This post was last Updated on May 31, 2022 by Himanshu Tyagi to reflect the accuracy and up-to-date information on the page.
Python is a general-purpose programming language that you can use to develop apps, software, websites, machine learning models, or get things done via scripting. In this article, we will explore what is Python used for. Apart from that, we will also explore the applications of Python in data analysis, machine learning, automation, web development, fintech, and other vital areas.
Also Read: How To Display A Calendar In Python
What is Python and Why You Should Learn It?
Python is a high-level and interpreted programming language. Its syntax is pretty simple and relatively straightforward to learn than other coding languages like C++ and Java. Coding in Python feels like instructing computers to perform a job in English. Thus, making it one of the best programming languages for beginners.
Before we explore the various applications of Python, here are some reasons to learn Python in the first place.
- Beginner’s friendly, making it easier to grasp and enjoy the coding.
- Huge demand in the market for Python developers. Many Fortune 500 companies such as Google, Netflix, Meta, and others work on Python.
- Multiple use-cases. You can use Python for backend development, building machine learning algorithms, creating addictive games, and many others.
- With Python, you can automate boring stuff by leveraging vast libraries and open source community resources.
- Python supports built-in software testing frameworks such as PyTest and Splinter.
Also Read: How To Sort a List of Tuples in Python
Where is Python Used For?
Let’s now explore applications of Python in various industries.
1. Data analysis and visualization
Python is one of the best programming languages for data analysis and visualization. It lets you perform complex statistical calculations, report data visually, design ML algorithms, and complete any other data-related tasks.
NumPy and Pandas make it easier for data scientists to work with vast chunks of data. You can also use Python to draw data visualization elements such as line graphs, pie charts, 3D Plots, histograms, etc.
Popular Python libraries for data analysis and visualization are Matplotlib, Pandas, Pytorch, NumPy, Plotly, Seaborn, etc.
Also Read: Arithmetic Operators in Python [With Examples]
2. Machine Learning and Artificial Intelligence
Python is one of the most popular choices for data scientists to build machine learning algorithms and artificial intelligence projects. It offers an extensive range of libraries which makes solving complex problems more manageable.
A library is a pre-written code that programmers can use to solve various tasks. Python offers numerous ML and AI project libraries, often backed by an active open-source community. Thus, helping developers reduce development time and implement ML and AI algorithms in a tested environment.
Here are some of the most popular Python libraries for machine learning and AI.
- Machine Learning: TensorFlow, Keras, Sickit-learn, etc.
- AI: OpenCV, PyTorch, Apache Spark, etc.
Also Read: How to Create Python Empty Set
3. Software Testing and Development
Apart from data science, machine learning, and AI, you can also use Python for software development purposes. Python is a high-level object-oriented programming language with built-in data structures such as lists, dictionaries, etc.
Above all, it has a relatively simple syntax and offers libraries that you can use to develop desktop GUIs, code the backend functionality, and deploy the software application.
As Python is supported by all major operating systems such as Windows, macOS, and Linux, you will not face any challenges creating cross-platform or platform-specific software.
Not just for software development, you can also use Python for writing automated tests as it offers in-built support for unit testing. Of course, you can extend the functionality by using any other Python library for software testing.
Popular Python libraries for software development: Kivy, wxWidgets, pyside, pyqt, etc.
Popular Python libraries for software testing: pytest, Selenium, Unittest, DocTest, etc.
Also Read: Python Matrix Using Numpy [With Examples]
4. Website Development
Fortune 500 companies such as Meta, Google, Quora, and others use Python for their product development. This is simple—Python offers simplicity, flexibility, and scalability for performance-oriented tasks.
Above all, you can always extend the functionality of your website by leveraging the data science and machine learning capabilities of Python. Some of the most Python libraries for website development are Django, Flask, CherryPy, Web2Py, etc.
Also Read: 7 Reasons Why You Should Use Python for Web Development
5. FinTech
According to Statista, the total value of investment into fintech companies worldwide has reached 213.8 Billion US Dollars. Fintech companies have many complex problems, and Python efficiently helps them solve them.
