how can python solve real world problems

Top 12 Fascinating Python Applications in Real-World [2024]

It is a well-established fact that Python is one of the most popular programming languages in both the coding and Data Science communities. But have you ever wondered why Python is so popular? What is the secret behind Python’s worldwide success and fame?

We’ll give you the answer in one line – Python is one of the top programming languages of all with a slew of applications of Python.

Whatever be your development and Data Science need, you name it – Python can take care of it as well as other related Python applications. Python is an open-source, high-level, general-purpose programming language that incorporates the features of object-oriented, structural, and functional programming.

Some believe that Java is a better language. However, it goes with saying that the former is much faster, but Python is easier to handle/read, versatile, and comes with a simple syntax. As per Stack Overflow, Python—general use and interpreted language rank fourth on the list of most popular languages for coding. It also finds immense use for Python applications.

Created way back in 1989 by Guido Van Rossum, Python stresses on the DRY (Don’t Repeat Yourself) principle, which enhances the readability of Python code. Python’s robust string manipulation, a massive collection of user-friendly libraries, and easy shell access make it a useful tool for quickly automating repetitive tasks.

While Python’s simple syntax allows for writing readable code, which can be further applied to complex software development processes to facilitate test-driven software application development, machine learning, and data analytics. Python can run on all the major operating systems, including Windows, Linux, and iOS.

Since it functions on cross-platform operating systems, Python can be used to develop a host of applications, including web apps, gaming apps, enterprise-level applications, ML apps, image processing, text processing, and so much more. 

But beyond its innate simplicity and versatility, what makes Python stand out are its vast assortments of libraries and packages that can cater to a wide range of development as well as Data Science requirements.

Understanding Python applications

Given that Python is now used in data science and related applications, its growing popularity among developers is natural. The common applications of Python are so relevant that it has now become a significant resource for those looking to begin a career in data science. Having a good grasp of Python allows you to strengthen your analytical skills. Today, a data scientist or even someone in the IT sector is expected to come with relevant and new-age skills.

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A common query few might have in mind is, whether Python-run blockchain is tough to learn. It is a technology that is complex yet groundbreaking after all! However, this must never deter you from trying to get a grasp on the same. The core Blockchain concepts are mining, decentralization, and consensus mechanism. This is an immutable blockchain that is also secured cryptographically.

If you want to kick-start a career in this domain, begin by understanding the fundamentals of the same and upskill your Python programming skills to develop blockchain applications. Start your journey by studying these top four concepts of blockchain first-

•     Smart Contracts
•     Shared Ledger
•     Consensus/Trust Mechanism
•     Cryptography

12 Real-world Applications of Python

Python is a very stable programming language choice that is in use at the developers’ end as well as thought of as an apt choice for automation of deployment automation. Of course, it has a lot of uses in web-related development. Even the non-developer groups believe that once they have a hang of the framework in Python, it is a preferred language for conducting data-related work. Here is the list of Python applications,

1. Web Development 2. Game Development 3. Scientific and Numeric Applications 4. Artificial Intelligence and Machine Learning 5. Desktop GUI 6. Software Development 7. Enterprise-level/Business Applications 8. Education programs and training courses 9. Language Development 10. Operating Systems 11. Web Scraping Applications 12. Image Processing and Graphic Design Applications

Let’s discuss the applications & advantages of Python in detail

1. Web Development

When it comes to web development, Python should be your go-to tool. Why? 

That’s because Python offers numerous options for web development. For instance, you have Django, Pyramid, Flask, and Bottle for developing web frameworks and even advanced content management systems like Plone and Django CMS. These web frameworks are packed with standard libraries and modules which simplify tasks like content management, database interaction, and interfacing with internet protocols like HTTP, SMTP, XML, JSON, FTP, IMAP, and POP.

Python web frameworks are known for their security, scalability, and flexibility. To add to that, Python’s Package Index comes with useful libraries like Requests, BeautifulSoup, Paramiko, Feedparser, and Twisted Python. 

Web development is an amazing application of Python programming . The reason is it offers a broad range of frameworks like Flask, Django, Bottle, and more that streamline developers’ tasks. Python also contains inbuilt libraries and tools that make the web development process quite effortless. You can effectively build the best Python app using its characteristics like wonderful visualization, convenience in development, enhanced security, and quick development process.

2. Game Development

As we mentioned earlier, Python comes loaded with many useful extensions (libraries) that come in handy for the development of interactive games. For instance, libraries like PySoy (a 3D game engine that supports Python 3) and PyGame are two Python-based libraries used widely for game development . Python is the foundation for popular games like Battlefield 2, Frets on Fire, World of Tanks, Disney’s Toontown Online, Vega Strike, and Civilization-IV. 

Apart from game development, game designers can also use Python for developing tools to simplify specific actions such as level design or dialog tree creation, and even use those tools to export those tasks in formats that can be used by the primary game engine. Also, Python is used as a scripting language by many game engines.

Python is also used to develop many contemporary popular game titles like World of Tanks, Sims 4, Eve Online, and Civilization IV. A few other titles that use Python are Doki Doki Literature Club, Mount & Blade, Disney’s Toontown Online, and Frets on Fire. So, the application of Python programming is not only limited to the professional world but game development too.

3. Scientific and Numeric Applications

Thanks to its massive library base, Python has become a crucial tool in scientific and numeric computing. In fact, Python provides the skeleton for applications that deal with computation and scientific data processing. Apps like FreeCAD (3D modeling software) and Abaqus (finite element method software) are coded in Python.

Some of the most useful Python packages for scientific and numeric computation include:

• SciPy (scientific numeric library)
• Pandas (data analytics library)
• IPython (command shell)
• Numeric Python (fundamental numeric package)
• Natural Language Toolkit (Mathematical And text analysis)

  4. Artificial Intelligence and Machine Learning 

AI and ML models and projects are inherently different from traditional software models. When we talk about AI/ML projects, the tools and technologies used and the skillset required is totally different from those used in the development of conventional software projects. AI/ML applications require a language that is stable, secure, flexible, and is equipped with tools that can handle the various unique requirements of such projects. Python has all these qualities, and hence, it has become one of the most favored languages of Data Science professionals and Python is a must tool in data science courses.

Python’s simplicity, consistency, platform independence, great collection of resourceful libraries, and an active community make it the perfect tool for developing AI and ML applications. Some of the best Python packages for AI and ML are:

• SciPy for advanced computing
• Pandas for general-purpose data analysis
• Seaborn for data visualization
• Keras, TensorFlow, and Scikit-learn for ML
• NumPy for high-performance scientific computing and data analysis

  Apart from these libraries, there are also other Python-based libraries like NLTK, Caffee, PyTorch, and Accord.NET, that are useful for AI and ML projects.

Two of the trendiest subjects right now are Artificial Intelligence and Machine Learning. With the inbuilt tools and libraries, it facilitates the development of ML and AI algorithms. Moreover, it provides easy, concise, and readable code that makes it simpler for developers to write complicated algorithms.

Few of the built-in tools and libraries that enhance ML and AI processes through the application of Python programming are:

• Keras for Machine learning
• Numpy for complex data analysis
• SciPy for technical computing

5.Desktop GUI

Python not only boasts of an English-like syntax, but it also features a modular architecture and the ability to work on multiple operating systems. These aspects, combined with its rich text processing tools, make Python an excellent choice for developing desktop-based GUI applications. 

Python offers many GUI toolkits and frameworks that make desktop application development a breeze. PyQt, PyGtk, Kivy, Tkinter, WxPython, PyGUI, and PySide are some of the best Python-based GUI frameworks that allow developers to create highly functional Graphical User Interfaces (GUIs).

Python is a dynamic programming language that assists developers in easily and efficiently creating GUIs. It features a long list of inbuilt tools like kivy, PyQT,wxWidgets, and several other libraries. These libraries help you to efficiently and securely build a functional GUI.

Python’s modular programming approach and easy-to-understand syntax are the basis for responsive and super-fast GUI. So, they streamline the whole development process. A few of the prominent tools available for GUI development using applications of Python are Tkinter, PyQt, wxWidgets, Python GTK+, and Kivy.

6. Software Development

Python packages and applications aim to simplify the process of software development. From developing complex applications that involve scientific and numeric computing to developing desktop and web applications, Python can do it all. This is the reason why Software Developers use Python as a support language for build control, testing, and management.

For instance, SCons is designed explicitly for build control, Buildbot and Apache Gump allow for automated continuous compilation and testing, and Roundup and Trac are great for bug tracking and project management.

Python also supports data analyzation and visualization, thereby further simplifying the process of creating custom solutions minus the extra effort and time investment.

Python is ideal for software development. Famous applications like Google, Reddit, and Netflix use Python. It offers the following great features for software development:

• Platform independence 
• High compatibility
• Inbuilt frameworks and libraries to streamline development
• Enhanced code reusability and readability

Python also provides enhanced features to work with swiftly growing technologies like AI and ML. These features make applications of Python the famous choice for software development.

7. Enterprise-level/Business Applications

Enterprise-level software or business applications are strikingly different from standard applications, as in the former demands features like readability, extensibility, and scalability. Essentially, business applications are designed to fit the requirements of an organization rather than the needs of individual customers.

Thus, these applications must be capable of integrating with legacy systems like existing databases and non-web apps. Since business applications are developed, keeping in mind the custom requirements to cater to the specific needs of an organization’s operating model, the entire development process becomes very complicated. 

This is where Python can make a significant difference. Python high performance, scalability, flexibility, and readability are just the features required for developing fully-functional and efficient business applications. Furthermore, Python has other tools for business application development, like:

• Odoo, an all-in-one management software that forms a complete suite of enterprise management applications.
• Tryton, a three-tier, high-level, general-purpose application platform, is another fantastic tool for building business applications.

Learn more about: Top Python tools

Business applications vastly vary from average consumer software. Firstly, provide a set of explicit features instead of plenty of features. Secondly, they target a small user group, commonly an organization. One of the best things about Python is that it perfectly delivers performance-efficient custom solutions. So, it can work on both business applications and consumer applications.

One of the most crucial facets of any application is security. Python’s security features are standout when it comes to business applications. This is because it is built considering information security.  One of the great applications of Python programming is scalability through which a business can expand its horizon.

 8. Education programs and training courses

If there’s any beginner-friendly programming language, it is Python. We’ve said it many times before, and we’re repeating it – Python has an extremely straightforward syntax that’s similar to the English language. It has a short learning curve and hence, is an excellent choice for beginners. Python’s easy learning curve and simplicity are the two main reasons why it is one of the most used programming languages in educational programs, both at beginner and advanced levels. 

However, Python is not just great as an introductory language – even professional developers and coders all around the world rely heavily on Python.

Python features a shorter learning curve compared to other programming languages. So, you can quickly learn the development of applications of Python programming. This facet makes it one of the best options for educational programs. Platforms like Coursera, Udemy, edX, Harvard, and Python Institute are among the leading online providers of Python educational courses.

9. Language Development

Over the years, Python’s design and module architecture has been the inspiration behind the development of many new programming languages such as Boo, Swift, CoffeeScript, Cobra, and OCaml. All of these languages share numerous similarities with Python on grounds like object model, syntax, and indentation.

10. Operating Systems

Yes, Python is the secret ingredient behind many operating systems as well, most popularly of Linux distributions. Linux-based Ubuntu’s Ubiquity Installer and Fedora and Red Hat Enterprise’s Anaconda Installer are coded in Python. Even Gentoo Linux leverages Python Portage (package management system). Usually, Python is combined with the C programming language to design and develop operating systems.

11. Web Scraping Applications

Python is a nifty tool for extracting voluminous amounts of data from websites and web pages. The pulled data is generally used in different real-world processes, including job listings, price comparison, R&D, etc. 

BeautifulSoup, MechanicalSoup, Scrapy, LXML , Python Requests, Selenium, and Urllib are some of the best Python-based web scraping tools.

In other words, web scraping is an automated process for easily and quickly extracting information from websites. Python presents various features that make it appropriate for web scraping and justify the applications of Python programming . Some of the features are:

• Easy to understand and use
• A concise syntax that improves the readability and saves your time
• The web scraping process is made easy and efficient with various tools and libraries like matplotlib, Pandas, and Selenium

12. Image Processing and Graphic Design Applications:

Alongside all the uses mentioned above, Python also finds a unique use case in image processing and graphic design applications. The programming language is used globally to design and build 2D imaging software like Inkscape, GIMP, Paint Shop Pro, and Scribus. Also, Python is used in several 3D animation packages such as Blender, Houdini, 3ds Max, Maya, Cinema 4D, and Lightwave, to name a few.

With so many uses up its sleeve, Python ranks as a highly loved language for programming. It is a top pick of software engineers and hackers, too, since it is laced with flexibility, versatility, and object-oriented specifications.

How is Python Programming used in the real world?

