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Numpy Array - trigonometric functions, hyperbolic functions, inverse trigonometric functions in numpy

Numpy Array - trigonometric functions, hyperbolic functions, inverse trigonometric functions in numpy


In this numpy tutorial, we will discuss about:

  • trigonometric functions in numpy,
  • numpy hyperbolic functions,
  • numpy inverse trigonometric functions,

Before we discuss about numpy trigonometric functions, numpy hyperbolic functions, numpy inverse trigonometric functions, lets create one numpy array first.

 

numpy stands for numeric python which is used to perform mathematical operations on arrays.

It is a module in which we have to import from the python

Syntax to import:

import numpy

We can also use alias for the module

For example,

import numpy as np

We can directly use np to call the numpy module.

 

Create Numpy Array

An array is an one dimensional data structure used to store single data type data.

I.E It will only store all integer data or all string type data.or all float type data.

We can create an numpy array by using array() function.

Syntax:

numpy.array(elements)

where, elements are the input data elements.

 

trigonometric functions in numpy

We can perform numpy sine function operation,cosine and tangent operations on the numpy numeric array.

Syntax:

numpy.sin(array_data)
numpy.cos(array_data)
numpy.tan(array_data)

To perform numpy hyperbolic functions operations the syntax is

 

numpy.sinh(array_data)
numpy.cosh(array_data)
numpy.tanh(array_data)

To perform numpy inverse trigonometric functions operations the syntax is

 

numpy.arcsin(array_data)
numpy.arccos(array_data)
numpy.arctan(array_data)

Lets see examples related to numpy trigonometric functions, numpy hyperbolic functions, numpy inverse trigonometric functions.


numpy trigonometric functions

Example 1: In this example we will perform numpy trigonometric functions operations

#importing the numpy module
import numpy 


#create an array with 8 elements - integer type 
array_data=numpy.array([34,56,43,22,45,6,54,2])

#actual array
print(array_data)

print()

#perform sin operation
print(numpy.sin(array_data))

print()

#perform cos operation
print(numpy.cos(array_data))

print()

#perform tan operation
print(numpy.tan(array_data))

Outputtrigonometric functions in numpy result

[34 56 43 22 45  6 54  2]

[ 0.52908269 -0.521551   -0.83177474 -0.00885131  0.85090352 -0.2794155
 -0.55878905  0.90929743]

[-0.84857027  0.85322011  0.5551133  -0.99996083  0.52532199  0.96017029
 -0.82930983 -0.41614684]

[-0.62349896 -0.61127369 -1.49838734  0.00885166  1.61977519 -0.29100619
  0.6738001  -2.18503986]

Lets see an example of numpy hyperbolic functions.


numpy hyperbolic functions

Example 2: In this example we will perform numpy hyperbolic functions operations.

#importing the numpy module
import numpy 


#create an array with 8 elements - integer type 
array_data=numpy.array([34,56,43,22,45,6,54,2])

#actual array
print(array_data)

print()

#perform sinh operation
print(numpy.sinh(array_data))

print()

#perform cosh operation
print(numpy.cosh(array_data))

print()

#perform tanh operation
print(numpy.tanh(array_data))

Outputnumpy hyperbolic functions result

[34 56 43 22 45  6 54  2]

[2.91730871e+14 1.04582975e+24 2.36391973e+18 1.79245642e+09
 1.74671355e+19 2.01713157e+02 1.41537665e+23 3.62686041e+00]

[2.91730871e+14 1.04582975e+24 2.36391973e+18 1.79245642e+09
 1.74671355e+19 2.01715636e+02 1.41537665e+23 3.76219569e+00]

[1.         1.         1.         1.         1.         0.99998771
 1.         0.96402758]

Lets understand numpy inverse trigonometric functions with example.


numpy inverse trigonometric functions

Example 3: In this example we will perform numpy inverse trigonometric functions operation.

#importing the numpy module
import numpy 


#create an array with 8 elements - integer type 
array_data=numpy.array([34,56,43,22,45,6,54,2])

#actual array
print(array_data)

print()

#perform sin operation
print(numpy.arcsin(array_data))

print()

#perform arccos operation
print(numpy.arccos(array_data))

print()

#perform arctan operation
print(numpy.arctan(array_data))

Outputnumpy inverse trigonometric functions result

[34 56 43 22 45  6 54  2]

[nan nan nan nan nan nan nan nan]

[nan nan nan nan nan nan nan nan]

[1.54139304 1.55294108 1.5475447  1.52537305 1.54857776 1.40564765
 1.55227992 1.10714872]

This wraps up our session on numpy trigonometric functions, numpy hyperbolic functions, numpy inverse trigonometric functions.


Numpy

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About the Author
Gottumukkala Sravan Kumar 171FA07058
B.Tech (Hon's) - IT from Vignan's University. Published 1000+ Technical Articles on Python, R, Java, C#, LISP, PHP - MySQL and Machine Learning
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