**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?

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]] '''

**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?
- How to Convert a NumPy Array to a Python List? (1D, 2D, 0D)

## Programming Humor – Python

## Where to Go From Here?

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