Skip to content

C
cpython
Commit e4810b2a authored by Raymond Hettinger's avatar Raymond Hettinger Committed by GitHub
Browse files
Options

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

parent 8f9cc877
Show whitespace changes
Inline Side-by-side
Showing with 83 additions and 85 deletions
Doc/library/statistics.rst
View file @ e4810b2a
......@@ -26,10 +26,10 @@ numeric (:class:`Real`-valued) data.
Unless explicitly noted otherwise, these functions support :class:`int`,
:class:`float`, :class:`decimal.Decimal` and :class:`fractions.Fraction`.
Behaviour with other types (whether in the numeric tower or not) is
currently unsupported. Mixed types are also undefined 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.
``map(float, input_data)``.
currently unsupported. Collections with a mix of types are also undefined
and implementation-dependent. If your input data consists of mixed types,
you may be able to use :func:`map` to ensure a consistent result, for
example: ``map(float, input_data)``.
Averages and measures of central location
-----------------------------------------
......@@ -102,11 +102,9 @@ However, for reading convenience, most of the examples show sorted sequences.
.. note::
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
data points. For more robust, although less efficient, measures of
central location, see :func:`median` and :func:`mode`. (In this case,
"efficient" refers to statistical efficiency rather than computational
efficiency.)
for central location: the mean is not necessarily a typical example of
the data points. For more robust measures of central location, see
:func:`median` and :func:`mode`.
The sample mean gives an unbiased estimate of the true population mean,
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.
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
:func:`mean`. If the input dataset is empty, raises a
:exc:`StatisticsError`.
:class:`float`. The *data* may be a sequence or iterator. If the input
dataset is empty, raises a :exc:`StatisticsError`.
.. 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.
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,
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.
(However, this may change in the future.)
.. doctest::
>>> round(geometric_mean([54, 24, 36]), 9)
>>> round(geometric_mean([54, 24, 36]), 1)
36.0
.. versionadded:: 3.8
......@@ -174,7 +176,7 @@ However, for reading convenience, most of the examples show sorted sequences.
3.6
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
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
measure of central location.
If there are multiple modes, returns the first one encountered in the *data*.
If the smallest or largest of multiple modes is desired instead, use
``min(multimode(data))`` or ``max(multimode(data))``. If the input *data* is
empty, :exc:`StatisticsError` is raised.
If there are multiple modes with the same frequency, returns the first one
encountered in the *data*. If the smallest or largest of those is
desired instead, use ``min(multimode(data))`` or ``max(multimode(data))``.
If the input *data* is empty, :exc:`StatisticsError` is raised.
``mode`` assumes discrete data, and returns a single value. This is the
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])
3
The mode is unique in that it is the only statistic which also applies
to nominal (non-numeric) data:
The mode is unique in that it is the only statistic in this package that
also applies to nominal (non-numeric) data:
.. doctest::
......@@ -368,15 +370,16 @@ However, for reading convenience, most of the examples show sorted sequences.
.. function:: pvariance(data, mu=None)
Return the population variance of *data*, a non-empty iterable of real-valued
numbers. Variance, or second moment about the mean, is a measure of the
variability (spread or dispersion) of data. A large variance indicates that
the data is spread out; a small variance indicates it is clustered closely
around the mean.
Return the population variance of *data*, a non-empty sequence or iterator
of real-valued numbers. Variance, or second moment about the mean, is a
measure of the variability (spread or dispersion) of data. A large
variance indicates that the data is spread out; a small variance indicates
it is clustered closely around the mean.
If the optional second argument *mu* is given, it should be the mean of
*data*. If it is missing or ``None`` (the default), the mean is
automatically calculated.
If the optional second argument *mu* is given, it is typically the mean of
the *data*. It can also be used to compute the second moment around a
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
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)
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:
.. 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
s², also known as variance with N degrees of freedom.
If you somehow know the true population mean μ, you may use this function
to calculate the variance of a sample, giving the known population mean as
the second argument. Provided the data points are representative
(e.g. independent and identically distributed), the result will be an
unbiased estimate of the population variance.
If you somehow know the true population mean μ, you may use this
function to calculate the variance of a sample, giving the known
population mean as the second argument. Provided the data points are a
random sample of the population, the result will be an unbiased estimate
of the population variance.
.. 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
