# How to Convert List of Lists to NumPy Array?

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Short answer: Convert a list of lists—let’s call it `l`—to a NumPy array by using the standard `np.array(l)` function. This works even if the inner lists have a different number of elements.

## 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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