chunk_id stringlengths 36 36 | source stringclasses 35
values | source_url stringlengths 0 290 | upstream_license stringclasses 1
value | document_id stringlengths 36 36 | chunk_index int64 0 324k | retrieved_at stringclasses 2
values | chunker_version stringclasses 4
values | content_hash stringlengths 15 64 | content stringlengths 50 44.7k | namespace stringclasses 9
values | source_name stringclasses 35
values | raw_text stringlengths 50 44.7k | cleaned_text stringlengths 50 44.7k | tags stringclasses 49
values | collection_name stringclasses 11
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
10f6f2ba-d5b3-410d-9afb-cfd05b4950f6 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,366 | supabase-export-v2 | 66dae72f96f76318 | .. function:: getrandbits(k)
Returns a non-negative Python integer with *k* random bits. This method
is supplied with the Mersenne Twister generator and some other generators
may also provide it as an optional part of the API. When available,
:meth:`getrandbits` enables :meth:`randrange` to handle arbitrarily large
... | trusted_official_docs | CPython Docs | .. function:: getrandbits(k)
Returns a non-negative Python integer with *k* random bits. This method
is supplied with the Mersenne Twister generator and some other generators
may also provide it as an optional part of the API. When available,
:meth:`getrandbits` enables :meth:`randrange` to handle arbitrarily large
... | .. function:: getrandbits(k)
Returns a non-negative Python integer with *k* random bits. This method
is supplied with the Mersenne Twister generator and some other generators
may also provide it as an optional part of the API. When available,
:meth:`getrandbits` enables :meth:`randrange` to handle arbitrarily large
... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
11f58e0d-f79e-4289-a85f-d22047df3086 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,487 | supabase-export-v2 | c781e0ab0494612b | average_arrival_interval) next_server_available = servers[0] wait = max(0.0, next_server_available - arrival_time) waits.append(wait) service_duration = max(0.0, gauss(average_service_time, stdev_service_time)) service_completed = arrival_time + wait + service_duration heapreplace(servers, service_completed)
print(f'Me... | trusted_official_docs | CPython Docs | average_arrival_interval) next_server_available = servers[0] wait = max(0.0, next_server_available - arrival_time) waits.append(wait) service_duration = max(0.0, gauss(average_service_time, stdev_service_time)) service_completed = arrival_time + wait + service_duration heapreplace(servers, service_completed)
print(f'Me... | average_arrival_interval) next_server_available = servers[0] wait = max(0.0, next_server_available - arrival_time) waits.append(wait) service_duration = max(0.0, gauss(average_service_time, stdev_service_time)) service_completed = arrival_time + wait + service_duration heapreplace(servers, service_completed)
print(f'Me... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
14a0e16d-a094-4424-90e8-5367378f43ad | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,478 | supabase-export-v2 | 383c0b513f55bbc8 | 31, 95] means = sorted(mean(choices(data, k=len(data))) for i in range(100)) print(f'The sample mean of {mean(data):.1f} has a 90% confidence ' f'interval from {means[5]:.1f} to {means[94]:.1f}')
Example of a `resampling permutation test
<https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests>`_
to det... | trusted_official_docs | CPython Docs | 31, 95] means = sorted(mean(choices(data, k=len(data))) for i in range(100)) print(f'The sample mean of {mean(data):.1f} has a 90% confidence ' f'interval from {means[5]:.1f} to {means[94]:.1f}')
Example of a `resampling permutation test
<https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests>`_
to det... | 31, 95] means = sorted(mean(choices(data, k=len(data))) for i in range(100)) print(f'The sample mean of {mean(data):.1f} has a 90% confidence ' f'interval from {means[5]:.1f} to {means[94]:.1f}')
Example of a `resampling permutation test
<https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests>`_
to det... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
1f67c39d-0096-4b92-8b6b-3c92d8a6bc8a | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,386 | supabase-export-v2 | d000ced53f9d8ae4 | Return a *k* length list of unique elements chosen from the population sequence. Used for random sampling without replacement.
Returns a new list containing elements from the population while leaving the
original population unchanged. The resulting list is in selection order so that
all sub-slices will also be valid ... | trusted_official_docs | CPython Docs | Return a *k* length list of unique elements chosen from the population sequence. Used for random sampling without replacement.
Returns a new list containing elements from the population while leaving the
original population unchanged. The resulting list is in selection order so that
all sub-slices will also be valid ... | Return a *k* length list of unique elements chosen from the population sequence. Used for random sampling without replacement.
Returns a new list containing elements from the population while leaving the
original population unchanged. The resulting list is in selection order so that
all sub-slices will also be valid ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
1f9786f1-0f17-471c-83c2-ac20b71b3c43 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,418 | supabase-export-v2 | 45940adf5898e0a1 | .. function:: gammavariate(alpha, beta)
Gamma distribution. (*Not* the gamma function!) The shape and
scale parameters, *alpha* and *beta*, must have positive values. (Calling conventions vary and some sources define 'beta'
as the inverse of the scale). | trusted_official_docs | CPython Docs | .. function:: gammavariate(alpha, beta)
Gamma distribution. (*Not* the gamma function!) The shape and
scale parameters, *alpha* and *beta*, must have positive values. (Calling conventions vary and some sources define 'beta'
as the inverse of the scale). | .. function:: gammavariate(alpha, beta)
Gamma distribution. (*Not* the gamma function!) The shape and
scale parameters, *alpha* and *beta*, must have positive values. (Calling conventions vary and some sources define 'beta'
as the inverse of the scale). | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
21e82e8f-1a50-4eaa-8bdf-44b497ae9cba | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,344 | supabase-export-v2 | b76d7cf1a63f654e | With version 1 (provided for reproducing random sequences from older versions of Python), the algorithm for :class:`str` and :class:`bytes` generates a narrower range of seeds.
.. versionchanged:: 3.2
Moved to the version 2 scheme which uses all of the bits in a string seed. | trusted_official_docs | CPython Docs | With version 1 (provided for reproducing random sequences from older versions of Python), the algorithm for :class:`str` and :class:`bytes` generates a narrower range of seeds.
.. versionchanged:: 3.2
Moved to the version 2 scheme which uses all of the bits in a string seed. | With version 1 (provided for reproducing random sequences from older versions of Python), the algorithm for :class:`str` and :class:`bytes` generates a narrower range of seeds.
.. versionchanged:: 3.2
Moved to the version 2 scheme which uses all of the bits in a string seed. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
254161d4-95de-4231-aefc-ad6409f48b99 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,389 | supabase-export-v2 | b56133aed46fc301 | time or with the optional keyword-only *counts* parameter. For example, ``sample(['red', 'blue'], counts=[4, 2], k=5)`` is equivalent to ``sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5)``.
To choose a sample from a range of integers, use a :func:`range` object as an
argument. This is especially fast and spa... | trusted_official_docs | CPython Docs | time or with the optional keyword-only *counts* parameter. For example, ``sample(['red', 'blue'], counts=[4, 2], k=5)`` is equivalent to ``sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5)``.
To choose a sample from a range of integers, use a :func:`range` object as an
argument. This is especially fast and spa... | time or with the optional keyword-only *counts* parameter. For example, ``sample(['red', 'blue'], counts=[4, 2], k=5)`` is equivalent to ``sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5)``.
To choose a sample from a range of integers, use a :func:`range` object as an
argument. This is especially fast and spa... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
2a49e5d5-d905-476f-8062-5666ecfc92d1 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,423 | supabase-export-v2 | c8b086314502d7c4 | also called the Gaussian distribution. *mu* is the mean, and *sigma* is the standard deviation. This is slightly faster than the :func:`normalvariate` function defined below.
Multithreading note: When two threads call this function
simultaneously, it is possible that they will receive the
same return value. This can ... | trusted_official_docs | CPython Docs | also called the Gaussian distribution. *mu* is the mean, and *sigma* is the standard deviation. This is slightly faster than the :func:`normalvariate` function defined below.
Multithreading note: When two threads call this function
simultaneously, it is possible that they will receive the
same return value. This can ... | also called the Gaussian distribution. *mu* is the mean, and *sigma* is the standard deviation. This is slightly faster than the :func:`normalvariate` function defined below.
Multithreading note: When two threads call this function
simultaneously, it is possible that they will receive the
same return value. This can ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
2d12079a-ef1a-4e7f-9fda-5244a2bbac6a | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,383 | supabase-export-v2 | 544e3c1363a0954d | be generated. For example, a sequence of length 2080 is the largest that can fit within the period of the Mersenne Twister random number generator.
.. versionchanged:: 3.11
Removed the optional parameter *random*. | trusted_official_docs | CPython Docs | be generated. For example, a sequence of length 2080 is the largest that can fit within the period of the Mersenne Twister random number generator.
.. versionchanged:: 3.11
Removed the optional parameter *random*. | be generated. For example, a sequence of length 2080 is the largest that can fit within the period of the Mersenne Twister random number generator.
.. versionchanged:: 3.11
Removed the optional parameter *random*. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
2f6af437-d6ad-46bb-9179-b84618ee0e32 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,342 | supabase-export-v2 | 836ebf483c3457bd | If *a* is an int, its absolute value is used directly.
With version 2 (the default), a :class:`str`, :class:`bytes`, or :class:`bytearray`
object gets converted to an :class:`int` and all of its bits are used. | trusted_official_docs | CPython Docs | If *a* is an int, its absolute value is used directly.
With version 2 (the default), a :class:`str`, :class:`bytes`, or :class:`bytearray`
object gets converted to an :class:`int` and all of its bits are used. | If *a* is an int, its absolute value is used directly.
With version 2 (the default), a :class:`str`, :class:`bytes`, or :class:`bytearray`
object gets converted to an :class:`int` and all of its bits are used. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
3171a71c-8778-4edd-99c4-dc581f5af1a7 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,539 | supabase-export-v2 | a43baa769321f34a | random $ python -m random egg bacon sausage spam "Lobster Thermidor aux crevettes with a Mornay sauce" Lobster Thermidor aux crevettes with a Mornay sauce
$ # Random integer
$ python -m random 6
6 | trusted_official_docs | CPython Docs | random $ python -m random egg bacon sausage spam "Lobster Thermidor aux crevettes with a Mornay sauce" Lobster Thermidor aux crevettes with a Mornay sauce
$ # Random integer
$ python -m random 6
6 | random $ python -m random egg bacon sausage spam "Lobster Thermidor aux crevettes with a Mornay sauce" Lobster Thermidor aux crevettes with a Mornay sauce
$ # Random integer
$ python -m random 6
6 | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
34d2bc2a-64d2-43c3-865a-ffd3456183ce | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,490 | supabase-export-v2 | 0123a2b357d623f6 | for Hackers <https://www.youtube.com/watch?v=Iq9DzN6mvYA>`_ a video tutorial by `Jake Vanderplas <https://us.pycon.org/2016/speaker/profile/295/>`_ on statistical analysis using just a few fundamental concepts including simulation, sampling, shuffling, and cross-validation.
`Economics Simulation
<https://nbviewer.org/... | trusted_official_docs | CPython Docs | for Hackers <https://www.youtube.com/watch?v=Iq9DzN6mvYA>`_ a video tutorial by `Jake Vanderplas <https://us.pycon.org/2016/speaker/profile/295/>`_ on statistical analysis using just a few fundamental concepts including simulation, sampling, shuffling, and cross-validation.
`Economics Simulation
<https://nbviewer.org/... | for Hackers <https://www.youtube.com/watch?v=Iq9DzN6mvYA>`_ a video tutorial by `Jake Vanderplas <https://us.pycon.org/2016/speaker/profile/295/>`_ on statistical analysis using just a few fundamental concepts including simulation, sampling, shuffling, and cross-validation.
