{"id":79306,"date":"2020-07-25T10:51:44","date_gmt":"2020-07-25T05:21:44","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=79306"},"modified":"2021-05-09T13:13:37","modified_gmt":"2021-05-09T07:43:37","slug":"numpy-mathematical-functions","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/numpy-mathematical-functions\/","title":{"rendered":"NumPy Mathematical Functions – Trigonometric, Exponential, Hyperbolic"},"content":{"rendered":"

NumPy consists of a large number of built-in mathematical functions that can solve mathematical problems. The in-built math module is imported for mathematical calculations.<\/p>\n

It can perform trigonometric operations, rounding functions and can also handle complex numbers. NumPy mathematical functions include methods for exponential, logarithmic, and hyperbolic functions.<\/p>\n

Trigonometric Functions in NumPy<\/h2>\n

\"NumPy<\/a><\/p>\n

NumPy contains built-in trigonometric functions that can calculate and return the sine, cosine, and tangent. It returns the values in radian for the given angle. It also includes functions to calculate the inverse of sine, cosine, and tangent.<\/p>\n

1. np.sin()-<\/strong> It performs trigonometric sine calculation element-wise.<\/p>\n

import numpy as np  \r\narr = np.array([0, 30, 60, 90, 120, 150, 180]) \r\n#sine function  \r\n print(np.sin(arr * np.pi \/ 180))\r\n<\/pre>\n
Output<\/strong><\/p>\n
[0.00000000e+00 5.00000000e-01 8.66025404e-01 1.00000000e+00
\n8.66025404e-01 5.00000000e-01 1.22464680e-16]<\/div>\n
2. np.cos()-<\/strong> It performs trigonometric cosine calculation element-wise.<\/p>\n
import numpy as np  \r\narr = np.array([0, 30, 60, 90, 120, 150, 180])\r\n#cosine function  \r\nprint(np.cos(arr * np.pi \/ 180))<\/pre>\n
Output<\/strong><\/p>\n
[ 1.00000000e+00 8.66025404e-01 5.00000000e-01 6.12323400e-17
\n-5.00000000e-01 -8.66025404e-01 -1.00000000e+00]<\/div>\n
3. np.tan()-<\/strong> It performs trigonometric tangent calculation element-wise.<\/p>\n
import numpy as np  \r\narr = np.array([0, 30, 60, 90, 120, 150, 180])\r\n#tangent function  \r\nprint(np.tan(arr * np.pi \/ 180)) \r\n\r\n<\/pre>\n
Output<\/strong><\/p>\n
[ 0.00000000e+00 5.77350269e-01 1.73205081e+00 1.63312394e+16
\n-1.73205081e+00 -5.77350269e-01 -1.22464680e-16]<\/div>\n
4. np.arcsin()-<\/strong> It performs trigonometric inverse sine element-wise.<\/p>\n
import numpy as np  \r\narr = np.array([1,2,3])\r\n#inverse sine function\r\nprint(np.arcsin(arr * np.pi \/ 180)) \r\n<\/pre>\n
Output<\/strong><\/p>\n
[0.01745418 0.03491368 0.05238383]<\/div>\n
5.\u00a0 np.arccos()-<\/strong> It performs trigonometric inverse of cosine element-wise.<\/p>\n
import numpy as np  \r\narr = np.array([1,2,3])\r\n#inverse cosine function\r\nprint(np.arccos(arr * np.pi \/ 180))<\/pre>\n
Output<\/strong><\/p>\n
[1.55334215 1.53588265 1.5184125 ]<\/div>\n
6. np.arctan()-<\/strong> It performs trigonometric inverse of tangent element-wise.<\/p>\n
import numpy as np  \r\narr = np.array([1,2,3])\r\n#inverse tangent function\r\nprint(np.arctan(arr * np.pi \/ 180))<\/pre>\n
Output<\/strong><\/p>\n
[0.01745152 0.03489242 0.05231211]<\/div>\n
NumPy Hyperbolic Functions<\/h2>\n
\"NumPy<\/a><\/p>\n
There are functions for calculation of hyperbolic functions which are the analogs of the trigonometric functions. There are functions for the calculation of hyperbolic and inverse hyperbolic sine, cosine, and tangent.<\/p>\n
1. np.sinh()-<\/strong> This function returns the hyperbolic sine of the array elements.<\/p>\n
import numpy as np  \r\narr = np.array([30,60,90])\r\n#hyperbolic  sine function\r\nprint(np.sinh(arr * np.pi \/ 180)) \r\n<\/pre>\n
Output<\/strong><\/p>\n
[0.54785347 1.24936705 2.3012989 ]<\/div>\n
2. np.cosh()-<\/strong> This function returns the hyperbolic cosine of the array elements.<\/p>\n
import numpy as np  \r\narr = np.array([30,60,90])\r\n#hyperbolic  cosine function\r\nprint(np.cosh(arr * np.pi \/ 180)) \r\n<\/pre>\n
Output<\/strong><\/p>\n
[1.14023832 1.60028686 2.50917848]<\/div>\n
3. np.tanh()-<\/strong> This function returns the hyperbolic tan of the array elements.<\/p>\n
