Constants
正无穷
- numpy.inf
- numpy.Inf
- numpy.Infinity
- numpy.infty
- 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.
Returns
- y : float
A floating point representation of positive 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.inf
inf
>>> np.array([1]) / 0.
array([ Inf])
负无穷
- numpy.NINF
IEEE 754 floating point representation of negative infinity.
Returns
- y : float
A floating point representation of 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
正零
- numpy.PZERO
IEEE 754 floating point representation of positive zero.
Returns
- y : float
A floating point representation of positive zero.
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])
负零
- numpy.NZERO
IEEE 754 floating point representation of negative zero.
Returns
- y : float
A floating point representation of negative zero.
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])
非数值
- numpy.NAN
- numpy.NaN
- 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.
Returns
y : A floating point representation of Not a Number.
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])
自然常数e
- numpy.e
Euler’s constant, base of natural logarithms, Napier’s constant.
e = 2.71828182845904523536028747135266249775724709369995...
伽马
- numpy.euler_gamma
γ = 0.5772156649015328606065120900824024310421...
π
- numpy.pi
pi = 3.1415926535897932384626433...
None的别名
- numpy.newaxis
A convenient alias for None, useful for indexing arrays.
Examples
import numpy as np
x=np.array([[2,3,5],[5,6,7]],np.int32)
print(x,"\n\n")
print(x[np.newaxis,:,:],"\n\n")
print(x[:,np.newaxis,:],"\n\n")
print(x[:,:,np.newaxis],"\n\n")
'''
# 原始的x，形状为（2,3）。
[[2 3 5]
[5 6 7]]
# 在原先的第一维前面添加了一维，形状变成了（1,2,3）。
[[[2 3 5]
[5 6 7]]]
# 在原先第二维前面添加了一维，形状变成了（2,1,3）。
[[[2 3 5]]
[[5 6 7]]]
# 添加第三维，形状变成（2,3,1）
[[[2]
[3]
[5]]
[[5]
[6]
[7]]]
'''