`Economics Simulation
<https://nbviewer.org/... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
357c561c-3400-4f21-af16-50b5e32abe05 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,542 | supabase-export-v2 | ba34d031c130393b | $ # With explicit arguments $ python -m random --choice egg bacon sausage spam "Lobster Thermidor aux crevettes with a Mornay sauce" egg
$ python -m random --integer 6
3 | trusted_official_docs | CPython Docs | $ # With explicit arguments $ python -m random --choice egg bacon sausage spam "Lobster Thermidor aux crevettes with a Mornay sauce" egg
$ python -m random --integer 6
3 | $ # With explicit arguments $ python -m random --choice egg bacon sausage spam "Lobster Thermidor aux crevettes with a Mornay sauce" egg
$ python -m random --integer 6
3 | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
3651a242-3887-4f94-a3d1-2799c6bf6fb4 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,481 | supabase-export-v2 | 361fe6d1637329fc | 73, 53, 70, 73, 68, 52, 65, 65] placebo = [54, 51, 58, 44, 55, 52, 42, 47, 58, 46] observed_diff = mean(drug) - mean(placebo)
n = 10_000
count = 0
combined = drug + placebo
for i in range(n):
shuffle(combined)
new_diff = mean(combined[:len(drug)]) - mean(combined[len(drug):])
count += (new_diff >= observed_diff) | trusted_official_docs | CPython Docs | 73, 53, 70, 73, 68, 52, 65, 65] placebo = [54, 51, 58, 44, 55, 52, 42, 47, 58, 46] observed_diff = mean(drug) - mean(placebo)
n = 10_000
count = 0
combined = drug + placebo
for i in range(n):
shuffle(combined)
new_diff = mean(combined[:len(drug)]) - mean(combined[len(drug):])
count += (new_diff >= observed_diff) | 73, 53, 70, 73, 68, 52, 65, 65] placebo = [54, 51, 58, 44, 55, 52, 42, 47, 58, 46] observed_diff = mean(drug) - mean(placebo)
n = 10_000
count = 0
combined = drug + placebo
for i in range(n):
shuffle(combined)
new_diff = mean(combined[:len(drug)]) - mean(combined[len(drug):])
count += (new_diff >= observed_diff) | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
371c8891-84f0-4812-8901-a32bd7ac8bf8 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,458 | supabase-export-v2 | 722a69e7fc68b735 | number generator. By reusing a seed value, the same sequence should be reproducible from run to run as long as multiple threads are not running.
Most of the random module's algorithms and seeding functions are subject to
change across Python versions, but two aspects are guaranteed not to change: | trusted_official_docs | CPython Docs | number generator. By reusing a seed value, the same sequence should be reproducible from run to run as long as multiple threads are not running.
Most of the random module's algorithms and seeding functions are subject to
change across Python versions, but two aspects are guaranteed not to change: | number generator. By reusing a seed value, the same sequence should be reproducible from run to run as long as multiple threads are not running.
Most of the random module's algorithms and seeding functions are subject to
change across Python versions, but two aspects are guaranteed not to change: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
382611ad-81a4-4fed-8507-6278f02c600e | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,416 | supabase-export-v2 | f478b8edae1d24d9 | word in Python.) Returned values range from 0 to positive infinity if *lambd* is positive, and from negative infinity to 0 if *lambd* is negative.
.. versionchanged:: 3.12
Added the default value for ``lambd``. | trusted_official_docs | CPython Docs | word in Python.) Returned values range from 0 to positive infinity if *lambd* is positive, and from negative infinity to 0 if *lambd* is negative.
.. versionchanged:: 3.12
Added the default value for ``lambd``. | word in Python.) Returned values range from 0 to positive infinity if *lambd* is positive, and from negative infinity to 0 if *lambd* is negative.
.. versionchanged:: 3.12
Added the default value for ``lambd``. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
3bf8158d-0698-492e-8201-703d44100d31 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,410 | supabase-export-v2 | a4fad7f716a9d6b6 | The end-point value ``b`` may or may not be included in the range depending on floating-point rounding in the expression ``a + (b-a) * random()``.
.. function:: triangular(low, high, mode) | trusted_official_docs | CPython Docs | The end-point value ``b`` may or may not be included in the range depending on floating-point rounding in the expression ``a + (b-a) * random()``.
.. function:: triangular(low, high, mode) | The end-point value ``b`` may or may not be included in the range depending on floating-point rounding in the expression ``a + (b-a) * random()``.
.. function:: triangular(low, high, mode) | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
3dc44364-292a-4feb-b25d-71162ffcdc0d | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,335 | supabase-export-v2 | eda87c84fb5bd1b4 | T. Nishimura, "Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator", ACM Transactions on Modeling and Computer Simulation Vol. 8, No. 1, January pp.3--30 1998.
`Complementary-Multiply-with-Carry recipe
<https://code.activestate.com/recipes/576707-long-period-random-number-genera... | trusted_official_docs | CPython Docs | T. Nishimura, "Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator", ACM Transactions on Modeling and Computer Simulation Vol. 8, No. 1, January pp.3--30 1998.
`Complementary-Multiply-with-Carry recipe
<https://code.activestate.com/recipes/576707-long-period-random-number-genera... | T. Nishimura, "Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator", ACM Transactions on Modeling and Computer Simulation Vol. 8, No. 1, January pp.3--30 1998.
`Complementary-Multiply-with-Carry recipe
<https://code.activestate.com/recipes/576707-long-period-random-number-genera... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
3fe89100-64dc-480d-98e4-d32770cd57c0 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,376 | supabase-export-v2 | ca2e316e86a81e49 | includes integers, floats, and fractions but excludes decimals). Weights are assumed to be non-negative and finite. A :exc:`ValueError` is raised if all weights are zero.
For a given seed, the :func:`choices` function with equal weighting
typically produces a different sequence than repeated calls to
:func:`choice`. ... | trusted_official_docs | CPython Docs | includes integers, floats, and fractions but excludes decimals). Weights are assumed to be non-negative and finite. A :exc:`ValueError` is raised if all weights are zero.
For a given seed, the :func:`choices` function with equal weighting
typically produces a different sequence than repeated calls to
:func:`choice`. ... | includes integers, floats, and fractions but excludes decimals). Weights are assumed to be non-negative and finite. A :exc:`ValueError` is raised if all weights are zero.
For a given seed, the :func:`choices` function with equal weighting
typically produces a different sequence than repeated calls to
:func:`choice`. ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
4288dbdd-9146-470a-87ae-7e50fe951540 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,459 | supabase-export-v2 | 3d2fb18536e06022 | Most of the random module's algorithms and seeding functions are subject to change across Python versions, but two aspects are guaranteed not to change:
* If a new seeding method is added, then a backward compatible seeder will be
offered. | trusted_official_docs | CPython Docs | Most of the random module's algorithms and seeding functions are subject to change across Python versions, but two aspects are guaranteed not to change:
* If a new seeding method is added, then a backward compatible seeder will be
offered. | Most of the random module's algorithms and seeding functions are subject to change across Python versions, but two aspects are guaranteed not to change:
* If a new seeding method is added, then a backward compatible seeder will be
offered. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
450a8c59-a26a-45e9-bcef-558be2259a76 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,411 | supabase-export-v2 | 4b42eeb4bf4d7f4f | .. function:: triangular(low, high, mode)
Return a random floating-point number *N* such that ``low <= N <= high`` and
with the specified *mode* between those bounds. The *low* and *high* bounds
default to zero and one. The *mode* argument defaults to the midpoint
between the bounds, giving a symmetric distribution. | trusted_official_docs | CPython Docs | .. function:: triangular(low, high, mode)
Return a random floating-point number *N* such that ``low <= N <= high`` and
with the specified *mode* between those bounds. The *low* and *high* bounds
default to zero and one. The *mode* argument defaults to the midpoint
between the bounds, giving a symmetric distribution. | .. function:: triangular(low, high, mode)
Return a random floating-point number *N* such that ``low <= N <= high`` and
with the specified *mode* between those bounds. The *low* and *high* bounds
default to zero and one. The *mode* argument defaults to the midpoint
between the bounds, giving a symmetric distribution. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
458a290b-c440-483e-882a-6a95d3c567bb | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,424 | supabase-export-v2 | a9301678aded3ea9 | thread use a different instance of the random number generator. 2) Put locks around all calls. 3) Use the slower, but thread-safe :func:`normalvariate` function instead.
.. versionchanged:: 3.11
*mu* and *sigma* now have default arguments. | trusted_official_docs | CPython Docs | thread use a different instance of the random number generator. 2) Put locks around all calls. 3) Use the slower, but thread-safe :func:`normalvariate` function instead.
.. versionchanged:: 3.11
*mu* and *sigma* now have default arguments. | thread use a different instance of the random number generator. 2) Put locks around all calls. 3) Use the slower, but thread-safe :func:`normalvariate` function instead.
.. versionchanged:: 3.11
*mu* and *sigma* now have default arguments. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
45ac205c-5984-435b-a22e-ded4be45849e | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,336 | supabase-export-v2 | c654dac76c2680b4 | `Complementary-Multiply-with-Carry recipe <https://code.activestate.com/recipes/576707-long-period-random-number-generator/>`_ for a compatible alternative random number generator with a long period and comparatively simple update operations.
.. note::
The global random number generator and instances of :class:`Random... | trusted_official_docs | CPython Docs | `Complementary-Multiply-with-Carry recipe <https://code.activestate.com/recipes/576707-long-period-random-number-generator/>`_ for a compatible alternative random number generator with a long period and comparatively simple update operations.
.. note::
The global random number generator and instances of :class:`Random... | `Complementary-Multiply-with-Carry recipe <https://code.activestate.com/recipes/576707-long-period-random-number-generator/>`_ for a compatible alternative random number generator with a long period and comparatively simple update operations.
.. note::
The global random number generator and instances of :class:`Random... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
466ae6cb-7353-4d77-8b2a-4d87999bce90 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,479 | supabase-export-v2 | ab4d833bf7aab86d | `resampling permutation test <https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests>`_ to determine the statistical significance or `p-value <https://en.wikipedia.org/wiki/P-value>`_ of an observed difference between the effects of a drug versus a placebo::
# Example from "Statistics is Easy" by Denni... | trusted_official_docs | CPython Docs | `resampling permutation test <https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests>`_ to determine the statistical significance or `p-value <https://en.wikipedia.org/wiki/P-value>`_ of an observed difference between the effects of a drug versus a placebo::
# Example from "Statistics is Easy" by Denni... | `resampling permutation test <https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests>`_ to determine the statistical significance or `p-value <https://en.wikipedia.org/wiki/P-value>`_ of an observed difference between the effects of a drug versus a placebo::
# Example from "Statistics is Easy" by Denni... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
52903156-9704-4b64-afa4-434f280d34b6 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,345 | supabase-export-v2 | 6ed8d53c6260d537 | .. versionchanged:: 3.2 Moved to the version 2 scheme which uses all of the bits in a string seed.
.. versionchanged:: 3.11
The *seed* must be one of the following types:
``None``, :class:`int`, :class:`float`, :class:`str`,
:class:`bytes`, or :class:`bytearray`. | trusted_official_docs | CPython Docs | .. versionchanged:: 3.2 Moved to the version 2 scheme which uses all of the bits in a string seed.
.. versionchanged:: 3.11
The *seed* must be one of the following types:
``None``, :class:`int`, :class:`float`, :class:`str`,
:class:`bytes`, or :class:`bytearray`. | .. versionchanged:: 3.2 Moved to the version 2 scheme which uses all of the bits in a string seed.
.. versionchanged:: 3.11
The *seed* must be one of the following types:
``None``, :class:`int`, :class:`float`, :class:`str`,
:class:`bytes`, or :class:`bytearray`. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
54ec9df5-82c8-48b9-9b8d-dba3806cd922 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,362 | supabase-export-v2 | 3babf97420129711 | .. versionchanged:: 3.2 :meth:`randrange` is more sophisticated about producing equally distributed values. Formerly it used a style like ``int(random()*n)`` which could produce slightly uneven distributions.
.. versionchanged:: 3.12
Automatic conversion of non-integer types is no longer supported. Calls such as ``ran... | trusted_official_docs | CPython Docs | .. versionchanged:: 3.2 :meth:`randrange` is more sophisticated about producing equally distributed values. Formerly it used a style like ``int(random()*n)`` which could produce slightly uneven distributions.
.. versionchanged:: 3.12
Automatic conversion of non-integer types is no longer supported. Calls such as ``ran... | .. versionchanged:: 3.2 :meth:`randrange` is more sophisticated about producing equally distributed values. Formerly it used a style like ``int(random()*n)`` which could produce slightly uneven distributions.
