What is Python Used For? 7 Real-Life Python Uses

Python is currently (as of November 2022) the most popular programming language in the world, and its user base is constantly growing. But what is Python used for? Many industries and companies use Python to analyze data, build machine learning models, create websites, and program software.

Here, we explore the main Python uses, the reasons why Python is so popular, why you should learn it, and how long it takes. We also examine what jobs usually need Python programming skills. Curious about Python vs SQL? Check out our separate article comparing the two. 

What is Python?

Python is an open-source, object-oriented, high-level, general purpose programming language. Since this definition may look overwhelming, let's take a look at each characteristic separately to understand what each of them means:

• Open-source: it's free and available for further improvements, like adding helpful features or fixing bugs
• Object-oriented: based not on functions but on objects with defined attributes and methods
• High-level: human-friendly rather than computer-friendly
• General purpose: can be used to create any kind of programs

Python is extensively applied in data science, data analysis, machine learning, data engineering, web development, software development, and other fields.

To learn more about what Python is, you can find our blog article on the fundamentals of this popular language.

Why is Python So Popular?

Let's now discuss the major advantages of using Python that make it such a powerful and prevalent programming language:

• It has an intuitive syntax that resembles a natural English language and hence is easy to learn, especially for people who are just entering the world of programming
• Because of its human-friendly syntax, it's easy to write, read, and debug
• It provides an extensive standard library and a wide choice of well-documented and comprehensive additional libraries and modules
• It's free both for individuals and businesses
• Thanks to its huge supporting community, Python is constantly developed, improved, and expanded
• It can be integrated into any project and used for solving advanced problems
• Being a general-purpose language, it has various applications in many spheres

What is Python Used For? 7 Python Uses

We already mentioned more than once in this article that Python has a lot of applications. So, let's see where exactly we can use Python in the real world.

1. Python for Data Analysis

Data has become a truly precious asset in any modern industry, and most companies are interested in gathering, manipulating, and analyzing relevant data to extract meaningful business insights from it. And this is where Python goes beyond any competition. 

Python is particularly valuable because, apart from its comprehensive standard library, it provides an impressive collection of additional modules designed specifically for analytical purposes. 

The most famous Python libraries for conducting data analysis are pandas and NumPy. These tools allow you to do almost everything with your data, such as cleaning and wrangling it, exploring statistics, or visualizing hidden trends in your data.

Apart from these two libraries, you can use plenty of others  for various data-related tasks, such as data visualization, web scraping, or hypothesis testing.

2. Python for Data Visualization

Data visualization is a stand-alone part of data analysis that helps us represent the information, whether raw or cleaned and transformed, in a more compelling and insightful form. 

Here Python comes again into play, offering a wide spectrum of tools for visualizing the data. The most popular of them are matplotlib and, based on it, seaborn. Using them, we can create literally all kinds of visualizations from simple ones (line plots, bar charts, histograms) to more advanced ones (animated plots, streamlines, cluster maps, joy plots, etc.).

If you want to learn more about how to use these two Python libraries, you can take our courses Introduction to Data Visualization with Matplotlib and Introduction to Data Visualization with Seaborn.

There are also a plethora of other graphical libraries in Python: both multipurpose (such as Plotly, Bokeh, or Altair) and more focused ones (missingno for visualizing missing values, Toyplot for creating interactive plots for electronic publishing, GeoPandas for building maps, etc.).

3. Python for Machine Learning

Machine learning (ML) is at the core of the majority of data science tasks. It represents a field of artificial intelligence (AI) concerned with using algorithms to enable machines to learn patterns and trends from historical data to make predictions on unknown data. 

Using ML techniques, we can create models that can accurately predict the customer churn rate of the company, estimate the risk of a person having a certain disease, identify the optimal positioning of taxi vehicles, etc.

With Python, we can build an ML model using as few as three lines of code (see an example of such a model for predicting fraudulent bank transactions). Even though behind those few lines of code, there are complex processes and calculations, Python ML libraries do most of the work under the hood, which significantly facilitates the user's task. The most common libraries are scikit-learn, Keras, TensorFlow, and PyTorch.

4. Python for Software Development

Besides its many-sided application in data science areas, Python is used at each stage of software development, including build control, automated continuous compilation, prototyping, bug tracking, testing, and software maintenance. 

The flexibility and, at the same time, power of this programming language allows using it for creating both relatively simple and highly-advanced multi-protocol applications or those that involve complex numeric computations. 

