In Python, **vectors** are *one-dimensional arrays* and are the most commonly used data structure in NumPy.

🛑

Do not confuse NumPy vectors with mathematical vectors.

Let’s see how they’re created:

# Creation #

There are many ways to create 1-D arrays and we can create them according to our needs. Let’s discuss these different ways below:

## Method 1 #

We can create an array by entering the individual elements of an array. See the example below:

import numpy as npx = np.array([1, 3, 5, 7, 9])print(x)

In the code above, we are actually converting a Python `list`

to a vector using the `np.array()`

function with its input argument being a `list`

.

## Method 2 #

Another function to create an array is `np.ones(size)`

, which creates an array of the specified `size`

filled with the value 1.

There is an analogous function `np.zeros(size)`

to create an array filled with the value 0.

import numpy as npv1 = np.ones(5)v0 = np.zeros(5)print(v1)print(v0)

Note:Data type of values inside the vectors generated from`ones()`

and`zeros()`

functions are floating points.

## Method 3 #

We can initialize an array using the `arange()`

function. This function can take up to 3 arguments.

```
np.arange(start, end, step)
```

The first argument is the *start point*, second argument is the *end point* and third argument is the *step size*.

Let’s look at the possible argument configurations of the `arange()`

function in the `numpy`

module:

import numpy as npprint(np.arange(1, 7)) # Takes default steps of 1 and doesn't include 7print(np.arange(5)) # Starts at 0 by defualt and ends at 4, giving 5 numbersprint(np.arange(1, 10, 3)) # Starts at 1 and ends at less than 10,# with a step size of 3

In line 5, the array will be generated according to the sequence: $1, 4, 7, 10,...$ and so on. But since $10$ is the upper limit, the sequence stops at $7$.

Below is an illustration of this concept.

## Method 4 #

We can also use the `linspace()`

function to define an array with equally spaced numeric elements and both endpoints **included**.

```
np.linspace(start, end, size)
```

Run the code below to see the implementation of `linspace()`

:

import numpy as npprint(np.linspace(1, 12, 12))print(np.linspace(1, 12, 5))print(np.linspace(1, 12, 3))

In the next lesson, let’s learn about multidimensional arrays.