.. versionchanged:: 3.12
Automatic conversion of non-integer types is no longer supported. Calls such as ``ran... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
56e87929-7082-4c0e-83f4-1a97fe77003b | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,408 | supabase-export-v2 | 4e9f61d4eae8f3e2 | .. function:: uniform(a, b)
Return a random floating-point number *N* such that ``a <= N <= b`` for
``a <= b`` and ``b <= N <= a`` for ``b < a``. | trusted_official_docs | CPython Docs | .. function:: uniform(a, b)
Return a random floating-point number *N* such that ``a <= N <= b`` for
``a <= b`` and ``b <= N <= a`` for ``b < a``. | .. function:: uniform(a, b)
Return a random floating-point number *N* such that ``a <= N <= b`` for
``a <= b`` and ``b <= N <= a`` for ``b < a``. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
58fac8b0-e479-4c01-936d-9c38c5a2a858 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,499 | supabase-export-v2 | 6b731d30d63ed313 | the iterable." # Result will be in set(itertools.combinations_with_replacement(iterable, r)). pool = tuple(iterable) n = len(pool) indices = sorted(random.choices(range(n), k=r)) return tuple(pool[i] for i in indices)
def random_derangement(iterable):
"Choose a permutation where no element stays in its original positi... | trusted_official_docs | CPython Docs | the iterable." # Result will be in set(itertools.combinations_with_replacement(iterable, r)). pool = tuple(iterable) n = len(pool) indices = sorted(random.choices(range(n), k=r)) return tuple(pool[i] for i in indices)
def random_derangement(iterable):
"Choose a permutation where no element stays in its original positi... | the iterable." # Result will be in set(itertools.combinations_with_replacement(iterable, r)). pool = tuple(iterable) n = len(pool) indices = sorted(random.choices(range(n), k=r)) return tuple(pool[i] for i in indices)
def random_derangement(iterable):
"Choose a permutation where no element stays in its original positi... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
5c22341c-c2f8-4819-a7e5-f92f519cce01 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,477 | supabase-export-v2 | c911fa4a7bb46c5b | # https://www.thoughtco.com/example-of-bootstrapping-3126155 from statistics import fmean as mean from random import choices
data = [41, 50, 29, 37, 81, 30, 73, 63, 20, 35, 68, 22, 60, 31, 95]
means = sorted(mean(choices(data, k=len(data))) for i in range(100))
print(f'The sample mean of {mean(data):.1f} has a 90% co... | trusted_official_docs | CPython Docs | # https://www.thoughtco.com/example-of-bootstrapping-3126155 from statistics import fmean as mean from random import choices
data = [41, 50, 29, 37, 81, 30, 73, 63, 20, 35, 68, 22, 60, 31, 95]
means = sorted(mean(choices(data, k=len(data))) for i in range(100))
print(f'The sample mean of {mean(data):.1f} has a 90% co... | # https://www.thoughtco.com/example-of-bootstrapping-3126155 from statistics import fmean as mean from random import choices
data = [41, 50, 29, 37, 81, 30, 73, 63, 20, 35, 68, 22, 60, 31, 95]
means = sorted(mean(choices(data, k=len(data))) for i in range(100))
print(f'The sample mean of {mean(data):.1f} has a 90% co... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
5f127804-62dc-457c-a6a6-4feb7bee9bba | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,472 | supabase-export-v2 | d506a8f832bc66ac | >>> # Six roulette wheel spins (weighted sampling with replacement) >>> choices(['red', 'black', 'green'], [18, 18, 2], k=6) ['red', 'green', 'black', 'black', 'red', 'black']
>>> # Deal 20 cards without replacement from a deck
>>> # of 52 playing cards, and determine the proportion of cards
>>> # with a ten-value: t... | trusted_official_docs | CPython Docs | >>> # Six roulette wheel spins (weighted sampling with replacement) >>> choices(['red', 'black', 'green'], [18, 18, 2], k=6) ['red', 'green', 'black', 'black', 'red', 'black']
>>> # Deal 20 cards without replacement from a deck
>>> # of 52 playing cards, and determine the proportion of cards
>>> # with a ten-value: t... | >>> # Six roulette wheel spins (weighted sampling with replacement) >>> choices(['red', 'black', 'green'], [18, 18, 2], k=6) ['red', 'green', 'black', 'black', 'red', 'black']
>>> # Deal 20 cards without replacement from a deck
>>> # of 52 playing cards, and determine the proportion of cards
>>> # with a ten-value: t... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
6146d251-f0a9-42fd-a5d8-1d9319e8514e | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,343 | supabase-export-v2 | 0bf25b250d7ab371 | With version 2 (the default), a :class:`str`, :class:`bytes`, or :class:`bytearray` object gets converted to an :class:`int` and all of its bits are used.
With version 1 (provided for reproducing random sequences from older versions
of Python), the algorithm for :class:`str` and :class:`bytes` generates a
narrower ra... | trusted_official_docs | CPython Docs | With version 2 (the default), a :class:`str`, :class:`bytes`, or :class:`bytearray` object gets converted to an :class:`int` and all of its bits are used.
With version 1 (provided for reproducing random sequences from older versions
of Python), the algorithm for :class:`str` and :class:`bytes` generates a
narrower ra... | With version 2 (the default), a :class:`str`, :class:`bytes`, or :class:`bytearray` object gets converted to an :class:`int` and all of its bits are used.
With version 1 (provided for reproducing random sequences from older versions
of Python), the algorithm for :class:`str` and :class:`bytes` generates a
narrower ra... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
6770a05f-670d-404f-badf-2f868857db20 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,480 | supabase-export-v2 | a6799ddb1e2afde1 | # Example from "Statistics is Easy" by Dennis Shasha and Manda Wilson from statistics import fmean as mean from random import shuffle
drug = [54, 73, 53, 70, 73, 68, 52, 65, 65]
placebo = [54, 51, 58, 44, 55, 52, 42, 47, 58, 46]
observed_diff = mean(drug) - mean(placebo) | trusted_official_docs | CPython Docs | # Example from "Statistics is Easy" by Dennis Shasha and Manda Wilson from statistics import fmean as mean from random import shuffle
drug = [54, 73, 53, 70, 73, 68, 52, 65, 65]
placebo = [54, 51, 58, 44, 55, 52, 42, 47, 58, 46]
observed_diff = mean(drug) - mean(placebo) | # Example from "Statistics is Easy" by Dennis Shasha and Manda Wilson from statistics import fmean as mean from random import shuffle
drug = [54, 73, 53, 70, 73, 68, 52, 65, 65]
placebo = [54, 51, 58, 44, 55, 52, 42, 47, 58, 46]
observed_diff = mean(drug) - mean(placebo) | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
68981700-e220-4e75-9e8b-eaa64299da16 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,340 | supabase-export-v2 | c1a29744327c079c | Initialize the random number generator.
If *a* is omitted or ``None``, the current system time is used. If
randomness sources are provided by the operating system, they are used
instead of the system time (see the :func:`os.urandom` function for details
on availability). | trusted_official_docs | CPython Docs | Initialize the random number generator.
If *a* is omitted or ``None``, the current system time is used. If
randomness sources are provided by the operating system, they are used
instead of the system time (see the :func:`os.urandom` function for details
on availability). | Initialize the random number generator.
If *a* is omitted or ``None``, the current system time is used. If
randomness sources are provided by the operating system, they are used
instead of the system time (see the :func:`os.urandom` function for details
on availability). | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
6b357c54-8d20-4c28-a6af-39f113814820 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,419 | supabase-export-v2 | d57b5abad0bfb391 | shape and scale parameters, *alpha* and *beta*, must have positive values. (Calling conventions vary and some sources define 'beta' as the inverse of the scale).
The probability distribution function is:: | trusted_official_docs | CPython Docs | shape and scale parameters, *alpha* and *beta*, must have positive values. (Calling conventions vary and some sources define 'beta' as the inverse of the scale).
The probability distribution function is:: | shape and scale parameters, *alpha* and *beta*, must have positive values. (Calling conventions vary and some sources define 'beta' as the inverse of the scale).
The probability distribution function is:: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
702d5d2b-918d-4165-bef7-e4ab3b916f92 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,400 | supabase-export-v2 | 164ed1057e0da6c4 | sum(random() < p for i in range(n))
The number of trials *n* should be a non-negative integer. The probability of success *p* should be between ``0.0 <= p <= 1.0``. The result is an integer in the range ``0 <= X <= n``. | trusted_official_docs | CPython Docs | sum(random() < p for i in range(n))
The number of trials *n* should be a non-negative integer. The probability of success *p* should be between ``0.0 <= p <= 1.0``. The result is an integer in the range ``0 <= X <= n``. | sum(random() < p for i in range(n))
The number of trials *n* should be a non-negative integer. The probability of success *p* should be between ``0.0 <= p <= 1.0``. The result is an integer in the range ``0 <= X <= n``. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
737cc0bd-518e-45e7-81ce-d73ba3d6db4e | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,476 | supabase-export-v2 | acb7a3ea4e748bac | Example of `statistical bootstrapping <https://en.wikipedia.org/wiki/Bootstrapping_(statistics)>`_ using resampling with replacement to estimate a confidence interval for the mean of a sample::
# https://www.thoughtco.com/example-of-bootstrapping-3126155
from statistics import fmean as mean
from random import choices | trusted_official_docs | CPython Docs | Example of `statistical bootstrapping <https://en.wikipedia.org/wiki/Bootstrapping_(statistics)>`_ using resampling with replacement to estimate a confidence interval for the mean of a sample::
# https://www.thoughtco.com/example-of-bootstrapping-3126155
from statistics import fmean as mean
from random import choices | Example of `statistical bootstrapping <https://en.wikipedia.org/wiki/Bootstrapping_(statistics)>`_ using resampling with replacement to estimate a confidence interval for the mean of a sample::
# https://www.thoughtco.com/example-of-bootstrapping-3126155
from statistics import fmean as mean
from random import choices | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
7baddb2e-0fa3-4b90-9c2c-e2d35e4a3a0a | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,426 | supabase-export-v2 | 983478cc793831bf | .. function:: lognormvariate(mu, sigma)
Log normal distribution. If you take the natural logarithm of this
distribution, you'll get a normal distribution with mean *mu* and standard
deviation *sigma*. *mu* can have any value, and *sigma* must be greater than
zero. | trusted_official_docs | CPython Docs | .. function:: lognormvariate(mu, sigma)
Log normal distribution. If you take the natural logarithm of this
distribution, you'll get a normal distribution with mean *mu* and standard
deviation *sigma*. *mu* can have any value, and *sigma* must be greater than
zero. | .. function:: lognormvariate(mu, sigma)
Log normal distribution. If you take the natural logarithm of this
distribution, you'll get a normal distribution with mean *mu* and standard
deviation *sigma*. *mu* can have any value, and *sigma* must be greater than
zero. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
7efd264e-4149-4472-aa03-0b96aa6ad21a | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,422 | supabase-export-v2 | b4def3f303d89c18 | .. function:: gauss(mu=0.0, sigma=1.0)
Normal distribution, also called the Gaussian distribution. *mu* is the mean,
and *sigma* is the standard deviation. This is slightly faster than
the :func:`normalvariate` function defined below. | trusted_official_docs | CPython Docs | .. function:: gauss(mu=0.0, sigma=1.0)
Normal distribution, also called the Gaussian distribution. *mu* is the mean,
and *sigma* is the standard deviation. This is slightly faster than
the :func:`normalvariate` function defined below. | .. function:: gauss(mu=0.0, sigma=1.0)
Normal distribution, also called the Gaussian distribution. *mu* is the mean,
and *sigma* is the standard deviation. This is slightly faster than
the :func:`normalvariate` function defined below. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
83bd3409-d4ca-451c-b356-e743faa7fc37 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,328 | supabase-export-v2 | b09f9229bf0f7126 | extensively tested random number generators in existence. However, being completely deterministic, it is not suitable for all purposes, and is completely unsuitable for cryptographic purposes.
The functions supplied by this module are actually bound methods of a hidden
instance of the :class:`random.Random` class. You ... | trusted_official_docs | CPython Docs | extensively tested random number generators in existence. However, being completely deterministic, it is not suitable for all purposes, and is completely unsuitable for cryptographic purposes.
The functions supplied by this module are actually bound methods of a hidden
instance of the :class:`random.Random` class. You ... | extensively tested random number generators in existence. However, being completely deterministic, it is not suitable for all purposes, and is completely unsuitable for cryptographic purposes.
The functions supplied by this module are actually bound methods of a hidden
instance of the :class:`random.Random` class. You ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
848bbaf0-2199-427c-b586-d44bfa8f33df | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,491 | supabase-export-v2 | 74fea2ee65e5ef21 | Norvig <https://norvig.com/bio.html>`_ that shows effective use of many of the tools and distributions provided by this module (gauss, uniform, sample, betavariate, choice, triangular, and randrange).
