# NumPy asarray()

The asarray() method converts all array_like objects into an array.

### Example

import numpy as np

# create array-like objects
list1 = [1, 2, 3, 4, 5]
tuple1 = (1, 2, 3, 4, 5)

# convert them to arrays
array1 = np.asarray(list1)
array2 = np.asarray(tuple1)

print(array1)
print(array2)

'''
Output:
[1 2 3 4 5]
[1 2 3 4 5]
'''

## asarray() Syntax

The syntax of asarray() is:

numpy.asarray(a, dtype = None, order = None, like = None)

## asarray() Argument

The asarray() method takes the following arguments:

• a- any array_like input object
• dtype(optional)- type of output array(dtype)
• order(optional)- specifies the order in which the array elements are placed
• like(optional)- reference object to allow the creation of non-NumPy arrays

## asarray() Return Value

The asarray() method returns an array representation of a.

## Example 1: Convert to an Array Using asarray

import numpy as np

# create array-like objects
list1 = [1, 2, 3, 4, 5]

# convert them to arrays array1 = np.asarray(list1) array2 = np.asarray(list1, dtype = str)
print(array1) print(array2)

Output

[1 2 3 4 5]
['1' '2' '3' '4' '5']

Note: Using the dtype argument specifies the data type of the resultant array.

## Key Difference Between np.array() and np.asarray()

Both np.array() and np.asarray() are NumPy functions used to generate arrays from array_like objects but they have some differences in their behavior.

The array() method creates a copy of an existing object whereas asarray() creates a new object only when needed.

Let us look at an example.

import numpy as np

# create an array
array1 = np.arange(5)

# use np.array() on existing array array2 = np.array(array1) print('Using array():', array1 is array2) # makes a copy # use np.asarray() on existing array array3 = np.asarray(array1) print('Using asarray():', array1 is array3) # doesn't make a copy

Output

Using array(): False
Using asarray(): True