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Python 3.11.3

Contenu du module « statistics »

Liste des classes du module statistics

Nom de la classe Description
Counter Dict subclass for counting hashable items. Sometimes called a bag [extrait de Counter.__doc__]
Decimal Construct a new Decimal object. 'value' can be an integer, string, tuple, [extrait de Decimal.__doc__]
defaultdict defaultdict(default_factory=None, /, [...]) --> dict with default factory [extrait de defaultdict.__doc__]
Fraction This class implements rational numbers. [extrait de Fraction.__doc__]
groupby make an iterator that returns consecutive keys and groups from the iterable [extrait de groupby.__doc__]
LinearRegression LinearRegression(slope, intercept) [extrait de LinearRegression.__doc__]
NormalDist Normal distribution of a random variable [extrait de NormalDist.__doc__]
repeat repeat(object [,times]) -> create an iterator which returns the object [extrait de repeat.__doc__]

Liste des exceptions du module statistics

Nom de la classe d'exception Description
StatisticsError

Liste des fonctions du module statistics

Signature de la fonction Description
bisect_left(a, x, lo=0, hi=None, *, key=None) Return the index where to insert item x in list a, assuming a is sorted. [extrait de bisect_left.__doc__]
bisect_right(a, x, lo=0, hi=None, *, key=None) Return the index where to insert item x in list a, assuming a is sorted. [extrait de bisect_right.__doc__]
correlation(x, y) Pearson's correlation coefficient [extrait de correlation.__doc__]
covariance(x, y) Covariance [extrait de covariance.__doc__]
erf(x) Error function at x. [extrait de erf.__doc__]
exp(x) Return e raised to the power of x. [extrait de exp.__doc__]
fabs(x) Return the absolute value of the float x. [extrait de fabs.__doc__]
fmean(data, weights=None) Convert data to floats and compute the arithmetic mean. [extrait de fmean.__doc__]
fsum(seq) Return an accurate floating point sum of values in the iterable seq. [extrait de fsum.__doc__]
geometric_mean(data) Convert data to floats and compute the geometric mean. [extrait de geometric_mean.__doc__]
harmonic_mean(data, weights=None) Return the harmonic mean of data. [extrait de harmonic_mean.__doc__]
hypot hypot(*coordinates) -> value [extrait de hypot.__doc__]
linear_regression(x, y, /, *, proportional=False) Slope and intercept for simple linear regression. [extrait de linear_regression.__doc__]
log log(x, [base=math.e]) [extrait de log.__doc__]
mean(data) Return the sample arithmetic mean of data. [extrait de mean.__doc__]
median(data) Return the median (middle value) of numeric data. [extrait de median.__doc__]
median_grouped(data, interval=1.0) Estimates the median for numeric data binned around the midpoints [extrait de median_grouped.__doc__]
median_high(data) Return the high median of data. [extrait de median_high.__doc__]
median_low(data) Return the low median of numeric data. [extrait de median_low.__doc__]
mode(data) Return the most common data point from discrete or nominal data. [extrait de mode.__doc__]
mul(a, b) Same as a * b. [extrait de mul.__doc__]
multimode(data) Return a list of the most frequently occurring values. [extrait de multimode.__doc__]
namedtuple(typename, field_names, *, rename=False, defaults=None, module=None) Returns a new subclass of tuple with named fields. [extrait de namedtuple.__doc__]
pstdev(data, mu=None) Return the square root of the population variance. [extrait de pstdev.__doc__]
pvariance(data, mu=None) Return the population variance of ``data``. [extrait de pvariance.__doc__]
quantiles(data, *, n=4, method='exclusive') Divide *data* into *n* continuous intervals with equal probability. [extrait de quantiles.__doc__]
reduce reduce(function, iterable[, initial]) -> value [extrait de reduce.__doc__]
sqrt(x) Return the square root of x. [extrait de sqrt.__doc__]
stdev(data, xbar=None) Return the square root of the sample variance. [extrait de stdev.__doc__]
variance(data, xbar=None) Return the sample variance of data. [extrait de variance.__doc__]

Liste des variables globales du module statistics

Nom de la variable globale Valeur
tau 6.283185307179586