If you’re thinking about learning the Python programming language or have already started, you might be wondering, “What exactly can I do with Python?” That’s what we’ll answer in this article. In fact, Python applications are mainly summarized in three key areas:
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Web development
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Data science, including machine learning and big data analysis and visualization
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Scripting
Let’s explore them together.
What is Python?
Python is an interpreted, object-oriented programming language with dynamic typing. The Python interpreter reads one line of code at a time, converts it into low-level bytecode, and then executes it. As a result, runtime errors are commonly encountered. Python is one of the most popular programming languages due to its simplicity and readability, and it is open-source.
Python Applications and Uses
1. Web Development
There are several Python frameworks for developing the backend of web servers. The most popular and beginner-friendly ones are Django and Flask.
But what’s the difference between Django and Flask?
Key Differences:
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Flask offers simplicity, flexibility, and precise control. It gives you the freedom to decide how each component works.
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Django offers a complete experience—it includes an admin panel, database interfaces, ORM (Object-Relational Mapping), and a structured guide for your projects.
So, choose:
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Flask if you want to experiment or learn, or if you need full control over elements (like the database used).
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Django if you’re focused solely on the final product—especially if you’re building a backend for a news site, e-commerce store, or blog and want a clear, consistent development path.
In short, Flask is ideal for beginners due to its simplicity and minimal components, making it easy to handle. It’s also best for highly customized applications.
On the other hand, if you’re looking to build an application in a fast and straightforward manner, Django is a better choice.
2. Data Science (Machine Learning, Big Data Analysis, and Visualization)
Let’s first define machine learning.
Machine learning uses algorithms to automatically detect specific patterns in the input data.
For example: Suppose you want your machine learning program to identify whether an image contains a dog or a table. First, you’d provide 1,000 images of dogs and 1,000 of tables. The program would then learn the difference. When given a new image, it should be able to identify it correctly.
This process is very similar to how children learn to recognize things.
We don’t usually tell a child explicitly: “If it’s furry and has light brown hair, it’s probably a dog.”
Instead, we just say: “This is a dog. That’s also a dog. This is a table. That’s also a table.”
Machine learning algorithms work in the same way.
This applies to recommendation systems like those used by YouTube, Amazon, and Netflix, as well as facial or voice recognition software.
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scikit-learn contains the most commonly used machine learning algorithms.
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TensorFlow is a low-level library that allows the creation of custom machine learning algorithms.
If you’re a beginner, start with scikit-learn. Once you’re comfortable, move on to TensorFlow for more advanced tasks.
Data Analysis and Visualization with Python
Python offers powerful tools and libraries for data analysis and visualization, making it ideal for handling large datasets and presenting insights effectively.
3. Scripting
Scripting means writing small programs that automate simple tasks.
Python is perfect for this because its syntax is extremely easy to learn and write. It’s ideal for quickly developing and testing small tools and scripts.
Can Python Be Used for Game Development?
Yes, you can use Python’s PyGame library for game development. However, it’s not the best or most popular game engine. It’s typically used for hobby projects. If you’re serious about game development, Python is not recommended.
When it comes to game development, Unity is the better choice. It allows you to create games that run on many platforms, including Mac, Windows, iOS, and Android.
Can Python Be Used for Desktop App Development?
Python does support desktop app development via the Tkinter library, but it’s not the most optimal choice.
For desktop apps, it’s better to use traditional programming languages like Java, C++, or C#. Recently, some companies have also started using JavaScript for building desktop apps.
