To convert a Python list to a NumPy array, use either of the following two methods:
- The
np.array()function that takes an iterable and returns a NumPy array creating a new data structure in memory. - The
np.asarray()function that takes an iterable as argument and converts it to the array. The difference tonp.array()is thatnp.asarray()doesn’t create a new copy in memory if you pass a NumPy array. All changes made on the original array are reflected on the NumPy array.
Exercise: Create array b from array a using both methods. Then change a value in array a. What happens at array b?
NumPy vs Python Lists
The Python built-in list data type is powerful. However, the NumPy array has many advantages over Python lists. What are they?
| Advantages NumPy | Advantages Python Lists |
|---|---|
| Multi-dimensional Slicing | Library-Independent |
| Broadcasting Functionality | Intuitive |
| Processing Speed | Less Complicated |
| Memory Footprint | Heterogeneous List Data Allowed |
| Many Convenience Methods | Arbitrary Data Shape (Non-Square Matrix) |
To read more about the advantages of a NumPy array over a Python list, read my detailed blog tutorial.
How to Convert a 1D Python List to a NumPy Array?
Problem: Given a one-dimensional Python list. How to convert it to a NumPy array?
Example: You have the following 1D Python list of integers.
lst = [0, 1, 100, 42, 13, 7]
You want to convert it into a NumPy array.
array([ 0, 1, 100, 42, 13, 7])
Method 1: np.array(…)
The simplest way to convert a Python list to a NumPy array is to use the np.array() function that takes an iterable and returns a NumPy array.
import numpy as np
lst = [0, 1, 100, 42, 13, 7]
print(np.array(lst))
The output is:
# [ 0 1 100 42 13 7]
This creates a new data structure in memory. Changes on the original list are not visible to the variable that holds the NumPy array:
lst = [0, 1, 100, 42, 13, 7]
a = np.array(lst)
lst.append(999)
print(a)
# [ 0 1 100 42 13 7]
The element 999 which is now part of list lst is not part of array a.
Method 2: np.asarray(…)
An alternative is to use the np.asarray() function that takes one argument—the iterable—and converts it to the NumPy array. The difference to np.array() is that it doesn’t create a new copy in memory IF you pass a NumPy array. All changes made on the original array are reflected on the NumPy array! So be careful.
lst = [0, 1, 100, 42, 13, 7]
a = np.array(lst)
b = np.asarray(a)
a[0] = 99
print(b)
# [ 99 1 100 42 13 7]
The array b is created using the np.asarray() function, so if you change a value of array a, the change will be reflected on the variable b (because they point to the same object in memory).
[Video] How to Convert a List of Lists to a NumPy Array?
Convert List of Lists to 2D Array
Problem: Given a list of lists in Python. How to convert it to a 2D NumPy array?
Example: Convert the following list of lists
[[1, 2, 3], [4, 5, 6]]
into a NumPy array
[[1 2 3] [4 5 6]]
Solution: Use the np.array(list) function to convert a list of lists into a two-dimensional NumPy array. Here’s the code:
# Import the NumPy library
import numpy as np # Create the list of lists
lst = [[1, 2, 3], [4, 5, 6]] # Convert it to a NumPy array
a = np.array(lst) # Print the resulting array
print(a) '''
[[1 2 3] [4 5 6]] '''
Try It Yourself: Here’s the same code in our interactive code interpreter:
<iframe height="700px" width="100%" src="https://repl.it/@finxter/numpylistoflists?lite=true" scrolling="no" frameborder="no" allowtransparency="true" allowfullscreen="true" sandbox="allow-forms allow-pointer-lock allow-popups allow-same-origin allow-scripts allow-modals"></iframe>
Hint: The NumPy method np.array() takes an iterable as input and converts it into a NumPy array.
Convert a List of Lists With Different Number of Elements
Problem: Given a list of lists. The inner lists have a varying number of elements. How to convert them to a NumPy array?
Example: Say, you’ve got the following list of lists:
[[1, 2, 3], [4, 5], [6, 7, 8]]
What are the different approaches to convert this list of lists into a NumPy array?
Solution: There are three different strategies you can use. (source)
(1) Use the standard np.array() function.
# Import the NumPy library
import numpy as np # Create the list of lists
lst = [[1, 2, 3], [4, 5], [6, 7, 8]] # Convert it to a NumPy array
a = np.array(lst) # Print the resulting array
print(a) '''
[list([1, 2, 3]) list([4, 5]) list([6, 7, 8])] '''
This creates a NumPy array with three elements—each element is a list type. You can check the type of the output by using the built-in type() function:
>>> type(a)
<class 'numpy.ndarray'>
(2) Make an array of arrays.
# Import the NumPy library
import numpy as np # Create the list of lists
lst = [[1, 2, 3], [4, 5], [6, 7, 8]] # Convert it to a NumPy array
a = np.array([np.array(x) for x in lst]) # Print the resulting array
print(a) '''
[array([1, 2, 3]) array([4, 5]) array([6, 7, 8])] '''
This is more logical than the previous version because it creates a NumPy array of 1D NumPy arrays (rather than 1D Python lists).
(3) Make the lists equal in length.
# Import the NumPy library
import numpy as np # Create the list of lists
lst = [[1, 2, 3], [4, 5], [6, 7, 8, 9]] # Calculate length of maximal list
n = len(max(lst, key=len)) # Make the lists equal in length
lst_2 = [x + [None]*(n-len(x)) for x in lst]
print(lst_2)
# [[1, 2, 3, None], [4, 5, None, None], [6, 7, 8, 9]] # Convert it to a NumPy array
a = np.array(lst_2) # Print the resulting array
print(a) '''
[[1 2 3 None] [4 5 None None] [6 7 8 9]] '''
You use list comprehension to “pad” None values to each inner list with smaller than maximal length.
Related Articles
- How to Convert a List of Lists to a NumPy array?
- What are Advantages of NumPy arrays over Python lists?
Where to Go From Here?
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