What is Python used for? Should you learn it?
In this article, we’ll take a look at what makes Python so great, how it’s being used for, and why it should be on your radar as a developer.
Python is an interpreted programming language that may be executed on top of a variety of different operating systems. It first appeared in 1991, and to this day, its notoriety only seems to increase.
If you are considering studying Python, or if you have just recently started learning it, you might be wondering:
“What exactly is Python used for?”
That’s a difficult question to answer because Python has so many applications.
However, I have seen that there are several popular Python applications:
- Web Development
- Data Science
- Machine learning
- Web scrapping
- Game development
Let’s go over each one individually. But first, let me tell you a quick history of how this fantastic programming language came to be. After all, Python has been around for over 30 years, which means it has a lot of history.
The History of Python
History was about to be written in the late 1980s. It was around this period that Python development began.
Guido Van Rossum began undertaking application-based work at Centrum Wiskunde & Informatica (CWI) in the Netherlands in December 1989. It began as a hobby project since he was seeking an engaging project to keep him engaged during the holidays.
Amoeba Operating System (ABC)
Python is reported to have succeeded in the ABC Programming Language, which had interaction with the Amoeba Operating System and the feature of exception management. Van Rossum had previously assisted in the creation of ABC, and while he saw certain flaws, he loved the majority of the features. What he did after that was quite intelligent.
He had copied ABC’s syntax and some of its best features. It also came with a lot of complaints, so he thoroughly solved those faults and designed a good programming language that eliminated all flaws.
The BBC TV show ‘Monty Python’s Flying Circus’ inspired the programming language’s name. He was a big admirer of the TV show and wanted his creation to have a short, original, and slightly cryptic name. As a result, he dubbed it Python!
He was the “Benevolent dictator for life” (BDFL) until he stood down as leader on July 12, 2018. He previously worked for Google, but now he works for Dropbox.
In 1991, the language was eventually launched. When it was first released, it utilized far fewer codes to describe concepts than Java, C++, and C. Its design philosophy was also quite good. Its primary goal is to improve code readability and developer productivity. It had more than enough power when published to enable classes with inheritance, numerous core data types, exception handling, and functions.
Python is a fascinating programming language. Developers might think they’ve done everything they want, but it still has more to offer. Businesses now know how much money they could make if they built their apps in Python. Here’s what’s so great about Python:
- Clear syntax
- Python has a syntax that is clear and easy to read. It’s easy for the team to work together on the coding front, so even newbies can work on complex software development projects.
- Python’s simple syntax makes test-driven development easy for all of its applications.
- Python is popular among businesses because of its scalability. Google, Spotify, Netflix, Instagram, and many other organizations that require scalable applications are integrating Python language usage.
- It makes it possible to manage an extremely high volume of traffic quickly.
- Python’s practical applications, in contrast to the majority of programming languages, are not restricted to the development of mobile or online applications.
- It is a well-liked option for developing web applications, games, enterprise-grade apps, online shopping apps, machine learning, artificial intelligence apps, and many more.
Python’s use cases
In his book Python for Everybody, Charles R. Severance of the University of Michigan and Coursera says, “Writing programs is a very creative and rewarding thing to do. There are many reasons to write programs, such as to make a living, solve a hard data analysis problem, have fun, or help someone else solve a problem.”
Python is often used for building websites and software, automating tasks, analyzing and displaying data, and more. Since Python is easy to learn, many people who aren’t programmers use it for everyday tasks like organizing finances. These people include accountants and scientists.
I’ve enlisted the major use-cases that Python is being used for:
- Web development
- Data science
- Machine Learning
- Web scraping
- Game development
Let’s talk about each one of those, starting with:
Web application development is, without a doubt, one of the best real-world uses of Python. Python is now the most popular language for making web apps.
Recently, Python-based web frameworks like Django and Flask have become very popular for building websites.
These web frameworks help you write Python code for the server-side (also called “backend code”). That’s the code that runs on your server, not on the devices and browsers of your users (front-end code).
A web framework makes it easier to build backend logic that is used often. That includes mapping different URLs to pieces of Python code, working with databases, and making HTML files that users can see in their browsers.
Django and Flask are two of the Python web frameworks that are used the most. If you’re starting, I’d say to use one of them.
Django gives you everything you need: an admin panel, database interfaces, an ORM, and a directory structure for your apps and projects right out of the box.
If you care about the end result, you should probably choose Django. Especially if you’re working on a simple app like a news site, an online store, or a blog, and you want there to be one clear way to do things.
