NumPy is a powerful library for numerical computing in Python. It provides a wide range of functions and tools for manipulating arrays and performing mathematical operations on them.

One such function is the `full_like()`

function, which allows users to create a new array with the same shape and data type as an existing array but with a specified value for every element. This article will explore the definition and purpose of `full_like()`

and provide examples of how it can be used to manipulate numerical data.

## What is full_like() and why is it useful?

In NumPy, `full_like()`

is a function that creates a new array with the same shape and data type as an existing array but with a specified value for every element.

This function is useful for creating an array of a specific shape and data type filled with a particular value. It is especially useful when working with arrays of numerical data, as it allows users to easily create new arrays and manipulate them without having to worry about the minutiae of data types and shapes.

## Examples of how full_like() can be used

Let’s say we have an array of temperatures measured in Fahrenheit over a period of time, and we want to convert these temperatures to Celsius. We can do this by creating a new array with the same shape and data type as the Fahrenheit array but with Celsius values.

Here’s how we can use `full_like()`

to accomplish this:

import numpy as np

fahrenheit_temps = np.array([72, 65, 80, 90, 68])

celsius_temps = ((fahrenheit_temps - 32) * 5/9)

celsius_temps_new = np.full_like(fahrenheit_temps, celsius_temps)

print(celsius_temps_new)

### Output:

array([22.22222222, 18.33333333, 26.66666667, 32.22222222, 20.])

In this example, we first converted the Fahrenheit temperatures to Celsius using a mathematical expression. We then used `full_like()`

to create a new array with the same shape and data type as the Fahrenheit array but with the Celsius values.

This allows us to perform further mathematical operations on the Celsius temperatures without having to worry about the details of the data types and shapes.

## Syntax of full_like()

To better understand how to use `full_like()`

, let’s take a look at its syntax and the parameters it requires.

numpy.full_like(a, fill_value, dtype=None, order='K', subok=True, shape=None)

In this syntax, we have the following parameters:

`a`

: This is the input array whose shape and data type we want to mimic.`fill_value`

: This is the value we want to fill the new array with.`dtype`

: This is the data type of the new array. If not specified, it defaults to the data type of the input array.`order`

: This is the order in which the array is stored in memory. It can either be ‘C’ (row-major order) or ‘F’ (column-major order).`subok`

: This is a boolean value that specifies whether to return a subclass of the input array or a base class.`shape`

: This is the shape of the new array. If not specified, it defaults to the shape of the input array.

## Examples of passing different parameters to full_like()

Let’s say we have an array of integers and we want to create a new array of the same shape and data type but filled with a specific integer value. Here’s how we can use `full_like()`

to achieve this:

import numpy as np

x = np.array([1, 2, 3, 4, 5])

y = np.full_like(x, 10)

print(y)

### Output:

array([10, 10, 10, 10, 10])

In this example, we used `full_like()`

to create a new array `y`

with the same shape and data type as `x`

but with the value 10 for every element.

Now, let’s say we want to create a new array of a specific shape and data type but filled with a float value. Here’s how we can use `full_like()`

to do this:

import numpy as np

x = np.zeros((3, 2), dtype=int)

y = np.full_like(x, 1.23, dtype=float)

print(y)

### Output:

array([[1.23, 1.23],

[1.23, 1.23],

[1.23, 1.23]])

In this example, we used `full_like()`

to create a new array `y`

with the same shape as `x`

but with the data type set to float. We also specified the fill value in the function call instead of as a separate argument.

## Implementation of Numpy full_like()

To begin using the NumPy `full_like()`

function, we must first import the NumPy package. This can be done using the following code:

import numpy as np

This code imports the NumPy package and gives it an alias of “np”. With NumPy imported, we can now use `full_like()`

to create new arrays.

Let’s look at some example code to implement `full_like()`

in different scenarios:

### Example 1: Creating an array filled with ones

import numpy as np

a = np.array([1, 2, 3])

b = np.full_like(a, 1)

print(b)

### Output:

array([1, 1, 1])

In this example, we created an array “a” with the elements 1, 2, and 3. We then used `full_like()`

to create a new array “b” with the same shape and data type as “a” but filled with the value 1.

The resulting array was [1, 1, 1].

### Example 2: Creating an array of a specific shape and data type filled with a float value

import numpy as np

c = np.full_like(np.zeros((2, 3), dtype=int), 2.5, dtype=float)

print(c)

### Output:

array([[2.5, 2.5, 2.5],

[2.5, 2.5, 2.5]])

In this example, we created an array of zeros with the shape (2, 3) and data type “int”. We then used `full_like()`

to create a new array with the same shape and data type as the zeros array but filled with the value 2.5 and data type “float”.

The resulting array was [[2.5, 2.5, 2.5], [2.5, 2.5, 2.5]].

### Example 3: Creating an array with a different order

import numpy as np

d = np.array([[1, 2], [3, 4]], order='F')

e = np.full_like(d, 5, order='F')

print(e)

### Output:

array([[5, 5],

[5, 5]])

In this example, we created an array “d” with the elements [[1, 2], [3, 4]] and with a column-major order (“F”). We then used `full_like()`

to create a new array “e” with the same shape and data type as “d” but filled with the value 5 and with a column-major order.

The resulting array was [[5, 5], [5, 5]].

## Conclusion

In conclusion, NumPy `full_like()`

is a powerful function that is useful in a variety of scenarios when working with numerical data.

This function allows for the creation of new arrays with a specified data type and shape, filled with a particular value. By using `full_like()`

, users can easily manipulate arrays without having to worry about the details of data types and shapes.

In this article, we explored the definition and purpose of `full_like()`

, provided examples of how it can be used to manipulate numerical data, and provided a syntax breakdown. We then looked at some example code to implement `full_like()`

in different scenarios, such as creating arrays filled with ones, creating arrays with a specific data type, and changing the order of an array.

Overall, `full_like()`

is a great tool for anyone working with numerical data in NumPy, and its versatility allows for a wide range of applications in data analysis, machine learning, and scientific computing.

In conclusion, NumPy `full_like()`

function is an incredibly useful tool for creating arrays that have the same shape and data type as other arrays but with specific values assigned to each element.

It simplifies the process of numerical computing in Python considerably, allowing users to manipulate arrays without worrying about the details of data types and shapes. In this article, we explored the definition, purpose, syntax, and implementation of `full_like()`

, offering examples of how it can be used in different scenarios.

By using `full_like()`

, we can create new and powerful arrays tailored to our specific numerical computing needs, making `full_like()`

an essential function for any Python programmer working with numerical data.