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53cdfa76-2c89-4493-9012-7e354c96d7f0 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,769 | supabase-export-v2 | c0ca1662ad7afb13 | ordinary least squares. Simple linear regression describes the relationship between an independent variable *x* and a dependent variable *y* in terms of this linear function:
*y = slope \* x + intercept + noise* | trusted_official_docs | CPython Docs | ordinary least squares. Simple linear regression describes the relationship between an independent variable *x* and a dependent variable *y* in terms of this linear function:
*y = slope \* x + intercept + noise* | ordinary least squares. Simple linear regression describes the relationship between an independent variable *x* and a dependent variable *y* in terms of this linear function:
*y = slope \* x + intercept + noise* | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
54892a2f-e3fc-402c-a4f3-7b5a2a6c411f | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,689 | supabase-export-v2 | 59220215a057774e | .. function:: multimode(data)
Return a list of the most frequently occurring values in the order they
were first encountered in the *data*. Will return more than one result if
there are multiple modes or an empty list if the *data* is empty: | trusted_official_docs | CPython Docs | .. function:: multimode(data)
Return a list of the most frequently occurring values in the order they
were first encountered in the *data*. Will return more than one result if
there are multiple modes or an empty list if the *data* is empty: | .. function:: multimode(data)
Return a list of the most frequently occurring values in the order they
were first encountered in the *data*. Will return more than one result if
there are multiple modes or an empty list if the *data* is empty: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
549f9708-f10d-4c6a-812f-b58facfac49a | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,770 | supabase-export-v2 | 4b3555dd7f3a436b | *y = slope \* x + intercept + noise*
where ``slope`` and ``intercept`` are the regression parameters that are
estimated, and ``noise`` represents the
variability of the data that was not explained by the linear regression
(it is equal to the difference between predicted and actual values
of the dependent variable). | trusted_official_docs | CPython Docs | *y = slope \* x + intercept + noise*
where ``slope`` and ``intercept`` are the regression parameters that are
estimated, and ``noise`` represents the
variability of the data that was not explained by the linear regression
(it is equal to the difference between predicted and actual values
of the dependent variable). | *y = slope \* x + intercept + noise*
where ``slope`` and ``intercept`` are the regression parameters that are
estimated, and ``noise`` represents the
variability of the data that was not explained by the linear regression
(it is equal to the difference between predicted and actual values
of the dependent variable). | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
554dce92-aedb-4fc8-9540-e582c1d5782b | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,586 | supabase-export-v2 | 9d31a3fd23e5d8dc | >>> from fractions import Fraction as F >>> mean([F(3, 7), F(1, 21), F(5, 3), F(1, 3)]) Fraction(13, 21)
>>> from decimal import Decimal as D
>>> mean([D("0.5"), D("0.75"), D("0.625"), D("0.375")])
Decimal('0.5625') | trusted_official_docs | CPython Docs | >>> from fractions import Fraction as F >>> mean([F(3, 7), F(1, 21), F(5, 3), F(1, 3)]) Fraction(13, 21)
>>> from decimal import Decimal as D
>>> mean([D("0.5"), D("0.75"), D("0.625"), D("0.375")])
Decimal('0.5625') | >>> from fractions import Fraction as F >>> mean([F(3, 7), F(1, 21), F(5, 3), F(1, 3)]) Fraction(13, 21)
>>> from decimal import Decimal as D
>>> mean([D("0.5"), D("0.75"), D("0.625"), D("0.375")])
Decimal('0.5625') | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
59132730-6727-4bbb-81cf-ee9c083c70d7 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,866 | supabase-export-v2 | c9cb52cbb58eb554 | >>> ht = 6.0 # height >>> wt = 130 # weight >>> fs = 8 # foot size
Starting with a 50% `prior probability
<https://en.wikipedia.org/wiki/Prior_probability>`_ of being male or female,
we compute the posterior as the prior times the product of likelihoods for the
feature measurements given the gender: | trusted_official_docs | CPython Docs | >>> ht = 6.0 # height >>> wt = 130 # weight >>> fs = 8 # foot size
Starting with a 50% `prior probability
<https://en.wikipedia.org/wiki/Prior_probability>`_ of being male or female,
we compute the posterior as the prior times the product of likelihoods for the
feature measurements given the gender: | >>> ht = 6.0 # height >>> wt = 130 # weight >>> fs = 8 # foot size
Starting with a 50% `prior probability
<https://en.wikipedia.org/wiki/Prior_probability>`_ of being male or female,
we compute the posterior as the prior times the product of likelihoods for the
feature measurements given the gender: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
5dae2fb4-cc6d-4546-af29-9aadc4242819 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,721 | supabase-export-v2 | a1a2fc16ba5469d3 | argument *xbar* is given, it should be the *sample* mean of *data*. If it is missing or ``None`` (the default), the mean is automatically calculated.
Use this function when your data is a sample from a population. To calculate
the variance from the entire population, see :func:`pvariance`. | trusted_official_docs | CPython Docs | argument *xbar* is given, it should be the *sample* mean of *data*. If it is missing or ``None`` (the default), the mean is automatically calculated.
Use this function when your data is a sample from a population. To calculate
the variance from the entire population, see :func:`pvariance`. | argument *xbar* is given, it should be the *sample* mean of *data*. If it is missing or ``None`` (the default), the mean is automatically calculated.
Use this function when your data is a sample from a population. To calculate
the variance from the entire population, see :func:`pvariance`. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
5e25ae7b-1558-450f-9033-6c6bfc2b61aa | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,671 | supabase-export-v2 | ea95c911fe25e008 | to 50 years old ... 55: 22, # 50 to 60 years old ... 65: 6, # 60 to 70 years old ... }) ...
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. | trusted_official_docs | CPython Docs | to 50 years old ... 55: 22, # 50 to 60 years old ... 65: 6, # 60 to 70 years old ... }) ...
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. | to 50 years old ... 55: 22, # 50 to 60 years old ... 65: 6, # 60 to 70 years old ... }) ...
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. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
61f2437d-0bf5-465d-a591-d3bf6a69368e | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,672 | supabase-export-v2 | e35aeee8fd38848a | 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 e... | trusted_official_docs | CPython Docs | 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 e... | 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 e... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
62651e63-5929-4562-8244-bc513e45b33a | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,616 | supabase-export-v2 | c092e1be90fa6436 | >>> harmonic_mean([40, 60]) 48.0
Suppose a car travels 40 km/hr for 5 km, and when traffic clears,
speeds-up to 60 km/hr for the remaining 30 km of the journey. What
is the average speed? | trusted_official_docs | CPython Docs | >>> harmonic_mean([40, 60]) 48.0
Suppose a car travels 40 km/hr for 5 km, and when traffic clears,
speeds-up to 60 km/hr for the remaining 30 km of the journey. What
is the average speed? | >>> harmonic_mean([40, 60]) 48.0
Suppose a car travels 40 km/hr for 5 km, and when traffic clears,
speeds-up to 60 km/hr for the remaining 30 km of the journey. What
is the average speed? | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
6347d89d-e505-4c2d-b086-e1c63babdd55 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,759 | supabase-export-v2 | b4cf557c46096580 | Both inputs must be of the same length (no less than two), and need not to be constant, otherwise :exc:`StatisticsError` is raised.
Example with `Kepler's laws of planetary motion
<https://en.wikipedia.org/wiki/Kepler's_laws_of_planetary_motion>`_: | trusted_official_docs | CPython Docs | Both inputs must be of the same length (no less than two), and need not to be constant, otherwise :exc:`StatisticsError` is raised.
Example with `Kepler's laws of planetary motion
<https://en.wikipedia.org/wiki/Kepler's_laws_of_planetary_motion>`_: | Both inputs must be of the same length (no less than two), and need not to be constant, otherwise :exc:`StatisticsError` is raised.
Example with `Kepler's laws of planetary motion
<https://en.wikipedia.org/wiki/Kepler's_laws_of_planetary_motion>`_: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
64c10e6e-9707-4910-aa75-192dcd3e15f3 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,604 | supabase-export-v2 | 51fbef15efa48820 | 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 iterable. | trusted_official_docs | CPython Docs | 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 iterable. | 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 iterable. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
6641ef4d-6634-42bd-a67c-fff8898ca0d8 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,834 | supabase-export-v2 | e10b5fa80bd6b062 | .. doctest::
>>> birth_weights = NormalDist.from_samples([2.5, 3.1, 2.1, 2.4, 2.7, 3.5])
>>> drug_effects = NormalDist(0.4, 0.15)
>>> combined = birth_weights + drug_effects
>>> round(combined.mean, 1)
3.1
>>> round(combined.stdev, 1)
0.5 | trusted_official_docs | CPython Docs | .. doctest::
>>> birth_weights = NormalDist.from_samples([2.5, 3.1, 2.1, 2.4, 2.7, 3.5])
>>> drug_effects = NormalDist(0.4, 0.15)
>>> combined = birth_weights + drug_effects
>>> round(combined.mean, 1)
3.1
>>> round(combined.stdev, 1)
0.5 | .. doctest::
>>> birth_weights = NormalDist.from_samples([2.5, 3.1, 2.1, 2.4, 2.7, 3.5])
>>> drug_effects = NormalDist(0.4, 0.15)
>>> combined = birth_weights + drug_effects
>>> round(combined.mean, 1)
3.1
>>> round(combined.stdev, 1)
0.5 | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
69700588-3d73-4220-af03-375dfb70d685 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,576 | supabase-export-v2 | ef1fab99c922806b | These functions calculate statistics regarding relations between two inputs.
========================= =====================================================
:func:`covariance` Sample covariance for two variables. :func:`correlation` Pearson and Spearman's correlation coefficients. :func:`linear_regression` Slope and in... | trusted_official_docs | CPython Docs | These functions calculate statistics regarding relations between two inputs.
========================= =====================================================
:func:`covariance` Sample covariance for two variables. :func:`correlation` Pearson and Spearman's correlation coefficients. :func:`linear_regression` Slope and in... | These functions calculate statistics regarding relations between two inputs.
========================= =====================================================
:func:`covariance` Sample covariance for two variables. :func:`correlation` Pearson and Spearman's correlation coefficients. :func:`linear_regression` Slope and in... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
69be1017-cc67-4803-8284-78dc738a62b3 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,771 | supabase-export-v2 | fc92ee2239a871f3 | the data that was not explained by the linear regression (it is equal to the difference between predicted and actual values of the dependent variable).
Both inputs must be sequences of the same length (no less than two), and
the independent variable *x* cannot be constant;
otherwise a :exc:`StatisticsError` is raised... | trusted_official_docs | CPython Docs | the data that was not explained by the linear regression (it is equal to the difference between predicted and actual values of the dependent variable).
