13 Python Data Science and Machine Learning Libraries You Need to KnowBlog

Get started or improve your craft with these libraries


BY DiNG / ON May 31, 2020

Python is almost always the best choice for data scientists. This is due to its versatility and simplicity, but above all else, it’s thanks to the open-source packages distributed by the community and important companies. As it’s a general-purpose programming language, Python is used for web development (with Django and Flask), data science, machine learning, cybersecurity, and so on. Today, we’re going to discuss the 13 data science and machine learning libraries that every data scientist must know and should be using.

Basic Libraries for Data Science

These basic libraries make Python a favorable language for data science and machine learning. The following packages will allow us to analyze and visualize data:

  1. NumPy is the fundamental package for scientific computing with Python. Among other things, it contains a powerful N-dimensional array object, sophisticated (broadcasting) functions, tools for integrating C/C++, and Fortran code. And it is useful in linear algebra, Fourier transform, and random number capabilities. Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
  2. SciPy builds on NumPy by adding a collection of algorithms and high-level commands for manipulating and visualizing data. This package includes functions for computing integrals numerically, solving differential equations, optimization, and more.
  3. pandas is actually the best tool to visualize, read, and write data. I find myself using it often — especially while working with .csv files.
  4. Matplotlib is the standard Python library for creating 2D plots and graphs. It’s very flexible to use but a bit low-level, so it’s a little tricky to plot more complex graphs or plots. However, it’s a library that I use often — especially when working with datasets that do not require to be visualized. So, just to plot my models’ scores.

Libraries for Machine Learning

Machine learning lies in the intersection of artificial intelligence and statistical analysis. The following libraries offer Python the ability to apply many machine learning activities, from running basic regressions to forming complex neural networks.

  1. scikit-learn builds on NumPy and SciPy by adding a set of algorithms for common machine learning and data mining tasks, including clustering, regression, and classification. It contains lots of pre-trained machine learning models that data scientists use rather than creating their own models. Obviously, it depends on what ML model you need to use. If you are looking for something very specific for your intent, maybe it’s better to create your own model.
  2. Theano uses NumPy’s syntax to optimize and evaluate mathematical expressions. It uses the GPU to speed up its processes. Theano’s speed makes it especially valuable for deep learning and other computationally complex tasks. I find it very useful to work with TensorFlow and Keras.
  3. TensorFlow was developed by Google as an open-source successor to DistBelief, their previous framework for training neural networks. TensorFlow uses a system of multi-layered nodes that allow you to quickly set up, train, and deploy artificial neural networks with large datasets. It’s very practical and easy to use. It’s also used by its creator, Google, and there are tons of articles and tutorials that mention TensorFlow.
  4. pickle is an open-source package that allows us to serialize our ML models. I choose pickle over many other model serializers because I find it very simple to use and efficient. This is one of the most efficient ways to share your model or use it from another program.

Libraries for Data Mining and Natural Language Processing

“Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.” — Wikipedia

“Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.” — Wikipedia

  1. Scrapy is a fast high-level web crawling and web scraping framework used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.
  2. NLTK is a set of libraries designed for natural language processing. It’s often used with everything regarding text classification and analysis, from sentiment analysis to chatbots.
  3. Pattern is a web mining module for the Python programming language. It has tools for data mining (Google, Twitter, and Wikipedia API, a web crawler, an HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis, and canvas visualization.
  4. seaborn is a popular visualization library that builds on Matplotlib’s foundation. Once you try it after using Matplotlib, it will be like being in Eden. It’s very sophisticated, and unlike Matplotilib, it’s a high-level package. This means we can easily plot more complex types of plots, such as heat maps and so on.


Flask is a powerful Python-based web development framework. But why is it on the list of tools that data scientists need to know? And also, isn’t Django better for web development? Well, sometimes you may need to embed your ML model in a web app because that would mean that anyone could easily access your classification model from the internet. You could even create an online classification service! To answer the second question, yes, Django is actually better for web development and it’s also simple to use, but not as simple as Flask.

In general, I would definitely use Django to build a normal website. But if you just want your model to be embedded in a website, Flask is actually simpler and more intuitive.

All the libraries listed in this article are a small part of the open-source packages that can be found online. These were just basic data science and machine learning libraries that every data scientist must know.

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