Discover the 20 features of Python that makes it truly awesome for developers and also has many other features that make it easy to learn, write, read and maintain code. Here are 20 of them:
1. Simple and Elegant Syntax
One of the most attractive features of Python is its simple and elegant syntax. Python uses indentation to define blocks of code, which makes the code more readable and consistent. Python also has a clear and concise grammar that avoids unnecessary symbols and keywords. Python code looks like pseudocode which makes it easier to understand and express your logic.
2. Dynamic Typing
Python is a dynamically typed language which means that you do not have to declare the type of a variable before using it. Python automatically assume the type of a variable based on the value assigned to it. This makes Python more flexible and expressive than statically typed languages where you have to specify the type of every variable and function parameter.
3. Multiple Programming Paradigms
Python supports multiple programming paradigms which means that you can use different styles of programming depending on your needs and preferences.
- Python supports object-oriented programming which allows you to create objects and classes that encapsulate behavior and data.
- Python also supports imperative programming which allows you to write sequential commands that manipulate data.
- Python also supports functional programming which allows you to write pure functions that avoid side effects and use higher-order functions and lambda expressions.
- Python also supports procedural programming which allows you to write modular code that can be reused and tested.
4. Rich Built-in Data Types
Python has a rich set of built-in data types that can handle various kinds of data. Some of the most common data types are:
- Numbers: integers, floats, complex numbers, booleans
- Strings: sequences of characters enclosed in quotes
- Lists: ordered collections that can be modified
- Tuples: ordered collections of items that cannot be modified
- Dictionaries: unordered collections of key-value pairs
- Sets: unordered collections of unique items
- Ranges: sequences of numbers with a fixed step
- Bytes: sequences of bytes for binary data
Python also allows you to create your own custom data types by defining classes.
5. Extensive Standard Library
Python has an extensive standard library that provides a wide range of modules and packages that can perform various tasks and operations. Some of the categories of modules in the standard library are:
- Text processing: string, re, textwrap, difflib.
- Data structures: collections, heapq, bisect, queue.
- File and directory access: os, pathlib, shutil, glob.
- Data persistence: pickle, json, csv, sqlite3.
- Data compression and archiving: zlib, gzip, bz2, tarfile, zipfile.
- Cryptography: hashlib, hmac, secrets.
- Networking: socket, urllib, http, email.
- Internet data handling: xml, html, webbrowser.
- Structured markup processing: xml.etree.ElementTree, html.parser, markdown.
- Development tools: unittest , doctest , pdb.
- Debugging and profiling: traceback , sys , logging , timeit , cProfile.
- Software packaging and distribution: distutils , setuptools , pip.
- Operating system services: subprocess , multiprocessing , threading , signal.
- Interprocess communication and networking: socketserver , asyncio , selectors.
- Internet protocols and support: websockets , cgi , http.server , smtpd.
- Multimedia services: audioop , wave , aifc , sunau.
- Internationalization: locale , gettext , codecs , unicodedata.
- Program frameworks: tkinter , turtle , cmd , shlex.
- Graphical user interfaces with Tk: tkinter.ttk , tkinter.font , tkinter.messagebox , tkinter.filedialog.
The standard library is constantly updated and improved with new modules and features.
6. Cross-platform Compatibility
Python is a cross-platform language which means that it can run on various operating systems such as Windows, Linux, Mac OS X and others. Python also has a built-in interpreter that can execute Python code without compiling it first. This makes Python portable and easy to deploy on different platforms.
7. Open Source and Community Driven
Python is an open source language which means that its source code is freely available and anyone can contribute to its development and improvement. Python also has a large and active community of developers and users who provide feedback, support, documentation, tutorials, libraries, frameworks and tools for Python. Python has a diverse and inclusive community that welcomes newcomers and encourages collaboration and learning.
8. Batteries Included
Python follows the philosophy of “batteries included” which means that it provides a comprehensive set of features and modules that can handle most common tasks and problems. Python also has a rich ecosystem of frameworks and third-party libraries that can extend its functionality and capabilities. Some of the most popular and useful libraries and frameworks for Python are:
- NumPy: a library for numerical analysis and scientific computing
- SciPy: a library for technical computing and scientific
- Pandas: a library for manipulation and data analysis
- Matplotlib: a library for data visualization and plotting
- Scikit-learn: a library for data mining and machine learning
- TensorFlow: a library for deep learning and neural networks
- Keras: a high-level API for deep learning
- PyTorch: a library for deep learning and tensor computation
- Django: a web framework for rapid development and clean design
- Flask: a micro web framework for building web applications
- Requests: a library for making HTTP requests
- Beautiful Soup: a library for parsing XML documents and HTML
- Selenium: a library for automating web browser actions
- NLTK: a library for NLP
- Spacy: a library for advanced NLP
- Gensim: a library for document similarity analysis and topic modeling
- PyGame: a library for game development
- PyQT: a library for creating graphical user interfaces with Qt
- Pillow: a library for image processing
These are just some of the many libraries and frameworks that Python offers. You can find more on the Python Package Index (PyPI) which is the official repository of Python packages.
