Numpy provides a wide range of mathematical functions that can be performed on arrays.
Let's explore three different types of math functions in NumPy:
- Trigonometric Functions
- Arithmetic Functions
- Rounding Functions
1. Trigonometric Functions
NumPy provides a set of standard trigonometric functions to calculate the trigonometric ratios (sine, cosine, tangent, etc.)
Here's a list of commonly used trigonometric functions in NumPy.
| Trigonometric Function | Computes (in radians) |
|---|---|
sin() |
the sine of an angle |
cos() |
cosine of an angle |
tan() |
tangent of an angle |
arcsin() |
the inverse sine |
arccos() |
the inverse cosine |
arctan() |
the inverse tangent |
degrees() |
converts an angle in radians to degrees |
radians() |
converts an angle in degrees to radians |
Let's see the examples.
Output
Angles: [0 1 2] Sine values: [0. 0.84147098 0.90929743] Inverse Sine values: [0. 1.57079633 nan]
In this example, the sin() and arcsin() functions calculate the sine and inverse sine values, respectively, for each element in the angles array.
The resulting values are in radians.
Now let's see the examples for degrees() and radians().
Output
Initial angle in radian: 1.57079633 Angle in degrees: 90.0000001836389 Angle in radians (after conversion): 1.57079633
Here, we first initialized an angle in radians. Then we converted it to degrees using the degrees() function.
Similarly, we used radians() to convert the degrees back to radians.
2. Arithmetic Functions
NumPy provides a wide range of arithmetic functions to perform on arrays.
Here's a list of various arithmetic functions along with their associated operators:
| Operation | Arithmetic Function | Operator |
|---|---|---|
| Addition | add() |
+ |
| Subtraction | subtract() |
- |
| Multiplication | multiply() |
* |
| Division | divide() |
/ |
| Exponentiation | power() |
** |
| Modulus | mod() |
% |
Let's see the examples.
Output
Using the add() function: [ 3 7 11 15]
In the above example, first we created two arrays named: first_array and second_array. Then, we used the add() function to perform element-wise addition respectively.
To learn more about the Arithmetic Functions, visit NumPy Arithmetic Array Operations.
3. Rounding Functions
We use rounding functions to round the values in an array to a specified number of decimal places.
Here's a list of commonly used NumPy rounding functions:
| Rounding Functions | Functions |
|---|---|
round() |
returns the value rounded to the desired precision |
floor() |
returns the values of array down to the nearest integer that is less than each element |
ceil() |
returns the values of array up to the nearest integer that is greater than each element. |
Let's see an example.
Here, we used the round() function to round the values of array numbers. Notice the line,
np.round(numbers, 2)
We've given two arguments to the round() function.
- numbers - the array whose values are to be rounded
- 2 - denotes the number of decimal places to which the array is rounded
Now, let's see the example of other NumPy rounding functions.
Output
Array after floor(): [1. 2. 3. 4.] Array after ceil(): [2. 3. 4. 5.]
In the above example, the floor() function rounds the values of array1 down to the nearest integer that is less than or equal to each element.
Whereas, the ceil() function rounds the values of array1 up to the nearest integer that is greater than or equal to each element.