With Python, we can build audio or video programs based on AI or ML techniques, APIs (application programming interfaces), GUIs (graphical user interfaces), or any other type of software.

5. Python for Web Development

While for building the front end of a website (the part of a website accessible to its users), we would mostly use the languages such as HTML, CSS, and JavaScript, for its back end (the invisible part of a website), we would often opt for Python. 

For these purposes, Python is used for some common frameworks (like Django or Flask) with specialized built-in modules that enable sharing data with servers, processing information, accessing databases, URL routing, content management, and maintaining website security.

Among the giant websites and applications created using Python, it's worth mentioning Google, Facebook, Instagram, YouTube, Dropbox, and Reddit.

6. Python for Task Automation/Scripting

Python is a great tool for writing programs to automate different repetitive tasks. This process is also called scripting.

In particular, you can do scripting to work with files and folders. For example, you can create, rename, convert, split, merge, or delete files, check them for errors, duplicates, or certain text patterns, or update the file content. 

You can also use Python automation to search and download information from the internet, complete and submit online forms, and send regular notifications or emails. 

Doing all these things manually, apart from being extremely boring, would be very time-consuming and error-prone. Using Python scripts to perform such actions in just minutes would significantly increase your efficiency and save you precious time and effort for more creative tasks.

7. Miscellaneous Python Uses

Besides all those implementations considered above, what else is Python used for? Below are some fresh ideas:

• Monitoring the stock market
• Web scraping
• Keeping updated a to-do list
• Creating bots
• Generating lists of random names, colors, phone numbers, currencies, etc.

Finally, you can enjoy Python for pure fun:

• Converting text to ASCII art, like this: ʕ •`ᴥ•´ʔ
• Randomly pairing Secret Santa gifters and recipients
• Generating ASCII art pictures of a selected animal with a message
• Playing chess
• Reading your horoscope
• Using Tinder

Why Learn to Use Python? 

The benefits of knowing and using Python clearly show why it's a great choice for learning, especially as your first programming language. However, why would you personally decide to learn it? Here are some reasons that can convince you more:

• It can open you numerous opportunities to start a new career in many fields, and we'll soon take a granular look at which ones exactly.
• It can become a handy supplementary tool for your current job. For example, using Python you can automate many monotonous tasks like renaming many files at once, converting files to another format, or creating and updating work reports.
• It can make you stand out from the crowd even when applying for job positions that don't require knowing Python directly, (e.g., you can check out an example of a business analyst job description template).
• Python-related professions are currently very well-paid and highly in-demand, and this trend is only going to grow in the future. If you need proof, just take any of the jobs mentioned in the What Jobs Need Python? chapter of this article and check the respective average salary on Indeed and job openings on LinkedIn.
• Finally, being able to use Python you can do a lot of helpful things for yourself, like scraping websites or creating bots. 

How Long It Takes to Learn Python

How long would it take you to learn Python? Well, here, everything depends on your previous coding experience, your goals, your motivation, and how much time you're ready to dedicate to your studies. 

The good news is that even if you're a total beginner, you can learn the fundamentals of Python for any purpose in as few as 15 hours. 

If you want to learn the basics of Python for data science, or to learn how to use it in statistics, or you already know Python but would like to master writing the code efficiently, it will take you only about 4 hours. 

If instead, you need a more structured and multi-faceted approach to your studies to become a data analyst in Python, it can take you up to 32 hours. So it's up to you to choose what you will do with this language and start learning.

What Jobs Need Python?

Many professions need Python directly for their everyday work. Below, we’ve outlined what you can do with Python in a professional capacity:

• Data scientist
• Data analyst
• Data engineer
• Machine learning engineer
• Data journalist
• Data architect
• Full-stack web developer
• Back-end web developer
• DevOps engineer
• Software engineer

In addition, some other jobs may require Python skills for conducting certain tasks, like analyzing data, automating repetitive tasks, or monitoring market behavior: business analysts, bankers, and scientists are just a few examples. 

Choosing Python

So, what is Python used for? To sum up, we discussed many things regarding the implementations of Python for solving different tasks, in areas such as data visualization, data analysis, and machine learning.

We also learned what Python is and what the main pros of Python are that make it the most widely-used programming language in the world. 

If you're interested in continuing the exciting journey of learning Python to broaden your career horizons, explore the exhaustive beginner-friendly online programs of the following skill and career tracks of DataCamp:

• Python Fundamentals
• Python Programmer
• Data Analyst with Python
• Data Scientist with Python