[3.12] Sync main docs and docstring for median_grouped(). (gh-117214) (gh-117241)

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@ -79,7 +79,7 @@ or sample.
:func:`median` Median (middle value) of data.
:func:`median_low` Low median of data.
:func:`median_high` High median of data.
:func:`median_grouped` Median, or 50th percentile, of grouped data.
:func:`median_grouped` Median (50th percentile) of grouped data.
:func:`mode` Single mode (most common value) of discrete or nominal data.
:func:`multimode` List of modes (most common values) of discrete or nominal data.
:func:`quantiles` Divide data into intervals with equal probability.
@ -329,55 +329,56 @@ However, for reading convenience, most of the examples show sorted sequences.
be an actual data point rather than interpolated.
.. function:: median_grouped(data, interval=1)
.. function:: median_grouped(data, interval=1.0)
Return the median of grouped continuous data, calculated as the 50th
percentile, using interpolation. If *data* is empty, :exc:`StatisticsError`
is raised. *data* can be a sequence or iterable.
Estimates the median for numeric data that has been `grouped or binned
<https://en.wikipedia.org/wiki/Data_binning>`_ around the midpoints
of consecutive, fixed-width intervals.
The *data* can be any iterable of numeric data with each value being
exactly the midpoint of a bin. At least one value must be present.
The *interval* is the width of each bin.
For example, demographic information may have been summarized into
consecutive ten-year age groups with each group being represented
by the 5-year midpoints of the intervals:
.. doctest::
>>> median_grouped([52, 52, 53, 54])
52.5
>>> from collections import Counter
>>> demographics = Counter({
... 25: 172, # 20 to 30 years old
... 35: 484, # 30 to 40 years old
... 45: 387, # 40 to 50 years old
... 55: 22, # 50 to 60 years old
... 65: 6, # 60 to 70 years old
... })
...
In the following example, the data are rounded, so that each value represents
the midpoint of data classes, e.g. 1 is the midpoint of the class 0.5--1.5, 2
is the midpoint of 1.5--2.5, 3 is the midpoint of 2.5--3.5, etc. With the data
given, the middle value falls somewhere in the class 3.5--4.5, and
interpolation is used to estimate it:
The 50th percentile (median) is the 536th person out of the 1071
member cohort. That person is in the 30 to 40 year old age group.
The regular :func:`median` function would assume that everyone in the
tricenarian age group was exactly 35 years old. A more tenable
assumption is that the 484 members of that age group are evenly
distributed between 30 and 40. For that, we use
:func:`median_grouped`:
.. doctest::
>>> median_grouped([1, 2, 2, 3, 4, 4, 4, 4, 4, 5])
3.7
>>> data = list(demographics.elements())
>>> median(data)
35
>>> round(median_grouped(data, interval=10), 1)
37.5
Optional argument *interval* represents the class interval, and defaults
to 1. Changing the class interval naturally will change the interpolation:
The caller is responsible for making sure the data points are separated
by exact multiples of *interval*. This is essential for getting a
correct result. The function does not check this precondition.
.. doctest::
>>> median_grouped([1, 3, 3, 5, 7], interval=1)
3.25
>>> median_grouped([1, 3, 3, 5, 7], interval=2)
3.5
This function does not check whether the data points are at least
*interval* apart.
.. impl-detail::
Under some circumstances, :func:`median_grouped` may coerce data points to
floats. This behaviour is likely to change in the future.
.. seealso::
* "Statistics for the Behavioral Sciences", Frederick J Gravetter and
Larry B Wallnau (8th Edition).
* The `SSMEDIAN
<https://help.gnome.org/users/gnumeric/stable/gnumeric.html#gnumeric-function-SSMEDIAN>`_
function in the Gnome Gnumeric spreadsheet, including `this discussion
<https://mail.gnome.org/archives/gnumeric-list/2011-April/msg00018.html>`_.
Inputs may be any numeric type that can be coerced to a float during
the interpolation step.
.. function:: mode(data)