Here's why Python is so popular in Machine Learning

Sławomir Sawicki

25 October 2018, 7 min read

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What's inside

  1. Simple syntax
  2. Gentle learning curve
  3. Scalability
  4. Amazing community
  5. Plenty of ML libraries
  6. Graphics and visualization
  7. Reducing complexity without compromises

Machine Learning (ML) is all the rage right now, and organizations that want to take advantage of this technology for their data often turn to Python.

There are many reasons why Python is one of the most popular programming languages with developers and engineers who work on ML systems.

Here are some good reasons why engineers choose Python for Machine Learning projects.

Simple syntax

First, there's Python's undeniable strength: simple and straightforward syntax. It's one of the most commonly cited reasons behind the popularity of the language in many other areas beyond ML.

Note that the semantics of Python often correspond to mathematical ideas that are at the core of Machine Learning. That's why it's easier for engineers to express these ideas with the help of Python – and within relatively few lines of code.

Since Python is a dynamically-typed language, it allows skipping a massive amount of material related to low-level tasks and go straight to the point. Developers won’t be losing their nerve on identifying and correcting their mistakes.

It’s far more pleasant to read Python code than code writing in Java, C++, or C#. Installing Python and preparing the environment for work is straightforward as well. Learning Python with the help of online resources is just so much easier - the language is just far more understandable.

Gentle learning curve

Another significant advantage of Python is that it's easy to learn. That's another reason for its appeal among developers – today, it's the third most popular programming language, according to the TIOBE index. That means assembling a team of Python experts for your ML project will be easy.

As Python enthusiasts, we agree with developers who claim that Python's accessible syntax makes it a much more welcoming and easy to use language than others. The important thing is that Python's simplicity doesn't mean we're trading off on performance. In fact, Python offers a very nice balance of the two, making it an excellent technology for complex Machine Learning projects.

And Python’s gentle learning curve is a huge advantage to all those who are taking their first steps in data science. Instead of spending several months learning a new language, they can participate in the project immediately.


In comparison to languages like R, Python is far more scalable - and way faster than Matlab or Stata. Python’s scalability comes from the flexibility that it offers to developers in problem-solving. The variety and breadth of Python applications indicate that the language can be used successfully for fast-growing projects.

Amazing community

Python is surrounded by a vibrant community of passionate developers who believe in knowledge sharing and have created plenty of resources for that purpose. For example, developers can join some of these 15 data science Slack communities to access productive discussions about Python in ML or ask questions when in doubt.

Since so many people take advantage of Python, the support community is vast, and you can be sure that its collective knowledge will come to rescue whenever your team encounters a problem.

Plenty of ML libraries

Most importantly, Python makes it easy for developers and engineers start working on their projects by providing them with a collection of valuable tools that offer great help in working with machine learning systems.

The broad range of frameworks, libraries, and extensions make implementing Machine Learning tasks easier. For example, scikit-learn guides developers in using Python for Machine Learning and Google's TensorFlow helps to build custom ML algorithms. Natural Language Processing (NLP) is another popular area where Python comes in handy - have a look at this article to see the best NLP Python libraries.

Apart from these, developers can take advantage of core libraries for data structuring (Pandas, NumPy) and visualizations (Matplotlib, Plotly, Seaborn). And there’s also SciPy, a collection of libraries that are closely related to and sometimes even dependent on one another (SciPy, Scikit-Learn, NumPy, Pandas, Matplotlib).

Here are a few libraries every ML enthusiast should know:


This general-purpose library helps in building neural networks. Its main advantage hre is the multi-layer nodes system that allows quick learning on large data sets. It’s a real speed demon! TensorFlow was created by Google, and its most famous application is recognizing voice and objects on pictures.


This high-level library is for deep learning. You can use Theano or TensorFlow as backend - and even CNTK (Microsoft Cognitive Toolkit). Using Keras, you can easily build a neural network with only basic knowledge about the topic.


SciPy is for carrying out mathematical operations on data matrices. It’s closely connected to NumPy and contains the main modules for linear algebra, statistics, fourier transformation, as well as integration, optimization and processing of images.


This library from the SciPy Stack set is dedicated to machine learning and image processing. It sets the standard for machine learning in Python, combining ease of use, flexibility, high efficiency and excellent documentation (and high quality of code!).


One of the basic ML libraries in the SciPy Stack. It offers many handy functionalities related to operations on tables and matrices, boosting their efficiency significantly.


Another great library from the SciPy Stack, Pandas is used for carrying out operations on data sets such as adding/removing columns, filling out missing data, creating DataFrame from basic structures (lists, dictionaries), grouping, and aggregation. It helps to carry out complex operations easily and efficiently.


Used for visualizing data sets, Matplotlib has many tools to  draw various charts easily and quickly. It’s very useful for presentation of results obtained using ML and visualization of input data which significantly helps to understand the problem that we need to solve and how our models/algorithms work. It’s also part of the the SciPy toolkit.


A library (or actually a framework) for web crawling that helps to easily obtain data required for further processing. It was created by scrapinghub that has  been professionally acquiring data from websites for many years.

Want to learn more? Check out this list for more amazing Python machine learning libraries.

Graphics and visualization

Another area where Python can help is visualization. The language offers a variety of visualization options. The visualization packages help developers to get a better understanding of data, create charts, and create web-ready interactive plots.

To see this application in action, check out this post where Alex shows how to use a Python library called Matplotlib for data visualization.

Reducing complexity without compromises

Many other languages are used in Machine Learning - for example, Java, C, and Perl. Some developers describe these complex languages are responsible for “hard-coding,” whereas Python figures as a “toy language” that is more accessible to basic users. But in reality, Python is a fully functional alternative to these languages and their often complex syntax.

Python is easy and accessible – and that makes collaborative coding and implementation so much easier. Let's face it: your Machine Learning project isn’t going to be developed by an individual, but a group. And building a team of expert Python developers is much easier.

As a general-purpose language, Python helps to get a lot done quickly – which brings a lot of value if we consider the general complexity of Machine Learning projects.

Still not sure whether Python is the best tool for your Machine Learning project? Get in touch with us; we help companies pick the most promising technologies for their projects.

Sławomir Sawicki


business intelligence


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