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NumPy provides standard trigonometric functions: sin(), cos(), tan().
import numpy as np a = np.array([0,30,45,60,90]) print('Sine values of different angles:') # By multiplying pi/180 converted to radians print(np.sin(a*np.pi/180)) print ('\n') print('Cosine values of angles in the array:') print(np.cos(a*np.pi/180)) print ('\n') print('Tangent values of angles in the array:') print(np.tan(a*np.pi/180))
The output result is:
Sine values of different angles: [0. 0.5 0.70710678 0.8660254 1. ] Cosine values of angles in the array: [1.00000000e+00 8.66025404e-01 7.07106781e-01 5.00000000e-01 6.12323400e-17] Tangent values of angles in the array: [0.00000000e+00 5.77350269e-01 1.00000000e+00 1.73205081e+00 1.63312394e+16]
arcsin, arccos, and arctan functions return the inverse trigonometric functions of sin, cos, and tan for the given angle.
These functions' results can be converted from radians to degrees using the numpy.degrees() function.
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('Calculate the inverse sine of an angle, the return value is in radians:') inv = np.arcsin(sin) print(inv) print ('\n') print('Check the results by converting to angular units:') 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 cosine:') inv = np.arccos(cos) print(inv) print ('\n') print('Angular unit:') print(np.degrees(inv)) print ('\n') print('tan function:') tan = np.tan(a*np.pi/180) print(tan) print ('\n') print('Inverse tangent:') inv = np.arctan(tan) print(inv) print ('\n') print('Angular unit:') print(np.degrees(inv))
The output result is:
Array containing sine values: [0. 0.5 0.70710678 0.8660254 1. ] Calculate the inverse sine of an angle, the return value is in radians: [0. 0.52359878 0.78539816 1.04719755 1.57079633] Check the results by converting to angular units: [ 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 cosine: [0. 0.52359878 0.78539816 1.04719755 1.57079633] Angular unit: [ 0. 30. 45. 60. 90.] tan function: [0.00000000e+00 5.77350269e-01 1.00000000e+00 1.73205081e+00 1.63312394e+16] Inverse tangent: [0. 0.52359878 0.78539816 1.04719755 1.57079633] Angular unit: [ 0. 30. 45. 60. 90.]
The numpy.around() function returns the rounded value of the specified number.
numpy.around(a,decimals)
Parameter Description:
a: Array decimals: The number of decimal places to round to. The default value is 0. If negative, the integer will be rounded to the left of the decimal point.
import numpy as np a = np.array([1.0,5.55, 123, 0.567, 25.532]) print ('Original array:') print (a) print ('\n') print ('Rounded after:') print (np.around(a)) print (np.around(a, decimals =) 1)) print (np.around(a, decimals =) -1))
The output result is:
Original array: [ 1. 5.55 123. 0.567 25.532] Rounded after: [ 1. 6. 123. 1. 26.] [ 1. 5.6 123. 0.6 25.5] [ 0. 10. 120. 0. 30.]
numpy.floor() returns the largest integer less than or equal to the specified expression, i.e., rounding down.
import numpy as np a = np.array([-1.7, 1.5, -0.2, 0.6, 10]) print ('Provided array:') print (a) print ('\n') print ('Modified array:') print (np.floor(a))
The output result is:
Provided array: [-1.7 1.5 -0.2 0.6 10. ] Modified array: [-2. 1. -1. 0. 10.]
numpy.ceil() returns the smallest integer greater than or equal to the specified expression, i.e., rounding up.
import numpy as np a = np.array([-1.7, 1.5, -0.2, 0.6, 10]) print ('Provided array:') print (a) print ('\n') print ('Modified array:') print (np.ceil(a))
Provided array: [-1.7 1.5 -0.2 0.6 10. ] Modified array: [-1. 2. -0. 1. 10.]