Python, known for its versatility, finds application in a myriad of real-world scenarios spanning different domains. Here are some instances where Python programming is used:

• Web Development : Python’s frameworks like Django and Flask are used to build scalable and dynamic web applications, powering platforms like Instagram and Pinterest.
• Data Science and Machine Learning : Python’s rich ecosystem of libraries such as NumPy, Pandas, and scikit-learn facilitates data analysis, machine learning model development, and deployment, enabling companies like Spotify and Netflix to offer personalized recommendations.
• Artificial Intelligence and Natural Language Processing : Python’s libraries like TensorFlow and PyTorch are fundamental in AI research and development, contributing to advancements in speech recognition, language translation, and image processing used by organizations like Google and Facebook.
• Automation and Scripting : Python’s simplicity and readability make it ideal for automating repetitive tasks and writing scripts for system administration, saving time and effort for companies like Dropbox and NASA.
• Finance and Trading : Python is extensively used in financial modeling, algorithmic trading, and risk management due to libraries like QuantLib and pandas-datareader, empowering institutions like JP Morgan and Goldman Sachs to make data-driven decisions.
• Game Development : Python, along with libraries like Pygame and Panda3D, is employed in game development for prototyping, scripting, and building game engines, as seen in games like Civilization IV and EVE Online.
• Scientific Computing and Research : Python’s libraries like SciPy and Matplotlib are widely adopted in scientific computing and research for simulations, visualization, and data analysis, benefiting institutions like NASA and CERN in their explorations of space and particle physics.

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Is python good for real-time applications.

Python can be suitable for certain real-time applications, especially those with moderate performance requirements and where rapid development is crucial. However, for applications demanding ultra-low latency or high-performance computing, other languages like C++ or Rust might be preferred.

What makes Python the “Best of the Best?”

The top application of Python ensures that the language remains popular at a professional level. Here are some of the most noteworthy features of Python that make it an excellent tool for Python application professionals of all skill levels:

Python = Simplicity

We cannot stress this point enough, but Python is not only easy to learn but also easy to use and implement across any application of python. With a syntax similar to English, you can master the nitty-gritty of python application and coding in a few days. Moreover, Python is dynamically-typed, which makes indentation mandatory, thereby enhancing its readability factor. 

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It is an open-source language

You don’t need to pay charges to install and use Python – it is open-source. What this means is that the source code of Python is freely available to the public. You can download it from Python’s official website . Not only that, Python supports the FLOSS (Free/Libre and Open Source Software) model, which means you can also change it and distribute it. This allows the Python community to tweak it and improve its features continuously.

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It is a high-level language

Since Python is a high-level language, you need not remember its system architecture, not do you need to perform memory management. This feature contributes to Python’s user-friendliness. 

It is interpreted

Unlike compiled languages like C++ and Jave wherein you must compile the code and then run it, Python is an interpreted language. What this means is that instead of executing the source code all at once, Python executes it line by line. This makes it easier to debug a Python code because you can do it while writing the code.

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It is both object-oriented and functional

An object-oriented programming language is one that can model real-world data, while a functional language focuses on functions (code that can be reused). Python supports both object-oriented and functional programming features. Also, unlike Java, Python supports multiple inheritances. Naturally, this opens up a lot of scope around the topic- what are the applications of python?

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It is portable.

Python is portable and highly flexible, meaning, a Python code written for a Windows machine or a Linux machine can also run on iOS, and vice versa – you don’t need to make any alterations in the code. So, with Python eliminates the need to write different code for different machines (just make sure there’s no system-dependent feature in your Python code).

It is extensible and embeddable

Python is an extensible language, as it allows you to write specific parts of your Python code in other programming languages such as C++. Similarly, you can also embed your Python code in the source code of other languages. This allows you to integrate Python’s scripting functionalities into a code written in another language. This also leads to a number of applications of Python programming being developed as per need. When you look up on what are the applications of Python programming , you can get an idea of myriad Python uses .

It comes with a vast collection of libraries

When you download Python, you will automatically download the extensive collection of Python libraries with it. These libraries are built-in, so you don’t have to write individual code for every single thing. Python has libraries and packages for web browsers, threading, databases, regular expressions, image manipulation, documentation-generation, unit-testing, CGI, email, and much more.

Now that we’ve talked at length about how great a tool Python is let’s check out twelve real-world applications of Python or popular python uses.

One of the great things about Python is it owns a huge collection of data science libraries like SciPy, NumPy, and Matplotlib. All these libraries help you to conduct visualization and data analysis easily.

Python is famous in the scientific community because of its large collection of libraries for physics, math, machine learning, and engineering. For instance, TensorFlow is a Google Brain library used in ML projects. So, there are plenty of application areas of Python .

It supports different paradigms

Python is a versatile programming language and supports multiple paradigms like functional, object-oriented, and procedural programming. It suggests that you can choose the paradigm that best outfits your requirements and preferences.

Python features a simple syntax for ease of learning and use. This characteristic helps you to explore various application areas of Python, including automation.

It should be your foremost choice to learn various programming paradigms.

Python owns a huge standard library that contains modules for string handling, data manipulation, Internet protocols, file I/O, and operating system interfaces. Python’s syntax makes it easy to learn and understand python programs that are typically used for automation tasks.

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It owns a strong community of developers

Python comes with a powerful community of developers who are always dedicated to assisting you. Many online resources like chat rooms and forums are available. It aids you in solving different Python programming problems.

Python provides support for debugging tools and unit testing. It is, therefore, an ideal language for even advanced software development. You get ample resources to get started with the application of Python due to its vast and friendly community.

It is popular in data science and machine learning

Python has become quite popular in machine learning and data science applications. Its flexibility and readability make it a wonderful choice for prototyping new ideas and algorithms. Many libraries are available to help you easily execute complex data analysis tasks.

For instance, the SciPy library contains scientific and mathematical computation tools. The Pandas library is widely used for manipulation and data analysis. These are only a few of the numerous libraries that Python offers.

One of the key reasons why Python is also a popular programming language in machine learning is that it offers various powerful tools for data handling. These tools help you to conduct tasks like data modelling, transformation, and cleaning. Moreover, various machine learning libraries like Theano and TensorFlow help you to build an advanced application of Python . So, Python is worth considering if you are fascinated by machine learning and/or data science.

Check out all trending Python tutorial concepts in 2024

Other Real Time Applications of Python

• Python in IoT (Internet of Things)

Python has emerged as a prominent language in the realm of Internet of Things (IoT) development. Its versatility, simplicity, and a rich ecosystem of libraries make it an ideal choice for building robust IoT solutions. Python facilitates the development of IoT applications, from sensor data processing to communication with cloud platforms, ensuring seamless connectivity in the IoT ecosystem.

Python seamlessly integrates with a wide array of IoT devices and platforms. Its adaptability allows developers to connect and communicate with various sensors, actuators, and IoT hardware. Python’s compatibility with popular IoT platforms, such as Raspberry Pi and Arduino, empowers developers to create innovative IoT projects with ease.

Python serves as the backbone for numerous IoT projects, ranging from smart home automation to industrial IoT applications. Frameworks like MicroPython and Zerynth provide Python support for microcontrollers, enabling developers to deploy Python code directly on resource-constrained IoT devices. Additionally, popular IoT frameworks like Home Assistant leverage Python for building comprehensive home automation solutions.

• Advancements in Python Web Frameworks

Modern Python web frameworks play a pivotal role in web development, offering efficient tools for building scalable and feature-rich applications. Frameworks such as Django, Flask, and FastAPI provide developers with the scaffolding needed to streamline development workflows. Django, known for its batteries-included approach, offers a comprehensive set of features, while Flask embraces simplicity and flexibility. FastAPI, a newcomer, stands out for its speed and automatic OpenAPI documentation.

Each Python web framework has its unique strengths and use cases. Django, a high-level framework, is renowned for rapid development and a built-in admin interface. Flask, a microframework, grants developers more flexibility by allowing them to choose components as needed. FastAPI, designed for building APIs quickly, stands out for its automatic validation and support for asynchronous programming. Comparing these frameworks helps developers choose the one that aligns with their project requirements.

Python web development continues to evolve with emerging trends. Serverless architecture, powered by platforms like AWS Lambda and Azure Functions, is gaining traction, allowing developers to build scalable applications without managing server infrastructure. Microservices architecture, supported by Python frameworks, is fostering the development of modular and maintainable web applications. Moreover, the rise of frontend technologies like Vue.js and React, coupled with Python backend services, contributes to a more dynamic and interactive user experience.

• Python forNatural Language Processing (NLP)

Python has become a leading language for Natural Language Processing (NLP), enabling developers to work with and analyze human language data. Its simplicity and a wealth of NLP libraries make it accessible for tasks such as text parsing, sentiment analysis, and language translation. Python provides a conducive environment for handling the intricacies of natural language, making it a preferred choice for NLP practitioners.

Python boasts powerful libraries dedicated to NLP tasks. The Natural Language Toolkit (NLTK) offers a comprehensive set of tools for tasks like tokenization, stemming, and part-of-speech tagging. SpaCy, another popular library, stands out for its speed and efficiency in processing large amounts of text. These libraries empower developers to implement sophisticated NLP algorithms with ease, making Python a cornerstone in the field.

Python’s influence extends to the creation of advanced language models and chatbots. With frameworks like TensorFlow and PyTorch, developers can build and train complex language models for tasks like language generation and understanding. Python’s simplicity and extensive community support also contribute to the development of conversational agents and chatbots, enhancing user interactions across various platforms.

Some other real-world applications of Python:

• Automation and robotics through inbuilt tools and libraries like Dart, PyDy, pyro, and PyRobot
• Image processing through tools and libraries like OpenCV, Blender, PIL, and Houdini
• Scientific applications and the best Python app are developed through libraries like Pandas, SciPy, Matplotlib

Latest Trends and Updates in Python

To stay ahead in the tech world, it’s crucial for developers and enthusiasts alike to be aware of the latest trends, updates, and developments in the Python ecosystem. Let’s delve into the exciting advancements that are shaping the future of Python with the help of python course for beginners.

• Adoption of Python in Machine Learning and AI:

Python’s dominance in the realm of machine learning and artificial intelligence continues to grow. Libraries such as TensorFlow and PyTorch are witnessing widespread adoption, empowering developers to create sophisticated models and applications. The seamless integration of Python with machine learning frameworks reinforces its position as the go-to language for AI enthusiasts.

• Web Development with FastAPI:

FastAPI, a modern, fast, and highly performant web framework for building APIs with Python 3.7 and above, has gained significant traction. Its simplicity, automatic validation, and support for asynchronous programming make it an attractive choice for developers working on web applications and APIs. FastAPI’s rising popularity showcases Python’s adaptability to evolving web development needs.

• Serverless Computing and Python:

Serverless architecture has become a popular paradigm in cloud computing, and Python is playing a pivotal role in this space. Platforms like AWS Lambda, Azure Functions, and Google Cloud Functions seamlessly support Python, enabling developers to build scalable and cost-effective serverless applications of python.

• Increased Focus on Data Science and Visualization:

With the rise of data-driven decision-making, Python’s role in data science and visualization is more prominent than ever. Libraries like Pandas, NumPy, and Plotly continue to be instrumental in handling, analyzing, and visualizing data. Python’s simplicity and extensive ecosystem contribute to its widespread adoption in data-centric applications.

• Microservices Architecture

Python’s versatility extends to microservices architecture, with frameworks like Flask and Django providing robust support. Microservices enable developers to build scalable and maintainable applications by breaking them down into smaller, independent services. Python’s ease of use and diverse ecosystem contribute to the efficiency of microservices development.

• Enhanced Type Hinting and Static Analysis

Type hinting, introduced in Python 3.5, has gained momentum, leading to improved static analysis tools and support in popular IDEs. The push towards more statically-typed Python code enhances code quality, readability, and developer collaboration. Tools like MyPy and Pyright contribute to the growing emphasis on static analysis in the Python community.

• Python in DevOps and Automation

Python’s simplicity and readability make it a preferred choice for DevOps tasks and automation scripts. Infrastructure as Code (IaC) tools like Ansible leverage Python for defining and managing infrastructure. The language’s extensive standard library and third-party modules facilitate seamless automation across various domains.

• Quantum Computing with Python

As quantum computing gains momentum, Python is becoming a language of choice for quantum programming. Libraries like Qiskit and Cirq enable developers to experiment with quantum algorithms and simulations. Python’s accessibility is lowering the barrier for entry into the fascinating field of quantum computing.

After reading about all these versatile and diverse real-world applications of Python , it is safe to conclude that Python is capable of handling almost any development requirement. In the last few years, Python applications have gained newfound traction in the field of Data Science as well, particularly in Machine Learning.

Python has brought in a lot of changes to the industry given it is easy to use as well as comes packed with powerful libraries. Additionally, it offers a wide range of applications that boost productivity. The jobs in Python pay hefty packages over time and Python developers are in high demand. The ease of learning Python makes it an option to bag a steady and well-paying job too.

If you are curious to learn about python, data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

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Frequently Asked Questions (FAQs)

Python has a solution for every field. It is the most versatile language till now and has a bright future ahead. There is a long list of fields where Python is considered to be the most suitable programming language. Developers in this language are sought after because the language is gradually becoming the go to solution in a diverse set of different areas. The major fields include Machine Learning and AI, Web Development, Data Analytics, Game Development, IoT, Application Development, and Game Development. Many sectors including the healthcare sector, finance sector, aerospace sector, and banking sector rely heavily on Python. There are many big names that have either built their applications on Python or have completely shifted their tech stack to Python. Some of these include YouTube, Google, Instagram, and Pinterest.

