu, if given, should be the mean of
the data. If it is missing or None, the mean is automatically calculated. the data. If it is missing or None, the mean is automatically calculated.
@ -766,10 +766,6 @@ def pvariance(data, mu=None):
>>> pvariance(data, mu) >>> pvariance(data, mu)
1.25 1.25
This function does not check that ``mu`` is actually the mean of ``data``.
Giving arbitrary values for ``mu`` may lead to invalid or impossible
results.
Decimals and Fractions are supported: Decimals and Fractions are supported:
>>> from decimal import Decimal as D >>> from decimal import Decimal as D
@ -913,8 +909,8 @@ class NormalDist:
"NormalDist where mu is the mean and sigma is the standard deviation." "NormalDist where mu is the mean and sigma is the standard deviation."
if sigma < 0.0: if sigma < 0.0:
raise StatisticsError('sigma must be non-negative') raise StatisticsError('sigma must be non-negative')
self._mu = mu self._mu = float(mu)
self._sigma = sigma self._sigma = float(sigma)
@classmethod @classmethod
def from_samples(cls, data): def from_samples(cls, data):

1
Misc/ACKS
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@ -416,6 +416,7 @@ Dima Dorfman
Yves Dorfsman Yves Dorfsman
Michael Dorman Michael Dorman
Steve Dower Steve Dower
Allen Downey
Cesar Douady Cesar Douady
Dean Draayer Dean Draayer
Fred L. Drake, Jr. Fred L. Drake, Jr.