method
Draw samples from a uniform distribution.
Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by uniform.
Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0.
Upper boundary of the output interval. All values generated will be less than high. The high limit may be included in the returned array of floats due to floating-point rounding in the equation low + (high-low) * random_sample(). high - low must be non-negative. The default value is 1.0.
Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is None (default), a single value is returned if low and high are both scalars. Otherwise, np.broadcast(low, high).size samples are drawn.
Drawn samples from the parameterized uniform distribution.
See also
integers
Discrete uniform distribution, yielding integers.
random
Floats uniformly distributed over [0, 1).
Notes
The probability density function of the uniform distribution is
\[p(x) = \frac{1}{b - a}\]
anywhere within the interval [a, b), and zero elsewhere.
When high == low, values of low will be returned.
Examples
Draw samples from the distribution:
>>> rng = np.random.default_rng() >>> s = rng.uniform(-1,0,1000)
All values are within the given interval:
>>> np.all(s >= -1) True >>> np.all(s < 0) True
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt >>> count, bins, _ = plt.hist(s, 15, density=True) >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r') >>> plt.show()
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