10 Essential Data Science Packages for Python

photo by Creative Art

Data science has been extremely popular in the last several years and the interest in this field has risen a lot. Even though there are a lot of programming languages that can be used for machine learning and data science, Python is by far the most popular. 

Here you can find a list of the ten most popular data science packages for Python. 


With a gentle learning curve, Scikit-Learn is a Python module that is widely used. It has more than 90 releases and is used by companies like Spotify and J.P.Morgan for their data science work. There are several tutorials that will help you grasp the basics even if you are a beginner and want to learn how to analyze data. Scikit-Learn is built on top of NumPy and SciPy and requires Python 3.5 or higher, SciPy 0.17.0 or higher, NumPy 1.11.0, or higher.


PyTorch is great for engineers or academics who want better performance. It can be used for deep learning, language processing, or to compute tensors faster using a strong GPU. It has a bit more complicated installation and requires Python 3.6 or higher, Conda 4.6.0 or higher. 


This package is best for image recognition and image processing. You need to have at least some knowledge of machine learning but still, Caffe offers a relatively gentle learning curve. Requirements will depend on the specifics of your operating system.


One of the most popular, very powerful, and flexible machine learning libraries that uses dataflow graphs for numerical computation. Originally created by Google Brain, it is open-source today. Very useful especially if you need to process large data quickly. 



An open-source library for deep-learning development, Theano is most suitable for high-speed computation. Even though it stopped major developments, you should consider studying it to see how the innovations it provided later were implemented in other libraries. 


A flexible and powerful data analysis library that is best for large data analysis and manipulation. It is quite easy to learn and offers very useful features for data analysis. Pandas requires NumPy version 1.12.0 or higher, Setuptools version 24.2.0 or higher, Python dateutil 2.5.0 or higher.


Keras can run on top of other frameworks and is created for fast experimentation. Offers fast and easy prototyping and comes with an API that is easy to use. 


The foundational package when it comes to data science with Python. Great for researchers that are looking for a library that is easy to use and makes scientific computing easier. NumPy requires Python 2.6.x, 2.7.x, 3.2.x, or higher. 


A 2D plotting library that is perfect for data visualization and the creation of cross-platform figures and charts. It requires Python 3 or higher. 


A large data science library with packages focused on science, mathematics, and engineering mostly. Perfect for data scientists and engineers that want to have the whole package when it comes to scientific and technical computing. The only requirement for SciPy is to have NumPy.