NumPy数学函数


很可以理解,NumPy包含大量的各种数学运算。NumPy提供标准的三角函数,用于算术运算的函数,处理复数的函数等。

三角函数

NumPy具有标准的三角函数,可以以弧度返回给定角度的三角比。

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,arcosarctan 函数返回给定角度的sin,cos和tan的三角函数的倒数。这些函数的结果可以通过 numpy.degrees()函数 通过将弧度转换为度来验证。

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)

其产出如下 -

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.]

四舍五入的功能

numpy.around()

这是一个返回四舍五入到所需精度的函数。该功能采用以下参数。

numpy.around(a,decimals)

哪里,

Sr.No. 参数和说明
1 a
输入数据
2 decimals
要舍入的小数位数。默认值为0.如果为负值,则将整数舍入到位于小数点左侧的位置

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。 标量x 的底部是最大的 整数i ,使得 i <= x。请注意,在Python中,地板总是从0圆整。

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 标量x 的最小值是最小的 整数i ,使得 i > = x。

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)

它会产生以下输出 -

The given array:                                                              
[ -1.7   1.5  -0.2   0.6  10. ]

The modified array:                                                           
[ -1.   2.  -0.   1.  10.]