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# numpy.linspace

`numpy.``linspace`(start, stop, num=50, endpoint=True, retstep=False, dtype=None)

  Return evenly spaced numbers over a specified interval.

  Returns num evenly spaced samples, calculated over the interval [start, stop].

  The endpoint of the interval can optionally be excluded.

  Parameters: | start : scalar The starting value of the sequence. stop : scalar The end value of the sequence, unless endpoint is set to False. In that case, the sequence consists of all but the last of `num + 1` evenly spaced samples, so that stop is excluded. Note that the step size changes when endpoint is False. num : int, optional Number of samples to generate. Default is 50. Must be non-negative. endpoint : bool, optional If True, stop is the last sample. Otherwise, it is not included. Default is True. retstep : bool, optional If True, return (samples, step), where step is the spacing between samples. dtype : dtype, optional The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. New in version 1.9.0.
  Returns: | samples : ndarray There are num equally spaced samples in the closed interval `[start, stop]` or the half-open interval `[start, stop)` (depending on whether endpoint is True or False). step : float, optional Only returned if retstep is True Size of spacing between samples.

  See also

  `arange`
    Similar to `linspace`, but uses a step size (instead of the number of samples).
  `logspace`
    Samples uniformly distributed in log space.

  Examples

      >>> np.linspace(2.0, 3.0, num=5)
      array([ 2.  ,  2.25,  2.5 ,  2.75,  3.  ])
      >>> np.linspace(2.0, 3.0, num=5, endpoint=False)
      array([ 2. ,  2.2,  2.4,  2.6,  2.8])
      >>> np.linspace(2.0, 3.0, num=5, retstep=True)
      (array([ 2.  ,  2.25,  2.5 ,  2.75,  3.  ]), 0.25)

  Graphical illustration:

      >>> import matplotlib.pyplot as plt
      >>> N = 8
      >>> y = np.zeros(N)
      >>> x1 = np.linspace(0, 10, N, endpoint=True)
      >>> x2 = np.linspace(0, 10, N, endpoint=False)
      >>> plt.plot(x1, y, 'o')
      [<matplotlib.lines.Line2D object at 0x...>]
      >>> plt.plot(x2, y + 0.5, 'o')
      [<matplotlib.lines.Line2D object at 0x...>]
      >>> plt.ylim([-0.5, 1])
      (-0.5, 1)
      >>> plt.show()

  (Source code, png, pdf)