Python Frameworks for Data Science

Python is increasingly becoming the language of choice for technical applications such as Data Science and Machine Learning. Among its other desirable properties, its libraries have played a part in making work easier for many professionals whose jobs rely on numerical analysis and data manipulation.

In this guide, I will provide you with brief descriptions of some of the most commonly used Python frameworks for data science and machine learning including their common uses to give you a rough picture of what they entail. I also hope it opens your eyes and makes your life easier if you are having trouble with your project:


It is a Python library that handles most of the numerical computing done using Python. It provides support for multi-dimensional arrays and matrices and comes with an impressive collection of routines to operate the arrays. The ndarray object that deals with an n-dimensional array is the core functionality of NumPy.

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Unlike Python’s other built-in list data structure that is dynamic and allows different types of elements for input, these arrays take strictly homogeneous elemental entries.
At the same time, you cannot merely append a new element to an array in the same way that you do to the conventional Python array without creating a new array.

The routines that operate on the arrays include logical, shape manipulation, mathematical, sorting, input/output (I/O), random simulation, basic statistical operation, and many others.
NumPy has two key features that make it indispensable for numerical projects; broadcasting and vectorization.

  • Broadcasting is the element-by-element manner in which operations are performed. It encourages accuracy and simplicity. 
  • Vectorization is the property that allows you to perform advanced mathematical operations on vast sets of data using significantly fewer lines of code. This among other factors has made NumPy very popular with programmers interested in creating mathematics-heavy packages. 


SciPy is a Python library that is commonly used in applications that call for scientific computing by scientists, engineers, and other technical fields. It has modules for signal processing, integration, solving ODEs (Ordinary Differential Equations), linear algebra, Fourier Transforms, optimization, image processing, among others that handle the commonly-occurring scientific computing tasks.

SciPy uses NumPy arrays to take advantage of the ease with which mathematical functions can be performed on them. You can think of SciPy as a platform that adds more capabilities to NumPy.


TensorFlow is a platform that was created by the Google Brain Team with the sole purpose of making it easy for you to build Machine Learning (ML) models. Google use it in-house extensively for research and production, but it is also free and open-source.

Its architecture is flexible enough to allow you to deploy your projects on CPU, GPU, and TPU using any language. Therefore, you can deploy your projects on servers, desktop computers, mobile devices (iOS and Android), and other edge devices.

TensorFlow has many levels of abstraction to afford you the choice to pick one that caters to your needs. One of the most common APIs developers use is Keras – a high-level API that simplifies deep learning projects and simplifies machine learning (more on it to follow).

It is also the ideal environment for experimentation because it allows flexibility and control.
You can make use of the rich ecosystem of models and libraries TensorFlow provides to handle all kinds of ML training tasks irrespective of their size.


Keras is a Python-based API that can run on TensorFlow, Theano, or CNTK. It is essentially a neural-network library that is designed to facilitate quick experiments on neural networks.

It was not intended to work as a standalone framework but rather as an interface (working mainly with TensorFlow). When using Keras, you will find it easier to come up with deep learning models across various platforms due to its high-level abstractions.

Keras enables you to deploy deep models on the JVM, the web, and on smartphones (Android and iOS).
As a deep learning tool, it has the following merits:

  • It runs on multiple platforms and languages. It is supported on GPU and CPU.
  • It is versatile because it supports different kinds of networks; recurrent, convolutional, and combinations of both. 
  • It is user-friendly: It has simple APIs and it conducts routine functions without prompting extensive user action. It also makes it easier for you to spot and debug errors. 
  • It is extensible; you can add modules, classes, and functions making it ideal for research purposes. 


Matplotlib is mainly used for data visualization through plotting. Matplotlib is analogous to MATLAB in terms of application with the advantage of allowing you to program using Python which also means that it is open-source and free. (There is a Matplotlib module called Pyplot that has a similar interface to MATLAB if you want to shift quickly)

You can use Matplotlib to generate histograms, bar charts, power spectra, plots, and many other visualization tools in a simple, and convenient way. The advantage of using it is that you don’t need many lines of code to achieve that.

It can produce beautiful figures in interactive environments and hardcopy formats across platforms. It has an object-oriented API that it uses to embed 2D plots in applications using GUI toolkits.


Pandas is a library that is used for data computation and analysis. It is open source. It is extensively used for data wrangling which explains its popularity when any form of data analysis is involved.
Pandas creates data frames from other data structures. A data frame is an element that has rows and columns that is easier to work with than data in its other raw forms (For instance, when performing a function such as a list comprehension).

You can use Pandas for operations like data filtration, data merging, date range generation, column insertion, and deletion, reshaping and pivoting data sets, among others.

Panda frequently uses NumPy arrays for its operations which means that you will need to install that library first. You can also couple it with other numerical analysis-based libraries like Matplot lib if your project demands the added functionalities or for the sake of convenience.


Bokeh is a sensational visualization library for Python that targets modern web browsers. It is designed to provide elegant, concise construction of versatile graphics. Bokeh makes data scientists life easier with its high-performance interactivity over very large datasets.

With bokeh, you can generate interactive graphs, dashboards and data application. It will provide you a whole new dimension to the performance testing reports. The visualization will not only look pretty, but they empower you to illustrate concepts in an interactive way.

The best part about bokeh  is how easy it is to install and use. You can make simple yet useful interactive graphs painlessly because it is equipped with pre-made plot templates. And if you want more than that, there are always room for customization.

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Scikit-learn is a Python library for machine learning applications built on SciPy. Scikit-learn is an open source project with a BSD license.

It provides straightforward and effective tools to perform many of the standard machine-learning tasks. This makes users can rely on one library to carry out different algorithms such as classification, model selection, regression, pre-processing, clustering, and dimensionality reduction.

One of the main benefits of  Scikit-learn is that it has excellent documentations. Most of modules are accompanied with narrative examples and sample scripts that run on small data sets. Most importantly, Scikit-learn is easy-to-use that allows users to perform a multitude of processes without frustration.


Python is one of the most ubiquitous programming languages in software engineering today because you can use it for research, development, and production. From what we have just covered, you can now see that python frameworks for Data Science have made previously complex operations simpler.

Since Python is quite popular and open-source, there is no shortage of frameworks for you to choose from. Your solid understanding of each of them will help you in your technical applications as you seek to advance your career or venture to change the world.