Python is a very versatile language and if you have a good knowledge of Python, there can be various career opportunities in your hand. Some of these opportunities are as follows: You can be a Python developer right after acquiring the Python knowledge. Python developers are responsible for building websites, optimize data algorithms, or write clean and efficient Python codes. A data analyst has to deal with large sets of data, analyze them and create visualizations out of them. If you are a Python geek and love to play with data then this job is for you. Project management is in high demand as a project manager is highly responsible for the business and marketing of the companies. A machine learning engineer trains the machines or models for making predictions on the basis of the data provided to them.

Python has a wide range of rich libraries and modules but being a Python geek, you must be handy with the top and most used Python libraries. The following are some of the most popular Python libraries: TensorFlow is a boon to Machine Learning engineers. This library is developed by Google and can be considered a computational library. Numpy is again a machine learning library used by other Python libraries like TensorFlow to perform internal operations. Keras is another popular Python library that provides a convenient mechanism for neural networks.

Python has become a dominant language in Machine Learning and AI due to its simplicity, extensive libraries (such as TensorFlow and PyTorch), and readability. Real time applications of python provides a conducive environment for developing complex algorithms, making it a preferred choice for data scientists and machine learning engineers.

Python's popularity in software development can be attributed to its platform independence, high compatibility, extensive built-in frameworks and libraries, enhanced code reusability, and readability. These features make it an excellent tool for developing applications ranging from web development to enterprise-level solutions.

Python plays a crucial role in quantum computing, and libraries like Qiskit and Cirq enable developers to experiment with quantum algorithms and simulations. Python's accessibility and support for quantum programming are contributing to its adoption in this cutting-edge field.

Applications of Python in Class 9 include exploring its various uses in education.

Microsoft encourages building real-world applications with Python, highlighting its practicality.

Popular applications of Python in technology include web development, data analysis, and automation.

Python's applications in Class 8 may include introductory programming exercises and basic problem-solving tasks.

Python is suitable for real-time applications due to its efficiency and dynamic nature.

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What can you do with Python? 5 real-world Python applications

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From your first line of code, to your first day on the job — Educative has you covered. Join 2M+ developers learning in-demand programming skills.

The word is out on Python. The simplicity, limitless range of external libraries, and committed community of Pythonistas are fundamentals of Python that even a beginner developer has probably heard before. These features have also distinguished Python as the most used programming language in the world, currently [1] .

You’re not here to learn all the reasons why you should learn Python as either your first introduction to coding or your next learning endeavor in a long line of languages. You’re here to be shown why learning to code in Python is worth your time based on the career and project you see in your future. For our purposes, It helps to view Python as a tool . We’ll explore not necessarily the tool itself, but rather all that can be built by that tool . It’s pointless to learn how to grip and strike with a hammer if you don’t have an idea of what to do with it once you achieve proficiency. If you don’t know what your future holds just yet, that’s more than OK. Hopefully, the following sections can provide some inspiration through examples. Many major industries and companies are already using Python’s boundless applications to turn their visions into reality.

Python is a versatile and powerful tool that stretches to every corner of our world. The use cases of Python are in everything from Luke Skywalker’s lightsaber to your tedious health care plan. You don’t know Python until you know what it can do.

We’ll cover:

2. Entertainment

3. education, 5. health care, what python’s presence means for you, wrapping up and next steps, 5 real-world applications of python.

Python code is in everything. The Python-based libraries and modules that can be freely and easily used in any project make certain that the language can be everywhere. Some examples of these libraries are NumPy for machine learning and Pandas for data analysis . Python and its endless list of libraries are things that even the most different industries and companies have in common. As a tool, Python can lead you down any career path that you could dream of. Let’s take a look at just a few examples of where Python thrives.

The Python programming language brings you personalized playlists to brighten up your day. Spotify uses Python to support its back-end web development and data science . Have you ever wondered how Spotify knows exactly what to put in your personalized playlists? You can thank the data analysis capabilities of Python . Over 80% of Spotify’s back-end web development and data analysis processes are written in Python.

Spotify is also a vocal and proud member of the Python community , sponsoring large conferences such as PyCon and local groups such as NYC PyLadies. A thriving company like Spotify doesn’t connect its name to a language so enthusiastically unless it performs sensationally. Spotify is always hiring Python developers. If you’ve always pictured yourself working with music while using your favorite programming language, then these are opportunities you should keep an eye out for in the future.

You don’t have to write or act to get into the entertainment business. You can code too ! Python appears frequently in the entertainment media industry. Industrial Light and Magic, the visual effects company behind films such as Star Wars and Jurassic Park, has been using Python to run its CGI operating systems and lighting automation for decades. And Netflix has been becoming more and more Python-oriented every year. The company depends on Python to run its Cassandra database. Cassandra clusters and modules are used for automation (including the recommendations page that everybody loves), data analytics, and error monitoring.

Metaflow, a Python web framework , is responsible for machine learning projects at Netflix from the prototype to the production stage. The framework handles millions of data points and organizes them among thousands of CPUs. YouTube was also initially built using mostly Python and still heavily uses it today among other languages. Not just exclusive to Netflix, the machine learning abilities of Python are extensively used in our modern entertainment landscape.

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• Top 10 Uses Of Python In The Real World

Top 10 Uses of Python in Real World with Examples

Python has become the go-to programming language in 2024, dominating the market with a 28.11% share. Its simplicity and versatility make it ideal for a wide range of applications. 

In this blog, we’ll explore the top 10 real-world applications on uses of Python, illustrating why it remains the most popular choice in the tech community.

Let’s dive into the list

• Web Development
• Data Science
• Artificial Intelligence and Machine Learning
• Enterprise Applications
• Education Sector
• Web Scraping Applications
• Game Development
• Software Development
• Desktop GUI
• Operating Systems

Let’s examine some real-world applications of Python programming. These examples will also provide amazing python project ideas , helping you explore diverse language possibilities.

1. Web Development

Web development is a widely known python usage example for creating dynamic, scalable, and maintainable web applications. It is known for its readability and efficiency, making it a popular choice for backend and full-stack development.

Key Frameworks and Tools

• Django : A high-level Python web framework that encourages rapid development and clean, pragmatic design. It includes an ORM, authentication, and other essential tools.
• Flask : A micro-framework that provides the essentials to get an application up and running, offering flexibility to developers.

Real-World Example

Instagram, one of the most popular apps in Python’s real-time applications, uses Django to handle its massive user base and large amounts of data. Django’s ability to manage large-scale applications is ideal for such high-traffic websites.

Join the thousands of learners already benefiting from our Free Web Development Courses .

2. Data Science

Python is a dominant language in data science due to its simplicity, extensive libraries, and active community. It is used for data analysis, visualization, and predictive modeling.

Key Libraries and Tools

• Pandas : Provides data structures and data analysis tools.
• NumPy : Supports large, multi-dimensional arrays and matrices and a collection of mathematical functions.
• Matplotlib : A plotting library for creating static, interactive, and animated visualizations.
• SciPy : Provides scientific and technical computing capabilities, including optimization and integration.

Netflix uses Python for its recommendation algorithm and data analytics , leveraging its powerful libraries to enhance user experience by providing personalized content suggestions.

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3. Artificial Intelligence and Machine Learning

Python is extensively used in AI and machine learning due to its simplicity and robust libraries. It supports the development of algorithms that allow machines to learn and make decisions.

• TensorFlow: An open-source library developed by Google for machine learning and deep learning applications.
• Keras : An API designed for human beings, not machines, which allows for easy and fast prototyping.
• Scikit-learn : A library for machine learning built on NumPy, SciPy, and Matplotlib.
• PyTorch : An open-source machine learning library developed by Facebook’s AI Research lab.

Uber uses machine learning for ETA predictions, fraud detection, and dynamic pricing, which is another Python application in the real world that takes advantage of Python’s robust libraries to improve service efficiency and customer satisfaction.

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4. Enterprise Applications

Python is increasingly adopted for building enterprise-level applications due to its versatility, ease of integration, and a large ecosystem of libraries. It allows businesses to develop scalable solutions for various needs.

• Tryton: A high-level, open-source application platform that provides a set of modules for various business needs, including accounting, inventory, and project management.
• Odoo: A suite of open-source business applications, including CRM, eCommerce, and accounting, offering customization options and integration capabilities.
• Pyramid: Known for its flexibility and scalability, Pyramid is suitable for easily developing complex enterprise applications.

Dropbox utilizes Python for its backend infrastructure and to develop internal tools. Python’s simplicity and robustness make it ideal for managing the platform’s large-scale data storage and synchronization needs.

5. Education Sector

Python is widely adopted in education as an introductory programming language due to its simplicity and readability. Its English-like syntax, extensive standard library, and abundant learning resources help beginners grasp programming concepts easily.

Key Resources

• Python Tutor: An online tool for visualizing Python code and its execution, helping students understand programming concepts.
• Python.org Documentation: The official Python documentation provides comprehensive guides, tutorials, and reference materials for learners to explore Python’s features and capabilities.
• Abundance of Learning Resources: Free online tutorials, documentation, and interactive platforms make it easy for beginners to find diverse learning materials and practice coding in a supportive environment.

The University of Texas at Austin incorporates Python into its introductory computer science courses, leveraging its clear syntax and extensive libraries to teach fundamental programming concepts effectively. Python’s versatility allows students to transition smoothly from basic programming tasks to more complex projects.

Start learning Python for free with our expertly crafted “ Free Python Courses .”

6. Web Scraping Applications

Python’s rich ecosystem of libraries makes it an excellent choice for web scraping, the process of extracting data from websites. It allows developers to retrieve and analyze information from the web efficiently.

• Beautiful Soup : A Python library for parsing HTML and XML documents, making web scraping easier and more manageable.
• Scrapy : An open-source and collaborative web crawling framework for Python, providing robust features for extracting data from websites.
• Selenium : A portable framework for automating web browsers, useful for scenarios where dynamic interaction with web pages is required.

Price monitoring services often utilize Python for web scraping to gather pricing information from various e-commerce websites. This enables businesses to make data-driven decisions and stay competitive in the market.

Gain expertise in extracting data from the web with our expert-led “ Free Course On Web Scraping with Python .”

7. Game Development

Python’s versatility extends to game development. Several frameworks and libraries cater to game creation, including graphics rendering, physics simulations, and user interface design.

• Pygame : A set of Python modules designed for writing video games, providing functionality for graphics, sound, and input handling.
• PyKyra: A fast and versatile game development framework for Python, focusing on 2D games.
• Pyglet: A cross-platform windowing and multimedia library for Python, suitable for creating games and multimedia applications.
• PyOpenGL: A Python binding to OpenGL, allowing developers to create 3D graphics applications.
• Kivy: An open-source Python library for developing multitouch applications with a natural user interface, including games.
• Panda3D: A game engine and framework for 3D rendering and game development in Python and C++.
• Cocos2d: A framework for building 2D games, interactive demos, and other graphical/interactive applications.

Battlefield 2, a popular first-person shooter game, utilizes Python scripting for various game mechanics and functionalities, demonstrating Python’s capability to contribute to developing and enhancing gameplay experiences.

8. Software Development & Engineering

Python’s versatility extends to software development, which is used to build various applications, from command-line tools to desktop applications. 

Its clean syntax and robust standard library make it suitable for rapid prototyping and production-grade software development.

• PyQt: A set of Python bindings for the Qt application framework, enabling the development of cross-platform GUI applications.
• Tkinter: Python’s standard GUI toolkit, providing a simple and easy-to-use interface for building desktop applications.
• Click: A Python package for creating command-line interfaces, allowing developers to build robust and user-friendly command-line tools.

Dropbox Paper, a collaborative document-editing service, utilizes Python for its backend services and desktop application development, demonstrating Python’s capability to support software development across different domains.

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9. Desktop GUI

Python is used to develop desktop graphical user interface (GUI) applications. It offers libraries and frameworks that simplify the creation of interactive and visually appealing interfaces, making it suitable for building desktop applications across various platforms.

• Kivy: A Python framework for developing multitouch applications supporting various input methods and platforms.
• wxPython: A cross-platform GUI toolkit for Python based on the wxWidgets library, offering native-looking interfaces on each platform.
• PyGTK: Python bindings for the GTK+ toolkit, enabling developers to create graphical user interfaces for Linux and other Unix-like systems.

Blender, a popular open-source 3D creation suite, utilizes Python for its GUI development, allowing users to create, animate, and render 3D models and animations with a powerful and customizable interface.

10. Operating Systems

Python’s object-oriented design makes it suitable for building entire operating systems . Its compatibility with most platforms, such as Windows and Mac, and ease of use for native application development contribute to its effectiveness in this domain.

Key Tools and Techniques

• Python OS Module: This module provides a way to interact with the operating system, allowing tasks such as file management, process management, and system information retrieval.
• Python Shell Scripting: Python scripts can be used for system administration tasks, such as managing services, deploying updates, and configuring network settings.
• PyInstaller: Converts Python scripts into standalone executables, which are useful for packaging and distributing Python applications on various operating systems.

Red Hat Enterprise Linux (RHEL), a leading enterprise Linux distribution, incorporates Python for system administration and automation tasks.

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Python is one of the most versatile programming languages, evident in its top 10 real-world applications spanning various industries. 

