With so many programming languages in use, professional and aspiring developers often find themselves in a fix when deciding which language to focus on to direct their careers.

Among all languages, Python had a dream run in 2020, ranking as the most popular language for people to learn. Reports suggest Python triggered a 27% higher interest among developers last year compared to the previous year.

A top choice for data science and machine learning applications, Python, is now the second most popular programming language in the Tiobe index, having overtaken Java in popularity.

Let’s go over what makes Python so popular, what is python used for, the practical applications of Python, and discuss tips to start a career in Python.   

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Python is a powerful, high-level programming language that can be used for web development, operating systems, AI, machine learning, numerical computing, mobile applications, and game development. The recent surge in the use of Python is largely due to its simple syntax and ease of use, which makes it relatively easy to learn even by beginners and non-programmers.  

Python was originally developed by Guido Van Rossum in the early 1980s and continued to be used in a wide variety of domains in the tech industry. With the introduction of Python 2.0 in the early 2000s, the language evolved into its modern form while retaining its basic operational principles. Python uses object-oriented programing, which is perfect for large-scale applications of Python as well as for small programs.

Top 11 Practical Applications of Python

Operating Systems

The robust standard library of Python makes it perfect for building entire operating systems. The object-oriented design of the language ensures large projects are easily managed. Python is compatible with most operating systems and can be easily used to build native applications for Windows and Mac computers.

Example

While Python is not typically used to build entire operating systems, it is often used for scripting and automation tasks within operating systems. Python scripts can automate system administration tasks, manage files and directories, and interact with system APIs. Examples include writing scripts to automate backups, manage user accounts, or monitor system performance. 

Web and Internet Development

Python offers several choices for complex web development projects. HTML and JavaScript are the main languages used to build the front end of an application. But Python-based web frameworks like Django, Pyramid, and Flask are used to handle backend or server-side functionality of sites and services like Spotify, Reddit, and Mozilla. Giant platforms like Google and YouTube depend largely on Python for critical infrastructure. 

The standard library of Python also supports many Internet protocols like HTML and XML, JSON, Email processing, FTP, and IMAP. 

Example

Python is widely used in web development for building dynamic websites, web applications, and APIs. Frameworks like Django and Flask provide powerful tools for building web applications, handling HTTP requests, and interacting with databases. Popular websites and web applications like Instagram, Pinterest, and Spotify are built using Python and its web frameworks.

Game Development

Just like for web development, Python offers an array of tools and libraries for game development. Would you believe, Battlefield 2 – one of the most popular shooting games of the early 2000s, was developed with the use of Python. 

Python’s 2D and 3D game development libraries are PyGame, Pycap, Panda#D, Construct, PySoy, and PyOpenGL.

Python has been used to develop popular games, including Sims 4, World of Tanks, Eve Online, Mount & Blade, Doki Doki Literature Club, and Disney’s Toontown Online, to name a few.  

Example

Python is increasingly used in the game development industry for building games, game engines, and game development tools. Libraries like Pygame provide a framework for building 2D games, while engines like Panda3D and Godot support the development of both 2D and 3D games. Python's simplicity and ease of use make it an attractive choice for prototyping and rapid game development.

Scientific and Numeric Computing

The Python ecosystem offers numerous tools and libraries that help scientists and researchers in scientific and numeric computing.  

  • SciPy is a set of packages for mathematics, science, and engineering 
  • Pandas is a library used for data analysis and modeling
  • IPython is a strong interactive shell that provides hassle-free editing and recording of a work session and aid in visualizing and parallel computing. 
  • FreeCAD and Abaqus are real-life numerical and scientific applications built with Python

Example

Python is widely used in scientific computing and data analysis due to its rich ecosystem of libraries and tools. Libraries like NumPy, SciPy, and Pandas provide powerful tools for numerical computing, data manipulation, and statistical analysis. Python is also used in scientific research, engineering simulations, and data visualization tasks.

Artificial Intelligence and Machine Learning

The hottest buzzwords of the decade – Artificial Intelligence (AI) and Machine Learning are mostly about algorithms, code, and logic. Python, along with a few other programming languages, is increasingly being used for developing AI and ML-powered solutions. The scope and power of Python, along with its stability and security, make it ideal for running AI and ML systems. 

Some important libraries for the job are:

  • Scikit-Learn – for building various machine learning models
  • SciPy – for scientific and technical computing
  • TensorFlow – for state-of-the-art neural networks
  • Keras – for artificial neural networks
  • Pandas – for data analysis and manipulation  

Example

Python is the preferred language for artificial intelligence (AI) and machine learning (ML) applications due to its simplicity, flexibility, and extensive libraries. Frameworks like TensorFlow, PyTorch, and scikit-learn provide tools for building and training machine learning models, neural networks, and deep learning algorithms. Python's popularity in AI and ML has led to its widespread adoption in areas like natural language processing, computer vision, and predictive analytics.

