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Top 10 Python Libraries essential for Data Analytics!

Top 10 Python Libraries header

When talking about Python’s popularity in both the programming and Data Science community, the first thing that comes to mind is its simplicity. One of the best features of Python is its inherent simplicity and readability that makes it a beginner-friendly language. It has a neat and lucid syntax, thereby offering a shorter learning curve than most other languages. In fact, you could write a program much faster in Python that you probably could with other languages such as C++ or Java.

Python has a design philosophy that stresses allowing programmers to express concepts readably and in fewer lines of code. This philosophy makes the language suitable for a diverse set of use cases: simple scripts for web, large web applications (like YouTube), scripting language for other platforms (like Blender and Autodesk’s Maya), and scientific applications in several areas, such as astronomy, meteorology, physics, and data science.

It is not only comfortable to use and easy to learn but also very versatile. What we mean is that Python for machine learning development can run on any platform including Windows, MacOS, Linux, Unix, and twenty-one others. To transfer the process from one platform to another, developers need to implement several small-scale changes and modify some lines of code to create an executable form of code for the chosen platform. ​

The programming languages assist in defining structures, classes, and objects that deal with the real world. They also help to solve problems more efficiently and in an ordered fashion. Data can also be stored in these languages in such a way that they give better a view, analysis, and reports.

Learn more about Python's creator, Guido Van Rossum, his pioneering work with the Python programming language, and the status of the community today.

The Story of Python, by Its Creator, Guido van Rossum

Learn more about Python's creator, Guido Van Rossum, his pioneering work with the Python programming language, and the status of the community today.

Python is being used as an integration language in many places, to stick the existing components together. Python is easy to integrate with other lower-level languages such as C, C++, or Java. Similarly, it is easy to incorporate a Python based-stack with data scientist’s work, which allows it to bring efficiency into production.

Experienced Data Scientists and Machine Learning Engineers rely on code recipes to release projects faster. Code recipes are solved, ready-to-use code examples that you can plug-in to your project. You can use this when you are stuck and not sure how to make progress, or when you want to get work done faster. Code recipes are also helpful to answer coding questions in job interviews.

At this point, you’re already aware of how big corporations rely on AI and Machine Learning for numerous operations, which also calls for a huge demand for experts in these technologies. According to Jean Francois Puget, from IBM’s Machine Learning Department, Python is the most popular language for Machine Learning, based on trending search results on



If you are stuck somewhere in your code or on something, you can be sure that someone somewhere has faced such a problem before. So, there’s always a solution. You can connect with Python experts and community members on online platforms like Reddit and StackOverflow, or you can attend meetups/conferences and other gatherings... and in the near future, we hope to create our own online community.

In the meantime here is a list of our top 10 code libraries, which of course is always going to be updated but not without your input!, so please don't hesitate to contact us if you wish to provide your expertise into ongoing content.

1. Natural Language Toolkit (NLTK)

NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, etc. This library provides a practical introduction to programming for language processing. NLTK has been called “a wonderful tool for teaching and working in computational linguistics using Python,” and “an amazing library to play with natural language.”

Natural Language Toolkit - NLTK 3.4.5 documentation

NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.

2. TextBlob

TextBlob is a must for developers who are starting their journey with NLP in Python and want to make the most of their first encounter with NLTK. It basically provides beginners with an easy interface to help them learn most basic NLP tasks like sentiment analysis, pos-tagging, or noun phrase extraction.

TextBlob: Simplified Text Processing - TextBlob 0.15.2 documentation

Release v0.15.2. () TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.

3. NumPy

NumPy is a commonly used Python data analysis package. By using NumPy, you can speed up your workflow, and interface with other packages in the Python ecosystem, like scikit-learn, that use NumPy under the hood. NumPy was originally developed in the mid 2000s, and arose from an even older package called Numeric. This longevity means that almost every data analysis or machine learning package for Python leverages NumPy in some way.

NumPy - NumPy

NumPy is the fundamental package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object sophisticated (broadcasting) functions tools for integrating C/C++ and Fortran code useful 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.

4. SciPy

SciPy (pronounced “Sigh Pie”) is open-source software for mathematics, science, and engineering. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimisation. Together, they run on all popular operating systems, are quick to install, and are free of charge. NumPy and SciPy are easy to use, but powerful enough to be depended upon by some of the world’s leading scientists and engineers. If you need to manipulate numbers on a computer and display or publish the results, give SciPy a try! -

SciPy (pronounced "Sigh Pie") is a Python-based ecosystem of open-source software for mathematics, science, and engineering. In particular, these are some of the core packages: Large parts of the SciPy ecosystem (including all six projects above) are fiscally sponsored by NumFOCUS.

5. Pandas

Pandas is an extremely useful Python library, particularly for data science. Various Pandas functionalities make data preprocessing extremely simple.


pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

6. Matplotlib

Matplotlib is the O.G. of Python data visualization libraries. Despite being over a decade old, it's still the most widely used library for plotting in the Python community. It was designed to closely resemble MATLAB, a proprietary programming language developed in the 1980s.

Matplotlib: Python plotting - Matplotlib 3.1.3 documentation

Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.

7. Theano

Theano is a Python library for efficiently handling mathematical expressions involving multi-dimensional arrays (also known as tensors). It is a common choice for implementing neural network models. Theano has been developed in University of Montreal, in a group led by Yoshua Bengio, since 2008.

Welcome - Theano 1.0.0 documentation

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features: tight integration with NumPy - Use numpy.ndarray in Theano-compiled functions. transparent use of a GPU - Perform data-intensive computations much faster than on a CPU.

8. Keras

Keras has the capability and resources to run on top of popular deep learning libraries like TensorFlow, Theano, or CNTK. It also offers a relatively simple API that manages to also offer a lot of flexibility. This makes Keras easy to learn and easy to use. Isn’t that enough reason to start using Keras?

Home - Keras Documentation

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

9. TensorFlow

TensorFlow is an open-source software library for machine learning across a range of tasks. It is a symbolic math library, and also used as a system for building and training neural networks to detect and decipher patterns and correlations, analogous to human learning and reasoning. It is used for both research and production at Google often replacing its closed-source predecessor, DistBelief. TensorFlow was developed by the Google Brain team for internal Google use. It was released under the Apache 2.0 open source license on 9 November 2015. TensorFlow provides a Python API as well as C++, Haskell, Java, Go and Rust APIs.


An end-to-end open source machine learning platform

10. Scikit-learn

Scikit-learn is a Python library designed to provide an interface for developers to create machine learning software. When comparing scikit-learn with other Python libraries that broach similar subject matter, such as TensorFlow, it’s important to note that scikit-learn provides a higher-level interface and is set up with algorithms for machine-learning ready-to-use. For this reason, scikit-learn lands more squarely in the field of traditional machine learning.


"We use scikit-learn to support leading-edge basic research [...]" "I think it's the most well-designed ML package I've seen so far." "scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]."

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about 2 months ago by Duncan Carter