# 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`

.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

numpy.

`euler_gamma`

`γ = 0.5772156649015328606065120900824024310421...`

References

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.

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