Flask offers ease of use, versatility, and control down to the finest granularity. It does not hold opinions (it lets you decide how you want to implement things).
If you are more interested in the experience and the prospects for learning, or if you want more control over which components to employ, then you should probably go with Flask (such as what databases you want to use and how you want to interact with them).
In other words, if you’re starting, Flask is the best option because it has fewer parts and pieces for you to take care of. In addition, if you want more customization options, Flask is the best option.
Data science has recently risen to the top of the list of skills in great demand. It is rapidly gaining prominence as one of the most important domains where Python programming may be applied.
Python is essential in the domains of data science and mathematics. Because of its readability, productivity, versatility, and portability, the language has become popular among scientists. The Python environment surrounding research has evolved enormously. Almost every major discipline of math and science has mature Python solutions.
Data scientists must understand how to extract and process data using Python. It enables people to visualize the data using graphs. Both Matplotlib and Seaborn are used to visualize data.
Python, which is gaining popularity, is the first language that data scientists must learn. Working with research and data-driven companies is the first step. Some of the everyday use cases of data analysis are the following:
A descriptive analysis gives you information about what has taken place. It tells us what the data and its features are by summarizing the sample set or population of data. Most of the time, the sample is taken into account in descriptive statistics.
It is possible to do utilizing an exploratory data analysis.
Example: Researching the total number of chair units that were sold in the past along with the associated profits.
A predictive analysis looks at patterns in both current and past data to see if they are likely to happen again. It reveals what is going to take place. This lets businesses and investors change where they put their money and time to take advantage of what might happen in the future. Predictive analysis can also be used to improve the efficiency of operations and lower risks.
Constructing predictive models is one way to accomplish this goal.
Example: Estimating the number of chairs that will be purchased and the amount of profit we can anticipate shortly.
Prescriptive analytics is a part of Business Analytics that looks for the best way to solve problems every day. It is a type of data analytics that uses algorithms and the analysis of raw data to help people make better decisions for long and short periods. A prescriptive analysis gives strategy ideas based on possible scenarios, gathered statistics, and past and present databases from the consumer community.
Also, it provides instructions on how to make something take place. It is possible to accomplish this by extracting important insights and hidden patterns from the data.
Example: Investigating several avenues to boost chair sales and increase profits.
Python is one of the best programming languages used to accomplish this sort of data analysis.
Python’s data analysis libraries
Matplotlib is one of the most widely used data visualization libraries.
It’s an excellent library to start because:
- It’s simple to learn the basics.
- Other libraries, such as seaborn, are built atop it. So understanding Matplotlib will assist you in learning these other libraries later on.
Artificial Intelligence and Machine Learning are probably the most exciting ways that Python is used in the real world. Python can be used in AI solutions for advanced computing, data analytics, image recognition, text and data processing, and much more.
Python is also a safe and stable programming language that can do the calculations needed to build Machine Learning models. But what is Machine Learning? I think a simple example is the best way to explain what machine learning is.
Let’s say you want to make a program that can figure out what’s in a picture on its own.
So, given this image, you want your algorithm to recognize it’s a dog.
Now, given this one below, you want your app to recognize it as a boat.
You could think, “Well, I can just create some code to achieve that.” For example, if there are a lot of light brown pixels in the image, we can probably infer it’s a dog.
Or perhaps you can find out how to detect edges in a photograph. Then you may claim it’s a boat if it has a lot of straight edges.
This strategy, however, quickly becomes complicated. What if the photo includes a white dog with no brown hair? What if the image shows a yacht or a sailboat? Above the sea or the ground. What about trees and mountains in the image? That’ll become quite confusing for your algorithm pretty quickly.
How can Machine Learning help?
Most of the time, machine learning uses an algorithm that automatically finds patterns in the data.
Whether you show it a new picture of a dog or a ship, it will know which one it is. For example, you can show an algorithm 3,000 photos of a dog and 3,000 pictures of a ship. Then it will know that a dog is different from a boat.
I see some similarities between this and how a baby learns new things. How does a baby figure out that something looks like a dog and something else looks like a boat? Most likely from a lot of examples.
You probably don’t tell a newborn, “If it’s fuzzy and has light brown hair, it’s a dog.”
“That’s a dog,” you’d probably say. That is a dog as well. This one, on the other hand, is a boat. That’s also a boat.”
Machine learning algorithms operate similarly.
