In this article, we will learn how to initialize a NumPy Array in Python

**Table of Contents**

- What is initialization?
- Initialize a NumPy Array by directly passing values to the array
- Initialize a NumPy Array by using asarray()
- Initialize a NumPy Array by using zeros()
- Initialize a NumPy Array by using ones()
- Initialize a NumPy Array by using full()
- Initialize a NumPy Array by using arange()
- Initialize a NumPy Array by using random().
- Summary

## What is initialization?

Initialization is nothing but to set some value to a variable. There are multiple ways to initialize a NumPy array. Lets discuss all the methods one by one with proper approach and a working code example

## Initialize a NumPy Array by directly passing values to the array

The numpy module has an array() method, and it takes a sequence or list as an input and returns a ndarray.

**Syntax of array() function**

numpy.array(object)

- Parameters:
- object : array_like.

- Returns:
- A ndarray.

**Approach:**

- Import numpy library.
- Pass the values to be present in the array to the array().
- It retuns a NumPyArray, initialized with the given values.

**Source Code**

### Frequently Asked:

import numpy as np # initializing an array arr = np.array([1, 2, 3, 4, 5]) print(arr)

**Output:**

[1 2 3 4 5]

## Initialize a NumPy Array by using asarray()

The numpy module has an asarray() method, and it takes a sequence or an array_like object as an input and returns a ndarray, with the object values initialized.

**Syntax of asarray() function**

numpy.asarray(object)

- Parameters:
- object : array_like.

- Returns:
- A ndarray.

**Approach:**

- Import numpy library.
- Pass the values to be present in the array to the asarray().
- It retuns a array with the given values initialized.

**Source Code**

import numpy as np # initializing an array arr = np.asarray((1, 2, 3, 4, 5)) print(arr)

**Output:**

[1 2 3 4 5]

## Initialize a NumPy Array by using zeros()

The numpy module has a zeros() method, and it takes a shape as an input and returns an array of given shape, but filled with zeros. In other words, zeros() can be used to initialize an array with zeros.

**Syntax of zeros() function**

numpy.zeros(shape)

- Parameters:
- shape : int or tuple of ints

.

- shape : int or tuple of ints
- Returns:
- A ndarray.

**Approach:**

- Import numpy library.
- Pass the shape to the np.zeros() method.
- It returns an array of zeros with given shape.

**Source Code**

import numpy as np # initializing an array arr = np.zeros(5) print(arr)

**Output:**

[0. 0. 0. 0. 0.]

## Initialize a NumPy Array by using ones()

The numpy module has ones() method, and it takes a shape as an input and returns an array of ones with given shape. In other words, ones() method can be used to initialize an array with ones.

**Syntax of ones() function**

numpy.ones(shape)

- Parameters:
- shape : int or tuple of ints

.

- shape : int or tuple of ints
- Returns:
- A ndarray.

**Approach:**

- Import numpy library.
- Pass the shape to the ones() method.
- It returns an array of ones with given shape.

**Source Code**

import numpy as np # initializing an array arr = np.ones(5) print(arr)

**Output:**

[1. 1. 1. 1. 1.]

## Initialize a NumPy Array by using full()

The numpy module has full() method, and it takes a shape and a fill_value as input and returns an NumPy Array of given shape filled with the **fill_value**. In other words, the full() method can be used to initialize an entire array with the same value.

**Syntax of full() function**

numpy.full(shape, fill_value)

- Parameters:
- shape : int or tuple of ints
- fill_value : value to be filled in the array

- Returns:
- A ndarray of given shape.

**Approach:**

- Import numpy library.
- Pass the shape and fill value to the full() method.
- It returns an array of fill_value with the given shape.

**Source Code**

import numpy as np # initializing an array arr = np.full(shape = 5, fill_value = 4) print(arr)

**Output:**

[4 4 4 4 4]

## Initialize a NumPy Array by using arange()

The numpy module has arange() method, and it takes **start_value**, **stop_value** and **step** as input and returns evenly spaced values within a given interval. By default the **start_value** is 0 and **step_value** is 1.

**Syntax of arange() function**

numpy.arange(start, stop, step)

- Parameters:
- start : integer or real, optional. Start of interval. The interval includes this value. The default start value is 0.
- stop : integer or real. End of interval. The interval does not include this value.
- step : integer or real, optional. Spacing between values

- Returns:
- Return array of evenly spaced values within a given interval.

**Approach:**

- Import numpy library.
- Pass the start and stop to the arange() method.
- It returns an array of evenly spaced values within a given interval.

**Source Code**

import numpy as np # initializing an array arr = np.arange(5) print(arr)

**Output:**

[0 1 2 3 4]

## Initialize a NumPy Array by using random().

The numpy module has a random() method, and it takes a shape and return an array of given shape, initialized with random values.

**Syntax of random() function**

numpy.random(shape)

- Parameters:
- shape : int or tuple of ints

.

- shape : int or tuple of ints
- Returns:
- ndarray : array of random values with given shape.

**Approach:**

- Import numpy library.
- Pass the shape to the random() method.
- It returns an array of random values with given shape.

**Source Code**

import numpy as np # initializing an array arr = np.random.random(5) print(arr)

**Output:**

[0.15737063 0.09794934 0.29919287 0.41237273 0.94688651]

## Summary

Great! you made it, we have discussed all possible methods to initialize a NumPy array. Happy learning.