Both inputs must be sequences of the same length (no less than two), and
the independent variable *x* cannot be constant;
otherwise a :exc:`StatisticsError` is raised... | the data that was not explained by the linear regression (it is equal to the difference between predicted and actual values of the dependent variable).
Both inputs must be sequences of the same length (no less than two), and
the independent variable *x* cannot be constant;
otherwise a :exc:`StatisticsError` is raised... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
6c7312b1-fc67-4900-afa4-86c36faf7f29 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,640 | supabase-export-v2 | b4c3f492c3dbdeff | Return a function that makes a random selection from the estimated probability density function produced by ``kde(data, h, kernel)``.
Providing a *seed* allows reproducible selections. In the future, the
values may change slightly as more accurate kernel inverse CDF estimates
are implemented. The seed may be an integ... | trusted_official_docs | CPython Docs | Return a function that makes a random selection from the estimated probability density function produced by ``kde(data, h, kernel)``.
Providing a *seed* allows reproducible selections. In the future, the
values may change slightly as more accurate kernel inverse CDF estimates
are implemented. The seed may be an integ... | Return a function that makes a random selection from the estimated probability density function produced by ``kde(data, h, kernel)``.
Providing a *seed* allows reproducible selections. In the future, the
values may change slightly as more accurate kernel inverse CDF estimates
are implemented. The seed may be an integ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
6cea2a96-3274-42f0-9e2f-4d24e95178a7 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,777 | supabase-export-v2 | 520359efeefd0a69 | *y = slope \* x + noise*
Continuing the example from :func:`correlation`, we look to see
how well a model based on major planets can predict the orbital
distances for dwarf planets: | trusted_official_docs | CPython Docs | *y = slope \* x + noise*
Continuing the example from :func:`correlation`, we look to see
how well a model based on major planets can predict the orbital
distances for dwarf planets: | *y = slope \* x + noise*
Continuing the example from :func:`correlation`, we look to see
how well a model based on major planets can predict the orbital
distances for dwarf planets: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
6dd5e050-c57b-4510-8797-f02e8669ce3e | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,781 | supabase-export-v2 | a9cfffa7a018e6db | 111_845, 103_410, 1_680] # days >>> predicted_dist = [math.cbrt(slope * (p * p)) for p in orbital_periods] >>> list(map(round, predicted_dist)) [5912, 10166, 6806, 6459, 414]
>>> [5_906, 10_152, 6_796, 6_450, 414] # actual distance in million km
[5906, 10152, 6796, 6450, 414] | trusted_official_docs | CPython Docs | 111_845, 103_410, 1_680] # days >>> predicted_dist = [math.cbrt(slope * (p * p)) for p in orbital_periods] >>> list(map(round, predicted_dist)) [5912, 10166, 6806, 6459, 414]
>>> [5_906, 10_152, 6_796, 6_450, 414] # actual distance in million km
[5906, 10152, 6796, 6450, 414] | 111_845, 103_410, 1_680] # days >>> predicted_dist = [math.cbrt(slope * (p * p)) for p in orbital_periods] >>> list(map(round, predicted_dist)) [5912, 10166, 6806, 6459, 414]
>>> [5_906, 10_152, 6_796, 6_450, 414] # actual distance in million km
[5906, 10152, 6796, 6450, 414] | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
6ef5e3ea-caba-48ec-81db-3b5a0db08238 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,722 | supabase-export-v2 | ba71220242b1fa19 | Use this function when your data is a sample from a population. To calculate the variance from the entire population, see :func:`pvariance`.
Raises :exc:`StatisticsError` if *data* has fewer than two values. | trusted_official_docs | CPython Docs | Use this function when your data is a sample from a population. To calculate the variance from the entire population, see :func:`pvariance`.
Raises :exc:`StatisticsError` if *data* has fewer than two values. | Use this function when your data is a sample from a population. To calculate the variance from the entire population, see :func:`pvariance`.
Raises :exc:`StatisticsError` if *data* has fewer than two values. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
6fbcc057-c612-4c95-aa0e-bf32c3eccb6d | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,627 | supabase-export-v2 | 97184325b88676f9 | of smoothing is controlled by the scaling parameter *h* which is called the bandwidth. Smaller values emphasize local features while larger values give smoother results.
The *kernel* determines the relative weights of the sample data
points. Generally, the choice of kernel shape does not matter
as much as the more in... | trusted_official_docs | CPython Docs | of smoothing is controlled by the scaling parameter *h* which is called the bandwidth. Smaller values emphasize local features while larger values give smoother results.
The *kernel* determines the relative weights of the sample data
points. Generally, the choice of kernel shape does not matter
as much as the more in... | of smoothing is controlled by the scaling parameter *h* which is called the bandwidth. Smaller values emphasize local features while larger values give smoother results.
The *kernel* determines the relative weights of the sample data
points. Generally, the choice of kernel shape does not matter
as much as the more in... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
7155574d-22e8-4d97-8f35-7cf05edac55e | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,581 | supabase-export-v2 | a67e482b113beaf2 | Return the sample arithmetic mean of *data* which can be a sequence or iterable.
The arithmetic mean is the sum of the data divided by the number of data
points. It is commonly called "the average", although it is only one of many
different mathematical averages. It is a measure of the central location of
the data. | trusted_official_docs | CPython Docs | Return the sample arithmetic mean of *data* which can be a sequence or iterable.
The arithmetic mean is the sum of the data divided by the number of data
points. It is commonly called "the average", although it is only one of many
different mathematical averages. It is a measure of the central location of
the data. | Return the sample arithmetic mean of *data* which can be a sequence or iterable.
The arithmetic mean is the sum of the data divided by the number of data
points. It is commonly called "the average", although it is only one of many
different mathematical averages. It is a measure of the central location of
the data. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
71a99560-f0cb-4d2a-a255-2af3b7641fd2 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,670 | supabase-export-v2 | 85c4e9e7cf36dd27 | .. doctest::
>>> 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
... })
... | trusted_official_docs | CPython Docs | .. doctest::
>>> 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
... })
... | .. doctest::
>>> 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
... })
... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
72038e77-f130-4a63-a9a4-a800fc8b83e8 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,592 | supabase-export-v2 | 42ad7fa31ed18c1b | 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 *data* may be a sequence or iterable. If the input
dataset is empty, raises a :exc:`StatisticsError`. | trusted_official_docs | CPython Docs | 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 *data* may be a sequence or iterable. If the input
dataset is empty, raises a :exc:`StatisticsError`. | 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 *data* may be a sequence or iterable. If the input
dataset is empty, raises a :exc:`StatisticsError`. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
74039060-0bb9-48c3-a5dd-b13926c952ba | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,870 | supabase-export-v2 | 39cb9c33b47e1f0f | >>> posterior_female = (prior_female * height_female.pdf(ht) * ... weight_female.pdf(wt) * foot_size_female.pdf(fs))
The final prediction goes to the largest posterior. This is known as the
`maximum a posteriori
<https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation>`_ or MAP: | trusted_official_docs | CPython Docs | >>> posterior_female = (prior_female * height_female.pdf(ht) * ... weight_female.pdf(wt) * foot_size_female.pdf(fs))
The final prediction goes to the largest posterior. This is known as the
`maximum a posteriori
<https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation>`_ or MAP: | >>> posterior_female = (prior_female * height_female.pdf(ht) * ... weight_female.pdf(wt) * foot_size_female.pdf(fs))
The final prediction goes to the largest posterior. This is known as the
`maximum a posteriori
<https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation>`_ or MAP: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
74868336-6233-4c5f-a571-419e6286da7f | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,710 | supabase-export-v2 | c7a3001143a13483 | >>> from decimal import Decimal as D >>> pvariance([D("27.5"), D("30.25"), D("30.25"), D("34.5"), D("41.75")]) Decimal('24.815')
>>> from fractions import Fraction as F
>>> pvariance([F(1, 4), F(5, 4), F(1, 2)])
Fraction(13, 72) | trusted_official_docs | CPython Docs | >>> from decimal import Decimal as D >>> pvariance([D("27.5"), D("30.25"), D("30.25"), D("34.5"), D("41.75")]) Decimal('24.815')
>>> from fractions import Fraction as F
>>> pvariance([F(1, 4), F(5, 4), F(1, 2)])
Fraction(13, 72) | >>> from decimal import Decimal as D >>> pvariance([D("27.5"), D("30.25"), D("30.25"), D("34.5"), D("41.75")]) Decimal('24.815')
>>> from fractions import Fraction as F
>>> pvariance([F(1, 4), F(5, 4), F(1, 2)])
Fraction(13, 72) | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
77c27ed8-6769-4d57-b891-afc47e09baeb | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,848 | supabase-export-v2 | d5ab7cac9859a0b0 | .. doctest::
>>> def model(x, y, z):
... return (3*x + 7*x*y - 5*y) / (11 * z)
... >>> n = 100_000
>>> 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:... | trusted_official_docs | CPython Docs | .. doctest::
>>> def model(x, y, z):
... return (3*x + 7*x*y - 5*y) / (11 * z)
... >>> n = 100_000
>>> 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:... | .. doctest::
>>> def model(x, y, z):
... return (3*x + 7*x*y - 5*y) / (11 * z)
... >>> n = 100_000
>>> 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:... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
79fbdfd1-0550-436f-8c37-f26572d5164d | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,687 | supabase-export-v2 | 24e5608efaba775f | type :class:`list`, consider casting to :class:`tuple`. For mixed or nested inputs, consider using this slower quadratic algorithm that only depends on equality tests: ``max(data, key=data.count)``.
.. versionchanged:: 3.8
Now handles multimodal datasets by returning the first mode encountered. Formerly, it raised :ex... | trusted_official_docs | CPython Docs | type :class:`list`, consider casting to :class:`tuple`. For mixed or nested inputs, consider using this slower quadratic algorithm that only depends on equality tests: ``max(data, key=data.count)``.
.. versionchanged:: 3.8
Now handles multimodal datasets by returning the first mode encountered. Formerly, it raised :ex... | type :class:`list`, consider casting to :class:`tuple`. For mixed or nested inputs, consider using this slower quadratic algorithm that only depends on equality tests: ``max(data, key=data.count)``.
