Complete List of built-in functions in Python

Python, and open source development technology celebrated for its simplicity and efficiency, offers a plethora of tools to make programming tasks a breeze. One of its most valuable features is the extensive collection of built-in functions in Python. 

In this blog post, we’ll delve into the world of Python built-in functions, explore their versatility, discuss best practices for maintaining clean code, and take a closer look at the top 10 Python development frameworks.

Understanding Python Built-in Functions

Python built-in functions are predefined functions that are readily available for use without requiring any imports or installations. These functions cover a wide range of operations, from simple tasks like conversions to complex operations like sorting and iteration.

Function Name	Description
abs() Function	Return the absolute value of a number
aiter() Function	Creates an asynchronous iterator from an asynchronous iterable.
all() Function	Returns True if all elements in an iterable are true.
any() Function	Returns True if any element in an iterable is true.
anext() Function	Retrieves the next item from an asynchronous iterator.
ascii() Function	Returns a string containing a printable representation of an object.
bin() Function	Convert integer to a binary string
bool() Function	Converts a value to a Boolean.
breakpoint() Function	IInvokes the built-in debugger at the specified line.
bytearray() Function	Creates a mutable byte array from a sequence of bytes.
bytes() Function	Creates an immutable bytes object from a sequence of bytes.
callable() Function	Checks if an object is callable (can be called as a function).
chr() Function	Returns a string representing a character whose Unicode code point is the given integer.
classmethod() Function	Converts a method into a class method.
compile() Function	Compiles source code into a code or AST object
complex() Function	Creates Complex Number
delattr() Function	Delete the attribute from the object
dict() Function	Creates a Dictionary
dir() Function	Returns a list of the attributes of any object
divmod() Function	Returns the quotient and remainder of a division.
enumerate() Function	Returns an enumerate object that generates pairs of index and value from an iterable.t
eval() Function	Evaluates a Python expression as a string and returns the result.
exec() Function	Executes dynamically created Python code.
filter() Function	Filters elements of an iterable based on a given function.
float() Function	Converts a number or a string to a floating-point number.
format() Function	Formats a value based on a specified format string.
frozenset() Function	Creates an immutable frozenset from an iterable.
getattr() Function	Retrieves the value of an attribute from an object.
globals() Function	Returns a dictionary representing the current global symbol table.
hasattr() Function	Checks if an object has a given attribute.
hash() Function	Returns the hash value of an object.
help() Function	Displays help documentation for a module, function, class, etc
hex() Function	Converts an integer to a hexadecimal string.
id() Function	Return the identity of an object
input() Function	Reads a line from the user’s input
int() Function	Converts a number or a string to an integer
isinstance() Function	Checks if an object is an instance of a specified class.
issubclass() Function	Checks if a class is a subclass of another class.
iter() Function	Returns an iterator object from an iterable.
len() Function	Returns the length (number of items) of an object.
list() Function	Creates a list in Python
locals() Function	Returns a dictionary representing the current local symbol table.
map() Function	Applies a function to all items in an iterable.
max() Function	Returns the largest item in an iterable or a set of arguments.
memoryview() Function	Creates a memory view object of an object’s memory.
min() Function	Returns the smallest item in an iterable or a set of arguments.
next() Function	Retrieves the next item from an iterator.
object() Function	Returns a new featureless object.
oct() Function	Converts an integer to an octal string.x
open() Function	Opens a file for reading, writing, or both.
ord() Function	Returns an integer representing the Unicode character.
pow() Function	Returns the value of x to the power of y.
print() Function	Prints objects to the standard output.
property() Function	Gets, sets, or deletes an object’s property.
range() Function	Generate a sequence of numbers
repr() Function	Returns a string containing a printable representation of an object.
reversed() Function	Returns a reversed iterator of a sequence.
round() Function	Rounds a number to a specified number of decimal places.
set() Function	Creates a set.
setattr() Function	Assign the object attribute its value
slice() Function	Creates a slice object representing a range of indices.
sorted() Function	Returns a sorted list from an iterable.
staticmethod() Function	Converts a method into a static method.
str() Function	Converts an object to a string.
sum() Function	Calculates the sum of all elements in an iterable.
super() Function	Returns a temporary object of the superclass.
tuple() Function	Creates a tuple in Python
type() Function	Returns the type of the object
vars() Function	Returns the __dict__ attribute for a module, class, instance, or any other object
zip() Function	Combines multiple iterables element-wise.
__import__() Function	Used for dynamic importing of modules.

Best Practices for Writing Clean Python Code

Writing clean code is pivotal for maintainability and collaboration. Here are some Python coding best practices to follow

• Meaningful Naming: Use descriptive names for variables, functions, and classes.
• Consistent Formatting: Adhere to PEP 8 guidelines for consistent indentation, line length, and whitespace usage.
• Comments and Documentation: Add comments to clarify complex sections and provide docstrings for functions and modules.
• Modularization: Break down code into smaller, reusable functions.
• Avoid Magic Numbers: Use constants or named variables instead of hard-coded numbers.
• Readable Logic: Write code that reads like a story, with well-organized and logical flow.
• Error Handling: Implement appropriate error handling to make your code more robust.
• Version Control: Utilize version control systems like Git to track changes and collaborate effectively.
• Testing: Write unit tests to ensure your code behaves as expected.
• Refactoring: Regularly refactor your code to eliminate redundancy and improve efficiency.

