# How to Convert a List to a NumPy Array?

To convert a Python list to a NumPy array, use either of the following two methods:

1. The `np.array()` function that takes an iterable and returns a NumPy array creating a new data structure in memory.
2. 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?

## 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 = 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).

## 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.

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