NumPy - 数学函数

简述

可以理解的是，NumPy 包含大量各种数学运算。NumPy 提供标准三角函数、算术运算函数、处理复数等。

三角函数

NumPy 有标准的三角函数，它以弧度返回给定角度的三角比。

示例


import numpy as np 
a = np.array([0,30,45,60,90]) 
print 'Sine of different angles:' 
# Convert to radians by multiplying with pi/180 
print np.sin(a*np.pi/180) 
print '\n'  
print 'Cosine values for angles in array:' 
print np.cos(a*np.pi/180) 
print '\n'  
print 'Tangent values for given angles:' 
print np.tan(a*np.pi/180)

这是它的输出 -


Sine of different angles:
[ 0.          0.5         0.70710678  0.8660254   1.        ]
Cosine values for angles in array:
[  1.00000000e+00   8.66025404e-01   7.07106781e-01   5.00000000e-01
   6.12323400e-17]                                                            
Tangent values for given angles:
[  0.00000000e+00   5.77350269e-01   1.00000000e+00   1.73205081e+00
   1.63312394e+16]

arcsin, arcos,和arctan函数返回给定角度的 sin、cos 和 tan 的三角逆。这些函数的结果可以通过以下方式验证numpy.degrees() function通过将弧度转换为度数。

示例


import numpy as np 
a = np.array([0,30,45,60,90]) 
print 'Array containing sine values:' 
sin = np.sin(a*np.pi/180) 
print sin 
print '\n'  
print 'Compute sine inverse of angles. Returned values are in radians.' 
inv = np.arcsin(sin) 
print inv 
print '\n'  
print 'Check result by converting to degrees:' 
print np.degrees(inv) 
print '\n'  
print 'arccos and arctan functions behave similarly:' 
cos = np.cos(a*np.pi/180) 
print cos 
print '\n'  
print 'Inverse of cos:' 
inv = np.arccos(cos) 
print inv 
print '\n'  
print 'In degrees:' 
print np.degrees(inv) 
print '\n'  
print 'Tan function:' 
tan = np.tan(a*np.pi/180) 
print tan
print '\n'  
print 'Inverse of tan:' 
inv = np.arctan(tan) 
print inv 
print '\n'  
print 'In degrees:' 
print np.degrees(inv)

它的输出如下 -


Array containing sine values:
[ 0.          0.5         0.70710678  0.8660254   1.        ]
Compute sine inverse of angles. Returned values are in radians.
[ 0.          0.52359878  0.78539816  1.04719755  1.57079633] 
Check result by converting to degrees:
[  0.  30.  45.  60.  90.]
arccos and arctan functions behave similarly:
[  1.00000000e+00   8.66025404e-01   7.07106781e-01   5.00000000e-01          
   6.12323400e-17] 
Inverse of cos:
[ 0.          0.52359878  0.78539816  1.04719755  1.57079633] 
In degrees:
[  0.  30.  45.  60.  90.] 
Tan function:
[  0.00000000e+00   5.77350269e-01   1.00000000e+00   1.73205081e+00          
   1.63312394e+16]
Inverse of tan:
[ 0.          0.52359878  0.78539816  1.04719755  1.57079633]
In degrees:
[  0.  30.  45.  60.  90.]

舍入函数

numpy.around()

这是一个返回四舍五入到所需精度的值的函数。该函数采用以下参数。


numpy.around(a,decimals)

函数说明

序号	参数及说明
1	a 输入数据
2	decimals 要四舍五入的小数位数。默认为 0。如果为负数，则整数四舍五入到小数点左侧

示例


import numpy as np 
a = np.array([1.0,5.55, 123, 0.567, 25.532]) 
print 'Original array:' 
print a 
print '\n'  
print 'After rounding:' 
print np.around(a) 
print np.around(a, decimals = 1) 
print np.around(a, decimals = -1)

它产生以下输出 -


Original array:                                                               
[   1.       5.55   123.       0.567   25.532] 
After rounding:                                                               
[   1.    6.   123.    1.   26. ]                                               
[   1.    5.6  123.    0.6  25.5]                                          
[   0.    10.  120.    0.   30. ]

numpy.floor()

该函数返回不大于输入参数的最大整数。floorscalar x是最大的integer i, i <= x. 请注意，在 Python 中，floor总是从 0 舍入。

示例


import numpy as np 
a = np.array([-1.7, 1.5, -0.2, 0.6, 10]) 
print 'The given array:' 
print a 
print '\n'  
print 'The modified array:' 
print np.floor(a)

它产生以下输出 -


The given array:                                                              
[ -1.7   1.5  -0.2   0.6  10. ]
The modified array:                                                           
[ -2.   1.  -1.   0.  10.]

numpy.ceil()

ceil() 函数返回输入值的上限，即scalar x是最小的integer i, 这样i >= x.

示例


import numpy as np 
a = np.array([-1.7, 1.5, -0.2, 0.6, 10]) 
print 'The given array:' 
print a 
print '\n'  
print 'The modified array:' 
print np.ceil(a)

它将产生以下输出 -


The given array:                                                              
[ -1.7   1.5  -0.2   0.6  10. ]
The modified array:                                                           
[ -1.   2.  -0.   1.  10.]