.. versionchanged:: 3.8
Now handles multimodal datasets by returning the first mode encountered. Formerly, it raised :ex... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
7c55a236-f666-4ee7-9650-39871484fb36 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,856 | supabase-export-v2 | 85850e887b8dbc9d | solution using the cumulative binomial distribution >>> from math import comb, fsum >>> round(fsum(comb(n, r) * p**r * q**(n-r) for r in range(k+1)), 4) 0.8402
>>> # Approximation using a simulation
>>> from random import seed, binomialvariate
>>> seed(8675309)
>>> mean(binomialvariate(n, p) <= k for i in range(10_0... | trusted_official_docs | CPython Docs | solution using the cumulative binomial distribution >>> from math import comb, fsum >>> round(fsum(comb(n, r) * p**r * q**(n-r) for r in range(k+1)), 4) 0.8402
>>> # Approximation using a simulation
>>> from random import seed, binomialvariate
>>> seed(8675309)
>>> mean(binomialvariate(n, p) <= k for i in range(10_0... | solution using the cumulative binomial distribution >>> from math import comb, fsum >>> round(fsum(comb(n, r) * p**r * q**(n-r) for r in range(k+1)), 4) 0.8402
>>> # Approximation using a simulation
>>> from random import seed, binomialvariate
>>> seed(8675309)
>>> mean(binomialvariate(n, p) <= k for i in range(10_0... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
805f817b-4b12-4a64-9736-1c8ed6d6a288 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,756 | supabase-export-v2 | 2395ad1099850257 | <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_ for two sequence inputs. Pearson's correlation coefficient *r* takes values between -1 and +1. It measures the strength and direction of a linear relationship.
If *method* is "ranked", computes `Spearman's rank correlation coefficient
<https://en.wikipe... | trusted_official_docs | CPython Docs | <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_ for two sequence inputs. Pearson's correlation coefficient *r* takes values between -1 and +1. It measures the strength and direction of a linear relationship.
If *method* is "ranked", computes `Spearman's rank correlation coefficient
<https://en.wikipe... | <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_ for two sequence inputs. Pearson's correlation coefficient *r* takes values between -1 and +1. It measures the strength and direction of a linear relationship.
If *method* is "ranked", computes `Spearman's rank correlation coefficient
<https://en.wikipe... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
80fe289e-4439-4442-9a9f-86008d535769 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,748 | supabase-export-v2 | f5b5889703dbe28d | data point. This allows quantile estimates to be built up one sample point at a time becoming gradually more refined with each new data point.
.. function:: covariance(x, y, /) | trusted_official_docs | CPython Docs | data point. This allows quantile estimates to be built up one sample point at a time becoming gradually more refined with each new data point.
.. function:: covariance(x, y, /) | data point. This allows quantile estimates to be built up one sample point at a time becoming gradually more refined with each new data point.
.. function:: covariance(x, y, /) | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
81589f91-7b9b-4aa9-ac7f-558a0d672a6d | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,824 | supabase-export-v2 | 192836d185e84f19 | Divide the normal distribution 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 the normal distribution in... | trusted_official_docs | CPython Docs | Divide the normal distribution 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 the normal distribution in... | Divide the normal distribution 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 the normal distribution in... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
835e2fc5-e742-4bc2-a39e-9e03fdba40e7 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,626 | supabase-export-v2 | a82ef8ca2d79bfd1 | The basic idea is to smooth the data using `a kernel function <https://en.wikipedia.org/wiki/Kernel_(statistics)>`_. to help draw inferences about a population from a sample.
The degree of smoothing is controlled by the scaling parameter *h*
which is called the bandwidth. Smaller values emphasize local
features while... | trusted_official_docs | CPython Docs | The basic idea is to smooth the data using `a kernel function <https://en.wikipedia.org/wiki/Kernel_(statistics)>`_. to help draw inferences about a population from a sample.
The degree of smoothing is controlled by the scaling parameter *h*
which is called the bandwidth. Smaller values emphasize local
features while... | The basic idea is to smooth the data using `a kernel function <https://en.wikipedia.org/wiki/Kernel_(statistics)>`_. to help draw inferences about a population from a sample.
The degree of smoothing is controlled by the scaling parameter *h*
which is called the bandwidth. Smaller values emphasize local
features while... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
847ab70d-e4ad-41e5-833d-add1a27ff163 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,642 | supabase-export-v2 | d5e1223c36348101 | A :exc:`StatisticsError` will be raised if the *data* sequence is empty.
Continuing the example for :func:`kde`, we can use
:func:`kde_random` to generate new random selections from an
estimated probability density function: | trusted_official_docs | CPython Docs | A :exc:`StatisticsError` will be raised if the *data* sequence is empty.
Continuing the example for :func:`kde`, we can use
:func:`kde_random` to generate new random selections from an
estimated probability density function: | A :exc:`StatisticsError` will be raised if the *data* sequence is empty.
Continuing the example for :func:`kde`, we can use
:func:`kde_random` to generate new random selections from an
estimated probability density function: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
84c598ac-5eab-48b7-99b2-1b891d4bd8ce | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,842 | supabase-export-v2 | 792612b2f047d8f7 | >>> sat = NormalDist(1060, 195) >>> fraction = sat.cdf(1200 + 0.5) - sat.cdf(1100 - 0.5) >>> round(fraction * 100.0, 1) 18.4
Find the `quartiles <https://en.wikipedia.org/wiki/Quartile>`_ and `deciles
<https://en.wikipedia.org/wiki/Decile>`_ for the SAT scores: | trusted_official_docs | CPython Docs | >>> sat = NormalDist(1060, 195) >>> fraction = sat.cdf(1200 + 0.5) - sat.cdf(1100 - 0.5) >>> round(fraction * 100.0, 1) 18.4
Find the `quartiles <https://en.wikipedia.org/wiki/Quartile>`_ and `deciles
<https://en.wikipedia.org/wiki/Decile>`_ for the SAT scores: | >>> sat = NormalDist(1060, 195) >>> fraction = sat.cdf(1200 + 0.5) - sat.cdf(1100 - 0.5) >>> round(fraction * 100.0, 1) 18.4
Find the `quartiles <https://en.wikipedia.org/wiki/Quartile>`_ and `deciles
<https://en.wikipedia.org/wiki/Decile>`_ for the SAT scores: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
856a9e99-e062-42b7-b74a-648372771d1e | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,814 | supabase-export-v2 | e9120c119624f4b4 | 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
the word "density"). Since the likelihood is relative to ... | trusted_official_docs | CPython Docs | 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
the word "density"). Since the likelihood is relative to ... | 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
the word "density"). Since the likelihood is relative to ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
8748adce-f4ac-4786-a789-aa2291eb2173 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,699 | supabase-export-v2 | 14b5f3ddc0512604 | 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 *population*
mean of the *data*. It can also be used to compute the second moment around
a point that is n... | trusted_official_docs | CPython Docs | 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 *population*
mean of the *data*. It can also be used to compute the second moment around
a point that is n... | 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 *population*
mean of the *data*. It can also be used to compute the second moment around
a point that is n... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
89dad6ba-bba8-4a50-b21e-93b1c6d6acb2 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,628 | supabase-export-v2 | 82b5a8726ee950d2 | relative weights of the sample data points. Generally, the choice of kernel shape does not matter as much as the more influential bandwidth smoothing parameter.
Kernels that give some weight to every sample point include
*normal* (*gauss*), *logistic*, and *sigmoid*. | trusted_official_docs | CPython Docs | relative weights of the sample data points. Generally, the choice of kernel shape does not matter as much as the more influential bandwidth smoothing parameter.
Kernels that give some weight to every sample point include
*normal* (*gauss*), *logistic*, and *sigmoid*. | relative weights of the sample data points. Generally, the choice of kernel shape does not matter as much as the more influential bandwidth smoothing parameter.
Kernels that give some weight to every sample point include
*normal* (*gauss*), *logistic*, and *sigmoid*. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
8c724312-372d-4a93-9854-ca426267f46f | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,704 | supabase-export-v2 | ac206e205a1c3748 | >>> data = [0.0, 0.25, 0.25, 1.25, 1.5, 1.75, 2.75, 3.25] >>> pvariance(data) 1.25
If you have already calculated the mean of your data, you can pass it as the
optional second argument *mu* to avoid recalculation: | trusted_official_docs | CPython Docs | >>> data = [0.0, 0.25, 0.25, 1.25, 1.5, 1.75, 2.75, 3.25] >>> pvariance(data) 1.25
If you have already calculated the mean of your data, you can pass it as the
optional second argument *mu* to avoid recalculation: | >>> data = [0.0, 0.25, 0.25, 1.25, 1.5, 1.75, 2.75, 3.25] >>> pvariance(data) 1.25
If you have already calculated the mean of your data, you can pass it as the
optional second argument *mu* to avoid recalculation: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
8efa07dd-5c28-4fc3-b19c-52ecb0961e60 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,676 | supabase-export-v2 | 59099eaf72f4a2db | 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.
Inputs may be any numeric type that can be coerced to a float during
the interpolation step. | trusted_official_docs | CPython Docs | 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.
Inputs may be any numeric type that can be coerced to a float during
the interpolation step. | 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.
Inputs may be any numeric type that can be coerced to a float during
the interpolation step. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
92723e15-20ab-4e92-8bc8-8263f0184bfe | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,660 | supabase-export-v2 | 2e54f21266d192a6 | Return the high median of data. If *data* is empty, :exc:`StatisticsError` is raised. *data* can be a sequence or iterable.
The high median is always a member of the data set. When the number of data
points is odd, the middle value is returned. When it is even, the larger of
the two middle values is returned. | trusted_official_docs | CPython Docs | Return the high median of data. If *data* is empty, :exc:`StatisticsError` is raised. *data* can be a sequence or iterable.
The high median is always a member of the data set. When the number of data
points is odd, the middle value is returned. When it is even, the larger of
the two middle values is returned. | Return the high median of data. If *data* is empty, :exc:`StatisticsError` is raised. *data* can be a sequence or iterable.
The high median is always a member of the data set. When the number of data
points is odd, the middle value is returned. When it is even, the larger of
the two middle values is returned. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
95551557-8d92-43d8-a269-008893f2cf00 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,570 | supabase-export-v2 | c8d0428a55abed79 | 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. ======================= =========================================================... | trusted_official_docs | CPython Docs | 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. ======================= =========================================================... | 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. ======================= =========================================================... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
9579c066-33b7-453e-ad0f-9c0fca337988 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,851 | supabase-export-v2 | 2e1fd2ba0d1a89f8 | can be used to approximate `Binomial distributions <https://mathworld.wolfram.com/BinomialDistribution.html>`_ when the sample size is large and when the probability of a successful trial is near 50%.
