Applications of Python

Python is a language that is almost dominating all other computer programming languages. It is dominant, of course, because of its simplified syntax and extensive number of applications.

A language with simplified syntax that can be used in almost every field to interact with computers is something a computer programmer needs. Python is particularly dominant in the fields of automation, such as data science and artificial intelligence. But including this, here is the list of applications of the Python language, or what we can say Python is used for:

  1. Artificial Intelligence (AI) and Machine Learning (ML)
  2. Data Science
  3. Data Visualization
  4. Data Analytics
  5. Web Scraping Applications
  6. Search Engine Optimization (SEO)
  7. Web Development
  8. Game Development
  9. Desktop Graphical User Interface (GUI)
  10. Software Development
  11. Business Applications
  12. Image Processing Applications
  13. Text Processing Applications
  14. Design
  15. Embedded Applications
  16. Console-based Applications

The first two and the fifth are the three most famous categories where Python is used most of the time. Let me briefly elaborate on these applications of Python, one by one.

Python for Artificial Intelligence (AI) and Machine Learning (ML)

Python is a well-known language to be used in the field of artificial intelligence and machine learning. Let's talk about artificial intelligence first.

Python for Artificial Intelligence (AI)

Anything made or produced by human beings is known as artificial, whereas the ability to acquire and then apply knowledge and skills is known as intelligence.

If we combine the two, we can say that a system developed by a computer programmer performs the tasks such as decision-making, problem-solving, speech recognition, visual perception, translation, etc. automatically.

For example, an application that requires only the face picture of customers or users Then we have two choices: either we manually check and allow the customer's picture, if it is of their face, or we do not.Or, we implement the code that automatically scans the picture and stores it if it is of a face, otherwise displays the message to the customer to remind them of a message saying something like, "Only face pictures are allowed."

This is just an example. There are a lot of fields where we can implement artificial intelligence in our applications to automate the task, using, of course, the Python programming language.

Another example of artificial intelligence in action is when, while using YouTube, our day-to-day activities get analyzed by the system and we get the best recommendation based on our previous activities.

Note: Pandas is a library available in Python, mostly used for data analysis and modeling, where SciPy is mostly used for science, engineering, and mathematics.

Python for Machine Learning (ML)

Machine learning is one step toward artificial intelligence. We can say ML is a type of AI.

As humans, we learn things based on our successes and failures, or whatever incidents in our lives or past help us learn. Similarly, machines can also learn, and that is known as machine learning.

Therefore, we can say that the ability to learn by the computer system itself, using data statistics, past experience, or whatever the data needs to learn, is known as "machine learning."

Note: The two famous libraries of Python used in machine learning are NumPy and SciPy.

Python for data science

Data science is the process of extracting knowledge and insights from both structured and unstructured data across multiple applications. Data science encompasses data mining, data processing, and data modeling.

In a nutshell, "data mining" is the conversion of a large amount of raw data into meaningful or useful information.

Data processing is the collection of data to manipulate and produce meaningful information. Basically, data processing can be called a subset of information processing. The steps to process the data are:

Data modeling is used to provide the relationship of one data object to another. Basically, data modeling is the process of creating a data model for an information system using some techniques.

The Scrapy library is the most commonly used data mining library in Python. Whereas NumPy, Pandas, and TensorFlow are the most used libraries in Python for data processing and modeling,

Python for data visualization

Data visualization is basically a part of data science. That is, after extracting the knowledge and insights from the data, we need to present the information in the form of a graphical or pictorial representation.

Note: Matplotlib and Seaborn are the two most used libraries in Python for data visualization.

Python for Data Analytics

Data analytics is somewhat similar to data science. But in the case of data analytics, instead of extracting, the processes that go under data analytics are inspecting, cleansing, transforming, and modeling the data to, of course, acquire some useful information.

Note: The three most used libraries in the field of data analytics using Python are Pandas, Numpy, and Scipy.

Python for Web Scraping Applications

Web scraping is the process of extracting data from the web. For example, a search engine like Google, Yahoo, etc. crawls the web to scrape the data from all the websites to index and rank them.

It should be noted that the Scrapy library is the most commonly used library in Python for web scraping.

Python for Search Engine Optimization (SEO)

Search engine optimization, or SEO for short, is a process of improving the traffic to a website. In this field, too, Python plays an important role. For example, a Python developer can create an application that can analyze the website to find some improvements such as broken links, multiple main headings, unmatched title and headings, duplicate contents, and many more.

Python for web development

The development of a website over the internet is called web development. Most of the larger applications that require some AI and ML at the back-end of a website use Python. But it is not limited to AI and ML; a normal website connected to a database can also be implemented using Python.

Note: Django is the most popular web framework in Python.

Python for game development

The Pygame library is the most used Python library for game development using Python. Some other libraries for game development using Python are PyKyra, Pyglet,  PyOpenGL, Kivy, etc.

Python for Desktop Graphical User Interface (GUI)

A GUI is an application created to interact with electronic devices through graphical icons. Python is useful for developing desktop GUIs.

Note: The two most used libraries for GUI development using Python are Tkinter and Kivy.

Python for software development

Using Python, we can also develop a collection of instructions to perform specific tasks. In other words, Python is useful in the software development field too.

For example, if we need to implement a feature like ChatBot in our application, then Python is the best and recommended language to build the ChatBot for your application to, of course, automate the chat with your client.

There are many libraries available in Python that can be helpful in software development. But I'm not going to mention any particular one in this section, as it totally depends on the category requirements of your application.

Python for Business Applications

Basically, business applications fall under the category of software development. In business applications, a business person or an enterprise needs some applications to trend the business, measure and increase productivity, perform business functions, and many more like applications for communication, applications to manage things like files and employee details, etc., depending on the requirement.

Python for Image Processing Applications

Image processing applications are created to process the image to enhance it based on the requirements of the application. Python is sometimes useful for creating image processing applications that scan the image for enhancement, sharpening, restoration, color processing, detecting faces, recognizing fingerprints, or any other pattern recognition of the image, and so on.

Note: Some of the most commonly used Python libraries to create image processing applications are OpenCV, Pillow/PIL, and Scikit-Image.

Python for Text Processing Applications

Similar to image processing, text processing applications can also be created using Python. An application created to process the text is a text processing application.

Text processing is required when our application needs to analyze the text, extract the useful text, analyze and auto-reply to the text like ChatBots do, auto-correct the text, etc.

Python for design

As already explained under the section on Python for Desktop GUI, libraries like Kivy, Libavg, and many more are used to design an application or a part of an application using Python.

Python for Embedded Applications

From creating a small-purpose application to an embedded application, Python is useful. Embedded applications are basically developed to run a device for some particular task.

Python for Console-based Applications

A console-based application can also be called an application without a GUI. Basically, a console application is created to be used via a text-only computer interface like the command line, boot loader, kernel, etc.

Note: A famous console-based application in Python is IPython.

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