Geekflare is supported by our audience. We may earn affiliate commissions from buying links on this site.
In Development Last updated: July 14, 2023
Share on:
Invicti Web Application Security Scanner – the only solution that delivers automatic verification of vulnerabilities with Proof-Based Scanning™.

This article mentions and expounds on some of the best python libraries for Data scientists and the machine learning team.

Python is an ideal language famously used in these two fields mainly for the libraries it offers.

This is because of the Python libraries’ applications like data input/output I/O and data analysis, among other data manipulation operations that data scientists and machine learning experts use to handle and explore data.

Python libraries, what are they?

A Python library is an extensive collection of built-in modules containing pre-compiled code, including classes and methods, eradicating the need for the developer to implement code from scratch.

Importance of Python in Data Science and Machine Learning

Python has the best libraries for use by Machine learning and Data Science experts.

Its syntax is easy, thus making it efficient to implement complex machine learning algorithms. Moreover, the simple syntax shortens the learning curve and makes understanding easier.

Importance of Python in Data Science and Machine Learning

Python supports rapid prototype development and smooth testing of applications as well.

Python’s large community is handy for data scientists to readily seek solutions to their queries when needed.

How useful are Python libraries?               

Python libraries are instrumental in creating applications and models in machine learning and data science.

These libraries go a long way in helping the developer with code reusability. Therefore, you can import a relevant library that implements a specific feature within your program other than reinventing the wheel.

Python Libraries used in Machine Learning and Data Science                          

Data Science experts recommend various Python libraries that data science enthusiasts must be familiar with. Depending on their relevance in the application, the Machine learning and Data Science experts apply different Python libraries categorized into libraries for deploying models, mining and scraping data, data processing, and data visualization.

This article identifies some commonly used Python libraries in Data Science and Machine learning.

Let’s look at them now.


Numpy Python library, also Numerical Python Code in full, is built with well-optimized C code. Data Scientists prefer it for its profound mathematical calculations and scientific computations.



  1. Numpy has a high-level syntax that makes it easy for programmers with experience.
  2. The library’s performance is relatively high because of the well-optimized C code that makes it up.
  3. It has numerical computing tools, including Fourier transform capabilities, Linear Algebra, and Random Number Generators.
  4. It is open source, thus allowing for numerous contributions by other developers.

Numpy comes with other comprehensive features like vectorization of mathematical operations, indexing, and key concepts in implementing arrays and matrices.


Pandas is a famous library in Machine Learning that provides high-level data structures and numerous tools to analyze massive datasets effortlessly and effectively. With very few commands, this library can translate complex operations with data.


Numerous inbuilt methods that can group, index, retrieve, split, restructure data, and filter sets before inserting them into single and multidimensional tables; makes up this library.

Pandas library’s main features

  1. Pandas make labeling the data into the tables easy and automatically align and index the data.
  2. It can quickly load and save data formats like JSON and CSV.

It is highly efficient for its good data analysis functionality and high flexibility.


Matplotlib 2D graphical Python library can easily handle data from numerous sources. The visualizations it creates are static, animated, and interactive that the user can zoom in on, thus making it efficient for visualizations and creating charts. It also allows customization of the layout and visual style.


Its documentation is open source and offers a profound collection of tools required for implementation.

Matplotlib imports helper classes to implement year, month, day, and week, making it efficient to manipulate time series data.        


If you are considering a library to help you work with complex data, Scikit-learn should be your ideal library. Machine learning experts widely use Scikit-learn. The library is associated with other libraries like NumPy, SciPy, and matplotlib. It offers both supervised and unsupervised learning algorithms that can be used for production applications.


Features of Scikit-learn Python library

  1. Identifying object categories, for example, using algorithms like SVM and random forest in applications like image recognition.
  2. Prediction of continuous-valued attribute an object associates with a task called regression.
  3. Feature extraction.
  4. Dimensionality reduction is where you reduce the considered number of random variables.
  5. Clustering of similar objects into sets.

Scikit-learn library is efficient in feature extraction from text and image data sets. Moreover, it is possible to check for the accuracy of supervised models on unseen data. Its numerous available algorithms make possible data mining and other machine learning tasks.


SciPy (Scientific Python Code) is a machine learning library that provides modules applied to mathematical functions and algorithms which are widely applicable. Its algorithms solve algebraic equations, interpolation, optimization, statistics, and integration.


Its main feature is its extension to NumPy, which adds tools to solve the mathematical functions and provides data structures like sparse matrices.

SciPy uses high-level commands and classes to manipulate and visualize data. Its data processing and prototype systems make it an even more effective tool.

Moreover, SciPy’s high-level syntax makes it easy for programmers of any experience level to use.

SciPy’s only disadvantage is its sole focus on numerical objects and algorithms; therefore unable to offer any plotting function.