For example, an open source conference has 750 attendees and two rooms with a
500 person capacity. There is a talk abou... | trusted_official_docs | CPython Docs | can be used to approximate `Binomial distributions <https://mathworld.wolfram.com/BinomialDistribution.html>`_ when the sample size is large and when the probability of a successful trial is near 50%.
For example, an open source conference has 750 attendees and two rooms with a
500 person capacity. There is a talk abou... | can be used to approximate `Binomial distributions <https://mathworld.wolfram.com/BinomialDistribution.html>`_ when the sample size is large and when the probability of a successful trial is near 50%.
For example, an open source conference has 750 attendees and two rooms with a
500 person capacity. There is a talk abou... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
991f3139-3c8e-477a-9cc7-195cc09e3932 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,630 | supabase-export-v2 | 3a0257d934a85950 | Kernels that only give weight to sample points within the bandwidth include *rectangular* (*uniform*), *triangular*, *parabolic* (*epanechnikov*), *quartic* (*biweight*), *triweight*, and *cosine*.
If *cumulative* is true, will return a cumulative distribution function. | trusted_official_docs | CPython Docs | Kernels that only give weight to sample points within the bandwidth include *rectangular* (*uniform*), *triangular*, *parabolic* (*epanechnikov*), *quartic* (*biweight*), *triweight*, and *cosine*.
If *cumulative* is true, will return a cumulative distribution function. | Kernels that only give weight to sample points within the bandwidth include *rectangular* (*uniform*), *triangular*, *parabolic* (*epanechnikov*), *quartic* (*biweight*), *triweight*, and *cosine*.
If *cumulative* is true, will return a cumulative distribution function. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
9c2371dd-097c-4b05-b698-c5648e89ef70 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,620 | supabase-export-v2 | cdb47b2714da872a | :exc:`StatisticsError` is raised if *data* is empty, any element is less than zero, or if the weighted sum isn't positive.
The current algorithm has an early-out when it encounters a zero
in the input. This means that the subsequent inputs are not tested
for validity. (This behavior may change in the future.) | trusted_official_docs | CPython Docs | :exc:`StatisticsError` is raised if *data* is empty, any element is less than zero, or if the weighted sum isn't positive.
The current algorithm has an early-out when it encounters a zero
in the input. This means that the subsequent inputs are not tested
for validity. (This behavior may change in the future.) | :exc:`StatisticsError` is raised if *data* is empty, any element is less than zero, or if the weighted sum isn't positive.
The current algorithm has an early-out when it encounters a zero
in the input. This means that the subsequent inputs are not tested
for validity. (This behavior may change in the future.) | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
9da1c39d-1db0-40a4-8fdb-5cff4cb6bd7b | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,743 | supabase-export-v2 | 95a82a64f058db13 | (m + 1)``. Given nine sample values, the method sorts them and assigns the following percentiles: 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%.
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 va... | trusted_official_docs | CPython Docs | (m + 1)``. Given nine sample values, the method sorts them and assigns the following percentiles: 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%.
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 va... | (m + 1)``. Given nine sample values, the method sorts them and assigns the following percentiles: 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%.
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 va... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
9f2b174e-985b-44f0-ba13-01f3ce9db842 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,750 | supabase-export-v2 | 6cb231ce2e4184d7 | Return the sample covariance of two sequence inputs *x* and *y*. Covariance is a measure of the joint variability of two inputs.
Both inputs must be of the same length (no less than two), otherwise
:exc:`StatisticsError` is raised. | trusted_official_docs | CPython Docs | Return the sample covariance of two sequence inputs *x* and *y*. Covariance is a measure of the joint variability of two inputs.
Both inputs must be of the same length (no less than two), otherwise
:exc:`StatisticsError` is raised. | Return the sample covariance of two sequence inputs *x* and *y*. Covariance is a measure of the joint variability of two inputs.
Both inputs must be of the same length (no less than two), otherwise
:exc:`StatisticsError` is raised. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a0944e99-8ea7-4c40-931a-718ec1511ba9 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,734 | supabase-export-v2 | 1bcc012cf187f559 | .. note::
This is the sample variance s² with Bessel's correction, also known as
variance with N-1 degrees of freedom. Provided that the data points are
representative (e.g. independent and identically distributed), the result
should be an unbiased estimate of the true population variance. | trusted_official_docs | CPython Docs | .. note::
This is the sample variance s² with Bessel's correction, also known as
variance with N-1 degrees of freedom. Provided that the data points are
representative (e.g. independent and identically distributed), the result
should be an unbiased estimate of the true population variance. | .. note::
This is the sample variance s² with Bessel's correction, also known as
variance with N-1 degrees of freedom. Provided that the data points are
representative (e.g. independent and identically distributed), the result
should be an unbiased estimate of the true population variance. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a2122527-0a60-472f-8662-5297121e7768 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,566 | supabase-export-v2 | b266222299a7471d | float('NaN'),19.2, 18.3, float('NaN'), 14.4] >>> sorted(data) # This has surprising behavior [20.7, nan, 14.4, 18.3, 19.2, nan] >>> median(data) # This result is unexpected 16.35
>>> sum(map(isnan, data)) # Number of missing values
2
>>> clean = list(filterfalse(isnan, data)) # Strip NaN values
>>> clean
[20.7, 19.... | trusted_official_docs | CPython Docs | float('NaN'),19.2, 18.3, float('NaN'), 14.4] >>> sorted(data) # This has surprising behavior [20.7, nan, 14.4, 18.3, 19.2, nan] >>> median(data) # This result is unexpected 16.35
>>> sum(map(isnan, data)) # Number of missing values
2
>>> clean = list(filterfalse(isnan, data)) # Strip NaN values
>>> clean
[20.7, 19.... | float('NaN'),19.2, 18.3, float('NaN'), 14.4] >>> sorted(data) # This has surprising behavior [20.7, nan, 14.4, 18.3, 19.2, nan] >>> median(data) # This result is unexpected 16.35
>>> sum(map(isnan, data)) # Number of missing values
2
>>> clean = list(filterfalse(isnan, data)) # Strip NaN values
>>> clean
[20.7, 19.... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a6fad42d-a170-418e-9971-df3b4ae71d99 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,758 | supabase-export-v2 | e2e6216444ecdfac | Spearman's correlation coefficient is appropriate for ordinal data or for continuous data that doesn't meet the linear proportion requirement for Pearson's correlation coefficient.
Both inputs must be of the same length (no less than two), and need
not to be constant, otherwise :exc:`StatisticsError` is raised. | trusted_official_docs | CPython Docs | Spearman's correlation coefficient is appropriate for ordinal data or for continuous data that doesn't meet the linear proportion requirement for Pearson's correlation coefficient.
Both inputs must be of the same length (no less than two), and need
not to be constant, otherwise :exc:`StatisticsError` is raised. | Spearman's correlation coefficient is appropriate for ordinal data or for continuous data that doesn't meet the linear proportion requirement for Pearson's correlation coefficient.
Both inputs must be of the same length (no less than two), and need
not to be constant, otherwise :exc:`StatisticsError` is raised. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a7abff67-0fe3-40e3-8165-992cf2808046 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,789 | supabase-export-v2 | cb7a5651a41dd662 | :class:`NormalDist` objects ---------------------------
: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
class that treats the mean and standard deviation of data
measurements as a single entity. | trusted_official_docs | CPython Docs | :class:`NormalDist` objects ---------------------------
: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
class that treats the mean and standard deviation of data
measurements as a single entity. | :class:`NormalDist` objects ---------------------------
: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
class that treats the mean and standard deviation of data
measurements as a single entity. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a91429fd-6117-41c8-8773-9d9180f8c34d | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,629 | supabase-export-v2 | 11643f22931e47ad | Kernels that give some weight to every sample point include *normal* (*gauss*), *logistic*, and *sigmoid*.
Kernels that only give weight to sample points within the bandwidth
include *rectangular* (*uniform*), *triangular*, *parabolic*
(*epanechnikov*), *quartic* (*biweight*), *triweight*, and *cosine*. | trusted_official_docs | CPython Docs | Kernels that give some weight to every sample point include *normal* (*gauss*), *logistic*, and *sigmoid*.
Kernels that only give weight to sample points within the bandwidth
include *rectangular* (*uniform*), *triangular*, *parabolic*
(*epanechnikov*), *quartic* (*biweight*), *triweight*, and *cosine*. | Kernels that give some weight to every sample point include *normal* (*gauss*), *logistic*, and *sigmoid*.
Kernels that only give weight to sample points within the bandwidth
include *rectangular* (*uniform*), *triangular*, *parabolic*
(*epanechnikov*), *quartic* (*biweight*), *triweight*, and *cosine*. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
abfaf638-cb53-4864-8438-441c689573a4 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,859 | supabase-export-v2 | a9df1fe6aa354aeb | Normal distributions commonly arise in machine learning problems.
Wikipedia has a `nice example of a Naive Bayesian Classifier
<https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Person_classification>`_. The challenge is to predict a person's gender from measurements of normally
distributed features including height... | trusted_official_docs | CPython Docs | Normal distributions commonly arise in machine learning problems.
Wikipedia has a `nice example of a Naive Bayesian Classifier
<https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Person_classification>`_. The challenge is to predict a person's gender from measurements of normally
distributed features including height... | Normal distributions commonly arise in machine learning problems.
Wikipedia has a `nice example of a Naive Bayesian Classifier
<https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Person_classification>`_. The challenge is to predict a person's gender from measurements of normally
distributed features including height... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
acb99677-8460-4f9b-b610-90fec69b04d5 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,698 | supabase-export-v2 | 472ac8b9adedd42b | .. function:: pvariance(data, mu=None)
Return the population variance of *data*, a non-empty sequence or 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... | trusted_official_docs | CPython Docs | .. function:: pvariance(data, mu=None)
Return the population variance of *data*, a non-empty sequence or 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... | .. function:: pvariance(data, mu=None)
Return the population variance of *data*, a non-empty sequence or 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... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
ad148059-e801-4687-b5dd-64dcd49de076 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,826 | supabase-export-v2 | 89f7fac363012dd6 | .. method:: NormalDist.zscore(x)
Compute the
`Standard Score <https://www.statisticshowto.com/probability-and-statistics/z-score/>`_
describing *x* in terms of the number of standard deviations
above or below the mean of the normal distribution:
``(x - mean) / stdev``. | trusted_official_docs | CPython Docs | .. method:: NormalDist.zscore(x)
Compute the
`Standard Score <https://www.statisticshowto.com/probability-and-statistics/z-score/>`_
describing *x* in terms of the number of standard deviations
above or below the mean of the normal distribution:
``(x - mean) / stdev``. | .. method:: NormalDist.zscore(x)
Compute the
`Standard Score <https://www.statisticshowto.com/probability-and-statistics/z-score/>`_
describing *x* in terms of the number of standard deviations
above or below the mean of the normal distribution:
``(x - mean) / stdev``. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
ad516550-b982-417f-9c7a-938da8d9cc71 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,562 | supabase-export-v2 | 58fbed37580c993e | 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.
