Constants¶
NumPy includes several constants:

numpy.
Inf
¶ IEEE 754 floating point representation of (positive) infinity.
Use
inf
becauseInf
,Infinity
,PINF
andinfty
are aliases forinf
. For more details, seeinf
.See Also
inf

numpy.
Infinity
¶ IEEE 754 floating point representation of (positive) infinity.
Use
inf
becauseInf
,Infinity
,PINF
andinfty
are aliases forinf
. For more details, seeinf
.See Also
inf

numpy.
NAN
¶ IEEE 754 floating point representation of Not a Number (NaN).
NaN
andNAN
are equivalent definitions ofnan
. Please usenan
instead ofNAN
.See Also
nan

numpy.
NINF
¶ IEEE 754 floating point representation of negative infinity.
Returns
 yfloat
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 FloatingPoint 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
 yfloat
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.
 isfiniteShows which elements are finite  not one of
Not a Number, positive infinity and negative infinity.
Notes
NumPy uses the IEEE Standard for Binary FloatingPoint 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
andNAN
are equivalent definitions ofnan
. Please usenan
instead ofNaN
.See Also
nan

numpy.
PINF
¶ IEEE 754 floating point representation of (positive) infinity.
Use
inf
becauseInf
,Infinity
,PINF
andinfty
are aliases forinf
. For more details, seeinf
.See Also
inf

numpy.
PZERO
¶ IEEE 754 floating point representation of positive zero.
Returns
 yfloat
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.
 isfiniteShows which elements are finite  not one of
Not a Number, positive infinity and negative infinity.
Notes
NumPy uses the IEEE Standard for Binary FloatingPoint 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
 yfloat
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 FloatingPoint 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
andinfty
are aliases forinf
.Examples
>>> np.inf inf >>> np.array([1]) / 0. array([ Inf])

numpy.
infty
¶ IEEE 754 floating point representation of (positive) infinity.
Use
inf
becauseInf
,Infinity
,PINF
andinfty
are aliases forinf
. For more details, seeinf
.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 FloatingPoint for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity.
NaN
andNAN
are aliases ofnan
.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.
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 tox[newaxis]
andx[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