What is Python Used For? 8 Real-Life Python Uses

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:

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:

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:

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