Unless explicitly noted, these functions support :class:`int`,
:class:`float`, :class:`~decimal.Decimal` and :class:`~fractions.Fraction`. B... | trusted_official_docs | CPython Docs | 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.
Unless explicitly noted, these functions support :class:`int`,
:class:`float`, :class:`~decimal.Decimal` and :class:`~fractions.Fraction`. B... | 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.
Unless explicitly noted, these functions support :class:`int`,
:class:`float`, :class:`~decimal.Decimal` and :class:`~fractions.Fraction`. B... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
adce1df6-0a5b-4612-a787-5d56036c74d7 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,855 | supabase-export-v2 | 86a7c79fbad0f503 | >>> # Approximation using the cumulative normal distribution >>> from math import sqrt >>> round(NormalDist(mu=n*p, sigma=sqrt(n*p*q)).cdf(k + 0.5), 4) 0.8402
>>> # Exact solution using the cumulative binomial distribution
>>> from math import comb, fsum
>>> round(fsum(comb(n, r) * p**r * q**(n-r) for r in range(k+1)... | trusted_official_docs | CPython Docs | >>> # Approximation using the cumulative normal distribution >>> from math import sqrt >>> round(NormalDist(mu=n*p, sigma=sqrt(n*p*q)).cdf(k + 0.5), 4) 0.8402
>>> # Exact solution using the cumulative binomial distribution
>>> from math import comb, fsum
>>> round(fsum(comb(n, r) * p**r * q**(n-r) for r in range(k+1)... | >>> # Approximation using the cumulative normal distribution >>> from math import sqrt >>> round(NormalDist(mu=n*p, sigma=sqrt(n*p*q)).cdf(k + 0.5), 4) 0.8402
>>> # Exact solution using the cumulative binomial distribution
>>> from math import comb, fsum
>>> round(fsum(comb(n, r) * p**r * q**(n-r) for r in range(k+1)... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
af3b600f-563d-49cd-a972-0eb150838d98 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,739 | supabase-export-v2 | fa2e5f2b9ba5bc01 | 100 for percentiles which gives the 99 cuts points that separate *data* into 100 equal sized groups. Raises :exc:`StatisticsError` if *n* is not least 1.
The *data* can be any iterable containing sample data. For meaningful
results, the number of data points in *data* should be larger than *n*. Raises :exc:`Statistics... | trusted_official_docs | CPython Docs | 100 for percentiles which gives the 99 cuts points that separate *data* into 100 equal sized groups. Raises :exc:`StatisticsError` if *n* is not least 1.
The *data* can be any iterable containing sample data. For meaningful
results, the number of data points in *data* should be larger than *n*. Raises :exc:`Statistics... | 100 for percentiles which gives the 99 cuts points that separate *data* into 100 equal sized groups. Raises :exc:`StatisticsError` if *n* is not least 1.
The *data* can be any iterable containing sample data. For meaningful
results, the number of data points in *data* should be larger than *n*. Raises :exc:`Statistics... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
af65455c-4c30-482a-bbba-fcc59d2f32c2 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,813 | supabase-export-v2 | 877d4377238189b2 | .. 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 limit of the ratio ``P(x <=
X < x+dx) / dx`` as *dx* approa... | trusted_official_docs | CPython Docs | .. 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 limit of the ratio ``P(x <=
X < x+dx) / dx`` as *dx* approa... | .. 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 limit of the ratio ``P(x <=
X < x+dx) / dx`` as *dx* approa... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
af90cee0-dfb1-44bd-a15e-8e26299b9b00 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,668 | supabase-export-v2 | 8763e9f3f989d36b | 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: | trusted_official_docs | CPython Docs | 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: | 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: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
aff0e014-4e90-4dd1-8365-5951d93bb850 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,657 | supabase-export-v2 | 736d4ccb9117e200 | >>> median_low([1, 3, 5]) 3 >>> median_low([1, 3, 5, 7]) 3
Use the low median when your data are discrete and you prefer the median to
be an actual data point rather than interpolated. | trusted_official_docs | CPython Docs | >>> median_low([1, 3, 5]) 3 >>> median_low([1, 3, 5, 7]) 3
Use the low median when your data are discrete and you prefer the median to
be an actual data point rather than interpolated. | >>> median_low([1, 3, 5]) 3 >>> median_low([1, 3, 5, 7]) 3
Use the low median when your data are discrete and you prefer the median to
be an actual data point rather than interpolated. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b08ea6ae-99fb-48f5-886a-20f333c0138c | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,854 | supabase-export-v2 | 257552a213a71d25 | >>> p = 0.65 # Preference for Python >>> q = 1.0 - p # Preference for Ruby >>> k = 500 # Room capacity
>>> # Approximation using the cumulative normal distribution
>>> from math import sqrt
>>> round(NormalDist(mu=n*p, sigma=sqrt(n*p*q)).cdf(k + 0.5), 4)
0.8402 | trusted_official_docs | CPython Docs | >>> p = 0.65 # Preference for Python >>> q = 1.0 - p # Preference for Ruby >>> k = 500 # Room capacity
>>> # Approximation using the cumulative normal distribution
>>> from math import sqrt
>>> round(NormalDist(mu=n*p, sigma=sqrt(n*p*q)).cdf(k + 0.5), 4)
0.8402 | >>> p = 0.65 # Preference for Python >>> q = 1.0 - p # Preference for Ruby >>> k = 500 # Room capacity
>>> # Approximation using the cumulative normal distribution
>>> from math import sqrt
>>> round(NormalDist(mu=n*p, sigma=sqrt(n*p*q)).cdf(k + 0.5), 4)
0.8402 | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b1bed624-eaae-46fd-bee5-ff55b63affeb | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,565 | supabase-export-v2 | 23762bd4e83e2c97 | >>> from statistics import median >>> from math import isnan >>> from itertools import filterfalse
>>> data = [20.7, float('NaN'),19.2, 18.3, float('NaN'), 14.4]
>>> sorted(data) # This has surprising behavior
[20.7, nan, 14.4, 18.3, 19.2, nan]
>>> median(data) # This result is unexpected
16.35 | trusted_official_docs | CPython Docs | >>> from statistics import median >>> from math import isnan >>> from itertools import filterfalse
>>> data = [20.7, float('NaN'),19.2, 18.3, float('NaN'), 14.4]
>>> sorted(data) # This has surprising behavior
[20.7, nan, 14.4, 18.3, 19.2, nan]
>>> median(data) # This result is unexpected
16.35 | >>> from statistics import median >>> from math import isnan >>> from itertools import filterfalse
>>> data = [20.7, float('NaN'),19.2, 18.3, float('NaN'), 14.4]
>>> sorted(data) # This has surprising behavior
[20.7, nan, 14.4, 18.3, 19.2, nan]
>>> median(data) # This result is unexpected
16.35 | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b202d887-16e7-45d4-ad5d-a0d276d9ae69 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,561 | supabase-export-v2 | 5405996cefacbb47 | This module provides functions for calculating mathematical statistics of numeric (:class:`~numbers.Real`-valued) data.
The module is not intended to be a competitor to third-party libraries such
as `NumPy <https://numpy.org>`_, `SciPy <https://scipy.org/>`_, or
proprietary full-featured statistics packages aimed at pr... | trusted_official_docs | CPython Docs | This module provides functions for calculating mathematical statistics of numeric (:class:`~numbers.Real`-valued) data.
The module is not intended to be a competitor to third-party libraries such
as `NumPy <https://numpy.org>`_, `SciPy <https://scipy.org/>`_, or
proprietary full-featured statistics packages aimed at pr... | This module provides functions for calculating mathematical statistics of numeric (:class:`~numbers.Real`-valued) data.
The module is not intended to be a competitor to third-party libraries such
as `NumPy <https://numpy.org>`_, `SciPy <https://scipy.org/>`_, or
proprietary full-featured statistics packages aimed at pr... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b2dd1f0a-87e6-47e2-8496-f7366755976d | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,741 | supabase-export-v2 | ed5f3ff23df1cab4 | points. For example, if a cut point falls one-third of the distance between two sample values, ``100`` and ``112``, the cut-point will evaluate to ``104``.
The *method* for computing quantiles can be varied depending on
whether the *data* includes or excludes the lowest and
highest possible values from the population... | trusted_official_docs | CPython Docs | points. For example, if a cut point falls one-third of the distance between two sample values, ``100`` and ``112``, the cut-point will evaluate to ``104``.
The *method* for computing quantiles can be varied depending on
whether the *data* includes or excludes the lowest and
highest possible values from the population... | points. For example, if a cut point falls one-third of the distance between two sample values, ``100`` and ``112``, the cut-point will evaluate to ``104``.