PayPal, Stripe, and Square are some of the most popular fintech platforms using Python. The topmost priorities for fintech companies are top-notch security, complex algorithms, and integration with other tools. Python can help fintech companies build a secure platform to handle large concurrent transactions.
Also Read: How To Create An Empty Dictionary in Python
6. Scientific and Numeric Computing
Here are some of the most popular Python libraries that developers can use to solve complex scientific problems and numeric computing:
- Pandas: For data analysis and modeling
- IPython: Powerful interactive shell that supports visualizations and parallel computing
- SciPy: Collection of libraries for science, mathematics, and engineering. It includes modules for integration, linear algebra, signal and image processing, statistics, and others.
- NumPy: NumPy or Numerical Python adds a multi-dimensional and fast array to Python.
- SymPy: You can use this Python library for symbolic mathematics.
- PyGSL: This Python library provides an interface for the GNU scientific library (GSL).
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7. Search Engine Optimization (SEO)
SEO or Search Engine Optimization is a set of activities to improve the organic visibility of a website in SERPs or search engine result pages. In the old days, SEO was a simple task where web admins only had to submit their website to search engines for indexing, and ranking at top positions was done. Mainly due to significantly less competition.
But in the 21st century, every company is battling a war for each search position on the first page. And there is a lot of data from various tools such as Google Analytics and Search Console at marketers’ disposal.
Python is the perfect tool to analyze several SEO problems such as broken links, keyword research, understanding users’ intent, and automated tasks. Explore the Python project linked below to understand better how to use Python for SEO purposes.
Also Read: Free SEO Rank Tracking Software Download: KWchecker by CodeItBro
8. Game Development
Some popular games like Battlefield and Pirates of the Caribbean use Python for some functionalities and add-ons. Python is still used for rapid prototyping despite not being the first choice as a game development language for programmers.
C++ is around 10 times faster than Python for game development. Python focuses more on coding performance rather than computation. PyGame, Pyglet, and Panda3D are some of the most popular libraries that you can use to try game development in Python. Read the article link below to understand the game development capabilities of Python.
Also Read: How To Use Python For Browser Games Development?
9. Blockchain
Blockchain is one of the hot topics these days. If you don’t know, then blockchain is a way to store data so that it is almost impossible to modify or hack it. Blockchain technology can track the origin of digital assets as it is a decentralized and distributed ledger.
Python is the perfect tool for blockchain developers as it is easy to use, maintain, secure, and flexible. For example, you can create a blockchain using Python in less than fifty lines of code.
Also Read: How to Handle String Index Out of Range Error In Python
10. Automation
Python is a scripting language. It is one of the most popular choices for hobbyists and professional developers alike to automate the boring stuff. You can use Python to automate likes on Instagram to send Gmail emails.
Honestly, your imagination is the only limitation for Python for automation. Recently, I created a Python project to automate Google search. Check it out to get a fair insight into Python’s capabilities as a scripting language.
Also Read: Sending Emails Using Python With Image And PDF Attachments
11. Web Scraping
Do you want to grab a list of Yelp businesses and information such as name, address, email, and other details? Then, you can use Python to scrape the website and store the results in CSV, XLS, or any other format.
Python offers a wide range of libraries, such as BeautifulSoup, Requests, and Selenium, making it easier for you to create a crawler to fetch web pages and extract the information you need to store.
Please note that web scraping can be illegal if prohibited by the website’s terms of use. So, before getting started with web scraping, check out the website’s policies.
Also Read: Python For Loop Index With Examples
Frequently Asked Questions (FAQs)
What is the primary use of Python?
You can mainly use Python for backend development, scripting, software testing and development, creating ML models, AI projects, and other use cases discussed in this article.
What apps can I make with Python?
Python is commonly used for making web and desktop-based applications. While you can choose to create mobile apps using Python, that path is less followed by professional app developers.
Where is Python used in real life?
Python is used everywhere in real-life projects. Python makes it easier for developers to design applications, from simple desktop utilities like digital clocks to complex AI-based recommendation systems.
What jobs can Python get you?
After learning Python, you can get the following jobs:
- Python Developer
- Software Engineer
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Educator
- Product Manager
