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One look at the TIOBE index is enough to see that Python is one of the most popular programming languages today.

And no wonder: Python is simple, versatile, and minimalistic. We love Python and use it in many of our projects. But we often get asked whether it’s the best technology out there to solve a specific problem.

When is it worth to use Python? And how to make sure that you’re making the most of Python in your project?

Here are projects that stand to benefit from Python most today.

 

1. Machine Learning (ML)

Python is an excellent choice for machine learning projects. Since machine learning projects are complicated, Python’s readability, versatility, and easiness come in handy. The speedy implementation of an idea in Python helps engineers to validate it quickly.

Python offers a wealth of machine learning libraries and frameworks such as scikit-learn and TensorFlow. While scikit-learn comes with popular machine learning algorithms, TensorFlow is a low-level library that helps developers build custom machine learning algorithms.

Most of them are free to use under GNU license and are easy to use – you can implement them even with a basic understanding of Python.

 

2. Data analysis and visualization

Python is also an excellent choice for projects that rely on data analysis and/or visualization, areas that are key to data science projects. One of the most popular libraries for data visualization is Matplotlib.

The good thing about it is that it’s easy to get started using it and learning it means that you’ll have an easier time learning other libraries later on (for example, seaborn which is based on is Matplotlib).

To see how Python works for data visualization with the help of Matplotlib, have a look at this recent post by my colleague, Aleksander.

 

3. Natural Language Processing (NLP)

Another area where Python brings many benefits is NLP. Many things about Python make it a top programming language for an NLP project: its transparent syntax and semantics, its simplicity, and its excellent support for integration with other languages and tools.

Python also offers extensive libraries that handle many NLP-related tasks such as document classification, topic modeling, part-of-speech (POS) tagging, and sentiment analysis.

Have a look at these 6 best Python Natural Language Processing libraries that have helped us deliver quality projects over the last few years.

 

4. Web development

Web frameworks based on Python such as Django, Flask, and Pyramid have become very popular in web development recently. They help developers create backend code, starting with mapping different URLs to Python code,  generating HTML files users see in browsers, and managing databases.

Which Python web framework should you be using? Have a look at these articles from our blog to get more information about them:

Choose Django if you care about building final product easily – Django supports both simple and complex applications. Flask or Pyramid are great options if you want more control of the components you’ll be using in your projects (for example, the ORM or template engine you want to use).

 

5. Automation (scripting)

Sometimes developers need to automate simple tasks. That’s when they turn to scripting – writing small programs that solve simple problems automatically. Python is an excellent language for this type of task because of its simple syntax and ease in writing.

Writing something small and testing it in Python is quick and straightforward. And that’s what we want when automating tasks.

 

The power of Python

As you can see, Python is useful in many different situations, starting with data science projects that rely on Machine Learning, data analysis, and visualization, or NLP to web development and automation.

If you’re still not sure whether Python is the right language for your project, don’t hesitate and get in touch with us.

We advise organizations about the best technologies for the type of problem they’re trying to solve and know where Python brings the best results.