The *method* for computing quantiles can be varied depending on
whether the *data* includes or excludes the lowest and
highest possible values from the population... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b3cd3ab2-13af-4fca-96a7-9013e18b39ad | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,598 | supabase-export-v2 | fe987616ff0b38b9 | >>> grades = [85, 92, 83, 91] >>> weights = [0.20, 0.20, 0.30, 0.30] >>> fmean(grades, weights) 87.6
If *weights* is supplied, it must be the same length as the *data* or
a :exc:`ValueError` will be raised. | trusted_official_docs | CPython Docs | >>> grades = [85, 92, 83, 91] >>> weights = [0.20, 0.20, 0.30, 0.30] >>> fmean(grades, weights) 87.6
If *weights* is supplied, it must be the same length as the *data* or
a :exc:`ValueError` will be raised. | >>> grades = [85, 92, 83, 91] >>> weights = [0.20, 0.20, 0.30, 0.30] >>> fmean(grades, weights) 87.6
If *weights* is supplied, it must be the same length as the *data* or
a :exc:`ValueError` will be raised. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b4ddd0d2-bc70-416d-b7f1-27b285691702 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,678 | supabase-export-v2 | 79a62abe1d1e3f1d | .. function:: mode(data)
Return the single most common data point from discrete or nominal *data*. The mode (when it exists) is the most typical value and serves as a
measure of central location. | trusted_official_docs | CPython Docs | .. function:: mode(data)
Return the single most common data point from discrete or nominal *data*. The mode (when it exists) is the most typical value and serves as a
measure of central location. | .. function:: mode(data)
Return the single most common data point from discrete or nominal *data*. The mode (when it exists) is the most typical value and serves as a
measure of central location. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b8acf733-c82a-495d-a795-1b2bce776b5e | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,780 | supabase-export-v2 | a15bff62026428ce | >>> model = linear_regression(period_squared, dist_cubed, proportional=True) >>> slope = model.slope
>>> # Dwarf planets: Pluto, Eris, Makemake, Haumea, Ceres
>>> orbital_periods = [90_560, 204_199, 111_845, 103_410, 1_680] # days
>>> predicted_dist = [math.cbrt(slope * (p * p)) for p in orbital_periods]
>>> list(ma... | trusted_official_docs | CPython Docs | >>> model = linear_regression(period_squared, dist_cubed, proportional=True) >>> slope = model.slope
>>> # Dwarf planets: Pluto, Eris, Makemake, Haumea, Ceres
>>> orbital_periods = [90_560, 204_199, 111_845, 103_410, 1_680] # days
>>> predicted_dist = [math.cbrt(slope * (p * p)) for p in orbital_periods]
>>> list(ma... | >>> model = linear_regression(period_squared, dist_cubed, proportional=True) >>> slope = model.slope
>>> # Dwarf planets: Pluto, Eris, Makemake, Haumea, Ceres
>>> orbital_periods = [90_560, 204_199, 111_845, 103_410, 1_680] # days
>>> predicted_dist = [math.cbrt(slope * (p * p)) for p in orbital_periods]
>>> list(ma... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
bbcbdeff-61d9-4f42-8687-bf1dd7063554 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,603 | supabase-export-v2 | c7e0f5c76a4bcf9e | 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). | trusted_official_docs | CPython Docs | 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). | 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). | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
bf1ac80f-002f-428e-a150-d8db9a2098c3 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,790 | supabase-export-v2 | 25af5519dd322521 | normal distributions of a `random variable <http://www.stat.yale.edu/Courses/1997-98/101/ranvar.htm>`_. It is a class that treats the mean and standard deviation of data measurements as a single entity.
Normal distributions arise from the `Central Limit Theorem
<https://en.wikipedia.org/wiki/Central_limit_theorem>`_ an... | trusted_official_docs | CPython Docs | normal distributions of a `random variable <http://www.stat.yale.edu/Courses/1997-98/101/ranvar.htm>`_. It is a class that treats the mean and standard deviation of data measurements as a single entity.
Normal distributions arise from the `Central Limit Theorem
<https://en.wikipedia.org/wiki/Central_limit_theorem>`_ an... | normal distributions of a `random variable <http://www.stat.yale.edu/Courses/1997-98/101/ranvar.htm>`_. It is a class that treats the mean and standard deviation of data measurements as a single entity.
Normal distributions arise from the `Central Limit Theorem
<https://en.wikipedia.org/wiki/Central_limit_theorem>`_ an... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c0e9358d-4326-4a29-ab98-a1462614ccb8 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,573 | supabase-export-v2 | 09cdf1dfecbccb3b | These functions calculate a measure of how much the population or sample tends to deviate from the typical or average values.
======================= =============================================
:func:`pstdev` Population standard deviation of data. :func:`pvariance` Population variance of data. :func:`stdev` Sample st... | trusted_official_docs | CPython Docs | These functions calculate a measure of how much the population or sample tends to deviate from the typical or average values.
======================= =============================================
:func:`pstdev` Population standard deviation of data. :func:`pvariance` Population variance of data. :func:`stdev` Sample st... | These functions calculate a measure of how much the population or sample tends to deviate from the typical or average values.
======================= =============================================
:func:`pstdev` Population standard deviation of data. :func:`pvariance` Population variance of data. :func:`stdev` Sample st... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c12422e4-741e-4e72-89f3-9bf92285827b | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,762 | supabase-export-v2 | 11050192afe8e953 | = [88, 225, 365, 687, 4331, 10_756, 30_687, 60_190] # days >>> dist_from_sun = [58, 108, 150, 228, 778, 1_400, 2_900, 4_500] # million km
>>> # Show that a perfect monotonic relationship exists
>>> correlation(orbital_period, dist_from_sun, method='ranked')
1.0 | trusted_official_docs | CPython Docs | = [88, 225, 365, 687, 4331, 10_756, 30_687, 60_190] # days >>> dist_from_sun = [58, 108, 150, 228, 778, 1_400, 2_900, 4_500] # million km
>>> # Show that a perfect monotonic relationship exists
>>> correlation(orbital_period, dist_from_sun, method='ranked')
1.0 | = [88, 225, 365, 687, 4331, 10_756, 30_687, 60_190] # days >>> dist_from_sun = [58, 108, 150, 228, 778, 1_400, 2_900, 4_500] # million km
>>> # Show that a perfect monotonic relationship exists
>>> correlation(orbital_period, dist_from_sun, method='ranked')
1.0 | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c24e3759-d1c2-45f8-a88d-55d1e6590b93 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,869 | supabase-export-v2 | 10af6625df996547 | >>> prior_male = 0.5 >>> prior_female = 0.5 >>> posterior_male = (prior_male * height_male.pdf(ht) * ... weight_male.pdf(wt) * foot_size_male.pdf(fs))
>>> posterior_female = (prior_female * height_female.pdf(ht) *
... weight_female.pdf(wt) * foot_size_female.pdf(fs)) | trusted_official_docs | CPython Docs | >>> prior_male = 0.5 >>> prior_female = 0.5 >>> posterior_male = (prior_male * height_male.pdf(ht) * ... weight_male.pdf(wt) * foot_size_male.pdf(fs))
>>> posterior_female = (prior_female * height_female.pdf(ht) *
... weight_female.pdf(wt) * foot_size_female.pdf(fs)) | >>> prior_male = 0.5 >>> prior_female = 0.5 >>> posterior_male = (prior_male * height_male.pdf(ht) * ... weight_male.pdf(wt) * foot_size_male.pdf(fs))
>>> posterior_female = (prior_female * height_female.pdf(ht) *
... weight_female.pdf(wt) * foot_size_female.pdf(fs)) | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c38a27f5-1476-4bc3-93ff-9982127b8407 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,679 | supabase-export-v2 | 9ed0b8fbc29de52f | data point from discrete or nominal *data*. The mode (when it exists) is the most typical value and serves as a measure of central location.
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... | trusted_official_docs | CPython Docs | data point from discrete or nominal *data*. The mode (when it exists) is the most typical value and serves as a measure of central location.
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 point from discrete or nominal *data*. The mode (when it exists) is the most typical value and serves as a measure of central location.
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... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c4930477-9afe-4a1d-b8fa-939cfb7dbb7b | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,700 | supabase-export-v2 | 97186af7153c72fb | 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
a better choice. | trusted_official_docs | CPython Docs | 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
a better choice. | 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
a better choice. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c647a909-96c0-4754-b524-c3bdae018714 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,686 | supabase-export-v2 | fb3faed03ea0f6a9 | >>> mode(["red", "blue", "blue", "red", "green", "red", "red"]) 'red'
Only hashable inputs are supported. To handle type :class:`set`,
consider casting to :class:`frozenset`. To handle type :class:`list`,
consider casting to :class:`tuple`. For mixed or nested inputs, consider
using this slower quadratic algorithm t... | trusted_official_docs | CPython Docs | >>> mode(["red", "blue", "blue", "red", "green", "red", "red"]) 'red'
Only hashable inputs are supported. To handle type :class:`set`,
consider casting to :class:`frozenset`. To handle type :class:`list`,
consider casting to :class:`tuple`. For mixed or nested inputs, consider
using this slower quadratic algorithm t... | >>> mode(["red", "blue", "blue", "red", "green", "red", "red"]) 'red'
Only hashable inputs are supported. To handle type :class:`set`,
consider casting to :class:`frozenset`. To handle type :class:`list`,
consider casting to :class:`tuple`. For mixed or nested inputs, consider
using this slower quadratic algorithm t... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c64c20d7-a090-4cf3-9362-37aa3bb1c54b | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,742 | supabase-export-v2 | 07a6cd3d4f3ded1a | The *method* for computing quantiles can be varied depending on whether the *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
a population that can have more extreme values than found in the
samples. The portio... | trusted_official_docs | CPython Docs | The *method* for computing quantiles can be varied depending on whether the *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
a population that can have more extreme values than found in the
samples. The portio... | The *method* for computing quantiles can be varied depending on whether the *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
a population that can have more extreme values than found in the
samples. The portio... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c66bbd9f-2ec9-425d-acfe-8db4fd47a228 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,747 | supabase-export-v2 | 8579c71fee84c0fb | .. versionadded:: 3.8
.. versionchanged:: 3.13
No longer raises an exception for an input with only a single data point. This allows quantile estimates to be built up one sample point
at a time becoming gradually more refined with each new data point. | trusted_official_docs | CPython Docs | .. versionadded:: 3.8
.. versionchanged:: 3.13
No longer raises an exception for an input with only a single data point. This allows quantile estimates to be built up one sample point
at a time becoming gradually more refined with each new data point. | .. versionadded:: 3.8
.. versionchanged:: 3.13
No longer raises an exception for an input with only a single data point. This allows quantile estimates to be built up one sample point
at a time becoming gradually more refined with each new data point. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c884d1ac-faaf-496b-8bfd-51f5c732a168 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,862 | supabase-export-v2 | 8bd70484dcfa6ece | .. doctest::
>>> height_male = NormalDist.from_samples([6, 5.92, 5.58, 5.92])
>>> height_female = NormalDist.from_samples([5, 5.5, 5.42, 5.75])
>>> weight_male = NormalDist.from_samples([180, 190, 170, 165])
>>> weight_female = NormalDist.from_samples([100, 150, 130, 150])
>>> foot_size_male = NormalDist.from_sampl... | trusted_official_docs | CPython Docs | .. doctest::
>>> height_male = NormalDist.from_samples([6, 5.92, 5.58, 5.92])
