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What is Python Used For? 8 Real-Life Python Uses
Python is 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 uses of Python, the reasons why Python is so popular, why you should learn it, and how long it takes. We also examined what jobs usually require 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 just entering the programming world.
- 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 to solve advanced problems.
- Being a general-purpose language, it has various applications in many spheres.
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What is Python Used For? 8 Python Uses
In this article, we have mentioned more than once that Python has many 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, in addition to 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 into play again, 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 using 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 most 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 there are complex processes and calculations behind those few lines of code, 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 in AI and deep learning
Python plays a central role in AI and deep learning, driving advancements in technologies like natural language processing (NLP) and computer vision. These domains are experiencing significant growth, and Python's ecosystem of libraries and frameworks makes it the go-to language for developing AI solutions.
Libraries like spaCy, NLTK, and Hugging Face Transformers enable tasks such as sentiment analysis, language translation, text summarization, and chatbot development. Python's versatility allows researchers and developers to implement state-of-the-art models and fine-tune pre-trained ones for custom applications.
Python is at the forefront of computer vision innovation, with libraries like OpenCV, PyTorch, and TensorFlow. These tools are employed to develop applications such as facial recognition, object detection, image classification, and video analysis. Python's ease of use and the availability of pre-trained models make it accessible for both beginners and professionals.
5. 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 power of this programming language allow it to create 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.
6. 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 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), which have specialized built-in modules that enable sharing data with servers, processing information, accessing databases, URL routing, content management, and maintaining website security. In recent years, we have also seen the increasing use of FastAPI for developing APIs due to its performance and ease of use.
Among the giant websites and applications created using Python, Google, Facebook, Instagram, YouTube, Dropbox, and Reddit are worth mentioning.
7. Python for task automation and scripting
Python is a great tool for writing programs that automate repetitive tasks, a process also known as 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.
Besides being extremely boring, doing all these things manually would be time-consuming and error-prone. Using Python scripts to perform such actions in minutes would significantly increase your efficiency and save you precious time and effort for more creative tasks.
Python also plays an important role in DevOps and Infrastructure as Code (IaC), using libraries like Ansible and Terraform.
8. 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 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 to numerous opportunities to start a new career in many fields, and we'll soon examine 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 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 will only grow in the future.
- Finally, by using Python, you can do many 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? Everything depends on your previous coding experience, goals, motivation, and the 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 30 hours. Here's a detailed table with timelines depending on your specific goals:
Purpose of learning Python | Estimated time to learn | Description |
---|---|---|
Basic syntax & programming concepts | 15-30 hours | Ideal for beginners to understand the fundamental syntax and programming logic of Python. |
Data analysis | 50-75 hours | Includes learning libraries like pandas and NumPy for data manipulation and basic analysis. |
Web development | 75-100 hours | Covers learning frameworks like Django or Flask, along with basics of front-end technologies. |
Machine learning | 100-150 hours | Involves understanding algorithms, machine learning concepts, and libraries such as scikit-learn. |
Automation & scripting | 40-60 hours | Focused on learning scripting for automating mundane tasks and writing simple scripts. |
Advanced data science | 150-200 hours | Includes deep learning, TensorFlow, and PyTorch. Suitable for those aiming to specialize in AI/ML roles. |
Full-stack development | 200-250 hours | Comprehensive learning including both back-end and front-end development. |
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, along with other skills required:
Career path | Required skills |
---|---|
Data Scientist | Python, pandas, NumPy, scikit-learn, machine learning, data visualization |
Data Analyst | Python, SQL, pandas, data cleaning, data visualization |
Machine Learning Engineer | Python, TensorFlow, PyTorch, deep learning, model deployment |
Web Developer | Python, Django/Flask, HTML/CSS, JavaScript, web security |
Software Engineer | Python, software design principles, testing, version control systems |
DevOps Engineer | Python, cloud platforms (AWS, GCP), CI/CD pipelines, Docker, Kubernetes |
Data Engineer | Python, ETL processes, big data tools (Hadoop, Spark), data warehousing |
Data Journalist | Python, storytelling with data, data visualization, journalism principles |
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.
Conclusion
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 its main pros, making it the world's most widely used programming language.
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:
Become a Python Developer
Python FAQs
Why is Python so popular?
There are a few reasons, including:
- It's easy to learn, write, debug, and read due to its intuitive syntax
- It's free for everyone
- It provides an extensive standard library and many additional modules
- It's constantly developed, improved, and expanded
- It can be used for solving advanced problems in various fields.
What are the main Python applications?
Data science, data analysis, data visualization, machine learning, artificial intelligence, data engineering, web development, software development, and task automation.
Is Python worth learning in 2024?
Definitely yes! The knowledge of Python can help you start a new well-paid and highly in-demand profession in many spheres, or it can become an additional tool for your current job or personal tasks. Python can also make you stand out from the crowd even when applying for jobs that don't require such skills directly.
How long does it take to learn Python?
Approximately 4 to 100 hours, depending on your previous coding experience (or its absence), your goals, your motivation, how much time you're ready to dedicate to your studies, and what exactly you're going to do with this programming language.
Is Python a good choice to learn for a beginner in coding?
Absolutely! Python has a beginner-friendly, intuitive syntax that resembles a natural language, and it also offers well-described and comprehensive libraries, so this language is really easy to learn and use. If you're a total beginner, you can find helpful this skill track on Python fundamentals.
Is Python more preferred than R for solving data science tasks?
Yes, and it's recently becoming more popular because of its low entry barrier, simple syntax, vast supporting community, and the wide choice of multipurpose or narrowly specialized well-documented data science libraries that can be used for solving any complex task.
What jobs need Python?
Data scientists, data analysts, data engineers, machine learning engineers, data journalists, data architects, full-stack web developers, back-end web developers, software engineers, DevOps engineers, business analysts, bankers, and scientists in different fields.
How can I use Python for my personal needs?
For monitoring the stock market, automating reports, scraping websites, completing and sending online forms, updating a to-do list, creating bots, and generating random data of a certain category. You can also use Python for various funny tasks, like generating ASCII art text and pictures, playing chess, pairing Secret Santa gifters and recipients, reading your horoscope, or even using Tinder.

IBM Certified Data Scientist (2020), previously Petroleum Geologist/Geomodeler of oil and gas fields worldwide with 12+ years of international work experience. Proficient in Python, R, and SQL. Areas of expertise: data cleaning, data manipulation, data visualization, data analysis, data modeling, statistics, storytelling, machine learning. Extensive experience in managing data science communities and writing/reviewing articles and tutorials on data science and career topics.
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