Desktop GUI

Python is an excellent choice for desktop GUI (Graphical User Interface) programming. The language offers numerous options for developers to build a fully functional GUI. The comprehensive syntax and modular programming approach of the Python framework help create a super-fast and responsive GUI. 

Some prominent applications of Python tools for GUI development are PyQt, Tkinter, wxWidgets, Python GTK+, and Kivy. Standard applications like Dropbox and BitTorrent are primarily written in Python.  

Example

Python can be used to develop desktop graphical user interface (GUI) applications using libraries like Tkinter, PyQt, and wxPython. These libraries provide tools for creating windows, dialogs, buttons, and other GUI components. Python's simplicity and cross-platform compatibility make it suitable for building desktop applications for various operating systems.

Business Applications

Applications of Python also include building ERP and ecommerce systems. Business applications are different from typical consumer software because they offer a set of specific features instead of a variety of features. Besides, they target a very tight-knit user group, usually an organization. 

Python is perfect for delivering best-performance custom solutions for business applications as well as consumer applications. 

Odoo is a well-rounded management software that offers numerous business applications that constitute a complete set of enterprise management applications.

Tryton is a three-tier high-level application platform designed for general purposes.

Example

Python is used in business applications for a wide range of purposes, including data analysis, automation, and web development. Python scripts can automate repetitive tasks, generate reports, and interact with business databases and APIs. Python's versatility and ease of integration with other technologies make it a valuable tool for building custom business applications.

Console-based Application

Console-based applications are text-based programs that interact with users through a command-line interface. These applications are typically used for tasks such as data processing, system administration, or automation scripts.

Example

A simple console-based calculator program written in Python. This program prompts the user to enter mathematical expressions and evaluates them to produce the result.

# Simple console-based calculator
while True:
 expression = input("Enter a mathematical expression (or 'quit' to exit): ")
 if expression.lower() == 'quit':
 break
 try:
 result = eval(expression)
 print("Result:", result)
 except:
 print("Invalid expression. Please try again.")

Audio or Video-based Applications

Python can be used to create applications that manipulate audio or video data, such as media players, editors, or streaming services. Libraries like PyDub and MoviePy provide tools for processing audio and video files in Python.

Example

A simple audio player application written in Python using the PyDub library. This application allows users to play, pause, and stop audio files.

from pydub import AudioSegment
from pydub.playback import play


# Load audio file
audio_file = AudioSegment.from_file("example.mp3")


# Play audio
play(audio_file)

3D CAD Applications

Python can be used in 3D computer-aided design (CAD) applications for tasks such as modeling, rendering, and simulation. Libraries like Blender provide a comprehensive set of tools for creating and manipulating 3D models programmatically.

Example

A script written in Python to create a simple 3D model using Blender's scripting interface. This script generates a cube and exports it to a file in the Wavefront OBJ format.

import bpy


# Create a cube
bpy.ops.mesh.primitive_cube_add(size=2)


# Export the cube to OBJ format
bpy.ops.export_scene.obj(filepath="cube.obj")

Image Processing Application

Python is commonly used for image processing tasks such as image enhancement, segmentation, and object detection. Libraries like OpenCV and Pillow provide tools for manipulating and analyzing images in Python.

Example

A simple image processing application written in Python using the OpenCV library. This application loads an image, converts it to grayscale, and applies a Gaussian blur filter to smooth the image.

import cv2


# Load image
image = cv2.imread("example.jpg")


# Convert image to grayscale
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)


# Apply Gaussian blur
blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)


# Display original and processed images
cv2.imshow("Original Image", image)
cv2.imshow("Blurred Image", blurred_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Why Use Python in Web Development?

Technology giants such as Spotify, Instagram, and Google use the open-source, easy-to-understand Python programming language for developing enterprise-level, robust, and responsive web applications.

Packaged to expedite installation, the ActivePython software, with a comprehensive set of extensions, allows Python developers to start working with state-of-the-art Python frameworks such as Tornado, Django, and Flask within minutes.

Integrating the Python programming language, the ActivePython software package, offered via the ActiveState Platform, also permits users to connect to AWS (Amazon Web Services) API and SDK libraries. This heightens the appeal of the software, making it the preferred choice for web developers around the world.

Now, before we learn what is Python used for, here are the top advantages of using Python in web development.

Python is Easy to Learn

Compared to other programming languages like C++ or Java, the design philosophy of Python - emphasizing general expressions and code readability with significant use of whitespace - makes the programming language easy to understand, even for beginners. Additionally, developers using Python can get their job done by writing much less code.