>>> height_female = NormalDist.from_samples([5, 5.5, 5.42, 5.75])
>>> weight_male = NormalDist.from_samples([180, 190, 170, 165])
>>> weight_female = NormalDist.from_samples([100, 150, 130, 150])
>>> foot_size_male = NormalDist.from_sampl... | .. doctest::
>>> height_male = NormalDist.from_samples([6, 5.92, 5.58, 5.92])
>>> height_female = NormalDist.from_samples([5, 5.5, 5.42, 5.75])
>>> weight_male = NormalDist.from_samples([180, 190, 170, 165])
>>> weight_female = NormalDist.from_samples([100, 150, 130, 150])
>>> foot_size_male = NormalDist.from_sampl... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
cd54364b-4c38-4982-955d-18754131bb56 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,595 | supabase-export-v2 | 60bf8b230cf5a21c | >>> fmean([3.5, 4.0, 5.25]) 4.25
Optional weighting is supported. For example, a professor assigns a
grade for a course by weighting quizzes at 20%, homework at 20%, a
midterm exam at 30%, and a final exam at 30%: | trusted_official_docs | CPython Docs | >>> fmean([3.5, 4.0, 5.25]) 4.25
Optional weighting is supported. For example, a professor assigns a
grade for a course by weighting quizzes at 20%, homework at 20%, a
midterm exam at 30%, and a final exam at 30%: | >>> fmean([3.5, 4.0, 5.25]) 4.25
Optional weighting is supported. For example, a professor assigns a
grade for a course by weighting quizzes at 20%, homework at 20%, a
midterm exam at 30%, and a final exam at 30%: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
d027875e-f8fc-4f17-8f5d-cf3746e4534c | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,634 | supabase-export-v2 | 5f08424de0321643 | .. doctest::
>>> sample = [-2.1, -1.3, -0.4, 1.9, 5.1, 6.2]
>>> f_hat = kde(sample, h=1.5)
>>> xarr = [i/100 for i in range(-750, 1100)]
>>> yarr = [f_hat(x) for x in xarr] | trusted_official_docs | CPython Docs | .. doctest::
>>> sample = [-2.1, -1.3, -0.4, 1.9, 5.1, 6.2]
>>> f_hat = kde(sample, h=1.5)
>>> xarr = [i/100 for i in range(-750, 1100)]
>>> yarr = [f_hat(x) for x in xarr] | .. doctest::
>>> sample = [-2.1, -1.3, -0.4, 1.9, 5.1, 6.2]
>>> f_hat = kde(sample, h=1.5)
>>> xarr = [i/100 for i in range(-750, 1100)]
>>> yarr = [f_hat(x) for x in xarr] | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
d5bad3f0-9ebd-4eed-a372-d7ff3c3e9237 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,816 | supabase-export-v2 | 633d9307a48f5508 | .. method:: NormalDist.cdf(x)
Using a `cumulative distribution function (cdf)
<https://en.wikipedia.org/wiki/Cumulative_distribution_function>`_,
compute the probability that a random variable *X* will be less than or
equal to *x*. Mathematically, it is written ``P(X <= x)``. | trusted_official_docs | CPython Docs | .. method:: NormalDist.cdf(x)
Using a `cumulative distribution function (cdf)
<https://en.wikipedia.org/wiki/Cumulative_distribution_function>`_,
compute the probability that a random variable *X* will be less than or
equal to *x*. Mathematically, it is written ``P(X <= x)``. | .. method:: NormalDist.cdf(x)
Using a `cumulative distribution function (cdf)
<https://en.wikipedia.org/wiki/Cumulative_distribution_function>`_,
compute the probability that a random variable *X* will be less than or
equal to *x*. Mathematically, it is written ``P(X <= x)``. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
d829c606-165d-453b-b18e-399da7153283 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,761 | supabase-export-v2 | 2e82e84543eccebb | .. doctest::
>>> # Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune
>>> orbital_period = [88, 225, 365, 687, 4331, 10_756, 30_687, 60_190] # days
>>> dist_from_sun = [58, 108, 150, 228, 778, 1_400, 2_900, 4_500] # million km | trusted_official_docs | CPython Docs | .. doctest::
>>> # Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune
>>> orbital_period = [88, 225, 365, 687, 4331, 10_756, 30_687, 60_190] # days
>>> dist_from_sun = [58, 108, 150, 228, 778, 1_400, 2_900, 4_500] # million km | .. doctest::
>>> # Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune
>>> orbital_period = [88, 225, 365, 687, 4331, 10_756, 30_687, 60_190] # days
>>> dist_from_sun = [58, 108, 150, 228, 778, 1_400, 2_900, 4_500] # million km | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
d99409ff-44e4-482d-a0e0-0d43fc142171 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,569 | supabase-export-v2 | cce6d622154f04af | These functions calculate an average or typical value from a population or sample.
======================= ===============================================================
:func:`mean` Arithmetic mean ("average") of data. :func:`fmean` Fast, floating-point arithmetic mean, with optional weighting. :func:`geometric_mean`... | trusted_official_docs | CPython Docs | These functions calculate an average or typical value from a population or sample.
======================= ===============================================================
:func:`mean` Arithmetic mean ("average") of data. :func:`fmean` Fast, floating-point arithmetic mean, with optional weighting. :func:`geometric_mean`... | These functions calculate an average or typical value from a population or sample.
======================= ===============================================================
:func:`mean` Arithmetic mean ("average") of data. :func:`fmean` Fast, floating-point arithmetic mean, with optional weighting. :func:`geometric_mean`... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
d99fae86-59c1-44ac-a82b-81f466794d28 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,735 | supabase-export-v2 | 2022e6eaceb1535f | freedom. Provided that the data points are representative (e.g. independent and identically distributed), the result should be an unbiased estimate of the true population variance.
If you somehow know the actual population mean μ you should pass it to the
:func:`pvariance` function as the *mu* parameter to get the var... | trusted_official_docs | CPython Docs | freedom. Provided that the data points are representative (e.g. independent and identically distributed), the result should be an unbiased estimate of the true population variance.
If you somehow know the actual population mean μ you should pass it to the
:func:`pvariance` function as the *mu* parameter to get the var... | freedom. Provided that the data points are representative (e.g. independent and identically distributed), the result should be an unbiased estimate of the true population variance.
If you somehow know the actual population mean μ you should pass it to the
:func:`pvariance` function as the *mu* parameter to get the var... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
d9b6d7bb-bd42-4e43-b620-98215d0f9ae5 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,772 | supabase-export-v2 | 14bdfee6aa1485cb | inputs must be sequences of the same length (no less than two), and the independent variable *x* cannot be constant; otherwise a :exc:`StatisticsError` is raised.
For example, we can use the `release dates of the Monty
Python films <https://en.wikipedia.org/wiki/Monty_Python#Films>`_
to predict the cumulative number ... | trusted_official_docs | CPython Docs | inputs must be sequences of the same length (no less than two), and the independent variable *x* cannot be constant; otherwise a :exc:`StatisticsError` is raised.
For example, we can use the `release dates of the Monty
Python films <https://en.wikipedia.org/wiki/Monty_Python#Films>`_
to predict the cumulative number ... | inputs must be sequences of the same length (no less than two), and the independent variable *x* cannot be constant; otherwise a :exc:`StatisticsError` is raised.
For example, we can use the `release dates of the Monty
Python films <https://en.wikipedia.org/wiki/Monty_Python#Films>`_
to predict the cumulative number ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
da987e6c-9d14-4f45-8cc2-0ba54f1de907 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,654 | supabase-export-v2 | f8fd946e004f562f | Return the low median of numeric data. If *data* is empty, :exc:`StatisticsError` is raised. *data* can be a sequence or iterable.
The low median is always a member of the data set. When the number of data
points is odd, the middle value is returned. When it is even, the smaller of
the two middle values is returned. | trusted_official_docs | CPython Docs | Return the low median of numeric data. If *data* is empty, :exc:`StatisticsError` is raised. *data* can be a sequence or iterable.
The low median is always a member of the data set. When the number of data
points is odd, the middle value is returned. When it is even, the smaller of
the two middle values is returned. | Return the low median of numeric data. If *data* is empty, :exc:`StatisticsError` is raised. *data* can be a sequence or iterable.
The low median is always a member of the data set. When the number of data
points is odd, the middle value is returned. When it is even, the smaller of
the two middle values is returned. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
dc566256-8e41-4624-be54-3360df00c8c5 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,643 | supabase-export-v2 | 2590cd5996c19213 | Continuing the example for :func:`kde`, we can use :func:`kde_random` to generate new random selections from an estimated probability density function:
>>> data = [-2.1, -1.3, -0.4, 1.9, 5.1, 6.2]
>>> rand = kde_random(data, h=1.5, seed=8675309)
>>> new_selections = [rand() for i in range(10)]
>>> [round(x, 1) for x... | trusted_official_docs | CPython Docs | Continuing the example for :func:`kde`, we can use :func:`kde_random` to generate new random selections from an estimated probability density function:
>>> data = [-2.1, -1.3, -0.4, 1.9, 5.1, 6.2]
>>> rand = kde_random(data, h=1.5, seed=8675309)
>>> new_selections = [rand() for i in range(10)]
>>> [round(x, 1) for x... | Continuing the example for :func:`kde`, we can use :func:`kde_random` to generate new random selections from an estimated probability density function:
>>> data = [-2.1, -1.3, -0.4, 1.9, 5.1, 6.2]
>>> rand = kde_random(data, h=1.5, seed=8675309)
>>> new_selections = [rand() for i in range(10)]
>>> [round(x, 1) for x... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
dea472ad-82a1-4b19-8832-0a0cbd5b8049 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,574 | supabase-export-v2 | dde4074704312dee | ============================================= :func:`pstdev` Population standard deviation of data. :func:`pvariance` Population variance of data. :func:`stdev` Sample standard deviation of data. :func:`variance` Sample variance of data. ======================= =============================================
Statistics f... | trusted_official_docs | CPython Docs | ============================================= :func:`pstdev` Population standard deviation of data. :func:`pvariance` Population variance of data. :func:`stdev` Sample standard deviation of data. :func:`variance` Sample variance of data. ======================= =============================================
Statistics f... | ============================================= :func:`pstdev` Population standard deviation of data. :func:`pvariance` Population variance of data. :func:`stdev` Sample standard deviation of data. :func:`variance` Sample variance of data. ======================= =============================================
Statistics f... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
e1343c73-9909-4d20-bd49-28dd57090311 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,818 | supabase-export-v2 | 8477ae5c5b66c7b1 | .. method:: NormalDist.inv_cdf(p)
Compute the inverse cumulative distribution function, also known as the
`quantile function <https://en.wikipedia.org/wiki/Quantile_function>`_
or the `percent-point
<https://web.archive.org/web/20190203145224/https://www.statisticshowto.datasciencecentral.com/inverse-distribution-fu... | trusted_official_docs | CPython Docs | .. method:: NormalDist.inv_cdf(p)
Compute the inverse cumulative distribution function, also known as the
`quantile function <https://en.wikipedia.org/wiki/Quantile_function>`_
or the `percent-point
<https://web.archive.org/web/20190203145224/https://www.statisticshowto.datasciencecentral.com/inverse-distribution-fu... | .. method:: NormalDist.inv_cdf(p)
Compute the inverse cumulative distribution function, also known as the
`quantile function <https://en.wikipedia.org/wiki/Quantile_function>`_
or the `percent-point
<https://web.archive.org/web/20190203145224/https://www.statisticshowto.datasciencecentral.com/inverse-distribution-fu... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
e15282ee-f1a4-46b3-a31d-c8082bf0a4aa | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,612 | supabase-export-v2 | 8d0f476ed632b279 | *a*, *b* and *c* will be equivalent to ``3/(1/a + 1/b + 1/c)``. If one of the values is zero, the result will be zero.