This diverse machine learning library efficiently implements tensor computations with GPU acceleration, creating dynamic computational graphs and automatic gradients calculations. The Torch library, an open-source machine learning library developed on C, builds the PyTorch library.


Key features include:

  1. A provision of frictionless development and smooth scaling because of its good support on major cloud platforms.
  2. A robust ecosystem of tools and libraries supports computer vision development and other areas like Natural Language Processing (NLP).
  3. It provides a smooth transition between eager and graph modes using Torch Script while it uses the TorchServe to speed up its path to production.
  4. The Torch distributed backend allows distributed training and performance optimization in research and production.

You can use PyTorch in developing NLP applications.


Keras is an open-source machine learning Python library used to experiment with deep neural networks.                          


It is famous for offering utilities that support tasks like model compiling and graphs visualizations, among others. It applies Tensorflow for its backend. Alternatively, you can use Theano or neural networks like CNTK in the backend. This backend infrastructure helps it to create computational graphs used to implement operations.

Key Features of the library

  1. It can efficiently run on both Central Processing Unit and Graphical Processing Unit.
  2. Debugging is easier with Keras because it is based on Python.
  3. Keras is modular, thus making it expressive and adaptable.
  4. You can deploy Keras anywhere by directly exporting its modules to JavaScript to run it on the browser.

Applications of Keras include neural network building blocks like layers and objectives, among other tools that facilitate working with images and text data.


Seaborn is another valuable tool in statistical data visualization.


Its advanced interface can implement attractive and informative statistical graphics drawings.


Plotly is a 3D web-based visualization tool built on the Plotly JS library. It has wide support for various chart types such as line charts, scatter plots, and box types sparklines.


Its application includes creating web-based data visualizations in Jupyter notebooks.

Plotly is suitable for visualization because it can point out outliers or abnormalities in the graph with its hover tool. You can also customize the graphs to fit your preference.

On Plotly’s downside, its documentation is outdated; therefore, using it as a guide can be difficult for the user. Moreover, it has numerous tools the user should learn. It may be challenging to keep track of all of them.

Features of Plotly Python library

  1. The 3D charts it avails allow multiple points of interaction.
  2. It has a simplified syntax.
  3. You can maintain your code’s privacy while you still share your points.


SimpleITK is an image analysis library that offers an interface to Insight Toolkit(ITK). It is based on C++ and is open-source.


Features of SimpleITK library

  1. Its image file I/O supports and can convert up to 20 image file formats like JPG, PNG, and DICOM.
  2. It provides numerous image segmentation workflow filters, including Otsu, level sets, and watersheds.
  3. It interprets images as spatial objects rather than an array of pixels.

Its simplified interface is available in various programming languages like R, C#, C++, Java, and Python.


Statsmodel estimates statistical models, implements statistical tests and explores statistical data using classes and functions.


Specifying models use R-style formulas, NumPy arrays, and Pandas data frames.


This open-source package is a preferred tool for retrieving(scraping) and crawling data from a website. It is asynchronous and, therefore, relatively fast. Scrapy has architecture and features that make it efficient.

On the con side, its installation differs for different Operating Systems. Furthermore, you cannot use it on websites built on JS. Also, it can only work with Python 2.7 or later versions.

Data Science experts apply it in data mining and automated testing.


  1. It can export feeds in JSON, CSV, and XML and store them in multiple backends.
  2. It has built-in functionality to collect and extract data from HTML/XML sources.
  3. You can use a well-defined API to extend Scrapy.


Pillow is a Python imaging library that manipulates and processes images.

It adds to the Python interpreter image processing features, supports various file formats, and offers an excellent internal representation.


Data stored in basic file formats can easily be accessed thanks to Pillow.

Wrapping Up

That sums up our exploration of some of the best Python libraries for data scientists and machine learning experts.

As this article shows, Python has more useful machine learning and data science packages. Python has other libraries you can apply in other areas.

You may want to know about some of the best data science notebooks.

Happy learning!

  • Neema Muganga
    Neema is an experienced technical author and proficient web developer. She loves to teach about development through writing and code.
Thanks to our Sponsors
More great readings on Development
Power Your Business
Some of the tools and services to help your business grow.
  • Invicti uses the Proof-Based Scanning™ to automatically verify the identified vulnerabilities and generate actionable results within just hours.
    Try Invicti
  • Web scraping, residential proxy, proxy manager, web unlocker, search engine crawler, and all you need to collect web data.
    Try Brightdata
  • is an all-in-one work OS to help you manage projects, tasks, work, sales, CRM, operations, workflows, and more.
    Try Monday
  • Intruder is an online vulnerability scanner that finds cyber security weaknesses in your infrastructure, to avoid costly data breaches.
    Try Intruder