`A Concrete Introduction to Probability (using Python)
<https://nbviewer.org/url/norvig.com/ipython/Probability.ipynb>... | trusted_official_docs | CPython Docs | Norvig <https://norvig.com/bio.html>`_ that shows effective use of many of the tools and distributions provided by this module (gauss, uniform, sample, betavariate, choice, triangular, and randrange).
`A Concrete Introduction to Probability (using Python)
<https://nbviewer.org/url/norvig.com/ipython/Probability.ipynb>... | Norvig <https://norvig.com/bio.html>`_ that shows effective use of many of the tools and distributions provided by this module (gauss, uniform, sample, betavariate, choice, triangular, and randrange).
`A Concrete Introduction to Probability (using Python)
<https://nbviewer.org/url/norvig.com/ipython/Probability.ipynb>... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
86a34c39-6595-4bc5-962a-1b8973142c5b | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,504 | supabase-export-v2 | 19811bf89fe5348f | random_combination('ABCDEFG', 4) ('B', 'C', 'D', 'G') >>> random_combination('ABCDEFG', 3) ('B', 'E', 'G') >>> random_combination('ABCDEFG', 2) ('E', 'G') >>> random_combination('ABCDEFG', 1) ('C',) >>> random_combination('ABCDEFG', 0) ()
>>> random.seed(8675309)
>>> random_combination_with_replacement('ABCDEFG', 7)
... | trusted_official_docs | CPython Docs | random_combination('ABCDEFG', 4) ('B', 'C', 'D', 'G') >>> random_combination('ABCDEFG', 3) ('B', 'E', 'G') >>> random_combination('ABCDEFG', 2) ('E', 'G') >>> random_combination('ABCDEFG', 1) ('C',) >>> random_combination('ABCDEFG', 0) ()
>>> random.seed(8675309)
>>> random_combination_with_replacement('ABCDEFG', 7)
... | random_combination('ABCDEFG', 4) ('B', 'C', 'D', 'G') >>> random_combination('ABCDEFG', 3) ('B', 'E', 'G') >>> random_combination('ABCDEFG', 2) ('E', 'G') >>> random_combination('ABCDEFG', 1) ('C',) >>> random_combination('ABCDEFG', 0) ()
>>> random.seed(8675309)
>>> random_combination_with_replacement('ABCDEFG', 7)
... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
8debbdae-e81e-4633-b419-c81dec650591 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,390 | supabase-export-v2 | e2a69846e43eaf19 | range of integers, use a :func:`range` object as an argument. This is especially fast and space efficient for sampling from a large population: ``sample(range(10000000), k=60)``.
If the sample size is larger than the population size, a :exc:`ValueError`
is raised. | trusted_official_docs | CPython Docs | range of integers, use a :func:`range` object as an argument. This is especially fast and space efficient for sampling from a large population: ``sample(range(10000000), k=60)``.
If the sample size is larger than the population size, a :exc:`ValueError`
is raised. | range of integers, use a :func:`range` object as an argument. This is especially fast and space efficient for sampling from a large population: ``sample(range(10000000), k=60)``.
If the sample size is larger than the population size, a :exc:`ValueError`
is raised. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
8e66d8df-7b58-43d6-883e-ba9391007e54 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,483 | supabase-export-v2 | 2626c94e5c2f74e9 | print(f'The one-sided p-value of {count / n:.4f} leads us to reject the null') print(f'hypothesis that there is no difference between the drug and the placebo.')
Simulation of arrival times and service deliveries for a multiserver queue:: | trusted_official_docs | CPython Docs | print(f'The one-sided p-value of {count / n:.4f} leads us to reject the null') print(f'hypothesis that there is no difference between the drug and the placebo.')
Simulation of arrival times and service deliveries for a multiserver queue:: | print(f'The one-sided p-value of {count / n:.4f} leads us to reject the null') print(f'hypothesis that there is no difference between the drug and the placebo.')
Simulation of arrival times and service deliveries for a multiserver queue:: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
8f18d305-5780-4809-a7fb-bfa97e4cdbdf | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,506 | supabase-export-v2 | 93a96d958eca468f | ('B', 'C', 'A', 'E', 'D') >>> # Identical inputs treated as distinct >>> identical = 20 >>> random_derangement((10, identical, 30, identical)) (20, 30, 10, 20)
The default :func:`.random` returns multiples of 2⁻⁵³ in the range
*0.0 ≤ x < 1.0*. All such numbers are evenly spaced and are exactly
representable as Python f... | trusted_official_docs | CPython Docs | ('B', 'C', 'A', 'E', 'D') >>> # Identical inputs treated as distinct >>> identical = 20 >>> random_derangement((10, identical, 30, identical)) (20, 30, 10, 20)
The default :func:`.random` returns multiples of 2⁻⁵³ in the range
*0.0 ≤ x < 1.0*. All such numbers are evenly spaced and are exactly
representable as Python f... | ('B', 'C', 'A', 'E', 'D') >>> # Identical inputs treated as distinct >>> identical = 20 >>> random_derangement((10, identical, 30, identical)) (20, 30, 10, 20)
The default :func:`.random` returns multiples of 2⁻⁵³ in the range
*0.0 ≤ x < 1.0*. All such numbers are evenly spaced and are exactly
representable as Python f... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
9413fd10-643b-44de-9dea-3e999bcac427 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,327 | supabase-export-v2 | 8f31468c5edb6994 | are functions to compute uniform, normal (Gaussian), lognormal, negative exponential, gamma, and beta distributions. For generating distributions of angles, the von Mises distribution is available.
Almost all module functions depend on the basic function :func:`.random`, which
generates a random float uniformly in the ... | trusted_official_docs | CPython Docs | are functions to compute uniform, normal (Gaussian), lognormal, negative exponential, gamma, and beta distributions. For generating distributions of angles, the von Mises distribution is available.
Almost all module functions depend on the basic function :func:`.random`, which
generates a random float uniformly in the ... | are functions to compute uniform, normal (Gaussian), lognormal, negative exponential, gamma, and beta distributions. For generating distributions of angles, the von Mises distribution is available.
Almost all module functions depend on the basic function :func:`.random`, which
generates a random float uniformly in the ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
97de2aa9-52d2-4b72-92d0-ba1803c9a758 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,496 | supabase-export-v2 | f3fd8c855483e8e9 | def random_product(*iterables, repeat=1): "Random selection from itertools.product(*iterables, repeat=repeat)" pools = tuple(map(tuple, iterables)) * repeat return tuple(map(random.choice, pools))
def random_permutation(iterable, r=None):
"Random selection from itertools.permutations(iterable, r)"
pool = tuple(iterab... | trusted_official_docs | CPython Docs | def random_product(*iterables, repeat=1): "Random selection from itertools.product(*iterables, repeat=repeat)" pools = tuple(map(tuple, iterables)) * repeat return tuple(map(random.choice, pools))
def random_permutation(iterable, r=None):
"Random selection from itertools.permutations(iterable, r)"
pool = tuple(iterab... | def random_product(*iterables, repeat=1): "Random selection from itertools.product(*iterables, repeat=repeat)" pools = tuple(map(tuple, iterables)) * repeat return tuple(map(random.choice, pools))
def random_permutation(iterable, r=None):
"Random selection from itertools.permutations(iterable, r)"
pool = tuple(iterab... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
9979ca02-68ab-4562-a1fc-d8f8102d214f | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,475 | supabase-export-v2 | e9488438c519fde9 | in middle two quartiles >>> def trial(): ... return 2_500 <= sorted(choices(range(10_000), k=5))[2] < 7_500 ... >>> sum(trial() for i in range(10_000)) / 10_000 0.7958
Example of `statistical bootstrapping
<https://en.wikipedia.org/wiki/Bootstrapping_(statistics)>`_ using resampling
with replacement to estimate a confi... | trusted_official_docs | CPython Docs | in middle two quartiles >>> def trial(): ... return 2_500 <= sorted(choices(range(10_000), k=5))[2] < 7_500 ... >>> sum(trial() for i in range(10_000)) / 10_000 0.7958
Example of `statistical bootstrapping
<https://en.wikipedia.org/wiki/Bootstrapping_(statistics)>`_ using resampling
with replacement to estimate a confi... | in middle two quartiles >>> def trial(): ... return 2_500 <= sorted(choices(range(10_000), k=5))[2] < 7_500 ... >>> sum(trial() for i in range(10_000)) / 10_000 0.7958
Example of `statistical bootstrapping
<https://en.wikipedia.org/wiki/Bootstrapping_(statistics)>`_ using resampling
with replacement to estimate a confi... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
9da67f78-dde3-48f4-bbdc-4305708b1c92 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,512 | supabase-export-v2 | f3ce6b4f9ef61eed | mantissa = 0x10_0000_0000_0000 | self.getrandbits(52) exponent = -53 x = 0 while not x: x = self.getrandbits(32) exponent += x.bit_length() - 32 return ldexp(mantissa, exponent)
All :ref:`real valued distributions <real-valued-distributions>`
in the class will use the new method:: | trusted_official_docs | CPython Docs | mantissa = 0x10_0000_0000_0000 | self.getrandbits(52) exponent = -53 x = 0 while not x: x = self.getrandbits(32) exponent += x.bit_length() - 32 return ldexp(mantissa, exponent)
All :ref:`real valued distributions <real-valued-distributions>`
in the class will use the new method:: | mantissa = 0x10_0000_0000_0000 | self.getrandbits(52) exponent = -53 x = 0 while not x: x = self.getrandbits(32) exponent += x.bit_length() - 32 return ldexp(mantissa, exponent)
All :ref:`real valued distributions <real-valued-distributions>`
in the class will use the new method:: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
9e1a2e94-1d93-4cee-9571-3d5610dc2f90 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,460 | supabase-export-v2 | 80f55416a87ade84 | * If a new seeding method is added, then a backward compatible seeder will be offered.
* The generator's :meth:`~Random.random` method will continue to produce the same
sequence when the compatible seeder is given the same seed. | trusted_official_docs | CPython Docs | * If a new seeding method is added, then a backward compatible seeder will be offered.
* The generator's :meth:`~Random.random` method will continue to produce the same
sequence when the compatible seeder is given the same seed. | * If a new seeding method is added, then a backward compatible seeder will be offered.
* The generator's :meth:`~Random.random` method will continue to produce the same
sequence when the compatible seeder is given the same seed. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
9fe86402-f714-420d-8112-422ec55bfed6 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,349 | supabase-export-v2 | d90c2100d8939a60 | .. function:: setstate(state)
*state* should have been obtained from a previous call to :func:`getstate`, and
:func:`setstate` restores the internal state of the generator to what it was at
the time :func:`getstate` was called. | trusted_official_docs | CPython Docs | .. function:: setstate(state)
*state* should have been obtained from a previous call to :func:`getstate`, and
:func:`setstate` restores the internal state of the generator to what it was at
the time :func:`getstate` was called. | .. function:: setstate(state)
*state* should have been obtained from a previous call to :func:`getstate`, and
:func:`setstate` restores the internal state of the generator to what it was at
the time :func:`getstate` was called. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a1cb142f-2410-4644-8ef1-0d7622ea6c39 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,325 | supabase-export-v2 | 1c1bc4430ac6944f | This module implements pseudo-random number generators for various distributions.
For integers, there is uniform selection from a range. For sequences, there is
uniform selection of a random element, a function to generate a random
permutation of a list in-place, and a function for random sampling without
replacement. | trusted_official_docs | CPython Docs | This module implements pseudo-random number generators for various distributions.
For integers, there is uniform selection from a range. For sequences, there is
uniform selection of a random element, a function to generate a random
permutation of a list in-place, and a function for random sampling without
replacement. | This module implements pseudo-random number generators for various distributions.
For integers, there is uniform selection from a range. For sequences, there is
uniform selection of a random element, a function to generate a random
permutation of a list in-place, and a function for random sampling without
replacement. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a2681b46-827f-4d84-8120-9869997674d0 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,388 | supabase-export-v2 | 68c6cd914424acba | Members of the population need not be :term:`hashable` or unique. If the population contains repeats, then each occurrence is a possible selection in the sample.
Repeated elements can be specified one at a time or with the optional
keyword-only *counts* parameter. For example, ``sample(['red', 'blue'],
counts=[4, 2],... | trusted_official_docs | CPython Docs | Members of the population need not be :term:`hashable` or unique. If the population contains repeats, then each occurrence is a possible selection in the sample.
