# NumPy sin()

The `sin()` function computes the element-wise sine of an array.

The sine is the trigonometric function that calculates the ratio of the length of the side opposite an angle to the length of the hypotenuse in a right-angled triangle.

### Example

``````import numpy as np

# create an array of angles in radians
angles = np.array([0, np.pi/6, np.pi/4, np.pi/3, np.pi/2])

# compute the sine of each angle
sine_values = np.sin(angles)

# print resulting sine values
print(sine_values)

# Output: [0.         0.5        0.70710678 0.8660254  1.        ]``````

## sin() Syntax

The syntax of `sin()` is:

``numpy.sin(array, out=None, dtype=None)``

## sin() Arguments

The `sin()` function takes following arguments:

• `array` - the input arrays
• `out` (optional) - the output array where the result will be stored
• `dtype` (optional) - data type of the output array

## sin() Return Value

The `sin()` function returns an array containing the element-wise sine values of the input array.

## Example 1: Compute Sine of the Angles

``````import numpy as np

# array of angles in radians
angles = np.array([0, 1, 2])
print("Angles:", angles)

# compute the sine of the angles
sine_values = np.sin(angles)

print("Sine values:", sine_values)``````

Output

```Angles: [0 1 2]
Sine values: [0.         0.84147098     0.90929743]```

In the above example, the `sin()` function calculates the sine values for each element in the angles array.

The resulting values are in radians.

## Example 2: Use out to Store the Result in Desired Location

``````import numpy as np

# create an array of angles in radians
angles = np.array([0, np.pi/6, np.pi/4, np.pi/3, np.pi/2])

# create an empty array to store the result
result = np.empty_like(angles)

# compute the sine of angles and store the result in the 'result' array
np.sin(angles, out=result)

print("Result:", result)``````

Output

`Result: [0.         0.5        0.70710678 0.8660254  1.        ]`

Here, we have used `sin()` with the `out` parameter to compute the sine of the angles array and store the result directly in the result array.

The resulting result array contains the computed sine values.

## Example 3: Use of dtype Argument in sin()

``````import numpy as np

# create an array of angles in radians
angles = np.array([0, np.pi/6, np.pi/4, np.pi/3, np.pi/2])

# compute the sine of angles with different data types
sin_float64 = np.sin(angles, dtype=np.float64)
sin_float32 = np.sin(angles, dtype=np.float32)

# print the resulting arrays
print("Sine values (float64):", sin_float64)
print("Sine values (float32):", sin_float32)``````

Output

```Sine values (float64): [0.         0.5        0.70710678 0.8660254  1.        ]
Sine values (float32): [0.         0.5        0.70710677 0.86602545 1.        ]```

In the above example, we have the array angles containing five angles in radians.

Here, we used `sin()` with the `dtype` argument to compute the sine of the angles with different data types.

Note: The `sin()` function typically returns floating-point values because the sine function can produce non-integer values for most inputs.