# # Constants

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.

Returns

y : float (A floating point representation of negative infinity.)

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

y : float A (floating point representation of negative zero.)

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 (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.

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.

Returns

y : float (A floating point representation of positive zero.)

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 (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...

exp : Exponential function log : Natural logarithm

References

https://en.wikipedia.org/wiki/E_%28mathematical_constant%29

• numpy.euler_gamma

γ = 0.5772156649015328606065120900824024310421...

References

https://en.wikipedia.org/wiki/Euler-Mascheroni_constant

• numpy.inf

IEEE 754 floating point representation of (positive) infinity.

Returns y : float (A floating point representation of positive infinity.)

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.

inf

• numpy.nan

IEEE 754 floating point representation of Not a Number (NaN).

Returns 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)

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.

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

https://en.wikipedia.org/wiki/Pi

Last Updated: 8/28/2019, 6:17:52 PM