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]]] 
'''