Table of Contents

The Python List Method will allow you to built-in methods and will do operations on the Python list/ arrays. Read this article to learn more about Python list Methods.

What is Python List Methods?

Python consists of a set of built-in methods that will be used on lists.

How to add Elements to the List?

The built-in Python methods are used to add the element to the list.

1. Python append() method

This method is used to add the element to the end of the list.

Syntax: list.append (element)

Example

class CustomList:
    def __init__(self):
        self.data = []

    def append(self, value):
        self.data += [value]

    def __str__(self):
        return str(self.data)


# Example usage
custom_list = CustomList()
custom_list.append(1)
custom_list.append(2)
custom_list.append(3)

print("Custom list after appending elements:", custom_list)

Output

Custom list after appending elements: [1, 2, 3]

2. Python insert() method

The Python insert() method will allow you to insert the element at the specified position.

Syntax:

list.insert(<position, element)

Example

class CustomList:
    def __init__(self):
        self.data = []

    def insert(self, index, value):
        self.data = self.data[:index] + [value] + self.data[index:]

    def __str__(self):
        return str(self.data)


# Example usage
custom_list = CustomList()
custom_list.insert(0, 1)
custom_list.insert(1, 2)
custom_list.insert(1, 3)

print("Custom list after inserting elements:", custom_list)

Output

Custom list after inserting elements: [1, 3, 2]

3. Python extend() method

The Python extend method will add the items of the iterable to the end of the list.

Syntax: List1.extend(List2)

Example

class CustomList:
    def __init__(self):
        self.data = []

    def extend(self, iterable):
        self.data += iterable

    def __str__(self):
        return str(self.data)


# Example usage
custom_list = CustomList()
custom_list.extend([1, 2, 3])
custom_list.extend([4, 5, 6])

print("Custom list after extending elements:", custom_list)

Output

Custom list after extending elements: [1, 2, 3, 4, 5, 6]

What are the important functions of the Python List?

Some of the important functions of the Python list are given below:

1. Python sum() method

The Python sum method will allow you to calculate the sum of the elements of the list.

Syntax: sum(List)

Example

class CustomList:
    def __init__(self):
        self.data = []

    def append(self, value):
        self.data += [value]

    def sum(self):
        return sum(self.data)

    def __str__(self):
        return str(self.data)


# Example usage
custom_list = CustomList()
custom_list.append(1)
custom_list.append(2)
custom_list.append(3)

print("Custom list:", custom_list)
print("Sum of elements in custom list:", custom_list.sum())

Output

Custom list: [1, 2, 3]
Sum of elements in custom list: 6

2. Python count() method

This method will sum the total occurrence of the given element of the list.

Syntax: List.count(element)

Example

class CustomList:
    def __init__(self):
        self.data = []

    def append(self, value):
        self.data.append(value)

    def count(self, value):
        return self.data.count(value)

    def __str__(self):
        return str(self.data)


# Example usage
custom_list = CustomList()
custom_list.append(1)
custom_list.append(2)
custom_list.append(3)
custom_list.append(1)  # Adding one more 1

print("Custom list:", custom_list)
print("Number of occurrences of '1' in custom list:", custom_list.count(1))

Output

Custom list: [1, 2, 3, 1]
Number of occurrences of '1' in custom list: 2

3.Python index() method

This method will return the index of the first occurrence. So, the start and end indexes are not the required parameters.

Syntax: List.index(element[,start[,end]])

Example

class CustomList:
    def __init__(self):
        self.data = []

    def append(self, value):
        self.data.append(value)

    def index(self, value):
        return self.data.index(value)

    def __str__(self):
        return str(self.data)


# Example usage
custom_list = CustomList()
custom_list.append(1)
custom_list.append(2)
custom_list.append(3)

print("Custom list:", custom_list)
print("Index of '2' in custom list:", custom_list.index(2))

Output

Custom list: [1, 2, 3]
Index of '2' in custom list: 1

4. Python min() method

This method will find the minimum of all the elements of the list.