Repeated elements can be specified one at a time or with the optional
keyword-only *counts* parameter. For example, ``sample(['red', 'blue'],
counts=[4, 2],... | Members of the population need not be :term:`hashable` or unique. If the population contains repeats, then each occurrence is a possible selection in the sample.
Repeated elements can be specified one at a time or with the optional
keyword-only *counts* parameter. For example, ``sample(['red', 'blue'],
counts=[4, 2],... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a5f36f63-2611-42bf-8681-5fd978872127 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,486 | supabase-export-v2 | c327466ed7451d3a | average_arrival_interval = 5.6 average_service_time = 15.0 stdev_service_time = 3.5 num_servers = 3
waits = []
arrival_time = 0.0
servers = [0.0] * num_servers # time when each server becomes available
heapify(servers)
for i in range(1_000_000):
arrival_time += expovariate(1.0 / average_arrival_interval)
next_ser... | trusted_official_docs | CPython Docs | average_arrival_interval = 5.6 average_service_time = 15.0 stdev_service_time = 3.5 num_servers = 3
waits = []
arrival_time = 0.0
servers = [0.0] * num_servers # time when each server becomes available
heapify(servers)
for i in range(1_000_000):
arrival_time += expovariate(1.0 / average_arrival_interval)
next_ser... | average_arrival_interval = 5.6 average_service_time = 15.0 stdev_service_time = 3.5 num_servers = 3
waits = []
arrival_time = 0.0
servers = [0.0] * num_servers # time when each server becomes available
heapify(servers)
for i in range(1_000_000):
arrival_time += expovariate(1.0 / average_arrival_interval)
next_ser... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a9063346-8ef9-4340-b6f9-c53f783c8c74 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,341 | supabase-export-v2 | 219ce5e172460fd8 | If randomness sources are provided by the operating system, they are used instead of the system time (see the :func:`os.urandom` function for details on availability).
If *a* is an int, its absolute value is used directly. | trusted_official_docs | CPython Docs | If randomness sources are provided by the operating system, they are used instead of the system time (see the :func:`os.urandom` function for details on availability).
If *a* is an int, its absolute value is used directly. | If randomness sources are provided by the operating system, they are used instead of the system time (see the :func:`os.urandom` function for details on availability).
If *a* is an int, its absolute value is used directly. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
a90fd89d-7fb5-4a54-8203-96d15037c45b | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,507 | supabase-export-v2 | 423327dc193d7bf3 | representable as Python floats. However, many other representable floats in that interval are not possible selections. For example, ``0.05954861408025609`` isn't an integer multiple of 2⁻⁵³.
The following recipe takes a different approach. All floats in the
interval are possible selections. The mantissa comes from a un... | trusted_official_docs | CPython Docs | representable as Python floats. However, many other representable floats in that interval are not possible selections. For example, ``0.05954861408025609`` isn't an integer multiple of 2⁻⁵³.
The following recipe takes a different approach. All floats in the
interval are possible selections. The mantissa comes from a un... | representable as Python floats. However, many other representable floats in that interval are not possible selections. For example, ``0.05954861408025609`` isn't an integer multiple of 2⁻⁵³.
The following recipe takes a different approach. All floats in the
interval are possible selections. The mantissa comes from a un... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
ab10ee7c-c822-4cd0-862f-d514e33f4d34 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,330 | supabase-export-v2 | f27484d8bfb455bd | also be subclassed if you want to use a different basic generator of your own devising: see the documentation on that class for more details.
The :mod:`!random` module also provides the :class:`SystemRandom` class which
uses the system function :func:`os.urandom` to generate random numbers
from sources provided by the ... | trusted_official_docs | CPython Docs | also be subclassed if you want to use a different basic generator of your own devising: see the documentation on that class for more details.
The :mod:`!random` module also provides the :class:`SystemRandom` class which
uses the system function :func:`os.urandom` to generate random numbers
from sources provided by the ... | also be subclassed if you want to use a different basic generator of your own devising: see the documentation on that class for more details.
The :mod:`!random` module also provides the :class:`SystemRandom` class which
uses the system function :func:`os.urandom` to generate random numbers
from sources provided by the ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
abbd9e9a-0f39-467d-a65d-fe931496beea | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,440 | supabase-export-v2 | fde49e54e138d16d | .. versionchanged:: 3.11 Formerly the *seed* could be any hashable object. Now it is limited to: ``None``, :class:`int`, :class:`float`, :class:`str`, :class:`bytes`, or :class:`bytearray`.
Subclasses of :class:`!Random` should override the following methods if they
wish to make use of a different basic generator: | trusted_official_docs | CPython Docs | .. versionchanged:: 3.11 Formerly the *seed* could be any hashable object. Now it is limited to: ``None``, :class:`int`, :class:`float`, :class:`str`, :class:`bytes`, or :class:`bytearray`.
Subclasses of :class:`!Random` should override the following methods if they
wish to make use of a different basic generator: | .. versionchanged:: 3.11 Formerly the *seed* could be any hashable object. Now it is limited to: ``None``, :class:`int`, :class:`float`, :class:`str`, :class:`bytes`, or :class:`bytearray`.
Subclasses of :class:`!Random` should override the following methods if they
wish to make use of a different basic generator: | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b432183f-8c2f-490c-a7e1-0f1cb648c1a6 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,361 | supabase-export-v2 | 1840d09f512e95a3 | Keyword arguments should not be used because they can be interpreted in unexpected ways. For example ``randrange(start=100)`` is interpreted as ``randrange(0, 100, 1)``.
.. versionchanged:: 3.2
:meth:`randrange` is more sophisticated about producing equally distributed
values. Formerly it used a style like ``int(rand... | trusted_official_docs | CPython Docs | Keyword arguments should not be used because they can be interpreted in unexpected ways. For example ``randrange(start=100)`` is interpreted as ``randrange(0, 100, 1)``.
.. versionchanged:: 3.2
:meth:`randrange` is more sophisticated about producing equally distributed
values. Formerly it used a style like ``int(rand... | Keyword arguments should not be used because they can be interpreted in unexpected ways. For example ``randrange(start=100)`` is interpreted as ``randrange(0, 100, 1)``.
.. versionchanged:: 3.2
:meth:`randrange` is more sophisticated about producing equally distributed
values. Formerly it used a style like ``int(rand... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b7a01c60-923d-4b73-a81e-883dbc2d8783 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,505 | supabase-export-v2 | 84566128ea2e2a5d | 'C', 'D', 'E', 'E', 'E', 'G') >>> random_combination_with_replacement('ABCDEFG', 3) ('A', 'B', 'E') >>> random_combination_with_replacement('ABCDEFG', 2) ('A', 'G') >>> random_combination_with_replacement('ABCDEFG', 1) ('E',) >>> random_combination_with_replacement('ABCDEFG', 0) ()
>>> random.seed(8675309)
>>> random_... | trusted_official_docs | CPython Docs | 'C', 'D', 'E', 'E', 'E', 'G') >>> random_combination_with_replacement('ABCDEFG', 3) ('A', 'B', 'E') >>> random_combination_with_replacement('ABCDEFG', 2) ('A', 'G') >>> random_combination_with_replacement('ABCDEFG', 1) ('E',) >>> random_combination_with_replacement('ABCDEFG', 0) ()
>>> random.seed(8675309)
>>> random_... | 'C', 'D', 'E', 'E', 'E', 'G') >>> random_combination_with_replacement('ABCDEFG', 3) ('A', 'B', 'E') >>> random_combination_with_replacement('ABCDEFG', 2) ('A', 'G') >>> random_combination_with_replacement('ABCDEFG', 1) ('E',) >>> random_combination_with_replacement('ABCDEFG', 0) ()
>>> random.seed(8675309)
>>> random_... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
b83dd031-0c9b-46ec-a41d-0f3b8f18f3be | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,455 | supabase-export-v2 | b7f7e7a148283a63 | .. class:: SystemRandom([seed])
Class that uses the :func:`os.urandom` function for generating random numbers
from sources provided by the operating system. Not available on all systems. Does not rely on software state, and sequences are not reproducible. Accordingly,
the :meth:`seed` method has no effect and is igno... | trusted_official_docs | CPython Docs | .. class:: SystemRandom([seed])
Class that uses the :func:`os.urandom` function for generating random numbers
from sources provided by the operating system. Not available on all systems. Does not rely on software state, and sequences are not reproducible. Accordingly,
the :meth:`seed` method has no effect and is igno... | .. class:: SystemRandom([seed])
Class that uses the :func:`os.urandom` function for generating random numbers
from sources provided by the operating system. Not available on all systems. Does not rely on software state, and sequences are not reproducible. Accordingly,
the :meth:`seed` method has no effect and is igno... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
bc13140a-7c9e-4cd7-adfc-64ff5d1073f9 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,482 | supabase-export-v2 | e2046f30a15f7450 | = 10_000 count = 0 combined = drug + placebo for i in range(n): shuffle(combined) new_diff = mean(combined[:len(drug)]) - mean(combined[len(drug):]) count += (new_diff >= observed_diff)
print(f'{n} label reshufflings produced only {count} instances with a difference')
print(f'at least as extreme as the observed differ... | trusted_official_docs | CPython Docs | = 10_000 count = 0 combined = drug + placebo for i in range(n): shuffle(combined) new_diff = mean(combined[:len(drug)]) - mean(combined[len(drug):]) count += (new_diff >= observed_diff)
print(f'{n} label reshufflings produced only {count} instances with a difference')
print(f'at least as extreme as the observed differ... | = 10_000 count = 0 combined = drug + placebo for i in range(n): shuffle(combined) new_diff = mean(combined[:len(drug)]) - mean(combined[len(drug):]) count += (new_diff >= observed_diff)
print(f'{n} label reshufflings produced only {count} instances with a difference')
print(f'at least as extreme as the observed differ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
beac2583-eb49-4ba2-b5dc-42f6347213ed | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,503 | supabase-export-v2 | 520a7bda815c63df | >>> random.seed(8675309) >>> random_permutation('ABCDEFG') ('D', 'B', 'E', 'C', 'G', 'A', 'F') >>> random_permutation('ABCDEFG', 5) ('A', 'G', 'D', 'C', 'B')
>>> random.seed(8675309)
>>> random_combination('ABCDEFG', 7)
('A', 'B', 'C', 'D', 'E', 'F', 'G')
>>> random_combination('ABCDEFG', 6)
('A', 'B', 'C', 'D', 'F... | trusted_official_docs | CPython Docs | >>> random.seed(8675309) >>> random_permutation('ABCDEFG') ('D', 'B', 'E', 'C', 'G', 'A', 'F') >>> random_permutation('ABCDEFG', 5) ('A', 'G', 'D', 'C', 'B')
>>> random.seed(8675309)
>>> random_combination('ABCDEFG', 7)
('A', 'B', 'C', 'D', 'E', 'F', 'G')
>>> random_combination('ABCDEFG', 6)
('A', 'B', 'C', 'D', 'F... | >>> random.seed(8675309) >>> random_permutation('ABCDEFG') ('D', 'B', 'E', 'C', 'G', 'A', 'F') >>> random_permutation('ABCDEFG', 5) ('A', 'G', 'D', 'C', 'B')
>>> random.seed(8675309)
>>> random_combination('ABCDEFG', 7)
('A', 'B', 'C', 'D', 'E', 'F', 'G')
>>> random_combination('ABCDEFG', 6)
('A', 'B', 'C', 'D', 'F... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c3ce1b92-a8a7-4f36-aa15-d76d9d3c3432 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,360 | supabase-export-v2 | 5d9f07877eaad9dc | The positional argument pattern matches the :func:`range` function.
Keyword arguments should not be used because they can be interpreted
in unexpected ways. For example ``randrange(start=100)`` is interpreted
as ``randrange(0, 100, 1)``. | trusted_official_docs | CPython Docs | The positional argument pattern matches the :func:`range` function.
Keyword arguments should not be used because they can be interpreted
in unexpected ways. For example ``randrange(start=100)`` is interpreted
as ``randrange(0, 100, 1)``. | The positional argument pattern matches the :func:`range` function.
