❮ Numpy Arithmetic Operations Numpy Linear Algebra ❯

NumPy Mathematical Functions

NumPy includes a large number of functions for various mathematical operations, including trigonometric functions, arithmetic functions, and functions for complex number handling.

Trigonometric Functions

NumPy provides standard trigonometric functions: sin(), cos(), and tan().

Example

import numpy as np

a = np.array([0, 30, 45, 60, 90])
print('Sine values for different angles:')
# Convert to radians by multiplying by pi/180
print(np.sin(a * np.pi / 180))
print('\n')
print('Cosine values for the angles in the array:')
print(np.cos(a * np.pi / 180))
print('\n')
print('Tangent values for the angles in the array:')
print(np.tan(a * np.pi / 180))

Output result:

Sine values for different angles:
[0.         0.5        0.70710678 0.8660254  1.        ]

Cosine values for the angles in the array:
[1.00000000e+00 8.66025404e-01 7.07106781e-01 5.00000000e-01
 6.12323400e-17]

Tangent values for the angles in the array:
[0.00000000e+00 5.77350269e-01 1.00000000e+00 1.73205081e+00
 1.63312394e+16]

The arcsin, arccos, and arctan functions return the inverse trigonometric functions for sin, cos, and tan of a given angle.

The results of these functions can be converted from radians to degrees using the numpy.degrees() function.

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('Calculate the arcsine of the angles, return value in radians:')
inv = np.arcsin(sin)
print(inv)
print('\n')
print('Check the results 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('Arccosine:')
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('Arctangent:')
inv = np.arctan(tan)
print(inv)
print('\n')
print('In degrees:')
print(np.degrees(inv))

Output result:

Array containing sine values:
[0.         0.5        0.70710678 0.8660254  1.        ]

Calculate the arcsine of the angles, return value in radians:
[0.         0.52359878 0.78539816 1.04719755 1.57079633]

Check the results 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]

Arccosine:
[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]

Arctangent:
[0.         0.52359878 0.78539816 1.04719755 1.57079633]

In degrees:
[ 0. 30. 45. 60. 90.]

Rounding Functions

The numpy.around() function returns the specified number rounded to the nearest value.

numpy.around(a, decimals)

Parameter description:

a: array
decimals: number of decimal places to round to. Default is 0. If negative, rounds 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))

Output result:

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.]

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:')
print(np.around(a))
print(np.around(a, decimals=1))
print(np.around(a, decimals=-1))

Output result:

Original array:
[  1.      5.55  123.      0.567  25.532]

Rounded:
[  1.   6. 123.   1.  26.]
[  1.    5.6 123.    0.6  25.5]
[  0.  10. 120.   0.  30.]

numpy.floor()

numpy.floor() returns the largest integer less than or equal to the specified expression, which is a downward rounding.

Example

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))

Output result:

Provided array:
[-1.7  1.5 -0.2  0.6 10. ]

Modified array:
[-2.  1. -1.  0. 10.]

numpy.ceil()

numpy.ceil() returns the smallest integer greater than or equal to the specified expression, which is an upward rounding.

Example

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))

Output result:

Provided array:
[-1.7  1.5 -0.2  0.6 10. ]

Modified array:
[-1.  2. -0.  1. 10.]

❮ Numpy Arithmetic Operations Numpy Linear Algebra ❯