# NumPy Indexing and Assignment

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 `round`

method:

`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[0]
#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:
```python
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[1] = 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 `new_array`

.

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[0] = 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`

:

```
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:

`mat[row, col]`

`mat[row][col]`

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[0]
#Next, let's get the last element of the first row:
mat[0][-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 `if`

statement.

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.

## Moving On

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.