The harmonic mean is a type of average, a measure of the central
location of the data. It is often appropriate when averaging
ratios or rates, for example speeds. | trusted_official_docs | CPython Docs | *a*, *b* and *c* will be equivalent to ``3/(1/a + 1/b + 1/c)``. If one of the values is zero, the result will be zero.
The harmonic mean is a type of average, a measure of the central
location of the data. It is often appropriate when averaging
ratios or rates, for example speeds. | *a*, *b* and *c* will be equivalent to ``3/(1/a + 1/b + 1/c)``. If one of the values is zero, the result will be zero.
The harmonic mean is a type of average, a measure of the central
location of the data. It is often appropriate when averaging
ratios or rates, for example speeds. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
e3b71493-6ae6-4250-b3ee-e8158efe82dd | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,675 | supabase-export-v2 | d31ed76a185e2a44 | >>> data = list(demographics.elements()) >>> median(data) 35 >>> round(median_grouped(data, interval=10), 1) 37.5
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. | trusted_official_docs | CPython Docs | >>> data = list(demographics.elements()) >>> median(data) 35 >>> round(median_grouped(data, interval=10), 1) 37.5
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. | >>> data = list(demographics.elements()) >>> median(data) 35 >>> round(median_grouped(data, interval=10), 1) 37.5
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. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
e3f39898-49e1-4902-8c79-7f5761be9864 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,651 | supabase-export-v2 | 8bde8cb740c1effd | This is suited for when your data is discrete, and you don't mind that the median may not be an actual data point.
If the data is ordinal (supports order operations) but not numeric (doesn't
support addition), consider using :func:`median_low` or :func:`median_high`
instead. | trusted_official_docs | CPython Docs | This is suited for when your data is discrete, and you don't mind that the median may not be an actual data point.
If the data is ordinal (supports order operations) but not numeric (doesn't
support addition), consider using :func:`median_low` or :func:`median_high`
instead. | This is suited for when your data is discrete, and you don't mind that the median may not be an actual data point.
If the data is ordinal (supports order operations) but not numeric (doesn't
support addition), consider using :func:`median_low` or :func:`median_high`
instead. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
e450a7d4-02f1-4554-ac5a-3c275248d5e4 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,567 | supabase-export-v2 | 0f8221e462b8827f | 19.2, 18.3, 14.4] >>> sorted(clean) # Sorting now works as expected [14.4, 18.3, 19.2, 20.7] >>> median(clean) # This result is now well defined 18.75
Averages and measures of central location
----------------------------------------- | trusted_official_docs | CPython Docs | 19.2, 18.3, 14.4] >>> sorted(clean) # Sorting now works as expected [14.4, 18.3, 19.2, 20.7] >>> median(clean) # This result is now well defined 18.75
Averages and measures of central location
----------------------------------------- | 19.2, 18.3, 14.4] >>> sorted(clean) # Sorting now works as expected [14.4, 18.3, 19.2, 20.7] >>> median(clean) # This result is now well defined 18.75
Averages and measures of central location
----------------------------------------- | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
e7162a70-2eb5-4df0-a6ed-e8bff6b6dab1 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,832 | supabase-export-v2 | 6dac2a408d6805fd | Dividing a constant by an instance of :class:`NormalDist` is not supported because the result wouldn't be normally distributed.
Since normal distributions arise from additive effects of independent
variables, it is possible to `add and subtract two independent normally
distributed random variables
<https://en.wikipe... | trusted_official_docs | CPython Docs | Dividing a constant by an instance of :class:`NormalDist` is not supported because the result wouldn't be normally distributed.
Since normal distributions arise from additive effects of independent
variables, it is possible to `add and subtract two independent normally
distributed random variables
<https://en.wikipe... | Dividing a constant by an instance of :class:`NormalDist` is not supported because the result wouldn't be normally distributed.
Since normal distributions arise from additive effects of independent
variables, it is possible to `add and subtract two independent normally
distributed random variables
<https://en.wikipe... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
ebd87902-969c-47fa-8ff0-37a262753344 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,729 | supabase-export-v2 | 90d707b5331d66da | not attempt to verify that you have passed the actual mean as *xbar*. Using arbitrary values for *xbar* can lead to invalid or impossible results.
Decimal and Fraction values are supported: | trusted_official_docs | CPython Docs | not attempt to verify that you have passed the actual mean as *xbar*. Using arbitrary values for *xbar* can lead to invalid or impossible results.
Decimal and Fraction values are supported: | not attempt to verify that you have passed the actual mean as *xbar*. Using arbitrary values for *xbar* can lead to invalid or impossible results.
Decimal and Fraction values are supported: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
ecc56f86-8bdc-4894-a8c3-f585294dfdf7 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,663 | supabase-export-v2 | 7b1b46a051a5c74b | >>> median_high([1, 3, 5]) 3 >>> median_high([1, 3, 5, 7]) 5
Use the high median when your data are discrete and you prefer the median to
be an actual data point rather than interpolated. | trusted_official_docs | CPython Docs | >>> median_high([1, 3, 5]) 3 >>> median_high([1, 3, 5, 7]) 5
Use the high median when your data are discrete and you prefer the median to
be an actual data point rather than interpolated. | >>> median_high([1, 3, 5]) 3 >>> median_high([1, 3, 5, 7]) 5
Use the high median when your data are discrete and you prefer the median to
be an actual data point rather than interpolated. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
ef016839-484b-4baa-a82f-f4e62973eb25 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,582 | supabase-export-v2 | 6fd367b0f52d02e3 | commonly called "the average", although it is only one of many different mathematical averages. It is a measure of the central location of the data.
If *data* is empty, :exc:`StatisticsError` will be raised. | trusted_official_docs | CPython Docs | commonly called "the average", although it is only one of many different mathematical averages. It is a measure of the central location of the data.
If *data* is empty, :exc:`StatisticsError` will be raised. | commonly called "the average", although it is only one of many different mathematical averages. It is a measure of the central location of the data.
If *data* is empty, :exc:`StatisticsError` will be raised. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
ef03d6d9-a692-4610-be8a-0d53fde28d85 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,863 | supabase-export-v2 | 312ef49db060c067 | NormalDist.from_samples([180, 190, 170, 165]) >>> weight_female = NormalDist.from_samples([100, 150, 130, 150]) >>> foot_size_male = NormalDist.from_samples([12, 11, 12, 10]) >>> foot_size_female = NormalDist.from_samples([6, 8, 7, 9])
Next, we encounter a new person whose feature measurements are known but whose
gende... | trusted_official_docs | CPython Docs | NormalDist.from_samples([180, 190, 170, 165]) >>> weight_female = NormalDist.from_samples([100, 150, 130, 150]) >>> foot_size_male = NormalDist.from_samples([12, 11, 12, 10]) >>> foot_size_female = NormalDist.from_samples([6, 8, 7, 9])
Next, we encounter a new person whose feature measurements are known but whose
gende... | NormalDist.from_samples([180, 190, 170, 165]) >>> weight_female = NormalDist.from_samples([100, 150, 130, 150]) >>> foot_size_male = NormalDist.from_samples([12, 11, 12, 10]) >>> foot_size_female = NormalDist.from_samples([6, 8, 7, 9])
Next, we encounter a new person whose feature measurements are known but whose
gende... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
f0e41534-9803-419a-8df5-3506218bc281 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,850 | supabase-export-v2 | ae447330bd33b707 | Approximating binomial distributions ************************************
Normal distributions can be used to approximate `Binomial
distributions <https://mathworld.wolfram.com/BinomialDistribution.html>`_
when the sample size is large and when the probability of a successful
trial is near 50%. | trusted_official_docs | CPython Docs | Approximating binomial distributions ************************************
Normal distributions can be used to approximate `Binomial
distributions <https://mathworld.wolfram.com/BinomialDistribution.html>`_
when the sample size is large and when the probability of a successful
trial is near 50%. | Approximating binomial distributions ************************************
Normal distributions can be used to approximate `Binomial
distributions <https://mathworld.wolfram.com/BinomialDistribution.html>`_
when the sample size is large and when the probability of a successful
trial is near 50%. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
f287481d-fb52-469d-b2ec-971cf545f3b1 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,755 | supabase-export-v2 | da8cd93f049aa9cb | .. function:: correlation(x, y, /, *, method='linear')
Return the `Pearson's correlation coefficient
<https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_
for two sequence inputs. Pearson's correlation coefficient *r* takes values
between -1 and +1. It measures the strength and direction of a linear
rel... | trusted_official_docs | CPython Docs | .. function:: correlation(x, y, /, *, method='linear')
Return the `Pearson's correlation coefficient
<https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_
for two sequence inputs. Pearson's correlation coefficient *r* takes values
between -1 and +1. It measures the strength and direction of a linear
rel... | .. function:: correlation(x, y, /, *, method='linear')
Return the `Pearson's correlation coefficient
<https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_
for two sequence inputs. Pearson's correlation coefficient *r* takes values
between -1 and +1. It measures the strength and direction of a linear
rel... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
f7f0f977-dcd5-4fbc-a213-dd882b5afa38 | CPython Docs | file://datasets/cpython/Doc/library/statistics.rst | unknown | 2728187e-f963-4035-be91-c4d7c16028dc | 40,641 | supabase-export-v2 | eca45436b6307c22 | future, the values may change slightly as more accurate kernel inverse CDF estimates are implemented. The seed may be an integer, float, str, or bytes.
A :exc:`StatisticsError` will be raised if the *data* sequence is empty. | trusted_official_docs | CPython Docs | future, the values may change slightly as more accurate kernel inverse CDF estimates are implemented. The seed may be an integer, float, str, or bytes.
A :exc:`StatisticsError` will be raised if the *data* sequence is empty. | future, the values may change slightly as more accurate kernel inverse CDF estimates are implemented. The seed may be an integer, float, str, or bytes.
A :exc:`StatisticsError` will be raised if the *data* sequence is empty. | python, official-docs, cpython, P0 | Local_Trusted_Corpus |
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