# 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

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 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

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 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

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 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

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 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() / 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.

Examples

```>>> newaxis is None
True
>>> x = np.arange(3)
>>> x
array([0, 1, 2])
>>> x[:, newaxis]
array([,
,
])
>>> x[:, newaxis, newaxis]
array([[],
[],
[]])
>>> 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