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# numpy.random.uniform

`numpy.random.``uniform`(low=0.0, high=1.0, size=None)

  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`.

  Parameters: | low : float, optional Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0. high : float Upper boundary of the output interval. All values generated will be less than high. The default value is 1.0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., `(m, n, k)`, then `m * n * k` samples are drawn. Default is None, in which case a single value is returned.
  Returns: | out : ndarray Drawn samples, with shape size.

  See also

  `randint`
    Discrete uniform distribution, yielding integers.
  `random_integers`
    Discrete uniform distribution over the closed interval `[low, high]`.
  `random_sample`
    Floats uniformly distributed over `[0, 1)`.
  `random`
    Alias for `random_sample`.
  `rand`
    Convenience function that accepts dimensions as input, e.g., `rand(2,2)` would generate a 2-by-2 array of floats, uniformly distributed over `[0, 1)`.

  Notes

  The probability density function of the uniform distribution is

  anywhere within the interval `[a, b)`, and zero elsewhere.

  Examples

  Draw samples from the distribution:

      >>> s = np.random.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, ignored = plt.hist(s, 15, normed=True)
      >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
      >>> plt.show()

  (Source code, png, pdf)