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Numpy Howto
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Numpy Howto
numpy.arrange
numpy.arange([_start_, ]_stop_, [_step_, ]_dtype=None_, _*_, _like=None_)
Return evenly spaced values within a given interval.
When using a non-integer step, such as 0.1, it is often better to use numpy.linspace
.
>>> np.arange(3)array([0, 1, 2])>>> np.arange(3.0)array([ 0., 1., 2.])>>> np.arange(3,7)array([3, 4, 5, 6])>>> np.arange(3,7,2)array([3, 5])
numpy.repeat
numpy.repeat(_a_, _repeats_, _axis=None_)
Repeat elements of an array.
>>> np.repeat(3, 4)array([3, 3, 3, 3])>>> x = np.array([[1,2],[3,4]])>>> np.repeat(x, 2)array([1, 1, 2, 2, 3, 3, 4, 4])>>> np.repeat(x, 3, axis=1)array([[1, 1, 1, 2, 2, 2],[3, 3, 3, 4, 4, 4]])>>> np.repeat(x, [1, 2], axis=0)array([[1, 2],[3, 4],[3, 4]])
numpy.concatenate
numpy.concatenate(_(a1_, _a2_, _...)_, _axis=0_, _out=None_, _dtype=None_, _casting="same_kind"_)
>>> a = np.array([[1, 2], [3, 4]])>>> b = np.array([[5, 6]])>>> np.concatenate((a, b), axis=0)array([[1, 2],[3, 4],[5, 6]])>>> np.concatenate((a, b.T), axis=1)array([[1, 2, 5],[3, 4, 6]])>>> np.concatenate((a, b), axis=None)array([1, 2, 3, 4, 5, 6])
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Last updated on 3/7/2023