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.
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
*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.
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
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.
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``.
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.
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
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.
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
values, the method sorts them and assigns the following percentiles:
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.
.. doctest::
......@@ -580,7 +579,7 @@ A single exception is defined:
:class:`NormalDist` is a tool for creating and manipulating normal
distributions of a `random variable
<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.
Normal distributions arise from the `Central Limit Theorem
......@@ -616,13 +615,14 @@ of applications in statistics.
.. classmethod:: NormalDist.from_samples(data)
Makes a normal distribution instance computed from sample data. The
*data* can be any :term:`iterable` and should consist of values that
can be converted to type :class:`float`.
Makes a normal distribution instance with *mu* and *sigma* parameters
estimated from the *data* using :func:`fmean` and :func:`stdev`.
If *data* does not 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.
The *data* can be any :term:`iterable` and should consist of values
that can be converted to type :class:`float`. If *data* does not
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)
......@@ -636,10 +636,10 @@ of applications in statistics.
.. method:: NormalDist.pdf(x)
Using a `probability density function (pdf)
<https://en.wikipedia.org/wiki/Probability_density_function>`_,
compute the relative likelihood that a random variable *X* will be near
the given value *x*. Mathematically, it is the ratio ``P(x <= X <
x+dx) / dx``.
<https://en.wikipedia.org/wiki/Probability_density_function>`_, compute
the relative likelihood that a random variable *X* will be near the
given value *x*. Mathematically, it is the limit of the ratio ``P(x <=
X < x+dx) / dx`` as *dx* approaches zero.
The relative likelihood is computed as the probability of a sample
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)
Returns a value between 0.0 and 1.0 giving the overlapping area for
the two probability density functions.
Measures the agreement between two normal probability distributions.
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,
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)
...
>>> n = 100_000
>>> seed = 86753099035768
>>> X = NormalDist(10, 2.5).samples(n, seed=seed)
>>> Y = NormalDist(15, 1.75).samples(n, seed=seed)
>>> Z = NormalDist(50, 1.25).samples(n, seed=seed)
>>> NormalDist.from_samples(map(model, X, Y, Z)) # doctest: +SKIP
NormalDist(mu=1.8661894803304777, sigma=0.65238717376862)
>>> X = NormalDist(10, 2.5).samples(n, seed=3652260728)
>>> Y = NormalDist(15, 1.75).samples(n, seed=4582495471)
>>> Z = NormalDist(50, 1.25).samples(n, seed=6582483453)
>>> quantiles(map(model, X, Y, Z)) # doctest: +SKIP
[1.4591308524824727, 1.8035946855390597, 2.175091447274739]
Normal distributions commonly arise in machine learning problems.
......
Lib/statistics.py
View file @ e4810b2a
......@@ -322,7 +322,6 @@ def fmean(data):
"""Convert data to floats and compute the arithmetic mean.
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.
>>> fmean([3.5, 4.0, 5.25])
......@@ -546,7 +545,8 @@ def mode(data):
>>> mode(["red", "blue", "blue", "red", "green", "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'])
'red'
......@@ -615,28 +615,28 @@ def multimode(data):
# position is that fewer options make for easier choices and that
# external packages can be used for anything more advanced.
def quantiles(dist, /, *, n=4, method='exclusive'):
"""Divide *dist* into *n* continuous intervals with equal probability.
def quantiles(data, /, *, n=4, method='exclusive'):
"""Divide *data* into *n* continuous intervals with equal probability.
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 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
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
maximum value is treated as the 100th percentile.
"""
if n < 1:
raise StatisticsError('n must be at least 1')
if hasattr(dist, 'inv_cdf'):
return [dist.inv_cdf(i / n) for i in range(1, n)]
data = sorted(dist)
if hasattr(data, 'inv_cdf'):
return [data.inv_cdf(i / n) for i in range(1, n)]
data = sorted(data)
ld = len(data)
if ld < 2:
raise StatisticsError('must have at least two data points')
......@@ -745,7 +745,7 @@ def variance(data, xbar=None):
def pvariance(data, mu=None):
"""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
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)
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:
>>> from decimal import Decimal as D
......@@ -913,8 +909,8 @@ class NormalDist:
"NormalDist where mu is the mean and sigma is the standard deviation."
if sigma < 0.0:
raise StatisticsError('sigma must be non-negative')
self._mu = mu
self._sigma = sigma
self._mu = float(mu)
self._sigma = float(sigma)
@classmethod
def from_samples(cls, data):
......
Misc/ACKS
View file @ e4810b2a
......@@ -416,6 +416,7 @@ Dima Dorfman
Yves Dorfsman
Michael Dorman
Steve Dower
Allen Downey
Cesar Douady
Dean Draayer
Fred L. Drake, Jr.
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment
GitLab Nexedi Edition | About GitLab | About Nexedi | 沪ICP备2021021310号-2 | 沪ICP备2021021310号-7