Essential Data Science & Machine Learning Python Libraries

Although, Python wasn’t originally built for Data Science, but when it comes to solving data science tasks and challenges, it never ceases to surprise its users. Most data professionals are already leveraging the power of this open-source language every day.

While it’s easy-to-learn, easy-to-debug, platform independent and open-source, the availability of plethora multi-purpose, ready-to-use libraries (over a thousand inbuilt) is what really makes Python the top choice for data scientists.

In this article, I’m discussing a few Python libraries which are very helpful in data science & machine learning related operations.

Let’s roll.

Top Python Libraries for Statistical Analysis

Statistics is one of the fundamentals of data science and Python comes with a number of libraries for the sole purpose of statistical analysis. Let’s cast a look at the best of them:


One of the most commonly used Python libraries, NumPy (Numerical Python) is meant for performing simple to complex mathematical and scientific computations. It contains a powerful N-dimensional array object and a collection of high-level mathematical functions and implemented methods that can be used for indexing, sorting, reshaping and conveying images and sound waves as an array of real numbers in multi-dimension.

Also, NumPy offers Fourier transforms, random number capabilities, and tools for integrating C/C++ and Fortran code. It has over twenty-one-thousand commits on GitHub and an active community of 846 contributors.


Built on top of NumPy, SciPy is another core library for scientific computing. It’s basically a collective of sub-packages that help in solving linear algebra, probability theory, integral calculus among others. SciPy is used to process the array elements defined using the NumPy library, so it is often used to compute mathematical equations that cannot be done using NumPy.

Backed by a strong community of over 700 contributors, SciPy is easy to get started with as all the functions offered by its various sub-modules are well documented.


While, NumPy is one of the best libraries for multi-dimensional arrays, Pandas is perfect for processing huge chunks of data.

This Python library provides expressive, fast, and flexible data structures and a vast variety of tools for analysis. The best feature of Pandas is the ability to translate rather complex data operations into a few commands. It contains many built-in functions for grouping, filtering, and combining data. It also contains time-series functionality, followed by impressive speed indicators.

It relies on the NumPy array for the purpose of processing pandas data objects. The library has 20K+ commits on GitHub and an active community of 1,600+ contributors.


Built on NumPy (arrays) and SciPy (scientific models), this Python module is popular for statistical computations, statistical testing, and data exploration. Along with the above two, Statsmodels also integrates with Pandas for effective data handling.

It’s perfect for performing statistical tests and hypothesis testing which are not found in NumPy and SciPy packages. Also, due it’s vast support for statistical computations, Statsmodels is often used to implement GLM and OLM models. It have around 12,000 commits on GitHub and a vibrant community of about 200 contributors.

Top Python Libraries for Data Visualization

Data visualization includes the implementation of graphs, histograms, charts, density plots, etc, in-short, it’s all about expressing the key insights from data effectively through graphical representations.

Now that we’ve been through the top python libraries for statistical analysis, let’s take a look some of the most commonly used and the most effective Python libraries for data visualization:


A two-dimensional plotting library, Matplotlib enables you to build diverse charts, from histograms and bar charts to non-Cartesian coordinates graphs. Also, many popular plotting libraries are designed to work in conjunction with this most basic data visualization package.

It makes it very easy to plot graphs by providing methods to choose appropriate line styles, font styles, formatting axes and so forth. One of the best features of this plotting library is the Pyplot module that provides an interface very similar to the MATLAB user interface. It has around 32,000 commits on GitHub and a very vibrant community of about 800 contributors.


The Matplotlib library forms the base of this higher-level API. In comparison to Matplotlib, Seaborn contains more suitable default settings for processing charts. Also, there is a rich gallery of visualizations including some complex types like time series, jointplots, and violin diagrams.

Along with extensive supports for data visualization, it also provides a dataset-oriented API, which can be used to examine the relationships among multiple variables. Seaborn has over two-thousand commits on GitHub and an active community of 102 contributors.


I don’t think there is any Python developer that does not know about Plotly. This very popular graphical Python package allows you to build sophisticated graphics easily. It is adapted to work in interactive web apps. Among its remarkable visualizations are clear and concise graphs, ternary-plots, heatmaps, 3D charts and so forth.

Top Python Libraries for Machine Learning

No data science project can be complete without a machine learning models that can accurately predict the outcome or solve a certain problem. Let’s go through the top Python libraries for Machine Learning:


This Python module based on NumPy and SciPy is one of the best libraries for data modeling and model evaluation. It comes with a plethora of functions for the sole purpose of creating a model. Scikit-learn contains all the Supervised and Unsupervised ML algorithms and it also comes with well-defined functions for Ensemble Learning and Boosting Machine Learning.

It has 24K+ commits on GitHub and a very vibrant community of over 1500 contributors.


Extreme Gradient Boosting aka XGBoost is one of the best Python libraries for performing Boosting Machine Learning. This library provide highly optimized, scalable and fast implementations of gradient boosting machines, which are used to improve the performance and accuracy of Machine Learning Models. It has over three-thousand commits on GitHub and a vibrant community of over 300 contributors.

Like XGBoost, LightGBM, and CatBoost too are equipped with well-defined functions and methods. All three solve a common problem and are used in almost the same way.

Top Python Libraries for Deep Learning

The biggest advancements in machine learning and artificial intelligence is been through deep learning. Python provides the best deep learning packages that help in building effective neural networks.


Developed in Google Brain, Tensorflow is one of the best Python libraries for Deep Learning. It provides abilities to work with multiple neural networks which help to accommodate large-scale projects and data sets. Among the most popular Tensorflow applications are object identification, speech recognition, and more.

Furthermore, it provides methods to perform statistical analysis. For e.g., it comes with in-built functions for creating probabilistic models and Bayesian Networks such as Bernoulli, Uniform, Gamma, and so on. Also, it comes with TensorBoard (a visualizer) that creates interactive graphs and visuals to understand the dependencies of data features.

The open-source library for high-performance numerical computations has 73K+ commits on GitHub and a very vibrant community of over 200 contributors.


Primarily developed by Facebook’s artificial intelligence research group, its an open-source, Python-based scientific computing package that allows you to perform tensor computations (like NumPy) with strong acceleration via GPU acceleration, create dynamic computational graphs and automatically calculate gradients. Moreover, it offers a rich API for solving applications related to neural networks.

Pytorch has over twenty-two-thousand commits on GitHub and an active community of 1236 contributors.

Top Python Libraries for Natural Language Processing

Do you know what’s the technology behind Alexa, Siri, and other chatbots? The answer is Natural Language Processing. It has played a huge role in designing AI-based systems that help in describing the interaction between human language and computers. In the end, let’s cast a look at top Python libraries for NLP:


A suite of libraries, Natural Language Toolkit is considered to be the best Python package for analyzing human language and behavior. With the help of NLTK, you can process and analyze text in a variety of ways, classify, tokenize, stem and tag it, extract information, etc. It is also used for prototyping and building research systems.

Preferred by most of the Data Scientists, NLTK has 13K+ commits on GitHub and a vibrant community of 284 contributors.


SpaCy is a NLP library with excellent examples, API documentation, and demo applications. The free, open-source Python library is written in the Cython language (C extension of Python). It supports a plethora of languages, provides easy deep learning integration and promises robustness and high accuracy. Unlike Natural Language Toolkit, which is widely used for teaching and research, SpaCy focuses on providing software for production usage.

SpaCy has 11K+ commits on GitHub and a vibrant community of 300+ contributors.

As you can see, all the source code is in GitHub, so you can check all the projects there.

Thanks for reading!

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