Syntax: min(iterable, *iterables[, key])

Example

class CustomList:
    def __init__(self):
        self.data = []

    def append(self, value):
        self.data.append(value)

    def min(self):
        return min(self.data)

    def __str__(self):
        return str(self.data)


# Example usage
custom_list = CustomList()
custom_list.append(3)
custom_list.append(1)
custom_list.append(5)
custom_list.append(2)

print("Custom list:", custom_list)
print("Minimum value in custom list:", custom_list.min())

Output

Custom list: [3, 1, 5, 2]
Minimum value in custom list: 1

5. Python max() method

The Python will calculate the maximum of all the elements of the list.

Syntax: max(iterable, *iterables[, key])

Example

class CustomList:
    def __init__(self):
        self.data = []

    def append(self, value):
        self.data.append(value)

    def max(self):
        return max(self.data)

    def __str__(self):
        return str(self.data)


# Example usage
custom_list = CustomList()
custom_list.append(3)
custom_list.append(1)
custom_list.append(5)
custom_list.append(2)

print("Custom list:", custom_list)
print("Maximum value in custom list:", custom_list.max())

Output

Custom list: [3, 1, 5, 2]
Maximum value in custom list: 5

6. Python sort() method

This method will allow us to sort the given data structure in ascending order. Whereas, the key and reverse flag won’t be considered as the necessary parameter and the reverse_ flag will be set to false when nothing is passed through the sorted().

Syntax: list.sort([key,[Reverse_flag]])

Example

class CustomList:
    def __init__(self):
        self.data = []

    def append(self, value):
        self.data.append(value)

    def sort(self):
        self.data.sort()

    def __str__(self):
        return str(self.data)


# Example usage
custom_list = CustomList()
custom_list.append(3)
custom_list.append(1)
custom_list.append(5)
custom_list.append(2)

print("Custom list before sorting:", custom_list)
custom_list.sort()
print("Custom list after sorting:", custom_list)

Output

Custom list before sorting: [3, 1, 5, 2]
Custom list after sorting: [1, 2, 3, 5]

7. Python reverse() method

The reverse method will reverse the order of the list.

Syntax: list. reverse()

Example

class CustomList:
    def __init__(self):
        self.data = []

    def append(self, value):
        self.data.append(value)

    def reverse(self):
        self.data = self.data[::-1]

    def __str__(self):
        return str(self.data)


# Example usage
custom_list = CustomList()
custom_list.append(1)
custom_list.append(2)
custom_list.append(3)

print("Custom list before reversing:", custom_list)
custom_list.reverse()
print("Custom list after reversing:", custom_list)

Output

Custom list before reversing: [1, 2, 3]
Custom list after reversing: [3, 2, 1]

Conclusion

Python lists are fundamental data structures that enable the storage and manipulation of collections of elements. For beginners learning Python, grasping basic list methods is crucial. These methods offer essential functionalities for managing lists effectively.

However, the append() method will allow adding elements to the end of a list, while extend() allows adding multiple elements from another iterable. insert() permits inserting elements at specific positions. For removing elements, students can utilize remove() to delete the first occurrence of a value or pop() to remove and return an element at a given index.

Python List Methods-FAQs

Hridhya Manoj

Hello, I’m Hridhya Manoj. I’m passionate about technology and its ever-evolving landscape. With a deep love for writing and a curious mind, I enjoy translating complex concepts into understandable, engaging content. Let’s explore the world of tech together

Table of Contents

Python – Loop Tuples

In the world of computer science, loops serve as iterative constructs, enabling the repetition of actions, while tuples act as immutable collections, securely storing our data. Read this article to learn more about Python-Loop Tuples

In Python, it is possible to traverse the items in the tuple with the help of a loop construct. So, traversal will be done with the help of an iterator or with the help of an index.

What is Loop Tuples in Python?

In Python, the loop through the tuple items will be operated with the help of a for loop. The for loop in Python will function to iterate over the sequence such as a list, tuple, array, or string.

Example -To loop through the Tuple

# Define a tuple
my_tuple = (1, 2, 3, 4, 5)

# Loop through the tuple
print("Elements of the tuple:")
for element in my_tuple:
    print(element)

Output

Elements of the tuple:
1
2
3
4
5

What is Loop through the Index Numbers in Python?