How Python Utilizes APIs for Web Application Interaction

Its versatility and rich library ecosystem make it an ideal candidate for interfacing with APIs. Here’s how Python APIs interact with web applications:

1. Sending HTTP Requests

Its libraries provide straightforward ways to send HTTP requests to web APIs. This enables Python programs to ask for data, send data, or trigger actions on remote servers.

2. Receiving and Parsing Responses

Upon sending a request, web APIs respond with data, often in formats like JSON or XML. The programming language can’t parse these responses using libraries like json or xml.etree.ElementTree, making it easy to extract and manipulate the relevant information.

3. Authenticating and Authorizing

Many APIs require authentication to ensure security. It can handle authentication by sending tokens or credentials along with the requests, enabling authorized access to protected resources.

4. Data Integration and Automation

Python application’s interaction with APIs enables seamless data integration between different systems. This is particularly useful for tasks like fetching weather data, stock market information, or social media posts automatically.

5. Web Scraping

While not strictly API interaction, Its ability to scrape data from web pages using libraries like BeautifulSoup or Scrapy can achieve similar results when APIs aren’t available.

Practical Applications of Python and APIs

The integration of Python and APIs opens the door to a multitude of applications:

1. Social Media Integration

Python web applications can interact with APIs provided by social media platforms to retrieve user data, post updates, or analyze trends.

2. Data Analysis and Visualization

Python apps can fetch data from APIs, process it using libraries like Pandas, and visualize insights using Matplotlib or other visualization tools.

3. Content Aggregation

Automatically gather news articles, blog posts, or any other content from various sources using APIs.

4. E-commerce Integration

Python development companies can help businesses streamline e-commerce operations by integrating APIs from payment gateways, inventory systems, and shipping providers.

5. IoT and Home Automation

Connect IoT devices to web services using APIs, allowing remote control and data exchange.

Top 10 Python Development Frameworks

It is home to a diverse array of development frameworks that cater to various needs. 

Let’s explore the top 10 frameworks:

Django:
A high-level web framework that encourages rapid Python application development and clean, pragmatic design. It follows the “batteries-included” philosophy, providing many built-in features like an ORM (Object-Relational Mapping), authentication, and an admin interface. Django is suitable for building complex web applications.

Flask:
A lightweight micro-framework that offers flexibility and minimalism. Flask gives you the freedom to choose components as you need them, making it suitable for both simple and complex applications. It’s a good choice for those who want to have more control over the components they use.

PyTorch:
Although primarily known as a deep learning framework, PyTorch can be used for general-purpose programming as well. It’s particularly popular in the field of machine learning and research due to its dynamic computation graph and ease of use.

TensorFlow:
Developed by Google, TensorFlow is an open-source machine learning framework. It’s widely used for building and training neural networks for tasks like image recognition, natural language processing, and more.

FastAPI:
A modern, fast web framework for building APIs with Python 3.7+ type hints. FastAPI is known for its speed and automatic generation of interactive API documentation, making it a great choice for building APIs quickly.

Tornado:
A scalable, non-blocking web server and web application framework. Tornado is designed to handle asynchronous I/O, making it suitable for applications that require real-time updates and high concurrency.

Pyramid:
A full-featured web framework that prioritizes flexibility and modularity. Pyramid is a great choice for building python web applications of varying sizes and complexities while allowing you to select the right components for your project.

CherryPy:
A minimalist framework that aims to be easy to learn and use. It’s a good option for building small python apps and services without the complexity of larger frameworks.

Bottle:
A micro-framework designed for simplicity and minimalism. It’s ideal for small-scale applications and quick prototypes due to its compact size and ease of use.

Kivy:
Although not a web framework, Kivy is a library for developing multitouch applications. It’s particularly useful for building python cross-platform applications with rich user interfaces for desktop and mobile platforms.

FAQs

Q1: Are built-in functions available without any imports?

A: Yes, built-in functions are inherent to Python and do not require import statements.

Q2: Can I create my own built-in functions?

A: While you can’t modify existing built-in functions, you can define your own functions in Python.

Q3: How can I learn more about Python’s standard library functions?

A: The official Python documentation provides comprehensive information about standard library functions.

Q4: What’s the advantage of using a micro-framework like Flask?

A: Micro-frameworks like Flask offer minimalistic, customizable solutions for web applications, allowing developers to choose components as needed.

Q5: How do APIs benefit Python programmers?

A: APIs empower Python developers to access and manipulate data from external sources, enabling tasks like data analysis, integration, and automation.

Conclusion

Its ability in interacting with web applications through APIs empowers developers to create versatile and integrated solutions. 

The language’s simplicity, along with libraries that facilitate HTTP requests, response parsing, and authentication, make Python a preferred choice for API-driven development. 

Whether you’re building data-driven applications, automating tasks, or integrating services, Python’s API interactions provide the foundation for innovation in today’s interconnected world.