In this lesson, we will explore indexing and assignment in NumPy arrays.
The Array I’ll Be Using In This Lesson
As before, I will be using a specific array through this lesson. This time it will be generated using the
np.random.rand method. Here’s how I generated the array:
arr = np.random.rand(5)
Here is the actual array:
array([0.69292946, 0.9365295 , 0.65682359, 0.72770856, 0.83268616])
To make this array easier to look at, I will round every element of the array to 2 decimal places using NumPy’s
arr = np.round(arr, 2)
Here’s the new array:
array([0.69, 0.94, 0.66, 0.73, 0.83])
How To Return A Specific Element From A NumPy Array
We can select (and return) a specific element from a NumPy array in the same way that we could using a normal Python list: using square brackets.
An example is below:
arr #Returns 0.69
We can also reference multiple elements of a NumPy array using the colon operator. For example, the index
[2:] selects every element from index 2 onwards. The index
[:3] selects every element up to and excluding index 3. The index
[2:4] returns every element from index 2 to index 4, excluding index 4. The higher endpoint is always excluded.
A few example of indexing using the colon operator are below.
arr[:] #Returns the entire array: array([0.69, 0.94, 0.66, 0.73, 0.83]) arr[1:] #Returns array([0.94, 0.66, 0.73, 0.83]) arr[1:4] #Returns array([0.94, 0.66, 0.73])
Element Assignment in NumPy Arrays
We can assign new values to an element of a NumPy array using the
= operator, just like regular python lists. A few examples are below (note that this is all one code block, which means that the element assignments are carried forward from step to step).
array([0.12, 0.94, 0.66, 0.73, 0.83]) arr #Returns array([0.12, 0.94, 0.66, 0.73, 0.83]) arr[:] = 0 arr #Returns array([0., 0., 0., 0., 0.])
In [ ]:
arr[2:5] = 0.5 arr #Returns array([0. , 0. , 0.5, 0.5, 0.5])
Array Referencing in NumPy
NumPy makes use of a concept called ‘array referencing’ which is a very common source of confusion for people that are new to the library.
To understand array referencing, let’s first consider an example:
new_array = np.array([6, 7, 8, 9]) second_new_array = new_array[0:2] second_new_array #Returns array([6, 7]) second_new_array = 4 second_new_array #Returns array([6, 4]), as expected new_array #Returns array([6, 4, 8, 9]) #which is DIFFERENT from its original value of array([6, 7, 8, 9]) #What the heck?
As you can see, modifying
second_new_array also changed the value of
Why is this?
By default, NumPy does not create a copy of an array when you reference the original array variable using the
= assignment operator. Instead, it simply points the new variable to the old variable, which allows the second variable to make modification to the original variable - even if this is not your intention.
This may seem bizarre, but it does have a logical explanation. The purpose of array referencing is to conserve computing power. When working with large data sets, you would quickly run out of RAM if you created a new array every time you wanted to work with a slice of the array.
Fortunately, there is a workaround to array referencing. You can use the
copy method to explicitly copy a NumPy array.
An example of this is below.
array_to_copy = np.array([1, 2, 3]) copied_array = array_to_copy.copy() array_to_copy #Returns array([1, 2, 3]) copied_array #Returns array([1, 2, 3])
As you can see below, making modifications to the copied array does not alter the original.
copied_array = 9 copied_array #Returns array([9, 2, 3]) array_to_copy #Returns array([1, 2, 3])
So far in the lesson, we have only explored how to reference one-dimensional NumPy arrays. We will now explore the indexing of two-dimensional arrays.
Indexing Two-Dimensional NumPy Arrays
To start, let’s create a two-dimensional NumPy array named
mat = np.array([[5, 10, 15],[20, 25, 30],[35, 40, 45]]) mat """ Returns: array([[ 5, 10, 15], [20, 25, 30], [35, 40, 45]]) """
There are two ways to index a two-dimensional NumPy array:
I personally prefer to index using the
mat[row][col] nomenclature because it is easier to visualize in a step-by-step fashion. For example:
#First, let's get the first row: mat #Next, let's get the last element of the first row: mat[-1]
You can also generate sub-matrices from a two-dimensional NumPy array using this notation:
mat[1:][:2] """ Returns: array([[20, 25, 30], [35, 40, 45]]) """
Array referencing also applies to two-dimensional arrays in NumPy, so be sure to use the
copy method if you want to avoid inadvertently modifying an original array after saving a slice of it into a new variable name.
Conditional Selection Using NumPy Arrays
NumPy arrays support a feature called
conditional selection, which allows you to generate a new array of boolean values that state whether each element within the array satisfies a particular
An example of this is below (I also re-created our original
arr variable since its been awhile since we’ve seen it):
arr = np.array([0.69, 0.94, 0.66, 0.73, 0.83]) arr > 0.7 #Returns array([False, True, False, True, True])
You can also generate a new array of values that satisfy this condition by passing the condition into the square brackets (just like we do for indexing).
An example of this is below:
arr[arr > 0.7] #Returns array([0.94, 0.73, 0.83])
Conditional selection can become significantly more complex than this. We will explore more examples in this section’s associated practice problems.
In this lesson, we explored NumPy array indexing and assignment in thorough detail. We will solidify your knowledge of these concepts further by working through a batch of practice problems in the next section.