Looping through the index numbers of the tuple items can be done by referring to their index numbers. Further, the range() and len() functions will be used to make the suitable iterable.

Example

# Define a tuple
my_tuple = (1, 2, 3, 4, 5)

# Loop through the tuple with index numbers
print("Index Numbers and Corresponding Elements:")
for index, element in enumerate(my_tuple):
    print("Index:", index, "Element:", element)

Output

Index Numbers and Corresponding Elements:
Index: 0 Element: 1
Index: 1 Element: 2
Index: 2 Element: 3
Index: 3 Element: 4
Index: 4 Element: 5

How to use the While Loop for the tuple items in Python?

While loop will function to loop through the tuple items. With the help of the len() function, you can evaluate the length of the tuple. It works in a way that it will begin at 0 and loop its way through the tuple items by referring to their indexes.

Note: It is recommended to increase the index by 1 after each of their iterations.

# Define a tuple
my_tuple = (1, 2, 3, 4, 5)

# Initialize index
index = 0

# Loop through the tuple using a while loop
print("Elements of the tuple using while loop:")
while index < len(my_tuple):
    print(my_tuple[index])
    index += 1

Output

Elements of the tuple using while loop:
1
2
3
4
5

Conclusion

In conclusion, mastering the art of looping through tuples in Python opens up a world of possibilities for efficiently handling data. Tuples, with their immutability, provide stability to your code, and by employing loops, you can effortlessly navigate through their elements.

Whether you’re accessing individual items or processing the entire tuple, loops offer a versatile toolset for your Python programming needs.

Moreover, the knowledge gained from this guide will allow you to upskill on tuples and loops in Python, empowering you to write cleaner, more effective code. Keep exploring and experimenting to uncover even more ways to leverage these fundamental concepts in your Python projects.

Python – Loop Tuples- FAQs

Hridhya Manoj

Hello, I’m Hridhya Manoj. I’m passionate about technology and its ever-evolving landscape. With a deep love for writing and a curious mind, I enjoy translating complex concepts into understandable, engaging content. Let’s explore the world of tech together

Table of Contents

The list Copy() method will help you to make a new shallow copy of the list. The article has listed the Python Copy List.

What is the List Copy () Method in Python?

The list Copy () function will allow you to make a copy of the list in Python. It consists of two main ways to create a copy of the list, a Shallow copy and a Deep copy.

Moreover, the list copy () function will make the copy of the list and won’t affect the values in the original list. So, it will provide the freedom to manipulate the data without worrying about data loss.

What is List copy() Method Syntax in Python?

list.copy()

Parameters

• The copy method won’t take any parameters.

Returns: It will return the shallow copy of the list.

How to Create the Simple Copy of a List in Python?

In Python, it is possible to create and copy a new list with the help of the copy() function . Let us look into the example given below.

Example

# Original list
original_list = [1, 2, 3, 4, 5]

# Copying the list using slicing
copied_list = original_list[:]

# Modifying the copied list
copied_list.append(6)

# Printing both lists
print("Original List:", original_list)
print("Copied List:", copied_list)

Output

Original List: [1, 2, 3, 4, 5]
Copied List: [1, 2, 3, 4, 5, 6]

What are the other Examples of the List copy() Method?

Let us look into the examples given below of the List Copy method.

Example 1: To create the Simple List Copy

The example provided below has allowed us to create the list of Python strings with the help of the copy method which is used to copy the list to another variable.

# Creating a list of strings
original_list = ["apple", "banana", "cherry", "date"]

# Copying the list using the copy() method
copied_list = original_list.copy()

# Modifying the copied list
copied_list.append("elderberry")

# Printing both lists
print("Original List:", original_list)
print("Copied List:", copied_list)

Output

Original List: ['apple', 'banana', 'cherry', 'date']
Copied List: ['apple', 'banana', 'cherry', 'date', 'elderberry']

Example 2: To Demonstrate the working of the List copy()

The example below will illustrate how to make the Python List and then make the shallow copy with the help of the copy() function.

