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NumPy Mathematical Functions

NumPy Mathematical Functions

In this chapter, we will discuss about NumPy Mathematical Functions. Quite understandably, NumPy contains a large number of various mathematical operations. N umPy provides standard trigonometric functions, functions for arithmetic operations, handling complex numbers, etc.

NumPy Mathematical Functions in trigonometric

N umPy has standard trigonometric functions which return trigonometric ratios for a given angle in radians.

Example

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)

Here is its output āˆ’

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, and arctan functions return the trigonometric inverse of sin, cos, and tan of the given angle. The result of these functions can be verified by numpy.degrees() function by converting radians to degrees.

Example

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)

Its output is as follows āˆ’

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.]

Functions for Rounding

numpy.around()

This is a function that returns the value rounded to the desired precision. The function takes the following parameters.

numpy.around(a,decimals)

Where,

Sr.No.Parameter & Description
1aInput data
2decimalsThe number of decimals to round to. Default is 0. If negative, the integer is rounded to position to the left of the decimal point

Example

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)

It produces the following output āˆ’

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()

This function returns the largest integer not greater than the input parameter. The floor of the scalar x is the largest integer i, such that i <= x. Note that in Python, flooring always is rounded away from 0.

Example

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)

It produces the following output āˆ’

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

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

i.e. the ceil of theĀ scalar xĀ is the smallestĀ integer i, such thatĀ i >= x.

Example

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)

following output āˆ’

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

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

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