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Showing content from https://docs.scipy.org/doc/numpy-1.17.0/reference/constants.html below:

Constants — NumPy v1.17 Manual

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

y : float

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

y : float

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.

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.

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

y : float

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.

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...

See Also

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.

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

numpy.doc.indexing

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

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