# Creating a Python list
original_list = [1, [2, 3], 4, [5, 6]]

# Making a shallow copy using the copy() function
copied_list = original_list.copy()

# Modifying the copied list
copied_list[1][0] = 10

# Printing both lists
print("Original List:", original_list)
print("Copied List:", copied_list)

Output

Original List: [1, [10, 3], 4, [5, 6]]
Copied List: [1, [10, 3], 4, [5, 6]]

What are Shallow Copy and Deep Copy?

The Deep copy will be referred to as the copy of the list, where it will add the element to any of the lists. So, only that list will be altered. Whereas, the Shallow copy will form the new array, but it won’t create new copies of the elements within the array. 

Example: To show the techniques of Shallow and Deep copy

The assignment operator, list copy() method, and copy.copy() method will allow us to create the list and the shallow copy.

Hence, a deep copy will be made using the deep copy() in Python. Hence, it will create changes to the original list and check if the other list is affected or not.

Example:

import copy

# Creating a list with nested lists
original_list = [1, [2, 3], 4, [5, 6]]

# Shallow copy
shallow_copied_list = copy.copy(original_list)

# Deep copy
deep_copied_list = copy.deepcopy(original_list)

# Modifying the copied lists
shallow_copied_list[1][0] = 10
deep_copied_list[1][0] = 20

# Printing all lists
print("Original List:", original_list)
print("Shallow Copied List:", shallow_copied_list)
print("Deep Copied List:", deep_copied_list)

Output

Original List: [1, [2, 3], 4, [5, 6]]
Shallow Copied List: [1, [10, 3], 4, [5, 6]]
Deep Copied List: [1, [20, 3], 4, [5, 6]]

How to Copy List Using the Slicing?

It is possible to copy the list using the list slicing method and then, we are providing the ‘a’ to the new list. Let us look into the example below:

Example

# Original list
original_list = [1, 2, 3, 4, 5]

# Copying the list using slicing
copied_list = original_list[:]

# Modifying the copied list
copied_list.append(6)

# Printing both lists
print("Original List:", original_list)
print("Copied List:", copied_list)

Output

Original List: [1, 2, 3, 4, 5]
Copied List: [1, 2, 3, 4, 5, 6]

Conclusion

To sum up, In Python, copying lists is crucial for maintaining data integrity and avoiding unintended side effects. There are several methods to copy lists, including slicing, the copy() method, shallow copying, and deep copying.

Python Copy List-FAQs

Hridhya Manoj

Hello, I’m Hridhya Manoj. I’m passionate about technology and its ever-evolving landscape. With a deep love for writing and a curious mind, I enjoy translating complex concepts into understandable, engaging content. Let’s explore the world of tech together

Table of Contents

Python Module has built-in functions, classes, and variables. There are several Python modules and each has their specific work. This article has listed the Python modules.

What is a Python Module?

The Python module consists of a file that has Python definitions and statements. A module will then define functions, classes, and variables. Additionally, the module has a runnable code.

Hence, categorizing the module will make the code easier to analyze. Therefore, the code will be more organized.

Create a Module

a.Create a new file, e.g., my_module.py.

b.Define your module content inside this file.

# File: my_module.py

def greet(name):
    """A simple function to greet the user."""
    return f"Hello, {name}! Welcome to my_module."

The syntax is given below:

import module

Use a Module

You will use the module that was created through the import statement.

# File: main_script.py
import my_module

# Using the greet function from my_module
name = "User"
message = my_module.greet(name)
print(message)

When you run main_script.py, it will import the my_module and take the greet function to print a welcome message.

These two files (my_module.py and main_script.py) are placed in the same directory where Python can find them.

Output

Hello, User! Welcome to my_module.

What are the examples of Importing Modules in Python?

The example illustrated below will import the calc for executing the add operation.

# File: main_script.py
import my_module

# Using the greet function from my_module
name = "User"
greeting_message = my_module.greet(name)
print(greeting_message)

# Using the add_numbers function from my_module
result = my_module.add_numbers(5, 7)
print(f"The result of addition is: {result}")

Output

Hello, User! Welcome to my_module.
The result of addition is: 12

How to Import Specific Attributes From a Python Module?

