**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.

## Where to Go From Here?

Enough theory. Let’s get some practice!

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