9. Multiple Implementations
Python has multiple implementations which means that there are different versions of Python that run on different platforms or have different features. Some of the most common implementations are:
- CPython: the default and most widely used implementation of Python, written in C
- Jython: an implementation of Python that runs on the Java Virtual Machine (JVM)
- IronPython: an implementation of Python that runs on the .NET Framework
- PyPy: an implementation of Python that uses a just-in-time (JIT) compiler to speed up execution
- MicroPython: an implementation of Python that runs on microcontrollers and embedded systems
Each implementation has its own advantages and disadvantages depending on your needs and preferences.
10. Interpreted Language
Python is an interpreted language which means that it does not need to be compiled before execution. Instead, it uses an interpreter that reads and executes the code line by line. This makes Python easier to debug, test and modify than compiled languages. It also allows you to use interactive shells or notebooks that let you write and run code on the fly.
11. Garbage Collection
Python has automatic memory management which means that it handles the allocation and deallocation of memory for your objects. Python uses a technique called garbage collection which periodically frees up the memory occupied by objects that are no longer in use. This saves you from having to manually manage memory or worry about memory leaks.
12. Exception Handling
Python has built-in support for exception handling which is a mechanism that allows you to deal with errors or unexpected situations in your code. Python provides several types of exceptions that represent different kinds of errors such as SyntaxError, ValueError, IndexError and KeyError. You can use the try-except-finally statements to catch, handle or clean up after exceptions. You can also raise your own exceptions using the raise keyword or create your own custom exception classes by inheriting from the Exception class.
13. Introspection
Python has introspection capabilities which means that it can examine its own objects and code at runtime. You can use various built-in functions and attributes to get information about your objects such as type(), dir(), id(), getattr() and hasattr(). You can also use the inspect module to get more detailed information about your code such as source code, documentation, and arguments.
14. Metaprogramming
Python supports metaprogramming which is the ability to write code that manipulates or generates other code. You can use various features of Python to perform metaprogramming such as:
- Decorators: functions or classes that modify or enhance the behavior of other functions or classes
- Descriptors: objects that define how attributes are accessed or modified in classes
- Metaclasses: classes that define how other classes are created or initialized
- Reflection: the ability to modify or access attributes
- Code generation: the ability to create or modify code using other code
- Macros: expressions that are expanded into code before execution
- Eval: a function that evaluates a string as Python code
- Exec: a function that executes a string or an object as Python code
Metaprogramming can be useful for creating dynamic or generic code but it can also be dangerous or complex if not used carefully.
15. Multiple Inheritance
Python supports multiple inheritance which is the ability of a class to inherit from more than one parent class. This allows you to create complex class hierarchies and combine the features and behaviors of different classes. You can use the super() function to access the methods and attributes of the parent classes in a consistent and cooperative way. You can also use the MRO (method resolution order) attribute to see the order in which the parent classes are searched for methods and attributes.
16. Operator Overloading
Python supports operator overloading which is the ability to redefine or customize the behavior of operators for user-defined classes. You can use special methods also known as dunder methods or magic methods to implement operator overloading. For example, you can use the add() method to define how the + operator works for your class. You can also use the str() method to define how your class is converted to a string. There are many other special methods that you can use to overload various operators and functions for your class.
17. Generators and Iterators
Python supports generators and iterators which are objects that can produce a sequence of values on demand. Generators are functions that use the yield keyword to return values one at a time. Iterators are objects that implement the iter() and next() methods to return values when called by the next() function. You can use generators and iterators to create lazy sequences that are efficient and memory-friendly. You can also use generator expressions and comprehensions to create generators in a concise and elegant way.
18. Decorators
Python supports decorators which are functions or classes that modify or enhance the behavior of other functions or classes. You can use the @ symbol to apply a decorator to a function or class definition. For example, you can use the @staticmethod decorator to define a static method in a class which is a method that does not depend on an instance of the class. You can also use the @property decorator to define a property in a class which is an attribute that has getter and setter methods. You can also create your own custom decorators by defining functions or classes that take another function or class as an argument and return a modified function or class.
19. Context Managers
Python supports context managers which are objects that provide a way to manage resources in a safe and convenient way. You can use the with statement to create a context using a context manager which will automatically perform some actions before and after the execution of a block of code. For example, you can use the open() function as a context manager to open a file which will automatically close the file when you exit the context. You can also use the lock() method of a threading.Lock object as a context manager to acquire and release a lock for thread synchronization. You can also create your own custom context managers by defining classes that implement the enter() and exit() methods or by using the @contextmanager decorator from the contextlib module.
20. Coroutines and Asyncio
Python supports coroutines and asyncio which are features that allow you to write asynchronous code that can perform multiple tasks concurrently without blocking.
- Coroutines are functions that use the async and await keywords to suspend and resume their execution at certain points.
- Asyncio is a module that provides an event loop, which is an object that manages and executes coroutines and other tasks in an efficient and non-blocking way.
You can use coroutines and asyncio to write concurrent code that can handle high-performance network or IO operations.
Conclusion
These are some of the 20 features of Python that make it awesome. Python is a versatile, powerful and easy-to-use language that has many applications and benefits. If you want to learn more about Python you can check out our Python Introduction article where we cover the basics of Python and also stay tuned for upcoming python tutorials. Happy coding!