Let us look into the example given below :

# File: main_script.py
from math import sqrt, factorial

# Using the sqrt function from math
number = 25
square_root = sqrt(number)
print(f"The square root of {number} is: {square_root}")

# Using the factorial function from math
fact_number = 5
factorial_result = factorial(fact_number)
print(f"The factorial of {fact_number} is: {factorial_result}")

Output

The square root of 25 is: 5.0
The factorial of 5 is: 120

What is * Import symbol?

The * symbol is used to import the statement function to import all the names from the module to the current namespace.

The syntax is given below

from module_name import *

Note:

The * symbol has its benefits and drawbacks. Suppose the purpose is known to you. However, it is not advised to use *.

# File: main_script.py
from math import sqrt, factorial

# Using the sqrt function from math
number = 25
square_root = sqrt(number)
print(f"The square root of {number} is: {square_root}")

# Using the factorial function from math
fact_number = 5
factorial_result = factorial(fact_number)
print(f"The factorial of {fact_number} is: {factorial_result}")

Output

The square root of 25 is: 5.0
The factorial of 5 is: 120

Locate Python Module

1. Current Directory:
   • First, Python checks if the module is in the same folder where your Python script is located.
2. PYTHON PATH:
   • If it’s not in the current folder, Python checks other places listed in the PYTHON PATH variable.
   • PYTHON PATH is like a list of folders where Python looks for modules. You can add more folders to this list if needed.
3. Installation-Dependent Directories:
   • If the module is still not found, Python checks places to set up when Python was installed.
   • These are special folders that come with Python and contain important modules that are needed for many programs.

What is the Directories List For Modules?

The sys. path has a built-in variable within the sys module. It has a list of directories, in which the interpreter will search in the particular module.

# File: main_script.py
import sys

# Now you can use functions and variables from the sys module
print("Python version:")
print(sys.version)
print("")

print("System platform:")
print(sys.platform)

Output

Python version:
3.8.5 (default, Jan 27 2021, 15:41:15)
[GCC 9.3.0]

System platform:
linux

How to Rename the Python Module?

The Python module will rename the module by importing it with the keyword.

The syntax is provided below

Syntax:  Import Module_name as Alias_name

# File: main_script.py
import math

# Using the sqrt function from math
number = 18  # Change the number to 18
square_root = math.sqrt(number)
print(f"The square root of {number} is: {square_root}")

# Using the factorial function from math
fact_number = 5
factorial_result = math.factorial(fact_number)
print(f"The factorial of {fact_number} is: {factorial_result}")

Output

The square root of 18 is: 4.242640687119285
The factorial of 5 is: 120

What is Python Built-in Modules?

Python consists of built-in modules that allow them to import whenever required.

# File: main_script.py
import math

# Using the sqrt function from math
number = 25
square_root = math.sqrt(number)
print(f"The square root of {number} is: {square_root}")

# Using the factorial function from math
fact_number = 5
factorial_result = math.factorial(fact_number)
print(f"The factorial of {fact_number} is: {factorial_result}")

# Using the pi function from math
pi_value = math.pi
print(f"The value of pi is: {pi_value}")

# Converting 2 radians to degrees
radians_to_degrees = math.degrees(2)
print(f"2 radians in degrees is: {radians_to_degrees}")

# Converting 60 degrees to radians
degrees_to_radians = math.radians(60)
print(f"60 degrees in radians is: {degrees_to_radians}")

# Calculating sine of 2 radians
sine_value = math.sin(2)
print(f"The sine of 2 radians is: {sine_value}")

# Calculating cosine of 0.5 radians
cosine_value = math.cos(0.5)
print(f"The cosine of 0.5 radians is: {cosine_value}")

# Calculating tangent of 0.23 radians
tangent_value = math.tan(0.23)
print(f"The tangent of 0.23 radians is: {tangent_value}")

Output

The square root of 25 is: 5.0
The factorial of 5 is: 120
The value of pi is: 3.141592653589793
2 radians in degrees is: 114.59155902616465
60 degrees in radians is: 1.0471975511965979
The sine of 2 radians is: 0.9092974268256817
The cosine of 0.5 radians is: 0.8775825618903728
The tangent of 0.23 radians is: 0.23414336235146526

Conclusion

To conclude, this article has offered the information about the Python Modules. It has also included how to create the module, Python import from the module, Renaming the Python Module, and several others.