Given its prevalence in data science and AI, exploring Python’s capabilities can open doors to lucrative career opportunities in these domains.

If you want to join this rewarding journey, consider enrolling in the Great Learning PG Program in Data Science & Business Analytics . 

With dual certification from UT Austin & Great Lakes, flexible learning options, weekly mentorship, and dedicated program support, you’ll be equipped to excel in the dynamic field of data science.

Python’s frameworks, like Django and Flask, offer rapid development, scalability, and maintainability, making it competitive with languages like JavaScript and PHP for web development. Its ease of use and robust ecosystem contribute to its popularity in this domain.

Python’s libraries, such as SciPy, Matplotlib, and NumPy, are real time applications of Python that are widely used in scientific computing for tasks such as data analysis, visualization, and simulation. Its ease of use and robust ecosystem make it a preferred tool for researchers across various scientific disciplines.

Python’s libraries, such as PySpark, Dask, and Apache Beam, enable distributed computing and parallel processing, making it suitable for big data processing and analytics tasks. These libraries leverage frameworks like Apache Spark and Hadoop for scalable data processing.

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Table of contents

What is Python Used For? 10+ Coding Uses for the Python Programming Language.

Hi! Please take a moment to think about this question:

How is Python applied in real-world scenarios?

If you are learning Python and you want to know the answer, then this article is for you.

Having a clear idea of the applications and vast potential of this programming language will give you the motivation that you will need throughout your journey.

Let's begin! ✨

🔸 Python in Real-World Scenarios

Python is used in virtually every industry and scientific field that you can imagine, including:

• Data Science.
• Machine Learning.
• Web Development.
• Computer Science Education.
• Computer Vision and Image Processing.
• Game Development.
• Medicine and Pharmacology.
• Biology and Bioinformatics.
• Neuroscience and Psychology.
• Other areas such as robotics, autonomous vehicles, business, meteorology, and graphical user interface (GUI) development.

This article covers a wide range of applications of this programming language in these industries with examples, use cases, and Python libraries. Let's start with the applications of Python in data science.

🔹 Data Science: Analysis and Visualization

Perhaps one of the most popular applications of Python is data science. The power of the Python libraries developed for data analysis and visualization is amazing. Let's see why.

Data Science Applications

With a Python data visualization library, you can create a wide variety of plots and visual representations, such as:

• Lines, Bars, and Markers.
• Images, contours and fields.
• Subplots, axes and figures.
• Statistics (Box Plots, Bar Charts, and Histograms).
• Pie and polar charts.

You can add text, labels, annotations, color, shapes, collections, animations, and interactivity to your plots depending on the package or library that you choose to work with.

💡 Tip: You can see some examples of data visualizations generated with Python in the image above.

Libraries and Packages

Let's see some of the most popular packages and libraries to work with Python in data science:

Python for Data Analysis

• NumPy : this package is described as "the fundamental package for scientific computing with Python". According to the official website of this package, "nearly every scientist working in Python draws on the power of NumPy."
• Pandas : is "a fast, powerful, flexible and easy to use open source data analysis and manipulation tool."

Python for Data Visualization

• Matplotlib : is "a comprehensive library for creating static, animated, and interactive visualizations in Python." If you are curious about what you can do with this library, check out the example gallery .
• Seaborn : is "a Python data visualization library based on matplotlib." If you are curious about what you can do with this library, check out the example gallery .
• ggplot2 : is "a system for declaratively creating graphics, based on The Grammar of Graphics". According to its official website: "you provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details."
• Bokeh : is "an interactive visualization library for modern web browsers".
• Pandas : this library has many tools for data visualization.

Learning Resources

If you want to learn data analysis and visualization using Python, Jupyter Notebooks, Numpy, Pandas, CSV files, data frames, and more, you can start your journey with freeCodeCamp's free Data Analysis with Python Certification :

During the certification, you work on and complete these projects:

• Mean-Variance-Standard Deviation Calculator.
• Demographic Data Analyzer.
• Medical Data Visualizer.
• Page View Time Series Visualizer.
• Sea Level Predictor.

freeCodeCamp's YouTube channel also has these great free tutorials to get you started:

• Data Analysis with Python – Full Course for Beginners (Numpy, Pandas, Matplotlib, Seaborn) by Santiago Basulto.
• Python for Data Science – Learn Pandas, Matplotlib, Numpy, and More by DataPublishing
• Matplotlib Crash Course by Keith Galli
• Python NumPy Tutorial for Beginners by Keith Galli

In addition, these are helpful resources if you want to learn how to work with these libraries:

• Matplolib Tutorials : free introductory, intermediate, and advanced tutorials to teach you how to create awesome visualizations.
• Pandas "Getting Started" section: free introductory tutorials.
• NumPy Learn section : a curated collection of external resources to help you get started.

🔸 Machine Learning

Python is an essential tool for every developer who wants to enter the fascinating area of Machine Learning. Let's see a brief introduction to Machine Learning.

What is Machine Learning?

Machine Learning is an area of Computer Science that creates systems that are able to learn on their own.

This type of system uses algorithms that are continuously improved based on input data that helps the system "learn". It learns how to respond autonomously to new scenarios by generating an appropriate output in new scenarios based on previous knowledge.

One of the most amazing things about these systems is that they are continually refined.

They are not like the programs that we typically write in a Python script where we specify every possible action that the program can take. In Machine Learning, the system is trained to "think" and make decisions based on previous knowledge.

This is why we say that machines "learn" from the data.

💡 Tip: This is an interesting talk by Google: Machine Learning Zero to Hero (Google I/O'19) .

Neural Networks: The Building-Blocks of Machine Learning

Neural networks are the processing units of the system. They try to simulate a real network of neurons of the brain. These virtual "neurons" receive input, learn how to process that input, and generate an output based on their previous knowledge.

This is very similar to what out brain does every single moment of every single day.

Thanks to neural networks, a Machine Learning algorithm can learn how to predict the expected output from a given input based on previous knowledge.

For example, when you see recommended videos on YouTube, those recommendations were generated by neural networks that predict what videos you might like to watch based on your previous patterns. Amazing, right?

💡 Tip: This is an interesting article by Google if you would like to read more about this YouTube example.

Python and Machine Learning

I'm sure you must be asking: what is the role of Python in this area? It is one of the most popular and powerful tools used to program this type of system.

One of the most popular libraries used by developers around the world to work with Python applied to Machine Learning is TensorFlow . It's a free open-source library developed by the Google Brain Team. This library is used for research and production at Google.

According to Jeff Dean, the lead of Google's Artificial Intelligence division:

Today it is used heavily in our speech recognition systems, in a new Google Photos product, Gmail, and Google Search. ( source )

The best part is that developers all over the world can use this library to tackle real-world problems.

💡 Tip: This is a great video about TensorFlow made by Google.

These are two other popular Python libraries used for Machine Learning:

• Keras – an open-source neural-network library written in Python.
• PyTorch – an open-source Machine Learning library used for developing and training neural networks.

Machine Learning Projects in Python

The potential of Machine Learning is really endless. It can be applied to virtually any area and context that you can think of. If the task requires learning from patterns and predicting output, then a Machine Learning model can definitely help.

For example, to give you an idea of the type of projects that you can create, freeCodeCamp's curriculum includes a free Machine Learning with Python Certification :

• Rock Paper Scissors.
• Cat and Dog Image Classifier.
• Book recommendation engine using K-Nearest Neighbors.
• Linear Regression health costs calculator.
• Neural Network SMS classifier.

More Examples of Real-World Applications

You can find more examples of the applications of Machine Learning in Kaggle , an "online community of data scientists and machine learning practitioners" owned by Google.

In this platform, you can practice your Python and Machine Learning skills by working on projects and participating in competitions.

To give you an idea of the type of projects that you can tackle with Machine Learning, previous competitions in Kaggle include:

• Predicting lung function decline.
• Predicting survival on the Titanic.
• Building tools for bird population monitoring.
• Labeling famous landmarks.
• Forecasting COVID-19 spread.
• Estimating the unit sales of Walmart retail goods.
• Identifying videos with face or sound manipulations.
• Predicting wait times at major city intersections.
• Detecting fraud from customer transactions.
• Predicting a movie's worldwide box office revenue.
• Predicting pet adoption.
• Identifying risk when pilots are distracted, sleepy, or in other dangerous cognitive states.

As you can see, just in this short list of projects, the applications range from medicine to business, from biology to risk detection, and from fraud detection to image processing. The possibilities are truly endless when you tackle real-world problems using Machine Learning.

freeCodeCamp's YouTube channel has these helpful tutorials to get you started with Machine Learning in Python:

• TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
• Develop an AI to play Connect Four - Python Tutorial
• Scikit-Learn Course - Machine Learning in Python Tutorial
• PyTorch for Deep Learning - Full Course / Tutorial

🔹 Web Development

Python is used in the field of web development to build the back-end of web applications. Let's start this section by talking a little bit about what the back-end is and how it helps us to create web applications.

Python for Back-End Web Development

In a web application, all the code used to interact with the user and create what the user sees is called the front-end part of the application.

Python is used to code the behind-the-scenes functionality of the application, the part that powers all the functionality of the application but that you don't see directly on screen.

It handles the server-side of the application, interacting with all the necessary databases when the user requests data. It returns the requested data to the user to make the application run as expected.

💡 Tip: Full-Stack Web Development involves both the front-end and back-end of a web application to make it presentable to the user while working with databases.

Web Frameworks

These are some popular Python web frameworks:

• Django : a "high-level Python Web framework that encourages rapid development and clean, pragmatic design."
• Flask : a very popular microframework used to develop web applications in Python.
• Pyramid : a "small, fast, down-to-earth Python web framework."
• Web2Py : a "free open source full-stack framework for rapid development of fast, scalable, secure and portable database-driven web-based applications."
• Bottle : a "fast, simple and lightweight WSGI micro web-framework for Python."

freeCodeCamp's YouTube channel has great free tutorials to learn web development in Python:

• Python Django Web Framework - Full Course for Beginners
• Learn Flask for Python - Full Tutorial
• Web Programming with Flask - Intro to Computer Science - Harvard's CS50 (2018)
• Full stack Python Flask tutorial - Build a social network

These are also great free resources to learn how to work with these frameworks:

• Django "First Steps" section
• Pyramid Tutorials
• Quick Tutorial for Pyramid

🔸 Computer Science Education

Python currently plays a key role in computer science education around the world.  Let's see why.

Why Python?

Python is so widely used as a teaching tool because:

• It is easy to learn: its syntax is simple and it can be learned quickly. Students start diving into more advanced aspects of computer science much more quickly than with other programming languages.
• It is powerful: it is used in real-world applications, so students immediately start acquiring valuable skills for their careers.
• It is versatile: it supports various programming paradigms including imperative programming, functional programming, procedural programming, and object-oriented programming.

The creator of the Python language, Guido van Rossum , stated that:

Now, it's my belief that Python is a lot easier than to teach to students programming and teach them C or C++ or Java at the same time because all the details of the languages are so much harder.

Python's syntax is simple and straightforward, so students can start learning computational thinking and problem-solving skills much more quickly, which is usually the main goal of introductory computer science courses.

Python in the Classroom and Online Learning

Many universities and schools around the world have decided to teach introductory programming and computer science courses using Python.

For example, MIT, one of the world's leading universities in the field of technology, teaches introductory computer science and programming using Python (both in the on-campus and online versions of the course on edX).

According to an article by MIT News published when the online version of the course reached 1.2 million enrollments, the course "has become the most popular MOOC in MIT history".

This clearly shows that Python's popularity continues to rise. In the article you can find testimonies of students who learned Python and how this new knowledge improved their lives.

In the article, Professor Ana Bell, lecturer in the EECS Department at MIT, states that:

“At its core, the 6.00 series teaches computational thinking...It does this using the Python programming language, but the course also teaches programming concepts that can be applied in any other programming language.”

This clearly shows the potential of Python as a teaching tool. It can be used to teach higher level concepts that can be applied to other programming languages.

And it does this without the extra layer of complexity that the syntax of other programming languages like Java or C might add to the learning process.

During the last few years, online courses have become an important part of the daily lives of learners of all ages worldwide. The variety of free online courses and resources has expanded tremendously in the last few years.  

For example, freeCodeCamp's curriculum includes three free certificates with projects to help you expand your Python skills in key areas with high demand worldwide:

• Scientific Computing with Python.
• Data Analysis with Python.
• Machine Learning with Python.

Harvard University also offers these online courses that can be audited for free:

• CS50's Introduction to Computer Science.
• CS50's Web Programming with Python and JavaScript.
• CS50's Introduction to Artificial Intelligence with Python.

Python has definitely become a key tool that has improved computer science education worldwide. And it will continue to do so in the future.

If you are thinking about teaching a course using Python or learning Python, I guarantee you that your time and effort will be totally worth it.

🔹 Computer Vision and Image Processing

Python is used for computer vision and image processing, fields that are expanding rapidly.

The goal of image processing is to process an image, apply transformations to it, and return a new version of the original image.  

In contrast, the goal of computer vision is more complex because it tries to make the computer understand and interpret an image and its content.