Python Has a Rich Ecosystem

The extensive Python library offers a wide range of packages and tools, such as SLQALchemy, Pygal, Pandas, and Numpy, which allow developers to access pre-defined code, ensuring fast and smooth application development.

Speedy Prototyping

In comparison to programming languages like C++ or Java, Python reduces the development time to a great extent, making things easier for developers to build prototypes quickly and gain feedback on their projects.

The Uses of Python Programming Language in Scientific Computing and Data Science

The Defense Advanced Research Projects Agency (DARPA) of the U.S. Department of Defense, which is responsible for promoting technology development for U.S. military use, funded $3 million in 2012 to build a top-notch data processing and data analytics library for Python.

With this high investment, the Python programming language now includes a variety of advanced tools and scientific packages for all facets of data science and scientific computing.

Including APIs, simple syntax, and outstanding features to address statistics, scientific, and mathematical functions, Python, according to seasoned developers and engineers, is best suited for scientific computing and data science.

Python’s data processing and data analytics library include.

Matplotlib

Ideal for generating data visualizations such as bar charts, histograms, scatterplots, and power spectra with minimal coding.

NumPy

With scientific computing and data science at its core, NumPy provides support for large-scale, multi-dimensional matrices and arrays with a range of first-rate mathematical functions.

Pandas

Built on NumPy, Pandas offers operations and data structures for manipulating time series and numerical tables.

SciPy

Working together with the NumPy arrays, SciPy facilitates powerful routines for optimization and numerical integration.

Scikit-learn

A free Machine Learning (ML) library for Python — developed on NumPy, Matplotlib, and SciPy — it features varied classification, clustering and regression algorithms, including SVM (Support Vector Machine).

Uses of Python in Machine Learning

As end-consumers seek enhanced user experience, including improved search functionalities, better personalization, and smarter recommendations, Machine Learning (ML) – an AI (Artificial Intelligence) application – in recent times, is increasingly gaining prominence.

AI and ML applications differ from customary software projects, especially in the overall technology infrastructure, the necessity for deep research, and the skills needed for AI-based projects.

ML and AI applications, therefore, require a high-class programming language, which is steady, yet agile, offering consistency and simplicity.

From project development to implementation and maintenance, the Python programming language, with its excellent frameworks and vast libraries, gives all of that, and more.

What is python coding used for Machine Learning? Well, search results refinement, web spam filtering, intelligent virtual assistants, facial recognition, smart product recommendations, predictive video analytics in surveillance, online fraud detection, and automatic translation services are some machine learning applications that What is Python Used For.

What is Python Used for in Startups?

The startup ecosystem is entirely different from how corporations and big companies operate.

Startups are innovative, young, and collaborative. They are also highly vulnerable, as most startups, in the initial stage, are not quite sure about whether the target audience will accept and appreciate their offerings.

That is why startups require high scalability, quicker development of MVP (Minimum Viable Product), efficacious iterations, the scope of technology integration, and time-monitored development processes.

Taking into account all the above factors, experts opine that the Python programming language is ideal for technology-driven startups. Here are some examples of how startups can benefit from Python software:

Scalability

Startups will grow over time, and eventually, they will seek scalability. Based on Python, the open-source and free Django web framework can help startups develop highly-scalable mobile and web applications, capable of handling huge traffic loads.

Versatile Programming Potential

The exceptionally versatile Python programming language works well on various platforms. With Python, startups can develop applications that everyone can access easily.

Unmatched Security

Python development frameworks, such as Django, incorporate excellent security features. Hiding the source code, Django protects applications from online security threats.

Fast MVP Development

Python allows startups to develop MVPs in a flash, reducing the time-to-market. This gives a leading edge in an intensely competitive business environment.

What is Python Used for in FinTech and the Financial Industry?

Emerging technologies are continually revolutionizing and disrupting the financial industry. Python, with its advanced, flexible frameworks, offers significant opportunities to cope with constant technology shifts.

The high-caliber programming language is excellent for Fin-Techs, when it comes to resolving challenges inherent in today’s financial landscape, in terms of regulation, compliance, analytics, and data volume.

As stated in HackerRank’s survey, an increasing number of financial institutions are now using the Python programming language to build futuristic finance applications. Here are the top factors that drive What is Python Used For in the financial sector -

Open-Sourced

Developed under OSI-approved open and flexible license, Python is a freely distributable and usable software that offers a broad range of toolsets and libraries for the financial industry.

Cross-Platform

All operating systems, be it the Mac OS, Linux, or Windows, can run the Python programming language.

Convenient

Python incorporates general-purpose data structures that web programmers can use to develop fast data structures runtime.

A dynamically-typed programming language, Python allows for easy deployment with reduced source code footprint.