Python Modules-FAQs

Hridhya Manoj

Hello, I’m Hridhya Manoj. I’m passionate about technology and its ever-evolving landscape. With a deep love for writing and a curious mind, I enjoy translating complex concepts into understandable, engaging content. Let’s explore the world of tech together

Table of Contents

Python Arguments will function to pass the data elements or the information to the functions. Hence, arguments will be enclosed in the parentheses after the function name. It is possible to enter as many parameters. This article has listed Python’s Arbitrary Arguments.

What are Python Function Arguments?

In the Python Function arguments, the user will pass their data values for running the functions. Functions consist of a set of arguments up until now. There is another way to define a function in Python and it has a variable number of arguments. Different types of function arguments are listed below:

What are the Different Types of Python Default Arguments?

1. Python Default
2. Python Keyword Argument
3. Python Arbitrary Argument

In Python, function arguments have default values that are assigned to them. This will be operated using the assignment operator (=) in the functional argument.

In programming, when you create a function, you can set default values for some of its inputs. This means that if someone uses the function but forgets to provide a value for a specific input, the function will use a predefined default value instead.

Note: The Python Default Arguments are known as the Python Optional arguments.

Example

def greet(name, greeting="Hello"):
    print(f"{greeting}, {name}!")

# Calling the function without providing a value for the 'greeting' parameter
greet("John")  # Output: Hello, John!

# Calling the function with a custom greeting
greet("Alice", "Hi")  # Output: Hi, Alice!

Output

Hello, John!
Hi, Alice!

1. In the first function call (greet("John")), since no specific greeting is provided, the default value “Hello” is used. So, it prints “Hello, John!”.
2. In the second function call (greet("Alice", "Hi")), a custom greeting “Hi” is provided. Therefore, it prints “Hi, Alice!”.

What are Python Keyword Arguments?

The arguments in Python are arguments that will be passed in the fixed positional order to function. Moreover, the Python Keyword Arguments are considered a contradiction to the Required Arguments and will offer the exact opposite function.

The example for the Python Keyword Argument is given below:

def person_info(name, age, city):
    print(f"Name: {name}, Age: {age}, City: {city}")

# Calling the function using keyword arguments
person_info(name="John", age=25, city="New York")

# Calling the function with a different order of keyword arguments
person_info(city="San Francisco", age=30, name="Alice")

Output

Name: John, Age: 25, City: New York
Name: Alice, Age: 30, City: San Francisco

In the example given above, the ‘person_info has three parameters such as ‘name’, ‘age’, and ‘city’. while calling the function, we will give the parameter names with their values using the keyword arguments. However, the order of arguments will be different from the order of parameters in the function definition.

What are Python Arbitrary Arguments?

In Python Arbitrary arguments, the programmer won’t know the number of arguments that are required to be passed into the function. Meanwhile, they will also use an asterisk (*) to indicate the method before the parameter in the function. This is known as the Python *args.

Python * args will provide a function that will accept any number of positional arguments and these arguments are non-keyword arguments and variable-length argument lists.

An example of Python Arbitrary arguments is given below:

def display_items(*items):
    print("Items:")
    for item in items:
        print(f"- {item}")

# Calling the function with different numbers of arguments
display_items("Apple", "Banana", "Orange")
display_items("Laptop", "Mouse")
display_items("Book", "Pen", "Notebook", "Pencil")

Output

Items:
- Apple
- Banana
- Orange

Items:
- Laptop
- Mouse

Items:
- Book
- Pen
- Notebook
- Pencil

Conclusion

To conclude, this article has described the arbitrary arguments and the different types of Python default arguments. This will help beginners to improve their skills and knowledge regarding Python arbitrary arguments.

Python Arbitrary Arguments- FAQs

Hridhya Manoj

Hello, I’m Hridhya Manoj. I’m passionate about technology and its ever-evolving landscape. With a deep love for writing and a curious mind, I enjoy translating complex concepts into understandable, engaging content. Let’s explore the world of tech together

Table of Contents

Function annotations are random expressions that are written with the functions and won’t be evaluated in the compile time. Moreover, the future annotation won’t exist at the run time. This article has listed in the future annotations in Python.

