# Vectors

In this lesson, we will learn about various methods to create vectors in Python.

We'll cover the following

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:

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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.

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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:

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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():

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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.