NumPy参考 > 常数
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
.
See Also
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
.
See Also
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
.
See Also
nan
numpy.
NINF
¶IEEE 754 floating point representation of negative infinity.
Returns
A floating point representation of negative infinity.
See Also
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)
Notes
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.
Examples
>>> np.NINF
-inf
>>> np.log(0)
-inf
numpy.
NZERO
¶IEEE 754 floating point representation of negative zero.
Returns
A floating point representation of negative zero.
See Also
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.
Not a Number, positive infinity and negative infinity.
Notes
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Negative zero is considered to be a finite number.
Examples
>>> 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
.
See Also
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
.
See Also
inf
numpy.
PZERO
¶IEEE 754 floating point representation of positive zero.
Returns
A floating point representation of positive zero.
See Also
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.
Not a Number, positive infinity and negative infinity.
Notes
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Positive zero is considered to be a finite number.
Examples
>>> 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...
See Also
exp : Exponential function log : Natural logarithm
References
numpy.
euler_gamma
¶γ = 0.5772156649015328606065120900824024310421...
References
numpy.
inf
¶IEEE 754 floating point representation of (positive) infinity.
Returns
A floating point representation of positive infinity.
See Also
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)
Notes
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
.
Examples
>>> 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
.
See Also
inf
numpy.
nan
¶IEEE 754 floating point representation of Not a Number (NaN).
Returns
y : A floating point representation of Not a Number.
See Also
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)
Notes
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
.
Examples
>>> 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.
See Also
Examples
>>> 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...
References