The same concept can be used in the following:
- recommendation engines (like Netflix and YouTube, for example)
- facial recognition
- voice recognition
You may have heard of the following machine learning algorithms:
- Neural networks
- Support vector machines
- Deep learning
- Random forest
You can solve the picture-labeling problem using any of the algorithms listed above.
Python’s machine learning libraries
Python is supported by a variety of prominent library and framework options for machine learning, such as:
|Keras||Keras is a deep learning framework for the business world that has an API made for people. It lets you try out new ideas and run new tests quickly. It adheres to best practices for lowering cognitive burden.|
|NLTK||NLTK is a platform for working with human language data in Python programs. It has libraries for categorizing, tokenizing, stemming, tagging, parsing, and figuring out what something means.|
|PyTorch||PyTorch is an open source machine learning framework that makes it faster to move from research prototyping to production deployment.|
|scikit-learn||scikit-learn is a machine learning library that is free and open source. It can be used for both supervised and unsupervised learning. It is a useful tool for analyzing data to make predictions that can be used by anyone and in many different situations.|
|TensorFlow||TensorFlow is an open source platform for machine learning that works from start to finish. It has a complete, flexible ecosystem of tools, libraries, and community resources that will help you build and deploy ML-powered apps.|
If you are just starting out with a project that involves machine learning, I would suggest that you start with scikit-learn. I would look into TensorFlow if you start having problems with performance and efficiency.
Scripting is commonly used to describe the creation of small programs that are intended to automate simple operations.
For numerous system maintenance duties, system administrators typically develop Bash scripts. Developers often use Shell scripts to automate tedious and time-consuming manual activities to increase efficiency.
Python can be used to build Shell scripts, and many developers do that. Bash does not provide all of the language features that we require from a modern scripting language, and Python excels in this matter.
Let me give you an example from my own experience.
I used to work for a tiny firm in Brazil that had an email support system. It was a technology that allowed us to react to emails from customers.
When I worked there, one of my responsibilities was to count the number of emails that contained specific keywords so that we could evaluate the emails we received.
We could have done it by hand, but I wrote a simple program/script to do this job automatically.
Back then, we did this with Python, and I’m still proud of that choice. That’s because it is easy to write, has a simple syntax and can be easily tested.
Web scraping of vast amounts of data is becoming a way for businesses to get helpful information about their customers and make good decisions. And guess what? Python can be used for web scraping, and in fact, it’s usually the first choice for this purpose.
In this real-world use of Python, many websites and web pages are scraped to get information for a specific purpose. It could be a list of jobs, comparing prices, a detailed description of something, and much more.
Python’s web scraping libraries
|Beautiful Soup||This is a Python library that pulls data from HTML and XML files into parse trees. The library has methods and Pythonic expressions that can be used to move through, search, change, and pull information from parse trees.|
|requests is a Python HTTP library that is both beautiful and powerful. It gives you an API that is easy to use and easy to understand.|
|Scrapy||Framework for crawling and scraping the web quickly and at a high level. You can crawl websites with it and get structured data from their pages.|
|urllib.request is a module that defines functions and classes for opening URLs. It also lets you work with basic and digest authentication, redirections, cookies, and other interesting features.|
Yes, Python can be used to create games! Making computer games is a great way to learn how to code in any language, not just Python. You’ll need to know how to use variables, loops, conditional statements, functions, object-oriented programming, and more if you want to make games. Making games is a great way to combine many different skills.
Computer games have been a big part of how people learn to code. Many people get into programming because they love games and want to remake their favorites or make their own. Creating computer games can be a fun and rewarding adventure because you can play the game you just made and see how great it is.
In the Python ecosystem, many tools, libraries, and frameworks make it easy to make games quickly. Here are just a few of them:
Python’s game development libraries
|Arcade||Arcade is a library for Python that lets you make 2D video games. It’s great for people learning to code because they don’t have to learn a complicated game framework to start making their own games.|
|PyGame||PyGame is a set of modules for Python that are made for making video games. It adds more features to the SDL library. It lets you make games and programs with lots of features. The library is very easy to move around, and it works on a number of platforms and operating systems.|
|pyglet||pyglet is a powerful Python library that can be used on Windows, macOS, and Linux to make games and other apps with a lot of graphics. It lets you use windows, handle user interface events, use OpenGL graphics, load images, play videos and music, and more.|
Why Should You Learn Python?
As you could see in this article, Python can be used in so many different ways. It is used for many things, from business apps to video games.
Python is becoming increasingly popular as a way to build all kinds of apps. We have many Python experts in our marketplace here at Talendor who are eager to work exclusively on your new project. Click here to learn more!