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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 or array_like of floats, 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 or array_like of floats 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. 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.
  Returns: | out : ndarray or scalar Drawn samples from the parameterized uniform distribution.

  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.

  When `high` == `low`, values of `low` will be returned. If `high` < `low`, the results are officially undefined and may eventually raise an error, i.e. do not rely on this function to behave when passed arguments satisfying that inequality condition.

  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)