What is Python Used For? 5 Major Real-World Applications
It’s one of those fine mornings when you don’t have much to do. While you make your coffee, you reach for the remote to play your morning songs on YouTube. When everything is set, you log onto Netflix to continue with your favorite series. From time to time you reach for your phone to check Facebook and Instagram notifications. Amazon reminds you to buy the rubber shoes you left on your cart. After a while, you board an Uber to go and see your friend. You then head to Google to search for the best restaurant in your local area for you to spend time in.
Well, guess what all these actions have in common — That’s right! Python!
Python is one of the most popular programming languages in the world today, but how exactly is it used?
In this article, we will cover five major industries that use Python every day, as well as the top python tools and frameworks that each industry uses.
Last Updated January 2020
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1) Web development
Python code drives the back-end of some of the world’s most popular apps and websites, including Instagram, Reddit, and YouTube. One key reason for Python’s popularity is the many Python frameworks and libraries that you can add to your projects.
Let’s look at some now:
Django is arguably the most popular Python web development framework. The primary advantage of Django is that it offers a single package that can build an entire web application, no matter its size or complexity.
Flask is a framework designed to help developers quickly prototype web apps and get them into production as quickly as possible. This is a great tool to use at a hackathon or startup competition to produce an application fast, but can be extended with third-party extensions to develop production applications
Bottle is a lightweight Python web framework. It doesn’t come with any “batteries included” besides the Python standard library. The Bottle framework is great when it comes to prototyping and executing simple web applications and personal projects. It is designed to run an application from one file and doesn’t have as many third-party extensions as Flask.
Pyramid calls itself the Goldilocks Solution of Python web frameworks. While frameworks like Django can be too large and restricting and microframeworks like Flask or Bottle can leave too much up to the developer, Pyramid tries to provide the best of both worlds.
The idea behind Pyramid is that, like an upside-down pyramid, developers can start small and use Pyramid’s extensibility to scale up. Like Django, Pyramid allows users to build big projects. However, like Flask and Bottle, it offers more flexible options for scaling up the project.
2) Data science and analytics
A 2018 post by Forbes Magazine reported that 2.5 quintillion—2.5 million trillion—bytes of data are generated daily. That’s a lot of data. For this data to be useful to business stakeholders, data scientists must analyze and present it visually. This is where Python comes in.
Data science projects involve data mining, data analysis, and data visualization. Python has many great libraries available for each of these steps, so let’s look at some now:
Scrapy works well when mining data to develop machine learning models. It allows us to “scrape” structured data from web pages, which we can later use to train machine learning models or analyze some other way. Hey, it’s much better than copying and pasting!
Pandas provides highly efficient data structures that help developers to store and manipulate large amounts of data.
Pandas is an essential data science library and will serve as the backbone of any Python data science project.
SciPy is a rich library for statistical analysis and scientific computing.
SciPy comes with everything a data scientist could ever dream of, with functions to perform everything from linear algebra to statistics.
Matplotlib and Seaborn
Matplotlib and Seaborn are two amazing data visualization libraries written in Python. Both can create stunning data visualizations to both make a point and to wow clients.
For example, check out these stunning violin plots created using Seaborn:
3) Machine learning & artificial intelligence
It’s probably an understatement to say that Machine learning and AI are hot topics these days. The fact that a computer can learn how to solve a complex problem for you, rather than having to be told explicitly how to do it, is nothing short of magical.
When it comes to machine learning and AI, Python is the undisputed champion of programming languages. One reason for this is the stunning libraries that are available to put some of the world’s most advanced technology at your fingertips.
Let’s look at some of these libraries now:
Scikit-learn is a one-stop-shop for machine learning and presents a variety of algorithms that can be used for clustering, classification, dimensionality reduction, and other big words. Some common algorithms that you may hear about include random forests (the best kind of forest, in my opinion), support vector machines, and k-means clustering.
If you’re interested in machine learning with Python, you absolutely must check out scikit-learn.
Tensorflow is the most popular open source frameworks for building neural networks. Companies like Uber, eBay, Dropbox, and Coca-cola use Tensorflow to identify consumer trends.
TensorFlow was created by Google’s team of developers and has become very popular over recent years. Creating multi-step neural networks is a powerful, but also computationally expensive technique. Where TensorFlow really shines is how it blends together ease of use with fast computational processing. This allows TensorFlow to build complex neural networks in a relatively short amount of time.
When it comes to using Python to develop neural networks for AI, you really can’t go wrong with TensorFlow.
4) Internet of things (IoT)
Imagine connecting the real world to the internet. Well, it’s already happening!
The Internet of things (IoT) is about connecting physical objects to the internet or a home’s/company’s internal wireless network so that the object’s behavior in the real world can change according to data being collected by various sensors.
Think of the smart fridges that remind you to buy milk, cleaning robots that clean your floors, and pet food dispensers that automatically dispense food and allow you to have a Skype call with your pet while you’re on holiday.
Python is a major technology in the IoT space, so let’s take a look at some ways Python is being used to make the future a reality.
Home Assistant is an open source Python project that provides a central control panel that you can use to control all the devices in your smart home. Things you can do from the Home Assistant dashboard include opening/closing doors, switching lights on/off, and controlling temperature. Home Assistant has over 1,600 integrations, so the possibilities are pretty much endless.
If you’ve looked into home IoT projects before, you’ve certainly come across the Raspberry Pi. The Raspberry Pi is a fully functioning computer… only it is the size of a credit card and costs about $35. What a time to be alive!
Raspberry Pi plays an important role in many Python development projects. It comes with everything that IoT makers and hobbyists could need, such as an in-built WiFi card, an ethernet port, HDMI port, USB port, and SD card support.
Because the Raspberry Pi runs a version of the Linux operating system, it comes with Python pre-installed, which makes it ready for DIY hacking and making right out of the box.
A blog covering Python tools for IoT would not be complete without mentioning MicroPython.
MicroPython is a modified version of the Python programming language that is designed to run on microcontrollers. Before MicroPython, makers, and hobbyists were required to learn relatively archaic languages such as C or C++ to make their projects run on their microcontroller boards. But now, thanks to MicroPython, the simplicity of the Python programming language has been made available to hackers and makers everywhere.
5) Application development
And Python is also very popular for quickly developing applications with fully functional graphical user interfaces (GUIs).
Rather than requiring your users to rummage around in a terminal, creating a GUI for your projects can increase usability and also give an air of professionalism and finesse.
Plus making stuff pop up on the screen never gets old.
Let’s look at some key libraries you will want to checkout for application development with Python.
TkInter is probably the #1 package for Python GUI development and is actually built into the Python standard library.
TkInter comes with an array of widgets that you can use to build your GUIs, from simple textboxes and buttons, all the way to different panes and clipboard management.
You can also style your application using various themes so that it looks good in deployment.
If you’re into GUI development, you’ll definitely want to look at TkInter.
Kivy is really cool. Kivy not only makes it possible to make widgets pop up on the screen, but it also allows you to create interfaces that work on touch screens.
These properties make Kivy a great go-to choice for Python developers who want to create software for smartphones and tablets. You write your Kivy in Python app, and then export the app into a format applicable for your desired domain, whether that be Windows, Mac OS X, Android, iOS, or all of them!
As you can see, Python is being used successfully in many high-profile domains, from web development, data science, and IoT, to artificial intelligence, machine learning, and application development.
With so many opportunities available, learning Python is one of the best investments you can ever make.
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