Image Processing

Let's start with image processing. With a Python library, you can perform operations such as:

• Cropping, flipping, and rotating.
• Manipulating exposure and color channels.
• Detecting edges and lines.
• Adding filters and restoring images.

Computer Vision

Now let's dive into computer vision. If you start researching this topic, you might be surprised by its current applications. Some of them are:

• Navigation.
• Object and Event Detection.
• Facial recognition.
• Image classification.

This scientific field is so important that Google developed a tool called Cloud Vision , which has a Python version for developers to incorporate this functionality into their programs.

According to the " Using the Vision API with Python " tutorial in Google Codelabs, the Google Cloud Vision API:

Allows developers to easily integrate vision detection features within applications, including image labeling, face and landmark detection, optical character recognition (OCR), and tagging of explicit content.

This set of tools provides functionality for face detection, landmark detection, logo detection, label detection, text detection, and more.

💡 Tip: One of the most amazing applications of computer vision is the development of software to control self-driving cars. These vehicles need to "see" where they are driving, where the lane is, and what objects surround them (including other vehicles). Computer vision plays a key role in this functionality.

Python Libraries

These are some awesome libraries for computer vision and image processing:

• OpenCV : an "open source computer vision and machine learning software library". Its Python version is called OpenCV-Python.
• scikit-image : a "collection of algorithms used for image processing".
• NumPy : it can be used to process the pixels of an image as a 2D array.
• SciPy : the scipy.ndimage package "contains various functions for multidimensional image processing."

🔸 Game Development

Games definitely shape lives and create timeless memories. They will continue to be part of our society in the years to come. Python is already there, lighting the spark of game creation.

Python Game Development Frameworks

According to the official Python Documentation , there are two main Python frameworks used to develop games:

• pygame : "the original and still very much active package for game development using Python. It allows Python to talk to SDL , a cross-platform, multimedia library. Because it needs to be compiled for each platform and each Python version, there can be a lag when a new Python version comes along."
• pyglet : this is "the newcomer, based on OpenGL. Because it is a pure Python package, it can be used as is even when a new Python version is released (except for the Python 2 to Python 3 transition)."

You can also use the turtle module to create simple games. Turtle is a built-in Python module that is installed automatically when you install Python in your computer. It helps you to create games with simple graphics and with a simple user interface.

If you want learn game development in Python, freeCodeCamp's YouTube channel has these great free tutorials:

• Learn Python by Building Five Games - Full Course
• Python and Pygame Tutorial - Build Tetris! Full GameDev Course
• Snake Game Python Tutorial

🔹 Medicine and Pharmacology

Python also has amazing applications in the medical field. You will be surprised by how technology is being combined with medical knowledge to provide accurate and efficient diagnoses and treatments to patients.

Applications

Some examples of the use of Python in medicine and pharmacology include:

• Making clinical diagnoses based on the patients' medical records and symptoms.
• Analyzing medical data.
• Making computational models to speed up the process of development of new medications.

These broad applications include thousands and thousands of examples around the world. I selected a few of them to illustrate how Python is shaping this field. Let's take a look at them.

Pharmaceutical Success Story: AstraZeneca

According to the official Python Documentation , one of the world's leading pharmaceutical companies, AstraZeneca , used Python to improve their existing computational models to make them "more robust, extensible, and maintainable".

Researchers used these models simulate the chemical structure of molecules and their effect in the body. This helped scientists identify potential molecules for new drugs and start testing them more quickly in the laboratory.

When he joined the team, Andrew Dalke, being a a "well-known advocate for Python in computational chemistry and biology" convinced the team that Python was exactly what they needed.

Python was chosen for this work because it is one of the best languages available for physical scientists, that is, for people who do not have a computer science background.

He stated that:

Python was designed to solve real-world problems faced by an expert programmer. The result is a language that scales well from small scripts written by a chemist to large packages written by a software developer.

Amazing, right? Python can power the computational models that pharmaceutical laboratories use to develop new drugs.

Red Blood Cells (RBC) Identification

Another interesting medical application of Python is related to Hematology. Usually, specialized professionals analyze blood tests by counting and identifying cells manually, but this can be improved with the help of automation.

Researchers found that Python can be the right tool for the job. Let's see an interesting project.

IdentiCyte The goal of this project is to identify and classify red blood cells shapes based on images taken from optical microscopes. According to this article , "RBC shape can help to diagnose diseases and disorders such as leukaemia, sickle cell anaemia and malaria."

The project was developed by researchers from the Bioresource Processing Research Institute Australia. It was programmed in Python and it used image processing Python packages and libraries such as numpy, scipy, opencv-python, scikit-learn, and matplotlib.

Python Medical Packages

• pyGeno : an open-source Python package developed by Tariq Daouda at the Institute for Research in Immunology and Cancer ( IRIC ). It's intended for "precision medicine applications that revolve around genomics and proteomics". It works with reference and personalized genomes.
• MedPy : an open-source Python library "for medical image processing in Python, providing basic functionalities for reading, writing and manipulating large images of arbitrary dimensionality."

Real-World Medical Applications (Examples)

• Gusztav Belteki presented another example during his talk at PyData Berlin 2018 " Python in Medicine: analysing data from mechanical ventilators ." The goal of his research was to "interpret large datasets retrieved from modern equipment used in neonatal intensive care, mechanical ventilators and patient monitors."
• At PyCon 2019, Jill Cates gave this presentation titled " How to Build a Clinical Diagnostic Model in Python ."

🔸 Biology and Bioinformatics

Python also has amazing applications in the world of Biology and Bioinformatics. These include processing DNA sequences, simulating population dynamics and genetics, and modeling biochemical structures.

Biopython is a Python framework with "freely available tools for biological computation". Its goal is to "address the needs of current and future work in bioinformatics."

According to its documentation , this framework includes functionality such as the ability to:

• Work with sequences and perform common operations on them such as transcription, translation, and weight calculations.
• Connect with biological databases.
• Perform classification of data using K-Nearest Neighbors, Naive Bayes, and Support Vector Machines.
• Work with phylogenetic trees and population genetics.

The documentation states that "the goal of Biopython is to make it as easy as possible to use Python for bioinformatics by creating high-quality, reusable modules and classes."

Rosalind: Practice Python by Solving Bioinformatics Challenges

Rosalind is "a platform for learning bioinformatics through problem solving." It is "free and open to the public" (the FAQ page indicates that it is in beta mode).

Python can be used to solve the challenges on the platform. Since this is a very popular programming language in the platform, there is a " Python Village " section where you can learn the basics of Python before tackling bioinformatics algorithms.

Users solve the problems by running their solutions on their computer, processing the given dataset, and copy/pasting the output to check the answer.

💡 Tip: The project's name commemorates Rosalind Franklin , "whose X-ray crystallography with Raymond Gosling facilitated the discovery of the DNA double helix by Watson and Crick".

Packages and Frameworks

• ProDy : a free and open-source package "for protein structural dynamics analysis" developed by Bahar Lab at the University of Pittsburgh.
• PySB : a "framework for building mathematical models of biochemical systems as Python programs" developed by members of the Lopez Lab at Vanderbilt University and the Sorger Lab at Harvard Medical School.
• The Community Simulator: this is a "freely available Python package for simulating microbial population dynamics in a reproducible, transparent and scalable way" developed by researchers at Boston University.

💡 Tip: If you would like to learn more about the applications of Python in Bioinformatics, here's a talk by Martin Schweitzer at PyCon Australia: " Python for Bioinformatics for learning Python ".

🔹 Neuroscience and Psychology

Python also has applications in neuroscience and experimental psychology research.

Python in Neuroscience

According to the article Python in neuroscience written by researchers from the Center for Brain Simulation, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland:

Computation is becoming essential across all sciences, for data acquisition and analysis, automation, and hypothesis testing via modeling and simulation.

In regards to Python, they state that:

It became clear to us in 2007 that we were on the cusp of an emerging Python in neuroscience ecosystem, particularly in computational neuroscience and neuroimaging, but also in electrophysiological data analysis and in psychophysics.

As you can see, Python and computation have been expanding across all sciences.

PsychoPy is "an open-source package for running experiments in Python" supported by the University of Nottingham. According to the official Documentation of this package:

It is used by many labs worldwide for psychophysics, cognitive neuroscience and experimental psychology.

The official website of this package states that it is:

• Easy for learning.
• Precise enough for Psychophysics.
• Online or lab-based depending on the user's choice.

🔸 Astronomy

Python also has applications in Astronomy and Astrophysics. Let's see three of the main Python packages used in this scientific area:

The Astropy package "contains various classes, utilities, and a packaging framework intended to provide commonly-used astronomy tools."

Astropy is part of a larger project called The Astropy Project, which is "is a community effort to develop a common core package for Astronomy in Python and foster an ecosystem of interoperable astronomy packages."

According to its About page, one of its goals is to "improve usability, interoperability, and collaboration between astronomy Python packages."

💡 Tip: You can see examples of projects made with Astropy in the Example Gallery .

The SunPy package is described as "the community-developed, free and open-source solar data analysis environment for Python." It builds upon the capabilities of Python packages such as NumPy, SciPy, Matplotlib, and Pandas.

The SpacePy package is "a package for Python, targeted at the space sciences, that aims to make basic data analysis, modeling and visualization easier."

According to its official Documentation :

The SpacePy project seeks to promote accurate and open research standards by providing an open environment for code development.

According to the description of its GitHub repository , it has superposed epoch classes, drift shell tracing, access to magnetic field models, streamline tracing, bootstrap confidence limits, time and coordinate conversions, and more.

🔹 Other Applications

Python can also be applied in many other areas, including:

• Robotics: Python can be used to program robots. A library written for this purpose is pybotics , "an open-source Python toolbox for robot kinematics and calibration".
• Autonomous vehicles: Python can be used to program the software that controls self-driving cars. These cars need computer vision to "see" where they are driving, where the lane is, and what objects surround them.
• Meteorology : The package climate-indices "contains Python implementations of various climate index algorithms which provide a geographical and temporal picture of the severity of precipitation and temperature anomalies useful for climate monitoring and research."
• Business: Python can be a powerful tool to analyze data generated by businesses and to forecast future trends.
• Graphical User Interface (GUI) Development : Python can be used to create graphical user interfaces with tools like tkinter .
• If you're interested in learning more about this, freeCodeCamp has a great tutorial on YouTube: Tkinter Course - Create Graphic User Interfaces in Python Tutorial.

🔸 In Summary

There are many applications of Python in every area that you can possibly imagine. I hope that this article gave you an idea of the wide range of real-world applications of this programming language in industries that are currently shaping our world.  

Remember that no matter which field you are in or which field you want to be in, learning Python will definitely open many doors for you. It is here to stay. And it has transformed and improved our current world and it will continue to do so for many years.

I really hope that you liked my article and found it helpful. Check out my online courses . Follow me on Twitter . ⭐️

Developer, technical writer, and content creator @freeCodeCamp. I run the freeCodeCamp.org Español YouTube channel.

If you read this far, thank the author to show them you care. Say Thanks

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Applying Math with Python: Over 70 practical recipes for solving real-world computational math problems

Discover easy-to-follow solutions and techniques to help you to implement applied mathematical concepts such as probability, calculus, and equations using Python's numeric and scientific libraries

Key Features

• Compute complex mathematical problems using programming logic with the help of step-by-step recipes
• Learn how to use Python libraries for computation, mathematical modeling, and statistics
• Discover simple yet effective techniques for solving mathematical equations and apply them in real-world statistics

Book Description

What you will learn.

• Become familiar with basic Python packages, tools, and libraries for solving mathematical problems
• Explore real-world applications of mathematics to reduce a problem in optimization
• Understand the core concepts of applied mathematics and their application in computer science
• Find out how to choose the most suitable package, tool, or technique to solve a problem
• Implement basic mathematical plotting, change plot styles, and add labels to plots using Matplotlib
• Get to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methods

Who this book is for

Whether you are a professional programmer or a student looking to solve mathematical problems computationally using Python, this is the book for you. Advanced mathematics proficiency is not a prerequisite, but basic knowledge of mathematics will help you to get the most out of this Python math book. Familiarity with the concepts of data structures in Python is assumed.

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Introduction to Python

• Learn Python Programming – One Stop Solution for Beginners
• What is Python language? Is it easy to learn?
• Python Tutorial – Python Programming For Beginners
• Python: Interesting Facts You Need To Know
• Which are the best books for Python?
• Top 10 Features of Python You Need to Know

Top 10 Python Applications in the Real World You Need to Know

• Python Anaconda Tutorial : Everything You Need To Know
• Top 10 Reasons Why You Should Learn Python
• What are Important Advantages and Disadvantages Of Python?
• Python and Netflix: What Happens When You Stream a Film?
• How to Learn Python 3 from Scratch – A Beginners Guide
• Top 10 Best IDE for Python: How to choose the best Python IDE?
• How To Use Python For DevOps?
• Python vs C: Know what are the differences
• Python vs C++: Know what are the differences
• Ruby vs Python : What are the Differences?