Keyword arguments should not be used because they can be interpreted
in unexpected ways. For example ``randrange(start=100)`` is interpreted
as ``randrange(0, 100, 1)``. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c742095f-30df-4954-8950-1b934050ae4e | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,326 | supabase-export-v2 | 3581d7a4a08e4fce | uniform selection of a random element, a function to generate a random permutation of a list in-place, and a function for random sampling without replacement.
On the real line, there are functions to compute uniform, normal (Gaussian),
lognormal, negative exponential, gamma, and beta distributions. For generating
distr... | trusted_official_docs | CPython Docs | uniform selection of a random element, a function to generate a random permutation of a list in-place, and a function for random sampling without replacement.
On the real line, there are functions to compute uniform, normal (Gaussian),
lognormal, negative exponential, gamma, and beta distributions. For generating
distr... | uniform selection of a random element, a function to generate a random permutation of a list in-place, and a function for random sampling without replacement.
On the real line, there are functions to compute uniform, normal (Gaussian),
lognormal, negative exponential, gamma, and beta distributions. For generating
distr... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c772fe84-e125-4f85-90c2-d909f7f828b8 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,415 | supabase-export-v2 | c71eedfddfd2cdb6 | .. function:: expovariate(lambd = 1.0)
Exponential distribution. *lambd* is 1.0 divided by the desired
mean. It should be nonzero. (The parameter would be called
"lambda", but that is a reserved word in Python.) Returned values
range from 0 to positive infinity if *lambd* is positive, and from
negative infinity to ... | trusted_official_docs | CPython Docs | .. function:: expovariate(lambd = 1.0)
Exponential distribution. *lambd* is 1.0 divided by the desired
mean. It should be nonzero. (The parameter would be called
"lambda", but that is a reserved word in Python.) Returned values
range from 0 to positive infinity if *lambd* is positive, and from
negative infinity to ... | .. function:: expovariate(lambd = 1.0)
Exponential distribution. *lambd* is 1.0 divided by the desired
mean. It should be nonzero. (The parameter would be called
"lambda", but that is a reserved word in Python.) Returned values
range from 0 to positive infinity if *lambd* is positive, and from
negative infinity to ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c860afa2-9277-4de2-991c-306cda874da6 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,334 | supabase-export-v2 | 3d55cf322f68fed8 | .. seealso::
M. Matsumoto and T. Nishimura, "Mersenne Twister: A 623-dimensionally
equidistributed uniform pseudorandom number generator", ACM Transactions on
Modeling and Computer Simulation Vol. 8, No. 1, January pp.3--30 1998. | trusted_official_docs | CPython Docs | .. seealso::
M. Matsumoto and T. Nishimura, "Mersenne Twister: A 623-dimensionally
equidistributed uniform pseudorandom number generator", ACM Transactions on
Modeling and Computer Simulation Vol. 8, No. 1, January pp.3--30 1998. | .. seealso::
M. Matsumoto and T. Nishimura, "Mersenne Twister: A 623-dimensionally
equidistributed uniform pseudorandom number generator", ACM Transactions on
Modeling and Computer Simulation Vol. 8, No. 1, January pp.3--30 1998. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c8e76514-889f-4cf9-bf78-2c4372bb5bea | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,382 | supabase-export-v2 | bf500d4503c34936 | To shuffle an immutable sequence and return a new shuffled list, use ``sample(x, k=len(x))`` instead.
Note that even for small ``len(x)``, the total number of permutations of *x*
can quickly grow larger than the period of most random number generators. This implies that most permutations of a long sequence can never b... | trusted_official_docs | CPython Docs | To shuffle an immutable sequence and return a new shuffled list, use ``sample(x, k=len(x))`` instead.
Note that even for small ``len(x)``, the total number of permutations of *x*
can quickly grow larger than the period of most random number generators. This implies that most permutations of a long sequence can never b... | To shuffle an immutable sequence and return a new shuffled list, use ``sample(x, k=len(x))`` instead.
Note that even for small ``len(x)``, the total number of permutations of *x*
can quickly grow larger than the period of most random number generators. This implies that most permutations of a long sequence can never b... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c9f60387-dc23-487f-ab9b-6846ceee1af3 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,404 | supabase-export-v2 | ea8bb0ba6cefef3a | Real-valued distributions -------------------------
The following functions generate specific real-valued distributions. Function
parameters are named after the corresponding variables in the distribution's
equation, as used in common mathematical practice; most of these equations can
be found in any statistics text. | trusted_official_docs | CPython Docs | Real-valued distributions -------------------------
The following functions generate specific real-valued distributions. Function
parameters are named after the corresponding variables in the distribution's
equation, as used in common mathematical practice; most of these equations can
be found in any statistics text. | Real-valued distributions -------------------------
The following functions generate specific real-valued distributions. Function
parameters are named after the corresponding variables in the distribution's
equation, as used in common mathematical practice; most of these equations can
be found in any statistics text. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
cc4708a2-ecd3-45d9-a90a-b5a9f311ae28 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,329 | supabase-export-v2 | 7d0dc8f6fee4ff5b | bound methods of a hidden instance of the :class:`random.Random` class. You can instantiate your own instances of :class:`Random` to get generators that don't share state.
Class :class:`Random` can also be subclassed if you want to use a different
basic generator of your own devising: see the documentation on that clas... | trusted_official_docs | CPython Docs | bound methods of a hidden instance of the :class:`random.Random` class. You can instantiate your own instances of :class:`Random` to get generators that don't share state.
Class :class:`Random` can also be subclassed if you want to use a different
basic generator of your own devising: see the documentation on that clas... | bound methods of a hidden instance of the :class:`random.Random` class. You can instantiate your own instances of :class:`Random` to get generators that don't share state.
Class :class:`Random` can also be subclassed if you want to use a different
basic generator of your own devising: see the documentation on that clas... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
cfb1e300-3312-4cab-9b87-2ed4539de41e | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,367 | supabase-export-v2 | bbf6f2c772e47916 | and some other generators may also provide it as an optional part of the API. When available, :meth:`getrandbits` enables :meth:`randrange` to handle arbitrarily large ranges.
.. versionchanged:: 3.9
This method now accepts zero for *k*. | trusted_official_docs | CPython Docs | and some other generators may also provide it as an optional part of the API. When available, :meth:`getrandbits` enables :meth:`randrange` to handle arbitrarily large ranges.
.. versionchanged:: 3.9
This method now accepts zero for *k*. | and some other generators may also provide it as an optional part of the API. When available, :meth:`getrandbits` enables :meth:`randrange` to handle arbitrarily large ranges.
.. versionchanged:: 3.9
This method now accepts zero for *k*. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
d2f7deb3-cd76-4110-81fe-1c0e130510d2 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,469 | supabase-export-v2 | 756638bce83fb44b | >>> deck = 'ace two three four'.split() >>> shuffle(deck) # Shuffle a list >>> deck ['four', 'two', 'ace', 'three']
>>> sample([10, 20, 30, 40, 50], k=4) # Four samples without replacement
[40, 10, 50, 30] | trusted_official_docs | CPython Docs | >>> deck = 'ace two three four'.split() >>> shuffle(deck) # Shuffle a list >>> deck ['four', 'two', 'ace', 'three']
>>> sample([10, 20, 30, 40, 50], k=4) # Four samples without replacement
[40, 10, 50, 30] | >>> deck = 'ace two three four'.split() >>> shuffle(deck) # Shuffle a list >>> deck ['four', 'two', 'ace', 'three']
>>> sample([10, 20, 30, 40, 50], k=4) # Four samples without replacement
[40, 10, 50, 30] | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
d7f6e7c0-df43-4c21-bc7f-994115293850 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,409 | supabase-export-v2 | af1ea96bf13ab4f5 | random floating-point number *N* such that ``a <= N <= b`` for ``a <= b`` and ``b <= N <= a`` for ``b < a``.
The end-point value ``b`` may or may not be included in the range
depending on floating-point rounding in the expression
``a + (b-a) * random()``. | trusted_official_docs | CPython Docs | random floating-point number *N* such that ``a <= N <= b`` for ``a <= b`` and ``b <= N <= a`` for ``b < a``.
The end-point value ``b`` may or may not be included in the range
depending on floating-point rounding in the expression
``a + (b-a) * random()``. | random floating-point number *N* such that ``a <= N <= b`` for ``a <= b`` and ``b <= N <= a`` for ``b < a``.
The end-point value ``b`` may or may not be included in the range
depending on floating-point rounding in the expression
``a + (b-a) * random()``. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
d81a0e8c-7865-485a-a4df-81caac6ee426 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,374 | supabase-export-v2 | 42a0bdb38e9c6973 | cumulative weights ``[10, 15, 45, 50]``. Internally, the relative weights are converted to cumulative weights before making selections, so supplying the cumulative weights saves work.
If neither *weights* nor *cum_weights* are specified, selections are made
with equal probability. If a weights sequence is supplied, it... | trusted_official_docs | CPython Docs | cumulative weights ``[10, 15, 45, 50]``. Internally, the relative weights are converted to cumulative weights before making selections, so supplying the cumulative weights saves work.
If neither *weights* nor *cum_weights* are specified, selections are made
with equal probability. If a weights sequence is supplied, it... | cumulative weights ``[10, 15, 45, 50]``. Internally, the relative weights are converted to cumulative weights before making selections, so supplying the cumulative weights saves work.
