This package helps us to create arrays and matrices. The main difference between arrays and matrices can be determined by dimensions. Matrix is a 2d array, but the array itself can be a 1d vector or higher N dimensions.

Here are some examples we can take a look at. First, let’s suppose we want a 1d array with 25 elements ranging from 0 to 24. We then reshape the vector into a 5 by 5 matrix.

`# Create a 1d array ranging from 0 to 24`

`np.arange(25)`

array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24])

`# Reshape the 1d array to 5 by 5 matrix`

```
arr.reshape(5,5)
```

array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24]])

Now we want to select values from the array. Let’s use an example by using

**conditional selection**. Create an array ranging from 1 to 10 and find the values greater than 5.

`# Create an array ranging from 1 to 10`

`arr = np.arange(1,11)`

`array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) `

`# Create a boolean variable and set as a condition to select elements from arr`

`bool_arr = arr>5 `

`arr[bool_arr]`

```
array([ 6, 7, 8, 9, 10])
```

We can also do

**operations**on arrays.

`# Create an array ranging from 1 to 10`

`arr = np.arange(1,11)`

`# Sum up arr and arr`

`arr + arr `

`array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20])`

`# Exponentiate the array`

`arr ** 2`

`array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100])`

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