Top 10 Python Libraries for Machine Learning

by Alex
Top 10 Python Libraries for Machine Learning

Data Science makes extensive use of the predictive capabilities of machine learning (ML) algorithms. Python, on the other hand, provides a convenient environment for experimenting with these algorithms because of its readability and efficiency. And the abundance of libraries makes it an even more attractive solution. A framework is an interface or tool that allows developers to simply create machine learning models without diving into the underlying algorithms. A library is a set of files containing code that can be imported into your application. A framework can be the set of libraries needed to build a model without understanding the specifics of the underlying algorithms. However, developers need to know how these algorithms work in order to interpret the results correctly.

#10 Matplotlib

#10 Matplotlib Matplotlib is an interactive cross-platform library for creating two-dimensional diagrams. It can be used to create high-quality graphs and charts in several formats. Advantages:

  • Flexibility. Supports Python and IPython, Python scripts, Jupyter Notebook, web application servers, and many interface tools (GTK+, Tkinter, Qt, and wxPython).
  • Provides a MATLAB-style interface for creating diagrams
  • Object-oriented interface gives full control over axis properties, fonts, line styles, and so on.
  • Compatible with various graphics engines and operating systems.
  • Often used in other libraries, such as Pandas.


  • Having two different interfaces (object-oriented and MATLAB-style) can be confusing to the novice developer.
  • Matplotlib is a library for visualization, not data analysis. For the latter, it needs to be combined with others, such as Pandas.

Official documentation: Tutorials on matplotlib in Russian: Installing matplotlib and graph architecture / plt 1.

#9 Natural Language Toolkit (NLTK)

#9 Natural Language Toolkit (NLTK) NLTK is a framework and a set of libraries for developing symbolic and statistical natural language processing (NLP). The standard toolkit for NLP in Python. Benefits:

  • The library contains graphical tools as well as data examples.
  • Includes a book and a set of examples for beginners.
  • Provides support for various ML operations such as classification, parsing, tokenization, and so on.
  • Works as a platform for prototyping and building research systems.
  • Compatible with several languages.


  • To work with NLTK you need to understand how to work with strings. However, documentation can help with this.
  • Tokenization comes at the expense of breaking text into sentences. This has a negative impact on performance.

Official documentation:

#8 Pandas

#8 Pandas This is the Python library for high-performance yet comprehensible data structures and data analysis tools in Python. Benefits:

  • Expressive, fast and flexible data structures.
  • Supports aggregation, concatenation, iteration, reindexing, and visualization operations.
  • Flexible and compatible with other Python libraries.
  • Intuitive data management with a minimal set of commands.
  • Supports a wide range of commercial and academic domains.
  • Performance.


  • Built on matplotlib, which means a beginner should be familiar with both to understand what is best to use for a particular problem.
  • Less suitable for n-dimensional arrays and statistical modeling. Better to use NumPy, SciPy, or SciKit Learn for that.

Official documentation: Brief documentation with examples: Introduction to the pandas library: installation and first steps / pd 1. Lessons on Pandas in Russian: Fundamentals of Pandas №1 // Reading files, DataFrame, data selection.

#7 Scikit-Learn

#7 Scikit-Learn This library is based on matplotlib, NumPy and SciPy. It provides several tools for data analysis and mining. Advantages:

  • Simple and efficient.
  • Quickly improving and updating.
  • Variety of algorithms, including cluster and factor analysis and principal components method.
  • Can extract data from images and text.
  • Can be used for NLP.


  • Designed for teacher-assisted learning and does not work well in non-teacher-assisted learning (e.g. Deep Learning).

Official documentation:

#6 Seaborn

#6 Seaborn A library for creating statistical graphs in Python. It is based on matplotlib and has integration with pandas data structures. Benefits of

  • Offers more visually appealing graphs compared to matplotlib.
  • Offers built-in graphs that matplotlib does not.
  • Uses less code for visualization.
  • Excellent integration with Pandas: a combination of data visualization and analysis.


  • Builds on matplotlib, so you need to understand which library to use in which case.
  • Relies on default themes, so the result is not as customizable as matplotlib.

Official documentation:

#5 NumPy

#5 NumPy NumPy adds multidimensional array and matrix processing to Python, as well as large datasets for high-level mathematical functions. It is commonly used for scientific calculations. Consequently, it is one of the most used Python packages for machine learning. Advantages:

  • Intuitive and interactive.
  • Offers Fourier transforms, capabilities to generate complex numbers, and other tools to integrate computer languages like C/C++ and Fortran.
  • Versatility – other machine learning libraries, such as scikit-learn and TensorFlow, use NumPy arrays as source values; and Pandas has NumPy under the hood.
  • Serious community contribution to development.
  • Simplifies complex mathematical implementations.


  • Can be overly complex – not worth using if you’re happy with regular Python lists.

Official documentation: Tutorials on NumPy in Russian: Introduction and installation of the NumPy / np 1.

#4 Keras

#4 Keras A very popular machine learning library in Python, providing a high-level neural network API that runs on top of TensorFlow, CNTK or Theano. Benefits:

  • Great solution for experimentation and rapid prototyping.
  • Portable.
  • Offers a lightweight representation of neural networks.
  • Easy to use for modeling and visualization.


  • Slow because it requires creating a computational graph before performing operations.

Official documentation: Lessons on Keras in Russian: Advantages and limitations of Keras / keras 1.

#3 SciPy

#3 SciPy Popular library with different modules for optimization, linear algebra, integration and statistics. Benefits:

  • Suitable for image management.
  • Provides simple processing of mathematical operations.
  • Offers efficient mathematical operations including integration and optimization.
  • Supports signal processing.


  • The name SciPy hides both a stack and a library. However, the library is part of the stack. This can be confusing.

Official documentation: Introduction to SciPy in Russian: Guide to SciPy: what it is, and how to use it.

#2 Pytorch

#2 Pytorch A popular library based on Torch, which, in turn, is made in C and wrapped in Lua. Originally created by Facebook, but now used by Twitter, Salefsorce, and many other organizations. Benefits:

  • Contains tools and libraries for computer vision, natural speech processing, deep learning, and more.
  • Developers can perform calculations on tensors using GPU acceleration.
  • Helps create computational diagrams.
  • Simulation process is simple and transparent.
  • The standard define-by-run mode is more like classic programming.
  • Uses familiar debugging tools such as pdb, ipdb, or the PyCharm debugger.
  • It uses a lot of pre-made models and modules that can be combined with each other.


  • Because PyTorch is relatively new, there aren’t many online resources. This makes it difficult to learn from scratch, though it’s still fairly intuitive.
  • It’s not that ready to be fully functional compared to TensorFlow.

Official documentation:

#1 TensorFlow

Топ-10 библиотек Python для машинного обучения Originally developed by Google, TensorFlow is a high-performance library for data flow graph computing. Under the hood, it is more of a framework for creating and running calculations that use tensors. TensorFlow is most often used in neural networks and deep learning. This makes it one of the most popular libraries. Benefits:

  • Supports reinforcement learning and other algorithms.
  • Provides computational graph abstraction.
  • Huge community.
  • Provides TensorBoard, a tool for visualizing models right in your browser.
  • Ready to run.
  • Can be deployed on multiple CPUs and GPUs.


  • Much slower than other CPU/GPU frameworks.
  • Steep learning curve compared to PyTorch.
  • Computational graphs can be slow.
  • Not commercially supported.
  • Not a great toolkit.

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Now you know the differences in Python libraries and frameworks. You can evaluate the advantages and disadvantages of the most popular machine learning libraries.

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