6 Essential Data Science Packages for Python
Written by Amna on March 03 2020

Thanks to data mining and big data, the interest in data science has seen a remarkable increase in the last few years. Python remains the most popular choice of language when it comes to data science and machine learning. As the language is already designed to facilitate data analysis and visualization it is excellent for custom big data solutions, artificial intelligence (AI) and natural language processing tasks.

Here are just some examples of companies using Python for their data science needs that we have covered in another post: DARPA ( Defense Advanced Research Projects Agency), Bank of America, Facebook, UC Berkeley and Netflix.

Since it’s the language of choice for machine learning, here’s our very own Python-centric list of six essential data science packages:


Data comes more often than not in huge Excel sheets with a large number of rows and columns. Analyzing the data contained within those cells and making decisions based on that usually requires high computational power and is considered to be very time-consuming. Python solves it via the use of parallel processing using libraries such as Pandas. Parallel processing in the most efficient way is done with the help of GPU which helps to cut down the time consuming part by double digit times - general purpose processors may have 4 or 8 cores while NVIDIA GPU may have thousands of cores and a pipeline that supports parallel processing on thousands of threads. Pandas is a powerful and flexible data analysis library written in Python. While not strictly a machine learning library, it’s well-suited for data analysis and manipulation for large data sets. 


Machine learning involves heavy mathematical tasks such as calculus, probability, and matrix operations over thousands of rows and columns making it a very high computational technique. All this can be made simple and cost-efficient with the help of scikit-learn machine learning library for Python. Scikit-Learn is a Python module for machine learning built on top of SciPy and NumPy. It started as a Google Summer of Code project and grew to over 20,000 commits and more than 90 releases. Companies such as J.P. Morgan and Spotify use it in their data science work!

Because Scikit-Learn has such a gentle learning curve, even the people on the business side of an organization can use it. For example, a range of tutorials on the Scikit-Learn website show you how to analyze real-world data sets. If you’re a beginner and want to pick up a machine learning library, Scikit-Learn is the one to start with.

Here’s what it requires:

  • Python 3.5 or higher.
  • NumPy 1.11.0 or higher.
  • SciPy 0.17.0 or higher.


TensorFlow is one of the most famous machine learning libraries for some very good reasons. It specializes in numerical computation using dataflow graphs. Originally developed by Google Brain, TensorFlow is open sourced. It uses dataflow graphs and differentiable programming across a range of tasks, making it one of the most highly flexible and powerful machine learning libraries ever created.


NumPy is the fundamental package needed for scientific computing with Python. It’s an excellent choice for researchers who want an easy-to-use Python library for scientific computing. In fact, NumPy was designed for this purpose; it makes array computing a lot easier.

Originally, the code for NumPy was part of SciPy. However, scientists who need to use the array object in their work were having to install the large SciPy package. To avoid that, a new package was separated from SciPy and called NumPy.


After any data has been analyzed, it needs to be presented in an easily understandable format. That is where pictographic representation or visualization of the data comes into play. Seeing plain numbers stretching from one end of the screen to the next will soon turn any vision blurry and surely makes it difficult to derive any meaningful insights from the existing data set. That is why it is necessary to present the data in the form of figures such as pie-charts, diagrams and bar graphs. That is where libraries such as Matplotlib make the hard things easy and the impossible into possible.

Matplotlib is a Python 2D plotting library that makes it easy to produce cross-platform charts and figures. It’s ideal for publication-quality charts and figures across platforms.


SciPy is a gigantic library of data science packages mainly focused on mathematics, science, and engineering. If you’re a data scientist or engineer who wants the whole kitchen sink when it comes to running technical and scientific computing, you’ve found your match with SciPy.

Since it builds on top of NumPy, SciPy has the same target audience. It has a wide collection of sub packages, each focused on niches such as Fourier transforms, signal processing, optimizing algorithms, spatial algorithms, and nearest neighbor. Essentially, this is the companion Python library for your typical data scientist.

As far as requirements go, you’ll need NumPy if you want SciPy. But that’s it.


At Thorgate we use Python for a variety of reasons, one being the great data science packages for Python. Smart developers are choosing Python as their go-to programming language for the myriad of benefits that make it particularly suitable for machine learning and deep learning projects. 

If you’re interested in some of the projects we’ve built using Python, do read about:
1. Coop: using Python we helped Coop to bring their extensive e-shop and assembly solution out to the market with only 8 months. Coop has more than 400 physical shops all over Estonia and now they sell more than their largest supermarkets also via their e-channels.

2. Krah Pipes: using Python we automated the whole factory for Krah Pipes. Now production planning, cooling station operations, quality control and reporting are all digital. At one point we needed help with all that and we were easily able to find the suitable partner for that

3. Vaheladu: using Python support libraries we built Vaheladu - the first logging management solution in Estonia. Vaheladu has a neat codebase and it is easily maintainable, although it is pretty extensive and we started building this product already more than 6 years ago.

Do you have a project you would like to build, scale or customize using Python? Write to me at!

This article sources part of the information from Kite which is a plugin for your IDE that uses machine learning to give you useful code completions for Python.

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