Python Installation

• Install Python On Windows – Python 3.X Installation Guide
• How To Run Python In Ubuntu (Linux)?
• What is Python Spyder IDE and How to use it?
• How To Add Python to Path?
• Introduction to Atom Python Text Editor and how to configure it
• Python 101 : Hello World Program

Python Fundamentals

• Python Basics: What makes Python so Powerful?
• Data Structures You Need To Learn In Python
• What is the use of self in Python?
• Python Programming – Beginners Guide To Python Programming Language
• What is print in Python and How to use its Parameters?
• Important Python Data Types You Need to Know
• PyCharm Tutorial: Writing Python Code In PyCharm (IDE)
• Python Visual Studio- Learn How To Make Your First Python Program
• What is the Main Function in Python and how to use it?
• What is Try Except in Python and how it works?
• What are Comments in Python and how to use them?
• How to fetch and modify Date and Time in Python?
• Inheritance In Python With Examples: All You Need To Know
• How To Best Utilize Python CGI In Day To Day Coding?
• Python Constructors: Everything You Need To Know
• How To Create Your First Python Metaclass?
• Init In Python: Everything You Need To Know

Learn How To Use Split Function In Python

• How to Read CSV File in Python?
• Stack in Python: How, why and where?
• Hash Tables and Hashmaps in Python: What are they and How to implement?
• What is Random Number Generator in Python and how to use it?
• How to find Square Root in Python?
• Arrays in Python – What are Python Arrays and how to use them?

Loops In Python: Why Should You Use One?

• What are Sets in Python and How to use them?
• What is Method Overloading in Python and How it Works?
• Python Functions : A Complete Beginners Guide
• Learn How To Use Map Function In Python With Examples
• Python time sleep() – One Stop Solution for time.sleep() Method
• How To Reverse A String In Python?
• How To Sort A Dictionary In Python : Sort By Keys , Sort By Values
• String Function In Python: How To Use It with Examples
• How To Convert Decimal To Binary In Python
• Python Tuple With Example: Everything You Need To Know
• How To Input a List in Python?
• How to Find Length of List in Python?

How to Reverse a List in Python: Learn Python List Reverse() Method

• Learn What is Range in Python With Examples
• Everything You Need To Know About Hash In Python
• What Isinstance In Python And How To Implement It?
• How To Best Implement Armstrong Number In Python?
• How To Implement Round Function In Python?
• How To Implement 2-D arrays in Python?
• Learn How To Make Python Pattern Programs With Examples
• Introduction To File Handling In Python
• What is Python JSON and How to implement it?

Threading In Python: Learn How To Work With Threads In Python

• How To Best Implement Multiprocessing In Python?

Know all About Robot Framework With Python

• What is Mutithreading in Python and How to Achieve it?
• Map, Filter and Reduce Functions in Python: All you need to know
• What is the Format Function in Python and How does it work?
• Python Database Connection: Know how to connect with database
• What are Lambda Functions and How to Use Them?
• What are Generators in Python and How to use them?
• Python Iterators: What is Iterator in Python and how to use it?
• Python For Loop Tutorial With Examples To Practice
• While Loop In Python : All You Need To Know
• What is Socket Programming in Python and how to master it?
• Regular Expression in Python With Example
• How to Parse and Modify XML in Python?

Python OOPs

• Object Oriented Programming Python: All you need to know
• Python Class – Object Oriented Programming
• What is Polymorphism in OOPs programming?
• Python String Concatenation : Everything You Need To Know

Everything You Need To Know About Print Exception In Python

Python libraries.

• Top 10 Python Libraries You Must Know In 2024
• How To Install NumPy In Python?
• Python NumPy Tutorial – Introduction To NumPy With Examples
• Python Pandas Tutorial : Learn Pandas for Data Analysis
• Python Matplotlib Tutorial – Data Visualizations In Python With Matplotlib
• Python Seaborn Tutorial: What is Seaborn and How to Use it?
• SciPy Tutorial: What is Python SciPy and How to use it?
• How To Make A Chatbot In Python?
• FIFA World Cup 2018 Best XI: Analyzing Fifa Dataset Using Python
• Scikit learn – Machine Learning using Python
• The Why And How Of Exploratory Data Analysis In Python
• OpenCV Python Tutorial: Computer Vision With OpenCV In Python
• Tkinter Tutorial For Beginners | GUI Programming Using Tkinter In Python
• Introduction To Game Building With Python's Turtle Module
• PyGame Tutorial – Game Development Using PyGame In Python
• PyTorch Tutorial – Implementing Deep Neural Networks Using PyTorch
• Scrapy Tutorial: How To Make A Web-Crawler Using Scrapy?

Web Scraping

• A Beginner's Guide to learn web scraping with python!
• Python Requests Module Tutorial – Sending HTTP Requests Using Requests Module
• Django Tutorial – Web Development with Python Django Framework
• Django vs Flask: Which is the best for your Web Application?
• Top 50 Django Interview Questions and Answers You Need to Know in 2024

Python Programs

• Palindrome in Python: How to Check a Number or String is Palindrome?
• How to Find Prime Numbers in Python
• How To Implement GCD In Python?
• How To Convert Lists To Strings In Python?
• How to Display Fibonacci Series in Python?
• How to implement Python program to check Leap Year?
• How to reverse a number in Python?
• How to Implement a Linked List in Python?
• How to implement Bubble Sort in Python?
• How to implement Merge Sort in Python?
• A 101 Guide On The Least Squares Regression Method

Career Oppurtunities

• Python Career Opportunities: Your Career Guide To Python Programming
• Top Python developer Skills you need to know
• Learn How To Make A Resume For A Python Developer
• What is the Average Python Developer Salary?
• How To Become A Python Developer : Learning Path For Python
• Why You Should Choose Python For Big Data

Interview Questions

• Top 100+ Python Interview Questions & Answers for 2024
• Top 50 OOPs Interview Questions and Answers in 2024
• Top Python Projects You Should Consider Learning

Data Science

Dynamic Typing, Built-In Data Structures , Powerful Libraries , Frameworks , Community Support are just some of the reasons which make Python an attractive language for rapidly developing any sort of application. In this article, we will see some of the areas where Python excels in application development. 

The following topics are to be covered in this article of Python Applications:

• Web Development
• Game Development
• Machine Learning and Artificial Intelligence
• Data Science and Data Visualization
• Desktop GUI
• Web Scraping Applications
• Business Applications
• Audio and Video Applications
• CAD Applications
• Embedded Applications

With that out of the way, let’s jump straight into the Python Applications. :)

Top 10 Trending Technologies to Learn in 2024 | Edureka

Python applications.

Python supports cross-platform operating systems which makes building applications with it all the more convenient. Some of the globally known applications such as YouTube , BitTorrent, DropBox, etc. use Python to achieve their functionality.

Find out our Python Training in Top Cities/Countries

1. Web Development

Python can be used to make web-applications at a rapid rate. Why is that? It is because of the frameworks Python uses to create these applications. There is common-backend logic that goes into making these frameworks and a number of libraries that can help integrate protocols such as HTTPS, FTP, SSL etc. and even help in the processing of JSON, XML, E-Mail and so much more.

Some of the most well-known frameworks are Django , Flask , Pyramid. Why use a framework? The security , scalability , convenience that they provide is commendable if we compare it to starting the development of a website from scratch.

2. Game Development

Python is also used in the development of interactive games . There are libraries such as PySoy which is a 3D game engine supporting Python 3, PyGame which provides functionality and a library for game development. Games such as Civilization-IV, Disney’s Toontown Online, Vega Strike etc. have been built using Python.

3. Machine Learning and Artificial Intelligence

Machine Learning and Artificial Intelligence are the talks of the town as they yield the most promising careers for the future. We make the computer learn based on past experiences through the data stored or better yet, create algorithms which makes the computer learn by itself. The programming language that mostly everyone chooses? It’s Python. Why? Support for these domains with the libraries that exist already such as Pandas , Scikit-Learn , NumPy and so many more.

Learn the algorithm, use the library and you have your solution to the problem. It is that simple. But if you want to go the hardcore way, you can design your own code which yields a better solution, which still is much easier when we compare it to other languages.

4. Data Science and Data Visualization

Data is money if you know how to extract relevant information which can help you take calculated risks and increase profits. You study the data you have, perform operations and extract the information required. Libraries such as Pandas, NumPy help you in extracting information.

You can even visualize the data libraries such as Matplotlib , Seaborn , which are helpful in plotting graphs and much more. This is what Python offers you to become a Data Scientist .

5. Desktop GUI

We use Python to program desktop applications . It provides the Tkinter library that can be used to develop user interfaces. There are some other useful toolkits such as the wxWidgets, Kivy , PYQT that can be used to create applications on several platforms.

You can start out with creating simple applications such as Calculators, To-Do apps and go ahead and create much more complicated applications.

6. Web Scraping Applications

Python is a savior when it comes to pull a large amount of data from websites which can then be helpful in various real-world processes such as price comparison, job listings, research and development and much more. 

Python has  BeautifulSoup which we use to pull such data. Here’s a full-fledged guide to learn Web scraping with Python .

7. Business Applications

Business Applications are different than our normal applications covering domains such as e-commerce, ERP and many more. They require applications which are scalable, extensible and easily readable and Python provides us with all these features. Platforms such as Tryton is available to develop such business applications.

8. Audio and Video Applications

We use Python to develop applications that can multi-task and also output media. Video and audio applications such as TimPlayer, Cplay have been developed using Python libraries. They provide better stability and performance in comparison to other media players.

9. CAD Applications

Computer-Aided Designing is quite challenging to make as many things have to be taken care of. Objects and their representation, functions are just the tip of the iceberg when it comes to something like this. Python makes this simple too and the most well-known application for CAD is Fandango.

10. Embedded Applications

Python is based on C which means that it can be used to create Embedded C software for embedded applications. This helps us to perform higher-level applications on smaller devices which can compute Python.

The most well-known embedded application could be the Raspberry Pi which uses Python for its computing. We can also use it as a computer or like a simple embedded board to perform high-level computations.

We use python in a variety of applications. No matter what field you take up, Python is rewarding. So I hope you have understood the Python Applications and what sets Python apart from every other programming language. To know more you can enroll with our Master in Python programming course.

Got a question for us? Please mention it in the comments section of this “ Top 10 Python Applications You Need to Know ” blog and we will get back to you as soon as possible

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Thankyou for sharing!! As am a beginner in Python Training,Now am more excited to work as it can help in Artificial Intelligence.

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Applying Functional Programming in Python to Solve Real-World Problems

Functional programming is a programming paradigm that models computations as the evaluation of mathematical functions. It emphasizes pure functions, immutable data, and avoidance of side effects. While Python is not a fully functional language, it does provide useful tools to apply functional programming concepts and techniques to solve real-world problems in fields like data science, machine learning, and web development. This guide will examine key functional programming principles and demonstrate how to leverage them in Python to write cleaner, more maintainable and testable code.

Table of Contents

Overview of functional programming, 1. first-class functions, 2. pure functions, 3. higher-order functions, 4. list comprehensions, 5. immutability, 6. recursion, benefits of functional programming in python, 1. use pure functions wherever possible, 2. make use of higher-order functions, 3. embrace immutability, 4. utilize list comprehensions, 5. write recursive algorithms, 6. pass functions as arguments, 7. return functions from other functions, data processing pipeline, parsing complex json, managing application state, parallel processing, when to avoid functional programming in python.

Functional programming languages like Haskell, Erlang, and Scala are built around mathematical functions and shun side effects. In contrast, Python is a multi-paradigm language that supports imperative, object-oriented, and functional styles. While not inherently functional, Python offers features like first-class functions, list comprehensions, and built-in functions like map() , filter() , and reduce() that facilitate a functional approach.

The key principles of functional programming include:

• Pure Functions - Functions that always return the same output for the same input and avoids manipulating external state.
• Immutable Data - Data that cannot be modified after creation. In Python, tuples are immutable while lists are mutable.
• First-class Functions - Treating functions as values that can be passed as arguments to other functions, returned from functions, or assigned to variables.

Higher-order Functions - Functions that take other functions as arguments or return functions as output. Examples are map() , filter() , reduce() .

Recursion - Solving problems by having functions call themselves until reaching a base case. Useful for tasks like tree traversal.

Avoid Side Effects - Not mutating state that exists outside a function’s scope. Enforces isolation and testing.

Following these principles leads to code that is more modular, testable, and reasoned about.

Key Functional Concepts in Python

While Python is not a functional language, it has adopted several functional concepts into its design:

Treating functions as first-class objects provides the basis for implementing core functional techniques in Python:

Pure functions always return the same output for the same input and avoid side effects:

Pure functions are easier to reason about and test.

Higher-order functions take other functions as input or return functions as output:

map() , filter() , reduce() facilitate functional programming.

List comprehensions provide a concise syntax for mapping and filtering without mutating existing data:

Using immutable objects prevents unintended side effects:

Python tuples , strings , numbers are immutable while lists and dictionaries are mutable.

Recursion allows elegant solutions by breaking problems down into simpler subproblems:

Recursive algorithms shine for tasks like tree traversal.

By leveraging these concepts, we can write more functional Python code.

Applying functional programming principles in Python code offers several advantages:

Modularity - Pure functions with no side effects are easier to combine into pipelines.

Testability - Immutable data and lack of side effects make functional code much easier to test.

Readability - Concise declarative syntax like list comprehensions are easier to read.

Parallelization - No shared state enables trivial parallelization of functional code.

