Skip to content
Related Articles
Convert Python List to numpy Arrays
• Last Updated : 08 Sep, 2020

A list in Python is a linear data structure that can hold heterogeneous elements they do not require to be declared and are flexible to shrink and grow. On the other hand, an array is a data structure which can hold homogeneous elements, arrays are implemented in Python using the NumPy library. Arrays require less memory than list.
The similarity between an array and a list is that the elements of both array and a list can be identified by its index value.
In Python lists can be converted to arrays by using two methods from the NumPy library:

• Using numpy.array()

## Python3

 `# importing library``import` `numpy` `# initilizing list``lst ``=` `[``1``, ``7``, ``0``, ``6``, ``2``, ``5``, ``6``]` `# converting list to array``arr ``=` `numpy.array(lst)` `# displaying list``print` `(``"List: "``, lst)` `# displaying array``print` `(``"Array: "``, arr)`

Output:

```List:  [1, 7, 0, 6, 2, 5, 6]
Array:  [1 7 0 6 2 5 6]
```
• Using numpy.asarray()

## Python3

 `# importing library``import` `numpy` `# initilizing list``lst ``=` `[``1``, ``7``, ``0``, ``6``, ``2``, ``5``, ``6``]` `# converting list to array``arr ``=` `numpy.asarray(lst)` `# displaying list``print` `(``"List:"``, lst)` `# displaying array``print` `(``"Array: "``, arr)`

Output:

```List:  [1, 7, 0, 6, 2, 5, 6]
Array:  [1 7 0 6 2 5 6]

```

The vital difference between the above two methods is that numpy.array() will make a duplicate of the original object and numpy.asarray() would mirror the changes in the original object. i.e :

When a copy of the array is made by using numpy.asarray() , the changes made in one array would be reflected in the other array also but doesn’t show the changes in the list by which if the array is made. However, this doesn’t happen with numpy.array().

## Python3

 `# importing library``import` `numpy` `# initilizing list``lst ``=` `[``1``, ``7``, ``0``, ``6``, ``2``, ``5``, ``6``]` `# converting list to array``arr ``=` `numpy.asarray(lst)` `# displaying list``print` `(``"List:"``, lst)` `# displaying array``print` `(``"arr: "``, arr)` `# made another array out of arr using asarray function``arr1 ``=` `numpy.asarray(arr)` `#displaying arr1 before the changes made``print``(``"arr1: "` `, arr1)` `#change made in arr1``arr1[``3``] ``=` `23` `#displaying arr1 , arr , list after the change has been made``print``(``"lst: "` `, lst)``print``(``"arr: "` `, arr)``print``(``"arr1: "` `, arr1)`

Output :

```List: [1, 7, 0, 6, 2, 5, 6]
arr:  [1 7 0 6 2 5 6]
arr1:  [1 7 0 6 2 5 6]
lst:  [1, 7, 0, 6, 2, 5, 6]
arr:  [ 1  7  0 23  2  5  6]
arr1:  [ 1  7  0 23  2  5  6]

```

In “arr” and “arr1” the change is visible at index 3 but not in 1st.

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course

My Personal Notes arrow_drop_up