If neither *weights* nor *cum_weights* are specified, selections are made
with equal probability. If a weights sequence is supplied, it... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
e53d16b9-c698-473b-919c-d70a344db785 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,468 | supabase-export-v2 | 0d468a059f3ea97e | >>> choice(['win', 'lose', 'draw']) # Single random element from a sequence 'draw'
>>> deck = 'ace two three four'.split()
>>> shuffle(deck) # Shuffle a list
>>> deck
['four', 'two', 'ace', 'three'] | trusted_official_docs | CPython Docs | >>> choice(['win', 'lose', 'draw']) # Single random element from a sequence 'draw'
>>> deck = 'ace two three four'.split()
>>> shuffle(deck) # Shuffle a list
>>> deck
['four', 'two', 'ace', 'three'] | >>> choice(['win', 'lose', 'draw']) # Single random element from a sequence 'draw'
>>> deck = 'ace two three four'.split()
>>> shuffle(deck) # Shuffle a list
>>> deck
['four', 'two', 'ace', 'three'] | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
e555634a-cfbe-4ca9-966a-aac2f8eedb61 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,514 | supabase-export-v2 | 4a66dd0dee30091c | >>> fr = FullRandom() >>> fr.random() 0.05954861408025609 >>> fr.expovariate(0.25) 8.87925541791544
The recipe is conceptually equivalent to an algorithm that chooses from
all the multiples of 2⁻¹⁰⁷⁴ in the range *0.0 ≤ x < 1.0*. All such
numbers are evenly spaced, but most have to be rounded down to the
nearest repres... | trusted_official_docs | CPython Docs | >>> fr = FullRandom() >>> fr.random() 0.05954861408025609 >>> fr.expovariate(0.25) 8.87925541791544
The recipe is conceptually equivalent to an algorithm that chooses from
all the multiples of 2⁻¹⁰⁷⁴ in the range *0.0 ≤ x < 1.0*. All such
numbers are evenly spaced, but most have to be rounded down to the
nearest repres... | >>> fr = FullRandom() >>> fr.random() 0.05954861408025609 >>> fr.expovariate(0.25) 8.87925541791544
The recipe is conceptually equivalent to an algorithm that chooses from
all the multiples of 2⁻¹⁰⁷⁴ in the range *0.0 ≤ x < 1.0*. All such
numbers are evenly spaced, but most have to be rounded down to the
nearest repres... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
e7d00119-a095-4dd7-85fd-54ae3c272e27 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,498 | supabase-export-v2 | a0769082e5953d05 | def random_combination(iterable, r): "Random selection from itertools.combinations(iterable, r)" pool = tuple(iterable) n = len(pool) indices = sorted(random.sample(range(n), r)) return tuple(pool[i] for i in indices)
def random_combination_with_replacement(iterable, r):
"Choose r elements with replacement. Order the ... | trusted_official_docs | CPython Docs | def random_combination(iterable, r): "Random selection from itertools.combinations(iterable, r)" pool = tuple(iterable) n = len(pool) indices = sorted(random.sample(range(n), r)) return tuple(pool[i] for i in indices)
def random_combination_with_replacement(iterable, r):
"Choose r elements with replacement. Order the ... | def random_combination(iterable, r): "Random selection from itertools.combinations(iterable, r)" pool = tuple(iterable) n = len(pool) indices = sorted(random.sample(range(n), r)) return tuple(pool[i] for i in indices)
def random_combination_with_replacement(iterable, r):
"Choose r elements with replacement. Order the ... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
ea316dff-dbbb-4d67-8871-841f4fbca95e | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,473 | supabase-export-v2 | e7e3d223d1d5201f | cards >>> # with a ten-value: ten, jack, queen, or king. >>> deal = sample(['tens', 'low cards'], counts=[16, 36], k=20) >>> deal.count('tens') / 20 0.15
>>> # Estimate the probability of getting 5 or more heads from 7 spins
>>> # of a biased coin that settles on heads 60% of the time. >>> sum(binomialvariate(n=7, p=0... | trusted_official_docs | CPython Docs | cards >>> # with a ten-value: ten, jack, queen, or king. >>> deal = sample(['tens', 'low cards'], counts=[16, 36], k=20) >>> deal.count('tens') / 20 0.15
>>> # Estimate the probability of getting 5 or more heads from 7 spins
>>> # of a biased coin that settles on heads 60% of the time. >>> sum(binomialvariate(n=7, p=0... | cards >>> # with a ten-value: ten, jack, queen, or king. >>> deal = sample(['tens', 'low cards'], counts=[16, 36], k=20) >>> deal.count('tens') / 20 0.15
>>> # Estimate the probability of getting 5 or more heads from 7 spins
>>> # of a biased coin that settles on heads 60% of the time. >>> sum(binomialvariate(n=7, p=0... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
eabb3215-c4ba-4b24-a880-20a53694924a | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,457 | supabase-export-v2 | cb2384fb908801dd | Notes on Reproducibility ------------------------
Sometimes it is useful to be able to reproduce the sequences given by a
pseudo-random number generator. By reusing a seed value, the same sequence should be
reproducible from run to run as long as multiple threads are not running. | trusted_official_docs | CPython Docs | Notes on Reproducibility ------------------------
Sometimes it is useful to be able to reproduce the sequences given by a
pseudo-random number generator. By reusing a seed value, the same sequence should be
reproducible from run to run as long as multiple threads are not running. | Notes on Reproducibility ------------------------
Sometimes it is useful to be able to reproduce the sequences given by a
pseudo-random number generator. By reusing a seed value, the same sequence should be
reproducible from run to run as long as multiple threads are not running. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
eedb45c8-b9af-461a-9f2f-8225dc0a26e4 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,538 | supabase-export-v2 | ce567a8c841018b9 | .. code-block:: console
$ # Choose one at random
$ python -m random egg bacon sausage spam "Lobster Thermidor aux crevettes with a Mornay sauce"
Lobster Thermidor aux crevettes with a Mornay sauce | trusted_official_docs | CPython Docs | .. code-block:: console
$ # Choose one at random
$ python -m random egg bacon sausage spam "Lobster Thermidor aux crevettes with a Mornay sauce"
Lobster Thermidor aux crevettes with a Mornay sauce | .. code-block:: console
$ # Choose one at random
$ python -m random egg bacon sausage spam "Lobster Thermidor aux crevettes with a Mornay sauce"
Lobster Thermidor aux crevettes with a Mornay sauce | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
ef3d86c4-74f8-4ad8-abd9-8e9449de16ec | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,373 | supabase-export-v2 | 6561175924edd638 | Return a *k* sized list of elements chosen from the *population* with replacement. If the *population* is empty, raises :exc:`IndexError`.
If a *weights* sequence is specified, selections are made according to the
relative weights. Alternatively, if a *cum_weights* sequence is given, the
selections are made according... | trusted_official_docs | CPython Docs | Return a *k* sized list of elements chosen from the *population* with replacement. If the *population* is empty, raises :exc:`IndexError`.
If a *weights* sequence is specified, selections are made according to the
relative weights. Alternatively, if a *cum_weights* sequence is given, the
selections are made according... | Return a *k* sized list of elements chosen from the *population* with replacement. If the *population* is empty, raises :exc:`IndexError`.
If a *weights* sequence is specified, selections are made according to the
relative weights. Alternatively, if a *cum_weights* sequence is given, the
selections are made according... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
f3af109c-346e-4624-9733-a1a7a16522eb | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,431 | supabase-export-v2 | 495c77fda797bd73 | .. function:: vonmisesvariate(mu, kappa)
*mu* is the mean angle, expressed in radians between 0 and 2\*\ *pi*, and *kappa*
is the concentration parameter, which must be greater than or equal to zero. If
*kappa* is equal to zero, this distribution reduces to a uniform random angle
over the range 0 to 2\*\ *pi*. | trusted_official_docs | CPython Docs | .. function:: vonmisesvariate(mu, kappa)
*mu* is the mean angle, expressed in radians between 0 and 2\*\ *pi*, and *kappa*
is the concentration parameter, which must be greater than or equal to zero. If
*kappa* is equal to zero, this distribution reduces to a uniform random angle
over the range 0 to 2\*\ *pi*. | .. function:: vonmisesvariate(mu, kappa)
*mu* is the mean angle, expressed in radians between 0 and 2\*\ *pi*, and *kappa*
is the concentration parameter, which must be greater than or equal to zero. If
*kappa* is equal to zero, this distribution reduces to a uniform random angle
over the range 0 to 2\*\ *pi*. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
f57f7c63-3f25-4076-a466-890620807f8f | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,497 | supabase-export-v2 | 9ca0664a0fbc097e | def random_permutation(iterable, r=None): "Random selection from itertools.permutations(iterable, r)" pool = tuple(iterable) r = len(pool) if r is None else r return tuple(random.sample(pool, r))
def random_combination(iterable, r):
"Random selection from itertools.combinations(iterable, r)"
pool = tuple(iterable)
n... | trusted_official_docs | CPython Docs | def random_permutation(iterable, r=None): "Random selection from itertools.permutations(iterable, r)" pool = tuple(iterable) r = len(pool) if r is None else r return tuple(random.sample(pool, r))
def random_combination(iterable, r):
"Random selection from itertools.combinations(iterable, r)"
pool = tuple(iterable)
n... | def random_permutation(iterable, r=None): "Random selection from itertools.permutations(iterable, r)" pool = tuple(iterable) r = len(pool) if r is None else r return tuple(random.sample(pool, r))
def random_combination(iterable, r):
"Random selection from itertools.combinations(iterable, r)"
pool = tuple(iterable)
n... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
f8668804-8fd3-41fc-bc4a-f012f9abcfe3 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,474 | supabase-export-v2 | 5e6ac3fcbc0ccfcb | # of a biased coin that settles on heads 60% of the time. >>> sum(binomialvariate(n=7, p=0.6) >= 5 for i in range(10_000)) / 10_000 0.4169
>>> # Probability of the median of 5 samples being in middle two quartiles
>>> def trial():
... return 2_500 <= sorted(choices(range(10_000), k=5))[2] < 7_500
... >>> sum(trial()... | trusted_official_docs | CPython Docs | # of a biased coin that settles on heads 60% of the time. >>> sum(binomialvariate(n=7, p=0.6) >= 5 for i in range(10_000)) / 10_000 0.4169
>>> # Probability of the median of 5 samples being in middle two quartiles
>>> def trial():
... return 2_500 <= sorted(choices(range(10_000), k=5))[2] < 7_500
... >>> sum(trial()... | # of a biased coin that settles on heads 60% of the time. >>> sum(binomialvariate(n=7, p=0.6) >= 5 for i in range(10_000)) / 10_000 0.4169
>>> # Probability of the median of 5 samples being in middle two quartiles
>>> def trial():
... return 2_500 <= sorted(choices(range(10_000), k=5))[2] < 7_500
... >>> sum(trial()... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
f935d7e6-1732-45fc-976e-c4325ae6cb33 | CPython Docs | file://datasets/cpython/Doc/library/random.rst | unknown | 184389ff-a8ac-467d-8c5d-8264b2e6fd8d | 19,541 | supabase-export-v2 | 978871516e15bf76 | $ # Random floating-point number $ python -m random 1.8 1.7080016272295635
$ # With explicit arguments
$ python -m random --choice egg bacon sausage spam "Lobster Thermidor aux crevettes with a Mornay sauce"
egg | trusted_official_docs | CPython Docs | $ # Random floating-point number $ python -m random 1.8 1.7080016272295635
$ # With explicit arguments
$ python -m random --choice egg bacon sausage spam "Lobster Thermidor aux crevettes with a Mornay sauce"
egg | $ # Random floating-point number $ python -m random 1.8 1.7080016272295635
$ # With explicit arguments
$ python -m random --choice egg bacon sausage spam "Lobster Thermidor aux crevettes with a Mornay sauce"
egg | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
2ee6e6bc-9878-49e1-83fc-62b1f912018f | CPython Docs | file://datasets/cpython/Doc/library/trace.rst | unknown | c1c92aac-e3b0-4b94-b83b-d3b520e5a639 | 19,614 | supabase-export-v2 | 6375a905365a0774 | import sys import trace
# create a Trace object, telling it what to ignore, and whether to
# do tracing or line-counting or both. tracer = trace.Trace(
ignoredirs=[sys.prefix, sys.exec_prefix],
trace=0,
count=1) | trusted_official_docs | CPython Docs | import sys import trace
# create a Trace object, telling it what to ignore, and whether to
# do tracing or line-counting or both. tracer = trace.Trace(
ignoredirs=[sys.prefix, sys.exec_prefix],
trace=0,
count=1) | import sys import trace
# create a Trace object, telling it what to ignore, and whether to
# do tracing or line-counting or both. tracer = trace.Trace(
ignoredirs=[sys.prefix, sys.exec_prefix],
trace=0,
count=1) | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
32a261cc-ace5-4984-ab38-bf80820b7cb1 | CPython Docs | file://datasets/cpython/Doc/library/trace.rst | unknown | c1c92aac-e3b0-4b94-b83b-d3b520e5a639 | 19,603 | supabase-export-v2 | b1835fbd9bcb7dab | .. method:: results()
Return a :class:`CoverageResults` object that contains the cumulative
results of all previous calls to ``run``, ``runctx`` and ``runfunc``
for the given :class:`Trace` instance. Does not reset the accumulated
trace results. | trusted_official_docs | CPython Docs | .. method:: results()
Return a :class:`CoverageResults` object that contains the cumulative
results of all previous calls to ``run``, ``runctx`` and ``runfunc``
for the given :class:`Trace` instance. Does not reset the accumulated
trace results. | .. method:: results()
Return a :class:`CoverageResults` object that contains the cumulative
results of all previous calls to ``run``, ``runctx`` and ``runfunc``
for the given :class:`Trace` instance. Does not reset the accumulated
trace results. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
53280b49-4653-4e9e-a3a2-403920122888 | CPython Docs | file://datasets/cpython/Doc/library/trace.rst | unknown | c1c92aac-e3b0-4b94-b83b-d3b520e5a639 | 19,598 | supabase-export-v2 | ebf694eb4ef3d359 | command and gather statistics from the execution with the current tracing parameters. *cmd* must be a string or code object, suitable for passing into :func:`exec`.
.. method:: runctx(cmd, globals=None, locals=None) | trusted_official_docs | CPython Docs | command and gather statistics from the execution with the current tracing parameters. *cmd* must be a string or code object, suitable for passing into :func:`exec`.
.. method:: runctx(cmd, globals=None, locals=None) | command and gather statistics from the execution with the current tracing parameters. *cmd* must be a string or code object, suitable for passing into :func:`exec`.