Mathematical - Mapping functions over data structures has an elegant mathematical appeal.

While functional code can sometimes be less performant due to immutability, the benefits of modularity and testability often outweigh any minor performance costs.

Functional Programming Techniques in Python

Let’s explore some of the main techniques for applying functional programming in Python:

Pure functions that avoid side effects facilitate code isolation and testing:

Use built-in higher order functions like map() , filter() , reduce() for data processing pipelines:

Use immutable objects like tuples and strings instead of lists and dictionaries:

List comprehensions provide a concise way to transform lists by mapping and filtering:

Use recursion techniques to traverse trees, break down problems, and enable elegant solutions:

Leverage first-class functions to pass behavior into other functions:

This allows abstraction and reusability.

Use closures to factory functions that can configure and return behavior:

Encapsulates logic elegantly.

By applying these techniques, we can reap the benefits of functional programming in Python.

Real World Examples

Let’s examine some real-world examples that demonstrate where a functional approach in Python shines:

Data pipelines often involve chaining together mapping, filtering, and reducing operations:

Functional programming allows clean data pipelines.

Parsing nested JSON can be elegantly handled via recursion:

Recursive algorithms shine for tree-like structures.

Representing state as immutable data helps manage complex applications:

Immutable state and pure functions provide stability.

Functional programming enables trivial parallelization:

No side effects means safely parallelizable.

As we’ve seen, taking a functional approach in Python can simplify everything from data processing to concurrency and state management.

While functional programming excels in many areas, there are a few cases where it may not be the best approach in Python:

Performance Critical Situations - Operations on immutable data and function calls can sometimes impose performance costs. Needs that demand optimized speed may justify an imperative approach.

Stateful Algorithms - Heavily stateful algorithms like simulations, neural networks, and physics engines can be awkward to express in a functional style. Mutable state offers a more natural fit.

Convenient APIs - Many Python libraries for tasks like machine learning and web development use stateful APIs for convenience. Interfacing with these may require non-functional design.

Legacy Systems - Integrating with older imperative systems can be difficult using pure functional code. Pragmatic compromises are sometimes necessary.

Functional programming emphasizes elegance, testability, and modularity, but integration with external systems and stateful algorithms may necessitate a more imperative style. The ideal approach depends heavily on the specific use case and problem domain.

Functional programming techniques enable writing Python code that is concise, readable, testable and modular. Key concepts like immutability, pure functions and avoidance of side effects can facilitate elegantly solving problems involving data processing, concurrency, and complex state management. While Python is not a purely functional language, its support of features like list comprehensions, higher order functions, and recursion allow idiomatic functional patterns that can simplify coding and testing. Learning to apply functional programming principles can make you a better and more versatile Python developer.

•     python
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Practical Machine Learning with Python

A Problem-Solver's Guide to Building Real-World Intelligent Systems

• © 2018
• Dipanjan Sarkar 0 ,
• Raghav Bali 1 ,
• Tushar Sharma 2

Embassy Paragon, Site No. 6/2 & 6/3, Intel Technology India Pvt Ltd Embassy Paragon, Site No. 6/2 & 6/3, Bangalore, India

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• A complete guide of theoretical, technical, and hands-on implementations for practical applications of machine learning across diverse domains in the industry
• Shows how data science and machine learning projects are executed in the real world
• Provides readers with the essential skills to tackle their own real-world problems with machine learning

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Table of contents (12 chapters)

Front matter, understanding machine learning, machine learning basics.

• Dipanjan Sarkar, Raghav Bali, Tushar Sharma

The Python Machine Learning Ecosystem

The machine learning pipeline, processing, wrangling, and visualizing data, feature engineering and selection, building, tuning, and deploying models, real-world case studies, analyzing bike sharing trends, analyzing movie reviews sentiment, customer segmentation and effective cross selling, analyzing wine types and quality, analyzing music trends and recommendations, forecasting stock and commodity prices, deep learning for computer vision, back matter.

• Machine Learning
• Natural Language Processing
• Deep Learning
• Social network analysis
• recommender systems
• image processing
• trend analysis

About this book

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.

Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries andframeworks are also covered.

Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem.

Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today!

• Execute end-to-end machine learning projects and systems
• Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks
• Review case studies depicting applications of machine learning and deep learning on diverse domains and industries
• Apply a wide range of machine learning models including regression, classification, and clustering.
• Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning.

Authors and Affiliations

Embassy paragon, site no. 6/2 & 6/3, intel technology india pvt ltd embassy paragon, site no. 6/2 & 6/3, bangalore, india.

Dipanjan Sarkar

Raghav Bali, Tushar Sharma

About the authors

Dipanjan Sarkar  is a Data Scientist at Intel, on a mission to make the world more connected and productive. He primarily works on data science, analytics, business intelligence, application development, and building large-scale intelligent systems. He holds a master of technology degree in Information Technology with specializations in Data Science and Software Engineering from the International Institute of Information Technology, Bangalore. He is also an avid supporter of self-learning, especially Massive Open Online Courses and also holds a Data Science Specialization from Johns Hopkins University on Coursera. 

  Raghav Bali has a master's degree (gold medalist) in Information

Raghav is a technology enthusiast who loves reading and playing around with new gadgets and technologies. He has also authored several books on R, Machine Learning and Analytics. He is a shutterbug, capturing moments when he isn't busy solving problems.

Apart from work Tushar enjoys watching movies, playing badminton and is an avid reader. He has also authored a book on R and social media analytics.

Bibliographic Information

Book Title : Practical Machine Learning with Python

Book Subtitle : A Problem-Solver's Guide to Building Real-World Intelligent Systems

Authors : Dipanjan Sarkar, Raghav Bali, Tushar Sharma

DOI : https://doi.org/10.1007/978-1-4842-3207-1

Publisher : Apress Berkeley, CA

eBook Packages : Professional and Applied Computing , Apress Access Books , Professional and Applied Computing (R0)

Copyright Information : Dipanjan Sarkar, Raghav Bali and Tushar Sharma 2018

Softcover ISBN : 978-1-4842-3206-4 Published: 22 December 2017

eBook ISBN : 978-1-4842-3207-1 Published: 20 December 2017

Edition Number : 1

Number of Pages : XXV, 530

Topics : Artificial Intelligence , Python , Open Source

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Python Practice for Beginners: 15 Hands-On Problems

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Want to put your Python skills to the test? Challenge yourself with these 15 Python practice exercises taken directly from our Python courses!

There’s no denying that solving Python exercises is one of the best ways to practice and improve your Python skills . Hands-on engagement with the language is essential for effective learning. This is exactly what this article will help you with: we've curated a diverse set of Python practice exercises tailored specifically for beginners seeking to test their programming skills.

These Python practice exercises cover a spectrum of fundamental concepts, all of which are covered in our Python Data Structures in Practice and Built-in Algorithms in Python courses. Together, both courses add up to 39 hours of content. They contain over 180 exercises for you to hone your Python skills. In fact, the exercises in this article were taken directly from these courses!

In these Python practice exercises, we will use a variety of data structures, including lists, dictionaries, and sets. We’ll also practice basic programming features like functions, loops, and conditionals. Every exercise is followed by a solution and explanation. The proposed solution is not necessarily the only possible answer, so try to find your own alternative solutions. Let’s get right into it!

Python Practice Problem 1: Average Expenses for Each Semester

John has a list of his monthly expenses from last year:

He wants to know his average expenses for each semester. Using a for loop, calculate John’s average expenses for the first semester (January to June) and the second semester (July to December).

Explanation

We initialize two variables, first_semester_total and second_semester_total , to store the total expenses for each semester. Then, we iterate through the monthly_spending list using enumerate() , which provides both the index and the corresponding value in each iteration. If you have never heard of enumerate() before – or if you are unsure about how for loops in Python work – take a look at our article How to Write a for Loop in Python .

Within the loop, we check if the index is less than 6 (January to June); if so, we add the expense to first_semester_total . If the index is greater than 6, we add the expense to second_semester_total .

After iterating through all the months, we calculate the average expenses for each semester by dividing the total expenses by 6 (the number of months in each semester). Finally, we print out the average expenses for each semester.

Python Practice Problem 2: Who Spent More?

John has a friend, Sam, who also kept a list of his expenses from last year:

They want to find out how many months John spent more money than Sam. Use a for loop to compare their expenses for each month. Keep track of the number of months where John spent more money.

We initialize the variable months_john_spent_more with the value zero. Then we use a for loop with range(len()) to iterate over the indices of the john_monthly_spending list.

Within the loop, we compare John's expenses with Sam's expenses for the corresponding month using the index i . If John's expenses are greater than Sam's for a particular month, we increment the months_john_spent_more variable. Finally, we print out the total number of months where John spent more money than Sam.

Python Practice Problem 3: All of Our Friends

Paul and Tina each have a list of their respective friends:

Combine both lists into a single list that contains all of their friends. Don’t include duplicate entries in the resulting list.

There are a few different ways to solve this problem. One option is to use the + operator to concatenate Paul and Tina's friend lists ( paul_friends and tina_friends ). Afterwards, we convert the combined list to a set using set() , and then convert it back to a list using list() . Since sets cannot have duplicate entries, this process guarantees that the resulting list does not hold any duplicates. Finally, we print the resulting combined list of friends.

If you need a refresher on Python sets, check out our in-depth guide to working with sets in Python or find out the difference between Python sets, lists, and tuples .

Python Practice Problem 4: Find the Common Friends

Now, let’s try a different operation. We will start from the same lists of Paul’s and Tina’s friends:

In this exercise, we’ll use a for loop to get a list of their common friends.

For this problem, we use a for loop to iterate through each friend in Paul's list ( paul_friends ). Inside the loop, we check if the current friend is also present in Tina's list ( tina_friends ). If it is, it is added to the common_friends list. This approach guarantees that we test each one of Paul’s friends against each one of Tina’s friends. Finally, we print the resulting list of friends that are common to both Paul and Tina.

Python Practice Problem 5: Find the Basketball Players

You work at a sports club. The following sets contain the names of players registered to play different sports:

How can you obtain a set that includes the players that are only registered to play basketball (i.e. not registered for football or volleyball)?

This type of scenario is exactly where set operations shine. Don’t worry if you never heard about them: we have an article on Python set operations with examples to help get you up to speed.

First, we use the | (union) operator to combine the sets of football and volleyball players into a single set. In the same line, we use the - (difference) operator to subtract this combined set from the set of basketball players. The result is a set containing only the players registered for basketball and not for football or volleyball.

If you prefer, you can also reach the same answer using set methods instead of the operators:

It’s essentially the same operation, so use whichever you think is more readable.

Python Practice Problem 6: Count the Votes

Let’s try counting the number of occurrences in a list. The list below represent the results of a poll where students were asked for their favorite programming language:

Use a dictionary to tally up the votes in the poll.

In this exercise, we utilize a dictionary ( vote_tally ) to count the occurrences of each programming language in the poll results. We iterate through the poll_results list using a for loop; for each language, we check if it already is in the dictionary. If it is, we increment the count; otherwise, we add the language to the dictionary with a starting count of 1. This approach effectively tallies up the votes for each programming language.

If you want to learn more about other ways to work with dictionaries in Python, check out our article on 13 dictionary examples for beginners .

Python Practice Problem 7: Sum the Scores

Three friends are playing a game, where each player has three rounds to score. At the end, the player whose total score (i.e. the sum of each round) is the highest wins. Consider the scores below (formatted as a list of tuples):

Create a dictionary where each player is represented by the dictionary key and the corresponding total score is the dictionary value.

This solution is similar to the previous one. We use a dictionary ( total_scores ) to store the total scores for each player in the game. We iterate through the list of scores using a for loop, extracting the player's name and score from each tuple. For each player, we check if they already exist as a key in the dictionary. If they do, we add the current score to the existing total; otherwise, we create a new key in the dictionary with the initial score. At the end of the for loop, the total score of each player will be stored in the total_scores dictionary, which we at last print.

Python Practice Problem 8: Calculate the Statistics

Given any list of numbers in Python, such as …

 … write a function that returns a tuple containing the list’s maximum value, sum of values, and mean value.

We create a function called calculate_statistics to calculate the required statistics from a list of numbers. This function utilizes a combination of max() , sum() , and len() to obtain these statistics. The results are then returned as a tuple containing the maximum value, the sum of values, and the mean value.

The function is called with the provided list and the results are printed individually.

Python Practice Problem 9: Longest and Shortest Words

Given the list of words below ..

… find the longest and the shortest word in the list.

To find the longest and shortest word in the list, we initialize the variables longest_word and shortest_word as the first word in the list. Then we use a for loop to iterate through the word list. Within the loop, we compare the length of each word with the length of the current longest and shortest words. If a word is longer than the current longest word, it becomes the new longest word; on the other hand, if it's shorter than the current shortest word, it becomes the new shortest word. After iterating through the entire list, the variables longest_word and shortest_word will hold the corresponding words.

There’s a catch, though: what happens if two or more words are the shortest? In that case, since the logic used is to overwrite the shortest_word only if the current word is shorter – but not of equal length – then shortest_word is set to whichever shortest word appears first. The same logic applies to longest_word , too. If you want to set these variables to the shortest/longest word that appears last in the list, you only need to change the comparisons to <= (less or equal than) and >= (greater or equal than), respectively.

