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DOC: Remove empty notes (numpy#27706)
* DOC: remove empty notes * DOC: remove empty line
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‎numpy/_core/_add_newdocs.py‎

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is a new array of the same shape as the input array, with dtype, order
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given by `dtype`, `order`.
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Notes
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Raises
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ComplexWarning

‎numpy/_core/code_generators/ufunc_docstrings.py‎

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ceil, floor, rint, fix
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Notes
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Examples
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>>> import numpy as np
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Notes
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rad2deg(x) is ``180 * x / pi``.
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Examples
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The output array, element-wise Heaviside step function of `x1`.
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$OUT_SCALAR_2
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References
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.. [1] Wikipedia, "Heaviside step function",
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power
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Examples
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>>> import numpy as np
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logaddexp2: Logarithm of the sum of exponentiations of inputs in base 2.
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Examples
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>>> import numpy as np
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logaddexp: Logarithm of the sum of exponentiations of the inputs.
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Examples
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>>> import numpy as np
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Notes
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The fmax is equivalent to ``np.where(x1 >= x2, x1, x2)`` when neither
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x1 nor x2 are NaNs, but it is faster and does proper broadcasting.
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The fmin is equivalent to ``np.where(x1 <= x2, x1, x2)`` when neither
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x1 nor x2 are NaNs, but it is faster and does proper broadcasting.
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Notes
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The behavior depends on the arguments in the following way.
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- If both arguments are 2-D they are multiplied like conventional

‎numpy/_core/fromnumeric.py‎

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By eliminating the inner loop in the description above, and using `s_` to
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build simple slice objects, `take` can be expressed in terms of applying
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fancy indexing to each 1-d slice::

‎numpy/_core/multiarray.py‎

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out : dtype
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The minimal data type.
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See Also
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result_type, promote_types, dtype, can_cast
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Notes
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The specific algorithm used is as follows.
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Categories are determined by first checking which of boolean,
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unravel_index
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>>> import numpy as np

‎numpy/_core/shape_base.py‎

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When called with only scalars, ``np.block`` is equivalent to an ndarray
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call. So ``np.block([[1, 2], [3, 4]])`` is equivalent to
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``np.array([[1, 2], [3, 4]])``.

‎numpy/lib/_arraysetops_impl.py‎

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`isin` is an element-wise function version of the python keyword `in`.
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``isin(a, b)`` is roughly equivalent to
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``np.array([item in b for item in a])`` if `a` and `b` are 1-D sequences.

‎numpy/lib/_index_tricks_impl.py‎

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diag_indices_from
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diag_indices
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‎numpy/lib/_stride_tricks_impl.py‎

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broadcast_arrays
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broadcast_shapes
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>>> import numpy as np

‎numpy/lib/_twodim_base_impl.py‎

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triu, tril, triu_indices, tril_indices
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"""
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"""
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‎numpy/ma/extras.py‎

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isin : Version of this function that preserves the shape of ar1.
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numpy.in1d : Equivalent function for ndarrays.
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in1d : Flattened version of this function.
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numpy.isin : Equivalent function for ndarrays.
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