import numpy as np  \r\narr = np.array([30,60,90])\r\n#hyperbolic  tangent function\r\nprint(np.tanh(arr * np.pi \/ 180)) \r\n \r\n<\/pre>\n
Output<\/strong><\/p>\n
[0.48047278 0.78071444 0.91715234]<\/div>\n
4. np.arcsinh()-<\/strong> This function returns the hyperbolic inverse sine of the array elements.<\/p>\n
import numpy as np  \r\narr = np.array([150,60,90])\r\n#hyperbolic inverse sine function\r\nprint(np.arcsinh(arr * np.pi \/ 180)) \r\n<\/pre>\n
Output<\/strong><\/p>\n
[1.69018317 0.91435666 1.23340312]<\/div>\n
5. np.arccosh()-<\/strong> This function returns the hyperbolic inverse cosine of the array elements.<\/p>\n
import numpy as np  \r\narr = np.array([150,60,90])\r\n#hyperbolic inverse cosine function\r\nprint(np.arccosh(arr * np.pi \/ 180)) \r\n<\/pre>\n
Output<\/strong><\/p>\n
[1.61690509 0.30604211 1.02322748]<\/div>\n
6. np.arctanh()-<\/strong> This function returns the hyperbolic inverse tan of the array elements.<\/p>\n
import numpy as np  \r\narr = np.array([1,2,3])\r\n#hyperbolic inverse tangent function\r\nprint(np.arctanh(arr * np.pi \/ 180)) \r\n<\/pre>\n
Output<\/strong><\/p>\n
[0.01745507 0.03492077 0.05240781]<\/div>\n
NumPy Rounding Functions<\/h2>\n
\"Rounding<\/a><\/p>\n
There are various rounding functions that are used to round decimal float numbers to a particular precision of decimal numbers. Let’s discuss them below:<\/p>\n
1. np.around()-<\/strong> This function is used to round off a decimal number to desired number of positions. The function takes two parameters: the input number and the precision of decimal places.<\/p>\n
import numpy as np  \r\narr = np.array([20.8999,67.89899,54.63409])  \r\nprint(np.around(arr,1))\r\n\r\n<\/pre>\n
Output<\/strong><\/p>\n
[20.9 67.9 54.6]<\/div>\n
2. np.floor()-<\/strong> This function returns the floor value of the input decimal value. Floor value is the largest integer number less than the input value.<\/p>\n
import numpy as np  \r\narr = np.array([20.8,67.99,54.09])  \r\nprint(np.floor(arr))\r\n<\/pre>\n
Output<\/strong><\/p>\n
[20. 67. 54.]<\/div>\n
3. np.ceil()-<\/strong> This function returns the ceiling value of the input decimal value. Ceiling value is the smallest integer number greater than the input value.<\/p>\n
import numpy as np  \r\narr = np.array([20.8,67.99,54.09])  \r\nprint(np.ceil(arr))\r\n\r\n<\/pre>\n
Output<\/strong><\/p>\n
[21. 68. 55.]<\/div>\n
NumPy Exponential and Logarithmic Functions<\/h2>\n
1. np.exp()-<\/strong> This function calculates the exponential of the input array elements.<\/p>\n
import numpy as np  \r\narr = np.array([1,8,4])\r\n#exponential function\r\nprint(np.exp(arr))\r\n\r\n<\/pre>\n
Output<\/strong><\/p>\n
[2.71828183e+00 2.98095799e+03 5.45981500e+01]<\/div>\n
2. np.log()-<\/strong> This function calculates the natural log of the input array elements. Natural Logarithm of a value is the inverse of its exponential value.<\/p>\n
import numpy as np  \r\narr = np.array([6,8,4])  \r\n#logarithmic funtion\r\nprint(np.log(arr))\r\n<\/pre>\n
Output<\/strong><\/p>\n
[1.79175947 2.07944154 1.38629436]<\/div>\n
NumPy Complex Functions<\/h2>\n
1. np.isreal()-<\/strong> This function returns the output of a test for real numbers.<\/p>\n
import numpy as np  \r\narr = np.array([1+2j])  \r\n#real test funtion\r\nprint(np.isreal(arr))\r\n<\/pre>\n
Output<\/strong><\/p>\n
[False]<\/div>\n
2. np.conj()-<\/strong> This function is useful for calculation of conjugate of complex numbers.<\/p>\n
import numpy as np  \r\narr = np.array([1+2j])  \r\n#conjugate funtion\r\nprint(np.conj(arr))\r\n \r\n<\/pre>\n
Output<\/strong><\/p>\n
[1.-2.j]<\/div>\n
Summary<\/h2>\n
The math module contains a wide range of NumPy functions functions for mathematical operations. Python supports all types of math functions. It is very useful for creating programs with complex calculations. This module further increases the capabilities of NumPy.<\/p>\n","protected":false},"excerpt":{"rendered":"
NumPy consists of a large number of built-in mathematical functions that can solve mathematical problems. The in-built math module is imported for mathematical calculations. 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