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API: Cleaning numpy/__init__.py and main namespace - Part 2 [NEP 52] (#24357)
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‎doc/source/reference/constants.rst

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.. currentmodule:: numpy
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*********
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Constants
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*********
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.. automodule:: numpy.doc.constants
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NumPy includes several constants:
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.. data:: e
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Euler's constant, base of natural logarithms, Napier's constant.
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``e = 2.71828182845904523536028747135266249775724709369995...``
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.. rubric:: See Also
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exp : Exponential function
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log : Natural logarithm
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.. rubric:: References
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https://en.wikipedia.org/wiki/E_%28mathematical_constant%29
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.. data:: euler_gamma
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``γ = 0.5772156649015328606065120900824024310421...``
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.. rubric:: References
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https://en.wikipedia.org/wiki/Euler-Mascheroni_constant
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.. data:: inf
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IEEE 754 floating point representation of (positive) infinity.
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.. rubric:: Returns
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y : float
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A floating point representation of positive infinity.
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.. rubric:: See Also
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isinf : Shows which elements are positive or negative infinity
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isposinf : Shows which elements are positive infinity
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isneginf : Shows which elements are negative infinity
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isnan : Shows which elements are Not a Number
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isfinite : Shows which elements are finite (not one of Not a Number,
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positive infinity and negative infinity)
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.. rubric:: Notes
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NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
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(IEEE 754). This means that Not a Number is not equivalent to infinity.
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Also that positive infinity is not equivalent to negative infinity. But
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infinity is equivalent to positive infinity.
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.. rubric:: Examples
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>>> np.inf
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inf
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>>> np.array([1]) / 0.
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array([inf])
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.. data:: nan
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IEEE 754 floating point representation of Not a Number (NaN).
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.. rubric:: Returns
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y : A floating point representation of Not a Number.
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.. rubric:: See Also
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isnan : Shows which elements are Not a Number.
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isfinite : Shows which elements are finite (not one of
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Not a Number, positive infinity and negative infinity)
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.. rubric:: Notes
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NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
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(IEEE 754). This means that Not a Number is not equivalent to infinity.
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`NaN` and `NAN` are aliases of `nan`.
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.. rubric:: Examples
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>>> np.nan
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nan
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>>> np.log(-1)
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np.float64(nan)
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>>> np.log([-1, 1, 2])
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array([ nan, 0. , 0.69314718])
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.. data:: newaxis
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A convenient alias for None, useful for indexing arrays.
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.. rubric:: Examples
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>>> np.newaxis is None
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True
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>>> x = np.arange(3)
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>>> x
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array([0, 1, 2])
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>>> x[:, np.newaxis]
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array([[0],
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[1],
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[2]])
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>>> x[:, np.newaxis, np.newaxis]
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array([[[0]],
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[[1]],
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[[2]]])
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>>> x[:, np.newaxis] * x
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array([[0, 0, 0],
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[0, 1, 2],
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[0, 2, 4]])
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Outer product, same as ``outer(x, y)``:
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>>> y = np.arange(3, 6)
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>>> x[:, np.newaxis] * y
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array([[ 0, 0, 0],
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[ 3, 4, 5],
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[ 6, 8, 10]])
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``x[np.newaxis, :]`` is equivalent to ``x[np.newaxis]`` and ``x[None]``:
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>>> x[np.newaxis, :].shape
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(1, 3)
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>>> x[np.newaxis].shape
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(1, 3)
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>>> x[None].shape
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(1, 3)
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>>> x[:, np.newaxis].shape
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(3, 1)
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.. data:: pi
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``pi = 3.1415926535897932384626433...``
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.. rubric:: References
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https://en.wikipedia.org/wiki/Pi

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