If you want to learn more about Python strings and what you can do with them, be sure to check out this overview on Python string methods .

Python Practice Problem 10: Filter a List by Frequency

Given a list of numbers …

… create a new list containing only the numbers that occur at least three times in the list.

Here, we use a for loop to iterate through the number_list . In the loop, we use the count() method to check if the current number occurs at least three times in the number_list . If the condition is met, the number is appended to the filtered_list .

After the loop, the filtered_list contains only numbers that appear three or more times in the original list.

Python Practice Problem 11: The Second-Best Score

You’re given a list of students’ scores in no particular order:

Find the second-highest score in the list.

This one is a breeze if we know about the sort() method for Python lists – we use it here to sort the list of exam results in ascending order. This way, the highest scores come last. Then we only need to access the second to last element in the list (using the index -2 ) to get the second-highest score.

Python Practice Problem 12: Check If a List Is Symmetrical

Given the lists of numbers below …

… create a function that returns whether a list is symmetrical. In this case, a symmetrical list is a list that remains the same after it is reversed – i.e. it’s the same backwards and forwards.

Reversing a list can be achieved by using the reverse() method. In this solution, this is done inside the is_symmetrical function.

To avoid modifying the original list, a copy is created using the copy() method before using reverse() . The reversed list is then compared with the original list to determine if it’s symmetrical.

The remaining code is responsible for passing each list to the is_symmetrical function and printing out the result.

Python Practice Problem 13: Sort By Number of Vowels

Given this list of strings …

… sort the list by the number of vowels in each word. Words with fewer vowels should come first.

Whenever we need to sort values in a custom order, the easiest approach is to create a helper function. In this approach, we pass the helper function to Python’s sorted() function using the key parameter. The sorting logic is defined in the helper function.

In the solution above, the custom function count_vowels uses a for loop to iterate through each character in the word, checking if it is a vowel in a case-insensitive manner. The loop increments the count variable for each vowel found and then returns it. We then simply pass the list of fruits to sorted() , along with the key=count_vowels argument.

Python Practice Problem 14: Sorting a Mixed List

Imagine you have a list with mixed data types: strings, integers, and floats:

Typically, you wouldn’t be able to sort this list, since Python cannot compare strings to numbers. However, writing a custom sorting function can help you sort this list.

Create a function that sorts the mixed list above using the following logic:

• If the element is a string, the length of the string is used for sorting.
• If the element is a number, the number itself is used.

As proposed in the exercise, a custom sorting function named custom_sort is defined to handle the sorting logic. The function checks whether each element is a string or a number using the isinstance() function. If the element is a string, it returns the length of the string for sorting; if it's a number (integer or float), it returns the number itself.

The sorted() function is then used to sort the mixed_list using the logic defined in the custom sorting function.

If you’re having a hard time wrapping your head around custom sort functions, check out this article that details how to write a custom sort function in Python .

Python Practice Problem 15: Filter and Reorder

Given another list of strings, such as the one below ..

.. create a function that does two things: filters out any words with three or fewer characters and sorts the resulting list alphabetically.

Here, we define filter_and_sort , a function that does both proposed tasks.

First, it uses a for loop to filter out words with three or fewer characters, creating a filtered_list . Then, it sorts the filtered list alphabetically using the sorted() function, producing the final sorted_list .

The function returns this sorted list, which we print out.

Want Even More Python Practice Problems?

We hope these exercises have given you a bit of a coding workout. If you’re after more Python practice content, head straight for our courses on Python Data Structures in Practice and Built-in Algorithms in Python , where you can work on exciting practice exercises similar to the ones in this article.

Additionally, you can check out our articles on Python loop practice exercises , Python list exercises , and Python dictionary exercises . Much like this article, they are all targeted towards beginners, so you should feel right at home!

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Solving the Least Cost Transportation Problem with Python

Minor cost transportation problems (LCTP) are among supply chain management’s most common and essential issues. LCTP involves determining the most cost-effective way to transport goods from many sources to multiple destinations with varying costs.

Linear programming concepts in linear algebra give us a robust framework for solving such optimization problems. These concepts also involve equation and constraint formation.

This article will define and solve an LCTP problem using the Python programming language and the Numpy library.

The Least Cost Transportation Problem (LCTP) can be solved using Python and the Numpy library. This method determines the most cost-effective routes from multiple sources to various destinations, optimizing the total transportation costs

Recommended: Maximizing Cost Savings Through Offshore Development: A Comprehensive Guide

Recommended: Delivery Route Optimization using Python: A Step-by-Step Guide

Understanding the Transportation Problem

In our problem, a company produces goods in three different factories, A, B, and C, and the goods have to be transported to three distribution centres, D1, D2, and D3. The transportation costs are as follows.

• From Factory A to Distribution Center D1: Rs 6
• From Factory A to Distribution Center D2: Rs 8
• From Factory A to Distribution Center D3: Rs 10
• From Factory B to Distribution Center D1: Rs 9
• From Factory B to Distribution Center D2: Rs 12
• From Factory B to Distribution Center D3: Rs 13
• From Factory C to Distribution Center D1: Rs 14
• From Factory C to Distribution Center D2: Rs 16
• From Factory C to Distribution Center D3: Rs 18

Moreover, each distribution center has a specific demand, and each factory has some capacity. Let’s also look at the additional constraints.

• Factory A Capacity: 30 units
• Factory B Capacity: 20 units
• Factory C Capacity: 50 units
• Distribution Center D1 Demand: 40 units
• Distribution Center D2 Demand: 70 units
• Distribution Center D3 Demand: 30 units

Now that we have defined the problem, let’s solve it using the Numpy library of the Python programming language.

Solving the Transportation Problem Using Python

Traditionally, we have formed constraints regarding the supply and demand centres, and using the simplex method, we have found a feasible solution to this transportation problem. The code below defines the supply, demand, and transportation costs from each factory to the distribution centre. We have used the power of the Numpy library to obtain our answer.

We used the Northwest Corner Rule to find a feasible solution initially. Then, we calculated the total transportation costs. Let us now look at the Python code step by step.

We solved this problem using the Numpy library of the Python programming language. Let’s move forward and define and initialize our variables.

We have a user-defined function with variables of supply, demand, and costs. We then create an array of supply and demand for inputs. After that, we use the northwest rule to loop it until the whole supply is finished.

Our objective function will be the total cost function, and we aim to minimize it. The Northwest rule attaches the minimum cost demand to the highest supplier. This rule is reiterated over and over until every demand and supply is exhausted. We then return the total cost and allocation.

We have defined the demand and supply array mentioned in the problem statement. After that, we run it through our user-defined function. We ultimately print our results.

Let us now look at the output for the code above.

From the output above, we can see that the most optimal solution would be –

• Thirty units are to be transported from A to D1.
• Twenty units are to be transported from A to D3.
• Fifty units are to be transported from A to D2.

This gives us the total transportation cost, which is the minimum of all the cases, to be Rs 1240. Please note that this problem is of an unbalanced category. Namely, demand and supply are not equal.

Here you go!! Now you know how to minimize costs in a transportation problem. There are multiple other methods to do the same like the Simplex method, Vogel’s Approximation, and the Hungarian approach. All of these approaches will help you determine the optimal solution.

Hope you enjoyed reading it!!

Recommended: Backtracking Line Search Algorithm for Unconstrained Optimization

Recommended: Optimizing Neural Networks with torch.optim in PyTorch

• Data Structures
• Linked List
• Binary Tree
• Binary Search Tree
• Segment Tree
• Disjoint Set Union
• Fenwick Tree
• Red-Black Tree
• Advanced Data Structures
• Tree Data Structure
• Data Structures | Binary Trees | Question 3
• Data Structures | Binary Trees | Question 4
• Data Structures | Binary Trees | Question 1
• Data Structures | Binary Trees | Question 9
• Data Structures | Binary Trees | Question 12
• Data Structures | Binary Trees | Question 14
• Data Structures | Binary Trees | Question 11
• Data Structures | Binary Trees | Question 13
• Data Structures | Binary Trees | Question 15
• Tango Tree Data Structure
• Applications of tree data structure
• Data Structures | Binary Search Trees | Question 7
• Data Structures | Binary Search Trees | Question 8
• Data Structures | Binary Search Trees | Question 5
• Data Structures | Binary Search Trees | Question 6
• Data Structures | Binary Search Trees | Question 2

Binary Tree Data Structure

A Binary Tree Data Structure is a hierarchical data structure in which each node has at most two children, referred to as the left child and the right child. It is commonly used in computer science for efficient storage and retrieval of data, with various operations such as insertion, deletion, and traversal.

Introduction:

• Introduction to Binary Tree – Data Structure and Algorithm Tutorials
• Properties of Binary Tree
• Types of Binary Tree
• Applications, Advantages and Disadvantages of Binary Tree
• Binary Tree (Array implementation)
• Complete Binary Tree
• Perfect Binary Tree

Basic Operations on Binary Tree:

• Tree Traversals (Inorder, Preorder and Postorder)
• Level Order Tree Traversal
• Find the Maximum Depth or Height of given Binary Tree
• Insertion in a Binary Tree
• Deletion in a Binary Tree
• Enumeration of Binary Trees

Some other important Binary Tree Traversals :

• Level order traversal in spiral form
• Reverse Level Order Traversal
• BFS vs DFS for Binary Tree
• Inorder Tree Traversal without Recursion
• Morris traversal for Preorder
• Iterative Preorder Traversal
• Iterative Postorder Traversal Using Two Stacks
• Diagonal Traversal of Binary Tree
• Boundary Traversal of binary tree

Easy Problems on Binary Tree Data Structure:

• Calculate depth of a full Binary tree from Preorder
• Construct a tree from Inorder and Level order traversals
• Check if a given Binary Tree is SumTree
• Check if two nodes are cousins in a Binary Tree
• Check if removing an edge can divide a Binary Tree in two halves
• Check whether a given binary tree is perfect or not
• Check if a Binary Tree contains duplicate subtrees of size 2 or more
• Check if two trees are Mirror
• Foldable Binary Trees
• Symmetric Tree (Mirror Image of itself)
• Write Code to Determine if Two Trees are Identical
• Subtree with given sum in a Binary Tree
• Succinct Encoding of Binary Tree
• Write a program to Calculate Size of a tree
• Diameter of a Binary Tree
• Get Level of a node in a Binary Tree

Medium Problems on Binary Tree Data Structure:

• Find all possible binary trees with given Inorder Traversal
• Populate Inorder Successor for all nodes
• Construct Complete Binary Tree from its Linked List Representation
• Minimum swap required to convert binary tree to binary search tree
• Convert a given Binary Tree to Doubly Linked List | Set 1
• Convert a tree to forest of even nodes
• Flip Binary Tree
• Print root to leaf paths without using recursion
• Check if given Preorder, Inorder and Postorder traversals are of same tree
• Check whether a given Binary Tree is Complete or not | Set 1 (Iterative Solution)
• Check if a binary tree is subtree of another binary tree | Set 2
• Find largest subtree sum in a tree
• Maximum sum of nodes in Binary tree such that no two are adjacent
• Lowest Common Ancestor in a Binary Tree | Set 1
• Height of a generic tree from parent array
• Find distance between two given keys of a Binary Tree

Hard Problems on Binary Tree Data Structure:

• Modify a binary tree to get Preorder traversal using right pointers only
• Construct Full Binary Tree using its Preorder traversal and Preorder traversal of its mirror tree
• Construct a special tree from given preorder traversal
• Construct tree from ancestor matrix
• Construct the full k-ary tree from its preorder traversal
• Construct Binary Tree from String with bracket representation
• Convert a Binary Tree into Doubly Linked List in spiral fashion
• Convert a Binary Tree to a Circular Doubly Link List
• Convert Ternary Expression to a Binary Tree
• Check if there is a root to leaf path with given sequence
• Remove all nodes which don’t lie in any path with sum>= k
• Maximum spiral sum in Binary Tree
• Sum of nodes at k-th level in a tree represented as string
• Sum of all the numbers that are formed from root to leaf paths
• Merge Two Binary Trees by doing Node Sum (Recursive and Iterative)
• Find root of the tree where children id sum for every node is given

Quick Links :

• ‘Practice Problems’ on Trees
• ‘Quizzes’ on Binary Trees
• ‘Videos’ on Trees

Recommended:

• Learn Data Structure and Algorithms | DSA Tutorial

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CS50: Introduction to Computer Science

An introduction to the intellectual enterprises of computer science and the art of programming.

Associated Schools

Harvard School of Engineering and Applied Sciences

What you'll learn.

A broad and robust understanding of computer science and programming

How to think algorithmically and solve programming problems efficiently

Concepts like abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development

Familiarity with a number of languages, including C, Python, SQL, and JavaScript plus CSS and HTML

How to engage with a vibrant community of like-minded learners from all levels of experience

How to develop and present a final programming project to your peers

Course description

This is CS50x , Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. The on-campus version of CS50x , CS50, is Harvard's largest course. 

Students who earn a satisfactory score on 9 problem sets (i.e., programming assignments) and a final project are eligible for a certificate. This is a self-paced course–you may take CS50x on your own schedule.

Instructors

David J. Malan

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