What are Future Annotations?

Function Annotations are the arbitrary Python expressions that will be associated with the various parts of functions. Hence, it won’t be checked at the compile time and doesn’t play any role during the existing time.

Moreover, function annotations are mostly used by third-party libraries as it is known that the different libraries have different benefits from these function annotations.

a.Future annotations with strings will work as messages at the compile time for describing the functionality of different methods, classes, and variables.

b. [def fxn(a:”int”, b:”float”=5.0) -> “int”]

In the example provided above, these annotations will define the data types of the parameters as Python will support the dynamic type checking and it is not possible to return type. Thus, libraries will use these kinds of annotations.

What is the syntax of the Future Annotations?

a. Future Annotation for simple parameters

def functionName(argumentName : expression):  

Colons are used after the argument name and then we can write the expression after the colon. This expression will appear similar to any data type of the argument.

Example

def fxn(var1 : expression1,var2:expression2:96):  

b. Function annotations for excess parameters

if it is required to use an arbitrary number of arguments as the function parameters with the same expression. Hence, it is possible to use the function annotation.

Example

def fxn(*arg1: expression1,**arg2:expression2):  

c. Function annotation for the nested parameters

If it is required to pass the list in the function call it as the argument and thus, apply the function annotations on the individual elements.

Example

def fxn((var1:expression,var2:expression),(var3:expression,var4:expression)):  

d. Function annotation for the return type:

The annotation of the return time will be operated with the help of the’>’ operator.

Example

def fxn(var1:expression) -> expression2:  

What are the examples of Function annotation in Python?

The examples of function annotation in Python are given below:

def add_numbers(x: int, y: int) -> int:
    """
    Adds two numbers and returns the result.

    :param x: The first number.
    :param y: The second number.
    :return: The sum of x and y.
    """
    result = x + y
    return result

# Example usage:
num1 = 5
num2 = 7
sum_result = add_numbers(num1, num2)

print(f"The sum of {num1} and {num2} is: {sum_result}")

Output

The sum of 5 and 7 is: 12

1.  Using ‘__annotations__’

The attribute __annotations__ will get every annotation in the function. It will return with the dictionary and has a pair of keys and values in which the key would act as an argument and the value will be in their expression.

def calculate_discount(original_price: float, discount_rate: float = 0.1) -> float:
    """
    Calculate the discounted price based on the original price and discount rate.

    :param original_price: The original price of the item.
    :param discount_rate: The discount rate (default is 10%).
    :return: The discounted price.
    """
    discounted_price = original_price - (original_price * discount_rate)
    return discounted_price

# Example usage:
item_price = 100.0
discounted_price = calculate_discount(item_price)

print(f"Original Price: ${item_price}")
print(f"Discounted Price: ${discounted_price}")

Output

Original Price: $100.0
Discounted Price: $90.0

b. Using the standard Python module:

In Python, there’s a module called pydoc that helps create documentation for Python code. While it doesn’t have a help() function to open a shell environment, you can still get information about function annotations.

c. Using the inspect module

In Python, there is another standard module which is known as inspect. This consists of information on a file, module, class, or object.

Example

import inspect

def example_function(x: int, y: str) -> float:
    return float(x)

# Get information about the function using inspect
function_info = inspect.getfullargspec(example_function)

# Print the information
print("Function Name:", example_function.__name__)
print("Parameters:", function_info.args)
print("Default Values:", function_info.defaults)
print("Annotations:", example_function.__annotations__)
print("Docstring:", example_function.__doc__)

Output

Function Name: example_function
Parameters: ['x', 'y']
Default Values: None
Annotations: {'x': <class 'int'>, 'y': <class 'str'>, 'return': <class 'float'>}
Docstring: None

Conclusion

To conclude, this article has described the Future Annotations in Python. It has also included the benefits of future annotations. This information can allow to improve skills and knowledge regarding the future annotation of Python.

Function Annotation in Python -FAQs

Hridhya Manoj

Hello, I’m Hridhya Manoj. I’m passionate about technology and its ever-evolving landscape. With a deep love for writing and a curious mind, I enjoy translating complex concepts into understandable, engaging content. Let’s explore the world of tech together