# Numpy Arrays Walk Through

## A quick reference to the rudiments of np arrays

One of the primary functions of NumPy is to provide access to array data types. The library supports fast and quick creation of example arrays. They’re powerful for many fields. And they’re important for data scientists. Let’s take a look at how they work.

The first thing we’ll do is `import numpy as np`

after that we can create an array of all zeroes. This is going to create array with 4 items. Allitems will be zeroes.

`import numpy as np`

# Create an array of 4 zeros (0s)

np.zeros(4)

For the expected output:

`array([0., 0., 0., 0.])`

Next we can also show the simple process of created a one dimensional array with a specific starting position, a specific stopping position, and a step or interval in the sequence.

The `np.arange()`

function is used to create an array with evenly spaced values within a defined interval. The syntax of the function is `np.arange(start, stop, step)`

, where:

`start`

is the starting value of the sequence (inclusive).`stop`

is the end value of the sequence (exclusive).`step`

is the spacing between each two consecutive values.

By default, `start`

is 0, and `step`

is 1 if they are not provided. The `stop`

parameter is required. The `np.arange()`

function can handle all numerical types and returns an array of a specific type inferred…