Python's **`numpy.divide()`** computes the element-wise division of array elements. The elements in the first array are divided by the elements in the second array. 

> `numpy.divide()` performs true division, which produces a floating-point result. 

# Syntax
`numpy.divide()` is declared as follows:

```python
numpy.divide(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'true_divide'>
```
In the syntax given above, `x1` and `x2` are non-optional parameters, and the rest are optional parameters. 

> A **universal function** (ufunc) is a function that operates on ndarrays in an element-by-element fashion. The `divide()` method is a universal function. 

# Parameters

The `numpy.divide()` method takes the following compulsory parameters:

* **`x1`** [*array-like*] - input array elements that act as the dividend. 

* **`x2`** [*array-like*] - input array elements that act as the divisor. If the #key# shape: the shape of an array is the number of elements in each dimension #key# of `x1` and `x2` is different, they must be broadcastable to a common shape for representing the output.   

The `numpy.divide()` method takes the following optional parameters:


# Return value
`numpy.divide()` returns the output of `x1`/`x2` in an output array. 

* If both `x1` and `x2` are scalars, the return type is also `scalar`. 

* If `x2` is zero, an error occurs. 


# Examples
The example below shows the result of dividing the elements in array `arr1` by elements in array `arr2`:

import numpy as np

arr1 = np.array([2,3,4])
arr2 = np.array([5,7,2])
print (np.divide(arr1,arr2))

The example below shows the result of dividing the elements in array `arr3` by elements in array `arr4`:

import numpy as np

arr3 = np.array([[1,2,3], [4,6,8]])
arr4 = np.array([[4,5,6], [2,3,8]])
print (np.divide(arr3,arr4))

Parameter	Description
out	represents the location into which the output of the method is stored.
where	True value indicates that a universal function should be calculated at this position.
casting	controls the type of datacasting that should occur. The same_kind option indicates that safe casting or casting within the same kind should take place.
order	controls the memory layout order of the output function. The option K means reading the elements in the order they occur in memory.
dtype	represents the desired data type of the array.
subok	decides if subclasses should be made or not. If True, subclasses will be passed through.

What is numpy.divide() in Python?

Syntax

Parameters

Return value

Examples