What is Python Used For? | 7 Practical Python Applications
Last Updated : 19 Jul, 2024
Python is an interpreted and object-oriented programming language commonly used for web development, data analysis, artificial intelligence, and more. It features a clean, beginner-friendly, and readable syntax. Due to its ecosystem of libraries, frameworks, and large community support, it has become a top preferred choice for developers in the industry over the last few years.
One of the best advantages of Python is its extensive libraries. If you want to learn web development, it offers frameworks like Flask and Django. For competitive programming, it provides libraries for data structures. If you’re interested in automation or machine learning, Python has all the necessary tools a programmer needs. So, let’s discuss some of the use cases and applications of Python.
What are Python’s Uses and Applications?
Being one of the most high-level languages, Python is vastly being used in solving real-life problems today. It is majorly suitable for scientists to work in diverse industries such as machine learning, artificial intelligence, robotics, etc.
There’s no surprise to the fact that Python was coined among the world’s most popular and highly used programming languages among developers worldwide. Today, it holds above 8 Million active developers, As of today, there are above 11,000 active jobs available (in India) and 121,000 (worldwide).
Here, in this article, we will see how Python can be useful to solve real-life problems and what are its applications developers are actively using these days.
7 Practical Python Applications
Python is one of the most popularly used language today, and with more people using it multiple kind of applications are coming up everyday. Some of them are useful for common people use and some are useful for developers to create amazing example. So, we will discuss some of the most practical and indemand application of Python that are widely used in the industry.
1. IoT
With the advancement of technology, IoT (Internet of Things) is spreading like fire and is definitely making a huge impact on our lives. As per reports, more than 10 Billion devices are active (integrated with IoT – *2022) and are expected to grow up by 75+ Billion by the end of 2025. Moreover, the market share of IoT has taken up pace and is expected to jump from USD 478 Billion (2022) – USD 2465 Billion (by the end of 2029). These stats are evidence that how far we’ve come in technology.
How is it useful?
- Developers use Raspberry Pi to enable any device for IoT purposes with the help of Python.
- Python language can be used to create embedded software, making Python fast on smaller objects that can use it.
- Python’s Flask can be easily used to work on the backend part of IoT as it offers easy setup on server-side I/O that helps IoT devices to work more smoothly.
2. Image Recognition and Text Processing
The algorithms are pretty similar to that of the human brain structure and in which different neurons have the capability to copy other functionality. In other words, this technology has become the most prominent one in today’s technology. Industries such as Gaming, and 3D weren’t possible without image processing. The ease of Python and its libraries such as SciPy, Scikit, etc. are among those top reasons why is it well known for using it when it comes to text processing and image recognition.
How is it useful?
- Python Program allows developers to prepare multi-dimensional images due to its capabilities and libraries for image processing and designing.
- Libraries such as NLTK (Natural Language Toolkit), CoreNLP, spaCy, etc. are one of the best tools for processing text using Python.
- Developers create apps using Python are the best solution for companies to identify images from a database (images)
If you’re looking to learn Python from scratch, we recommend you to check out this highly recommendable course – Python Programming Foundation – Self Paced
3. Scientific Computing
Over the past few years, the approach to processing mathematical calculations has drastically changed. Developers and data scientists have developed new methods to handle complexities. There are multiple reasons to choose Python for scientific computing, some of them are:
- Python has a suitable environment and built-in support for scientific computing. Packages like NumPy, Scikit, Matplotlib, etc. are among those toolkits that help scientists and developers to work flawlessly on application
- Python enables developers to interact directly with code using cFFI (C Foreign Function Interface) by using tools such as SWIG.
- Python carries long-supported options for the I/O function to support all types of I/O formats that includes both real-time and streaming formats,
4. Game Development
Python is also famous for developing a variety of games such as arcades, adventure, puzzles, etc. Over these years, developers have been able to deliver games like tic-tac-toe, ping-pong, etc. Libraries like Pygame are among the most popular libraries that developers use for game development due to their functionality of I/O. However, when it comes to building high-interactive 3D games, Python will not be able to provide the expected output that’s why there are limitations while building games using Python.
How and where is it most suitable?
- Pygame contains a wide range of modules for developing highly interactive apps
- It is good for developing number crunching and string manipulation
- Carries capability to go platform independence
5. Data Analysis & Visualization
Python is one of the most suitable languages for data analysis and visualization, its libraries offer capabilities to scientists to handle any scale of data. Below are some of the most popular libraries that are being used for analysis and visualization:
- Pandas: It is among the most popular libraries used by data scientists for data manipulation and analysis
- Matplotlib: It is extensively used for creating plotting and various proportion of graphs
- Scikit-learn: Much helpful for creating plotting functions, and descriptive statistics for different sets of data
- Seaborn: Highly used by scientists for preparing statistical data visualization.
- NumPy and SciPy: One of the most popular libraries in Python for processing numeric and scientific calculations
6. Artificial Intelligence
Python offers fascinating libraries such as Pybrain, NumPy, SciPy, etc. for different tasks and offers extensive support to developers to work on complex projects or any dedicated task. Being platform-independent, Python offers superb flexibility for different tasks. Let’s say you want to start an AI project, then packages like NumPy, scikit-learn, SciPy, etc. will provide the base for any basic form. Furthermore, today it is being used to solve many complex problems such as Image recognition, data processing, scientific calculation, etc.
There are multiple reasons to use Python for AI, ML, DL, etc. but among all of them, one major point that withholds this is its ease of readability. Besides this, it offers add-on features to create interactive, and informative designs.
You can also visit this article to learn in-depth about Python – Python Tutorial
7. Web Scraping
Many organizations are investing a lot of money just to keep ahead in their business and that’s where Python comes into the picture. With its advanced tools and capabilities, web scraping is way easy for extracting valuable data for making profitable business decisions. Below are some of the notable tools that are being used by scientists for fetching data:
- Beautiful Soup: It is one of the most essential tools for extracting useful information from HTML and XML files into parse trees. You can also check out this article to know more about scraping using beautiful soup – How to Scrape Websites with Beautifulsoup and Python?
- Scrapy: A high-end tool used for web crawling and scraping. It also parses HTML and XML documents for extracting data.
- Python Requests: It’s a fast, and superior Python library for HTML that offers elegant APIs for scientists to work on. (*Note: it only helps in extracting data from static content of the web page)
You can also check out this reference article for web scraping using Python – Python Web Scraping Tutorial
Conclusion
Python is a very popular programming language used by computer programmers. It is object-oriented, and its syntax is very simple and easy to remember. It is widely used for creating web applications with the help of frameworks like Django and Flask. It has a huge base of libraries, allowing developers to use Python in fields like data analysis and data science.
Python is also used for task automation and web scraping to enhance business processes with libraries like BeautifulSoup and Selenium. It is used for game development as well, with the help of its library called Pygame. Additionally, Python is essential for creating machine learning models and is heavily utilized in the field of artificial intelligence.
Python has extensive community support, which is very helpful for debugging code. Hence, if you are considering learning Python, it would be a great choice in today’s tech landscape.
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