NumPy includes several constants:
numpy.Inf
IEEE 754 floating point representation of (positive) infinity.
Use inf
because Inf
, Infinity
, PINF
and infty
are aliases for inf
. For more details, see inf
.
inf
numpy.Infinity
IEEE 754 floating point representation of (positive) infinity.
Use inf
because Inf
, Infinity
, PINF
and infty
are aliases for inf
. For more details, see inf
.
inf
numpy.NAN
IEEE 754 floating point representation of Not a Number (NaN).
NaN
and NAN
are equivalent definitions of nan
. Please use nan
instead of NAN
.
nan
numpy.NINF
IEEE 754 floating point representation of negative infinity.
y : float
isinf : Shows which elements are positive or negative infinity
isposinf : Shows which elements are positive infinity
isneginf : Shows which elements are negative infinity
isnan : Shows which elements are Not a Number
isfinite : Shows which elements are finite (not one of Not a Number, positive infinity and negative infinity)
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Also that positive infinity is not equivalent to negative infinity. But infinity is equivalent to positive infinity.
>>> np.NINF -inf >>> np.log(0) -inf
numpy.NZERO
IEEE 754 floating point representation of negative zero.
y : float
PZERO : Defines positive zero.
isinf : Shows which elements are positive or negative infinity.
isposinf : Shows which elements are positive infinity.
isneginf : Shows which elements are negative infinity.
isnan : Shows which elements are Not a Number.
isfinite : Shows which elements are finite - not one of
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Negative zero is considered to be a finite number.
>>> np.NZERO -0.0 >>> np.PZERO 0.0
>>> np.isfinite([np.NZERO]) array([ True]) >>> np.isnan([np.NZERO]) array([False]) >>> np.isinf([np.NZERO]) array([False])
numpy.NaN
IEEE 754 floating point representation of Not a Number (NaN).
NaN
and NAN
are equivalent definitions of nan
. Please use nan
instead of NaN
.
nan
numpy.PINF
IEEE 754 floating point representation of (positive) infinity.
Use inf
because Inf
, Infinity
, PINF
and infty
are aliases for inf
. For more details, see inf
.
inf
numpy.PZERO
IEEE 754 floating point representation of positive zero.
y : float
NZERO : Defines negative zero.
isinf : Shows which elements are positive or negative infinity.
isposinf : Shows which elements are positive infinity.
isneginf : Shows which elements are negative infinity.
isnan : Shows which elements are Not a Number.
isfinite : Shows which elements are finite - not one of
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Positive zero is considered to be a finite number.
>>> np.PZERO 0.0 >>> np.NZERO -0.0
>>> np.isfinite([np.PZERO]) array([ True]) >>> np.isnan([np.PZERO]) array([False]) >>> np.isinf([np.PZERO]) array([False])
numpy.e
Euler’s constant, base of natural logarithms, Napier’s constant.
e = 2.71828182845904523536028747135266249775724709369995...
exp : Exponential function log : Natural logarithm
numpy.euler_gamma
γ = 0.5772156649015328606065120900824024310421...
numpy.inf
IEEE 754 floating point representation of (positive) infinity.
y : float
isinf : Shows which elements are positive or negative infinity
isposinf : Shows which elements are positive infinity
isneginf : Shows which elements are negative infinity
isnan : Shows which elements are Not a Number
isfinite : Shows which elements are finite (not one of Not a Number, positive infinity and negative infinity)
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Also that positive infinity is not equivalent to negative infinity. But infinity is equivalent to positive infinity.
Inf
, Infinity
, PINF
and infty
are aliases for inf
.
>>> np.inf inf >>> np.array([1]) / 0. array([ Inf])
numpy.infty
IEEE 754 floating point representation of (positive) infinity.
Use inf
because Inf
, Infinity
, PINF
and infty
are aliases for inf
. For more details, see inf
.
inf
numpy.nan
IEEE 754 floating point representation of Not a Number (NaN).
y : A floating point representation of Not a Number.
isnan : Shows which elements are Not a Number.
isfinite : Shows which elements are finite (not one of Not a Number, positive infinity and negative infinity)
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity.
NaN
and NAN
are aliases of nan
.
>>> np.nan nan >>> np.log(-1) nan >>> np.log([-1, 1, 2]) array([ NaN, 0. , 0.69314718])
numpy.newaxis
A convenient alias for None, useful for indexing arrays.
>>> newaxis is None True >>> x = np.arange(3) >>> x array([0, 1, 2]) >>> x[:, newaxis] array([[0], [1], [2]]) >>> x[:, newaxis, newaxis] array([[[0]], [[1]], [[2]]]) >>> x[:, newaxis] * x array([[0, 0, 0], [0, 1, 2], [0, 2, 4]])
Outer product, same as outer(x, y)
:
>>> y = np.arange(3, 6) >>> x[:, newaxis] * y array([[ 0, 0, 0], [ 3, 4, 5], [ 6, 8, 10]])
x[newaxis, :]
is equivalent to x[newaxis]
and x[None]
:
>>> x[newaxis, :].shape (1, 3) >>> x[newaxis].shape (1, 3) >>> x[None].shape (1, 3) >>> x[:, newaxis].shape (3, 1)
numpy.pi
pi = 3.1415926535897932384626433...
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Licensed under the 3-clause BSD License.
https://docs.scipy.org/doc/numpy-1.17.0/reference/constants.html