.. method:: runctx(cmd, globals=None, locals=None) | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
57645604-1c5e-4c22-add0-f1b3f969cc51 | CPython Docs | file://datasets/cpython/Doc/library/trace.rst | unknown | c1c92aac-e3b0-4b94-b83b-d3b520e5a639 | 19,597 | supabase-export-v2 | 44636ea8024de3b8 | .. method:: run(cmd)
Execute the command and gather statistics from the execution with
the current tracing parameters. *cmd* must be a string or code object,
suitable for passing into :func:`exec`. | trusted_official_docs | CPython Docs | .. method:: run(cmd)
Execute the command and gather statistics from the execution with
the current tracing parameters. *cmd* must be a string or code object,
suitable for passing into :func:`exec`. | .. method:: run(cmd)
Execute the command and gather statistics from the execution with
the current tracing parameters. *cmd* must be a string or code object,
suitable for passing into :func:`exec`. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
5dfe84ec-aea0-4ce9-9f86-d2671f5cf926 | CPython Docs | file://datasets/cpython/Doc/library/trace.rst | unknown | c1c92aac-e3b0-4b94-b83b-d3b520e5a639 | 19,609 | supabase-export-v2 | 85c46c84ee709f77 | .. method:: write_results(show_missing=True, summary=False, coverdir=None,\ *, ignore_missing_files=False)
Write coverage results. Set *show_missing* to show lines that had no
hits. Set *summary* to include in the output the coverage summary per
module. *coverdir* specifies the directory into which the coverage
resu... | trusted_official_docs | CPython Docs | .. method:: write_results(show_missing=True, summary=False, coverdir=None,\ *, ignore_missing_files=False)
Write coverage results. Set *show_missing* to show lines that had no
hits. Set *summary* to include in the output the coverage summary per
module. *coverdir* specifies the directory into which the coverage
resu... | .. method:: write_results(show_missing=True, summary=False, coverdir=None,\ *, ignore_missing_files=False)
Write coverage results. Set *show_missing* to show lines that had no
hits. Set *summary* to include in the output the coverage summary per
module. *coverdir* specifies the directory into which the coverage
resu... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
6a4acc23-ca6d-4750-b814-11a14e29f928 | CPython Docs | file://datasets/cpython/Doc/library/trace.rst | unknown | c1c92aac-e3b0-4b94-b83b-d3b520e5a639 | 19,589 | supabase-export-v2 | 2034b930810312be | .. option:: --ignore-module=<mod>
Ignore each of the given module names and its submodules (if it is a
package). The argument can be a list of names separated by a comma. | trusted_official_docs | CPython Docs | .. option:: --ignore-module=<mod>
Ignore each of the given module names and its submodules (if it is a
package). The argument can be a list of names separated by a comma. | .. option:: --ignore-module=<mod>
Ignore each of the given module names and its submodules (if it is a
package). The argument can be a list of names separated by a comma. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
92970a09-ccde-4600-bd39-6bd0750e6c5d | CPython Docs | file://datasets/cpython/Doc/library/trace.rst | unknown | c1c92aac-e3b0-4b94-b83b-d3b520e5a639 | 19,550 | supabase-export-v2 | f53af8685563aefa | --------------
The :mod:`!trace` module allows you to trace program execution, generate
annotated statement coverage listings, print caller/callee relationships and
list functions executed during a program run. It can be used in another program
or from the command line. | trusted_official_docs | CPython Docs | --------------
The :mod:`!trace` module allows you to trace program execution, generate
annotated statement coverage listings, print caller/callee relationships and
list functions executed during a program run. It can be used in another program
or from the command line. | --------------
The :mod:`!trace` module allows you to trace program execution, generate
annotated statement coverage listings, print caller/callee relationships and
list functions executed during a program run. It can be used in another program
or from the command line. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
94a1a930-5308-468f-b338-47255ab5d288 | CPython Docs | file://datasets/cpython/Doc/library/trace.rst | unknown | c1c92aac-e3b0-4b94-b83b-d3b520e5a639 | 19,582 | supabase-export-v2 | 7486ed5905ae397f | .. option:: -R, --no-report
Do not generate annotated listings. This is useful if you intend to make
several runs with :option:`--count <-c>`, and then produce a single set of
annotated listings at the end. | trusted_official_docs | CPython Docs | .. option:: -R, --no-report
Do not generate annotated listings. This is useful if you intend to make
several runs with :option:`--count <-c>`, and then produce a single set of
annotated listings at the end. | .. option:: -R, --no-report
Do not generate annotated listings. This is useful if you intend to make
several runs with :option:`--count <-c>`, and then produce a single set of
annotated listings at the end. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
aa7a4140-0983-4e22-853d-55e9d75cd584 | CPython Docs | file://datasets/cpython/Doc/library/trace.rst | unknown | c1c92aac-e3b0-4b94-b83b-d3b520e5a639 | 19,600 | supabase-export-v2 | 6482fe1d984f7c68 | from the execution with the current tracing parameters, in the defined global and local environments. If not defined, *globals* and *locals* default to empty dictionaries.
.. method:: runfunc(func, /, *args, **kwds) | trusted_official_docs | CPython Docs | from the execution with the current tracing parameters, in the defined global and local environments. If not defined, *globals* and *locals* default to empty dictionaries.
.. method:: runfunc(func, /, *args, **kwds) | from the execution with the current tracing parameters, in the defined global and local environments. If not defined, *globals* and *locals* default to empty dictionaries.
.. method:: runfunc(func, /, *args, **kwds) | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
c8609f1f-7220-41f5-86a5-9b48b77fc9a9 | CPython Docs | file://datasets/cpython/Doc/library/trace.rst | unknown | c1c92aac-e3b0-4b94-b83b-d3b520e5a639 | 19,610 | supabase-export-v2 | 87b9b2ea64cd1bc6 | specifies the directory into which the coverage result files will be output. If ``None``, the results for each source file are placed in its directory.
If *ignore_missing_files* is ``True``, coverage counts for files that no
longer exist are silently ignored. Otherwise, a missing file will
raise a :exc:`FileNotFoundE... | trusted_official_docs | CPython Docs | specifies the directory into which the coverage result files will be output. If ``None``, the results for each source file are placed in its directory.
If *ignore_missing_files* is ``True``, coverage counts for files that no
longer exist are silently ignored. Otherwise, a missing file will
raise a :exc:`FileNotFoundE... | specifies the directory into which the coverage result files will be output. If ``None``, the results for each source file are placed in its directory.
If *ignore_missing_files* is ``True``, coverage counts for files that no
longer exist are silently ignored. Otherwise, a missing file will
raise a :exc:`FileNotFoundE... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
ca08f44e-5687-4f9a-9472-21abd739d674 | CPython Docs | file://datasets/cpython/Doc/library/trace.rst | unknown | c1c92aac-e3b0-4b94-b83b-d3b520e5a639 | 19,595 | supabase-export-v2 | a16b98bb2704b5ec | .. class:: Trace(count=1, trace=1, countfuncs=0, countcallers=0, ignoremods=(),\ ignoredirs=(), infile=None, outfile=None, timing=False)
Create an object to trace execution of a single statement or expression. All
parameters are optional. *count* enables counting of line numbers. *trace*
enables line execution tracin... | trusted_official_docs | CPython Docs | .. class:: Trace(count=1, trace=1, countfuncs=0, countcallers=0, ignoremods=(),\ ignoredirs=(), infile=None, outfile=None, timing=False)
Create an object to trace execution of a single statement or expression. All
parameters are optional. *count* enables counting of line numbers. *trace*
enables line execution tracin... | .. class:: Trace(count=1, trace=1, countfuncs=0, countcallers=0, ignoremods=(),\ ignoredirs=(), infile=None, outfile=None, timing=False)
Create an object to trace execution of a single statement or expression. All
parameters are optional. *count* enables counting of line numbers. *trace*
enables line execution tracin... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
d18b5299-1588-4b75-af76-48cde2035696 | CPython Docs | file://datasets/cpython/Doc/library/trace.rst | unknown | c1c92aac-e3b0-4b94-b83b-d3b520e5a639 | 19,599 | supabase-export-v2 | 06e7db2871fb1c1d | .. method:: runctx(cmd, globals=None, locals=None)
Execute the command and gather statistics from the execution with the
current tracing parameters, in the defined global and local
environments. If not defined, *globals* and *locals* default to empty
dictionaries. | trusted_official_docs | CPython Docs | .. method:: runctx(cmd, globals=None, locals=None)
Execute the command and gather statistics from the execution with the
current tracing parameters, in the defined global and local
environments. If not defined, *globals* and *locals* default to empty
dictionaries. | .. method:: runctx(cmd, globals=None, locals=None)
Execute the command and gather statistics from the execution with the
current tracing parameters, in the defined global and local
environments. If not defined, *globals* and *locals* default to empty
dictionaries. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
e1642522-62c4-413b-b6d6-6d63a6192586 | CPython Docs | file://datasets/cpython/Doc/library/trace.rst | unknown | c1c92aac-e3b0-4b94-b83b-d3b520e5a639 | 19,564 | supabase-export-v2 | bab95bd8215fa205 | .. option:: -c, --count
Produce a set of annotated listing files upon program completion that shows
how many times each statement was executed. See also
:option:`--coverdir <-C>`, :option:`--file <-f>` and
:option:`--no-report <-R>` below. | trusted_official_docs | CPython Docs | .. option:: -c, --count
Produce a set of annotated listing files upon program completion that shows
how many times each statement was executed. See also
:option:`--coverdir <-C>`, :option:`--file <-f>` and
:option:`--no-report <-R>` below. | .. option:: -c, --count
Produce a set of annotated listing files upon program completion that shows
how many times each statement was executed. See also
:option:`--coverdir <-C>`, :option:`--file <-f>` and
:option:`--no-report <-R>` below. | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
e6252e7e-feba-449c-8482-7107ad518708 | CPython Docs | file://datasets/cpython/Doc/library/trace.rst | unknown | c1c92aac-e3b0-4b94-b83b-d3b520e5a639 | 19,615 | supabase-export-v2 | 7c1eec93e7661071 | a Trace object, telling it what to ignore, and whether to # do tracing or line-counting or both. tracer = trace.Trace( ignoredirs=[sys.prefix, sys.exec_prefix], trace=0, count=1)
# run the new command using the given tracer
tracer.run('main()') | trusted_official_docs | CPython Docs | a Trace object, telling it what to ignore, and whether to # do tracing or line-counting or both. tracer = trace.Trace( ignoredirs=[sys.prefix, sys.exec_prefix], trace=0, count=1)
# run the new command using the given tracer
tracer.run('main()') | a Trace object, telling it what to ignore, and whether to # do tracing or line-counting or both. tracer = trace.Trace( ignoredirs=[sys.prefix, sys.exec_prefix], trace=0, count=1)
# run the new command using the given tracer
tracer.run('main()') | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
ef2d894f-dc8d-470e-b55c-6d4fdf33443b | CPython Docs | file://datasets/cpython/Doc/library/trace.rst | unknown | c1c92aac-e3b0-4b94-b83b-d3b520e5a639 | 19,562 | supabase-export-v2 | 75287820af04d3a7 | Main options ^^^^^^^^^^^^
At least one of the following options must be specified when invoking
:mod:`!trace`. The :option:`--listfuncs <-l>` option is mutually exclusive with
the :option:`--trace <-t>` and :option:`--count <-c>` options. When
:option:`--listfuncs <-l>` is provided, neither :option:`--count <-c>` nor
:... | trusted_official_docs | CPython Docs | Main options ^^^^^^^^^^^^
At least one of the following options must be specified when invoking
:mod:`!trace`. The :option:`--listfuncs <-l>` option is mutually exclusive with
the :option:`--trace <-t>` and :option:`--count <-c>` options. When
:option:`--listfuncs <-l>` is provided, neither :option:`--count <-c>` nor
:... | Main options ^^^^^^^^^^^^
At least one of the following options must be specified when invoking
:mod:`!trace`. The :option:`--listfuncs <-l>` option is mutually exclusive with
the :option:`--trace <-t>` and :option:`--count <-c>` options. When
:option:`--listfuncs <-l>` is provided, neither :option:`--count <-c>` nor
:... | python, official-docs, cpython, P0 | Local_Trusted_Corpus | |
0465d01f-eb69-4b30-a519-9dde434d958c | CPython Docs | file://datasets/cpython/Doc/library/xml.sax.rst | unknown | 51e3f59f-684e-4338-82cc-c2f09b013811 | 19,634 | supabase-export-v2 | 2bc601222688de24 | Similar to :func:`parse`, but parses from a buffer *string* received as a parameter. *string* must be a :class:`str` instance or a :term:`bytes-like object`.
.. versionchanged:: 3.5
Added support of :class:`str` instances. | trusted_official_docs | CPython Docs | Similar to :func:`parse`, but parses from a buffer *string* received as a parameter. *string* must be a :class:`str` instance or a :term:`bytes-like object`.
.. versionchanged:: 3.5
Added support of :class:`str` instances. | Similar to :func:`parse`, but parses from a buffer *string* received as a parameter. *string* must be a :class:`str` instance or a :term:`bytes-like object`.
.. versionchanged:: 3.5
Added support of :class:`str` instances. | python, official-docs, cpython, P0 | Local_Trusted_Corpus |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.