- numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)#
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.
Changed in version 1.16.0: Non-scalar start and stop are now supported.
Changed in version 1.20.0: Values are rounded towards
0when an integer
dtypeis specified. The old behavior can still be obtained with
np.linspace(start, stop, num).astype(int)
The starting value of the sequence.
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 + 1evenly spaced samples, so that stop is excluded. Note that the step size changes when endpoint is False.
- numint, optional
Number of samples to generate. Default is 50. Must be non-negative.
- endpointbool, optional
If True, stop is the last sample. Otherwise, it is not included. Default is True.
- retstepbool, optional
If True, return (samples, step), where step is the spacing between samples.
- dtypedtype, optional
The type of the output array. If
dtypeis not given, the data type is inferred from start and stop. The inferred dtype will never be an integer; float is chosen even if the arguments would produce an array of integers.
New in version 1.9.0.
- axisint, optional
The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end.
New in version 1.16.0.
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).
- stepfloat, optional
Only returned if retstep is True
Size of spacing between samples.
linspace, but uses a step size (instead of the number of samples).
linspace, but with numbers spaced evenly on a log scale (a geometric progression).
geomspace, but with the end points specified as logarithms.
- How to create arrays with regularly-spaced values
>>> 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)
>>> 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()