- scipy.stats.randint = <scipy.stats._discrete_distns.randint_gen object at 0x7f6169c4c7d0>¶
A uniform discrete random variable.
Discrete random variables are defined from a standard form and may require some shape parameters to complete its specification. Any optional keyword parameters can be passed to the methods of the RV object as given below:
x : array_like
q : array_like
lower or upper tail probability
low, high : array_like
loc : array_like, optional
location parameter (default=0)
size : int or tuple of ints, optional
shape of random variates (default computed from input arguments )
moments : str, optional
composed of letters [‘mvsk’] specifying which moments to compute where ‘m’ = mean, ‘v’ = variance, ‘s’ = (Fisher’s) skew and ‘k’ = (Fisher’s) kurtosis. Default is ‘mv’.
Alternatively, the object may be called (as a function) to fix the shape and
location parameters returning a “frozen” discrete RV object:
rv = randint(low, high, loc=0)
- Frozen RV object with the same methods but holding the given shape and location fixed.
The probability mass function for randint is:
randint.pmf(k) = 1./(high - low)
for k = low, ..., high - 1.
randint takes low and high as shape parameters.
Note the difference to the numpy random_integers which returns integers on a closed interval [low, high].
>>> from scipy.stats import randint >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(1, 1)
Calculate a few first moments:
>>> low, high = 7, 31 >>> mean, var, skew, kurt = randint.stats(low, high, moments='mvsk')
Display the probability mass function (pmf):
>>> x = np.arange(randint.ppf(0.01, low, high), ... randint.ppf(0.99, low, high)) >>> ax.plot(x, randint.pmf(x, low, high), 'bo', ms=8, label='randint pmf') >>> ax.vlines(x, 0, randint.pmf(x, low, high), colors='b', lw=5, alpha=0.5)
Alternatively, freeze the distribution and display the frozen pmf:
>>> rv = randint(low, high) >>> ax.vlines(x, 0, rv.pmf(x), colors='k', linestyles='-', lw=1, ... label='frozen pmf') >>> ax.legend(loc='best', frameon=False) >>> plt.show()
Check accuracy of cdf and ppf:
>>> prob = randint.cdf(x, low, high) >>> np.allclose(x, randint.ppf(prob, low, high)) True
Generate random numbers:
>>> r = randint.rvs(low, high, size=1000)
rvs(low, high, loc=0, size=1) Random variates. pmf(x, low, high, loc=0) Probability mass function. logpmf(x, low, high, loc=0) Log of the probability mass function. cdf(x, low, high, loc=0) Cumulative density function. logcdf(x, low, high, loc=0) Log of the cumulative density function. sf(x, low, high, loc=0) Survival function (1-cdf — sometimes more accurate). logsf(x, low, high, loc=0) Log of the survival function. ppf(q, low, high, loc=0) Percent point function (inverse of cdf — percentiles). isf(q, low, high, loc=0) Inverse survival function (inverse of sf). stats(low, high, loc=0, moments='mv') Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(low, high, loc=0) (Differential) entropy of the RV. expect(func, low, high, loc=0, lb=None, ub=None, conditional=False) Expected value of a function (of one argument) with respect to the distribution. median(low, high, loc=0) Median of the distribution. mean(low, high, loc=0) Mean of the distribution. var(low, high, loc=0) Variance of the distribution. std(low, high, loc=0) Standard deviation of the distribution. interval(alpha, low, high, loc=0) Endpoints of the range that contains alpha percent of the distribution