High Performance

Python offers outstanding code readability, robust integration, simple syntax, a clean design, increased process control, and superb text processing capabilities. All of these improve overall productivity.

How to Build a Career in Python

Python is the ‘next Big thing’ and a “must” for professionals interested in coding. Developers need to add Python to their resumes, not only to do faster programming but also for lucrative global careers. 

So, if you are wondering how to start out with Python or how to become a Python developer, here are a few ideas for you to get started. 

There are few coding languages as straightforward and versatile as Python. Any interested individual can learn Python, but an aspirant from a computer science or engineering background is desired for a job as a Python developer. A degree in science or engineering along with a Python certification and a complete project using Python can help you secure a well-paid job in Python development or data sciences domain. 

There are many online certifications and bootcamps for learning Python if you want to make a career in data science. Consider the Python training course from SimpliLearn – the online bootcamp experts that can help you master the basics or develop some more specific Python skills. 

Some popular Python-oriented Job roles are:

  • Software Engineer
  • Python Developer
  • Research Analyst
  • Data Analyst
  • Data Scientist
  • Software Developer

Python Job Skills

To build or advance your career with Python programming, you need to have the following skills:

  • Proficiency Using Common Python Libraries
  • Knowledge of Syntax, Semantics and Data Structures of Python
  • Familiarity with Python frameworks like Django, Flask
  • Using and linking applications through ORM
  • Knowledge of Relevant Terms and Actions
  • Good Communication Skills
  • Team handling Abilities
  • Result oriented
  • Problem Solving Aptitude

Python Developer Salary

Professionals that learn and code in Python face a huge demand in today’s job market, which explains the high salary and bonuses that Python developers and programmers earn. By upskilling yourself with the knowledge of this highly popular and practical programming language, you can gain well-paid skills and land high salaried jobs. 

According to Talent, the average annual salary of a python developer in the US is $115,000. While entry-level roles can fetch $90,000 per year, with more experience, you can easily earn up to $148,436 a year.

Talent’s UK average for a python developer salary is £55,000 per year. 

In India, an average Python developer can draw over ₹ 6 Lac per year, according to Indeed.com.   

Besides the base salary, many companies offer perks and bonuses, making Python-oriented jobs a lucrative one. 

Conclusion

From NASA to Facebook, and from Google to Instagram – leading technology giants all over the world use Python as a programming language for a wide variety of applications. Naturally, there’s a high demand for skilled Python professionals. 

As Python is in high demand, a career in Python can be the perfect path to achieve your career goals. Stay updated with developments in Python and data sciences with online certifications offered by Simplilearn, one of the leading online certification training providers in the world. You can get started right away with the Python Certification Course. It can help you cover all the fundamentals of the Python language and get work-ready training in the field. On the other hand, if you are looking to leverage your knowledge of Python to foray into a career in data science, Simplilearn’s Data Scientist Master’s program should fit the bill nicely. This comprehensive bootcamp offers a role-aligned learning path designed to help you gain in-demand skills needed to become a data scientist, fast.

FAQs

1. What is the main application of Python?

Python is a versatile programming language with a wide range of applications. However, its main application is in software development, particularly in areas such as web development, data science, artificial intelligence, automation, scientific computing, and more. Python's simplicity, readability, and extensive library support make it suitable for various domains and industries.

2. How is Python used in web development?

Python is widely used in web development for building dynamic websites, web applications, and web services. Frameworks like Django, Flask, and Pyramid provide powerful tools for developing and deploying web applications. Python's simplicity, ease of use, and large community support make it a popular choice for backend development, handling HTTP requests, interacting with databases, and generating dynamic content.

3. What role does Python play in data science?

Python is one of the most popular programming languages for data science and analytics. It is used for tasks such as data manipulation, cleaning, visualization, and analysis. Libraries like NumPy, Pandas, Matplotlib, and scikit-learn provide powerful tools for working with data in Python. Python's simplicity, readability, and extensive library support make it an ideal choice for data scientists and analysts to explore and analyze large datasets effectively.

4. Can Python be used for automation?

Yes, Python is commonly used for automation tasks across various domains, including system administration, network automation, test automation, and more. Python's simplicity, versatility, and extensive library support make it suitable for automating repetitive tasks, scripting, and building workflows. Libraries like Fabric, Paramiko, Selenium, and PyAutoGUI provide tools for automating tasks on different platforms and environments.

5. In what ways is Python applied in artificial intelligence (AI)?

Python is widely used in artificial intelligence (AI) and machine learning (ML) applications due to its simplicity, flexibility, and extensive library support. Frameworks like TensorFlow, PyTorch, and scikit-learn provide tools for building and training machine learning models, neural networks, and deep learning algorithms. Python's popularity in AI and ML has led to its widespread adoption in areas such as natural language processing, computer vision, robotics, and more.

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