Module « statistics » Python 3.11.3

Classe « NormalDist »

Informations générales

builtins.object
    NormalDist

class NormalDist(builtins.object):

Normal distribution of a random variable

Signature du constructeur	Description
__init__(self, mu=0.0, sigma=1.0)	NormalDist where mu is the mean and sigma is the standard deviation. _{[extrait de __init__.__doc__]}

Nom de la propriété	Description
mean	Arithmetic mean of the normal distribution. _{[extrait de __doc__]}
median	Return the median of the normal distribution _{[extrait de __doc__]}
mode	Return the mode of the normal distribution _{[extrait de __doc__]}
stdev	Standard deviation of the normal distribution. _{[extrait de __doc__]}
variance	Square of the standard deviation. _{[extrait de __doc__]}

Signature de l'opérateur	Description
__add__(x1, x2)	Add a constant or another NormalDist instance. _{[extrait de __add__.__doc__]}
__eq__(x1, x2)	Two NormalDist objects are equal if their mu and sigma are both equal. _{[extrait de __eq__.__doc__]}
__mul__(x1, x2)	Multiply both mu and sigma by a constant. _{[extrait de __mul__.__doc__]}
__neg__(x1)	Negates mu while keeping sigma the same. _{[extrait de __neg__.__doc__]}
__pos__(x1)	Return a copy of the instance. _{[extrait de __pos__.__doc__]}
__radd__(x1, x2)	Add a constant or another NormalDist instance. _{[extrait de __add__.__doc__]}
__rmul__(x1, x2)	Multiply both mu and sigma by a constant. _{[extrait de __mul__.__doc__]}
__rsub__(x1, x2)	Subtract a NormalDist from a constant or another NormalDist. _{[extrait de __rsub__.__doc__]}
__sub__(x1, x2)	Subtract a constant or another NormalDist instance. _{[extrait de __sub__.__doc__]}
__truediv__(x1, x2)	Divide both mu and sigma by a constant. _{[extrait de __truediv__.__doc__]}

Toutes les méthodes Méthodes d'instance Méthodes statiques

Signature de la méthode	Description
__getstate__(self)
__hash__(self)	NormalDist objects hash equal if their mu and sigma are both equal. _{[extrait de __hash__.__doc__]}
__repr__(self)
__setstate__(self, state)
cdf(self, x)	Cumulative distribution function. P(X <= x) _{[extrait de cdf.__doc__]}
from_samples(data)	Make a normal distribution instance from sample data. _{[extrait de from_samples.__doc__]}
inv_cdf(self, p)	Inverse cumulative distribution function. x : P(X <= x) = p _{[extrait de inv_cdf.__doc__]}
overlap(self, other)	Compute the overlapping coefficient (OVL) between two normal distributions. _{[extrait de overlap.__doc__]}
pdf(self, x)	Probability density function. P(x <= X < x+dx) / dx _{[extrait de pdf.__doc__]}
quantiles(self, n=4)	Divide into n continuous intervals with equal probability. _{[extrait de quantiles.__doc__]}
samples(self, n, *, seed=None)	Generate n samples for a given mean and standard deviation. _{[extrait de samples.__doc__]}
zscore(self, x)	Compute the Standard Score. (x - mean) / stdev _{[extrait de zscore.__doc__]}