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 to np.array[] is that np.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?
| 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
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:
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][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.
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This article explains how to convert a one-dimensional array to a two-dimensional array in Python, both for NumPy arrays ndarray and for built-in lists list.
- Convert a one-dimensional numpy.ndarray to a two-dimensional numpy.ndarray
- Convert a one-dimensional list to a two-dimensional list
On the contrary, see the following article on how to convert [= flatten] a multi-dimensional array to a one-dimensional array.
- How to flatten a list of lists in Python
Convert a one-dimensional numpy.ndarray to a two-dimensional numpy.ndarray
Use the reshape[] method to transform the shape of a NumPy array ndarray. Any shape transformation is possible, not limited to transforming from a one-dimensional array to a two-dimensional array.
By using -1, the size of the dimension is automatically calculated.
- NumPy: How to use reshape[] and the meaning of -1
import numpy as np a = np.arange[6] print[a] # [0 1 2 3 4 5] print[a.reshape[2, 3]] # [[0 1 2] # [3 4 5]] print[a.reshape[-1, 3]] # [[0 1 2] # [3 4 5]] print[a.reshape[2, -1]] # [[0 1 2] # [3 4 5]]
source: numpy_1d_to_2d.py
If you specify a shape that cannot be converted, an error is raised.
# print[a.reshape[3, 4]] # ValueError: cannot reshape array of size 6 into shape [3,4] # print[a.reshape[-1, 4]] # ValueError: cannot reshape array of size 6 into shape [4]
source: numpy_1d_to_2d.py
Convert a one-dimensional list to a two-dimensional list
With NumPy
With NumPy, you can convert list to numpy.ndarray and transform the shape with reshape[], and then return it to list.
l = [0, 1, 2, 3, 4, 5] print[np.array[l].reshape[-1, 3].tolist[]] # [[0, 1, 2], [3, 4, 5]] print[np.array[l].reshape[3, -1].tolist[]] # [[0, 1], [2, 3], [4, 5]]
source: numpy_1d_to_2d.py
See the following article on how to convert numpy.ndarray and list to each other.
- Convert numpy.ndarray and list to each other
Without NumPy
Without NumPy, you can use list comprehensions, range[], and slices as follows.
def convert_1d_to_2d[l, cols]: return [l[i:i + cols] for i in range[0, len[l], cols]] l = [0, 1, 2, 3, 4, 5] print[convert_1d_to_2d[l, 2]] # [[0, 1], [2, 3], [4, 5]] print[convert_1d_to_2d[l, 3]] # [[0, 1, 2], [3, 4, 5]] print[convert_1d_to_2d[l, 4]] # [[0, 1, 2, 3], [4, 5]]
The first argument is the original list, and the second argument is the number of elements of the inner list [= number of columns]. If there is a remainder, a list with a different number of elements will be stored, as in the last example.
If you want to specify the number of rows:
def convert_1d_to_2d_rows[l, rows]: return convert_1d_to_2d[l, len[l] // rows] print[convert_1d_to_2d_rows[l, 2]] # [[0, 1, 2], [3, 4, 5]] print[convert_1d_to_2d_rows[l, 3]] # [[0, 1], [2, 3], [4, 5]] print[convert_1d_to_2d_rows[l, 4]] # [[0], [1], [2], [3], [4], [5]]
The function in this example is just a simple one. If not divisible, the result is different from the specified number of rows, as in the last example.
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