Popular Python Libraries for Data Science, Machine Learning and More

by September 23, 2020 0 comments

Data Science

Python was first released in 1991 by Guido Van Rossum as a programming language

Python serves many purposes in diverse communities. Starting from data science to business, Python is familiar for its precise and efficient syntax, relatively flat learning curve, and good integration with other languages. Pythons’ numerous applications serve the purpose of thousands of entities.

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. The programming language has high-level built-in data structures, combined with dynamic typing and dynamic binding. It makes Python very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms and can be freely distributed. Programmers often get attracted to python for the increased productivity it provides.

Here are some domains that the python is being used extensively,

• Web and Internet development

• Scientific and numeric

• Education

• Desktop GUIs

• Software development

• Business applications

The footsteps of python go back to 1980s. But officially it was first released in 1991 by Guido Van Rossum, who created the programming language. The motto of python is to construct an object-oriented approach that aims to help programmers write clear logical code on small and large-scale projects. Python 2.7.18 is the recent release used for coding.

The language’s fame has concluded in a series of python packages being produced for data visualizationmachine learningNLP, complex data analysis, etc. Here is the collection of the most popular python libraries.



Astropy is a community effort to develop a core package for astronomy using the Python programming language. It improves usability, interoperability, and collaboration between astronomy packages.

The core Astropy package contains functionality aimed at professional astronomers and astrophysicists but may be useful to anyone developing astronomy software.



Biopython is a set of freely available tools for biological computation written in Python by an international team of developers. It is a distributed collaborative effort to develop Python libraries and applications which address the needs of current and future work in bioinformatics.

The collection contains classes to represent biological sequences and sequence annotation. The library also provisions to read and write to a variety of file formats.



Bokeh is a modern web library browser that provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. It acts as a quick solution to people who want to make interactive plots, dashboards, and data applications.



Cubes is a light-weight Python framework and set of tools for the development of reporting and analytical applications, Online Analytical Processing (OLAP), multidimensional analysis, and browsing of aggregated data. It is part of Data Brewery.

Cubes are meant to be used by application builders that want to provide analytical functionality.



Dask features parallel computing in Python. It is composed of two parts,

• Dynamic task scheduling optimized for computing which also works on interactive computational workloads.

• Big Data collection extends common interfaces on parallel arrays, data frames to larger-than-memory or distributed environment. These parallel collections run on top of dynamic task schedulers.



Distributed Evolutionary Algorithms in Python (DEAP) is a novel evolutionary computational framework for rapid prototyping and testing of ideas.

DEAP assimilates with data structures and tools required to implement most common evolutionary computation techniques, such as genetic algorithms, genetic programming, evolution strategies, particle swarm optimization, differential evolution, and estimation of distribution algorithms. It works in perfect harmony with the parallelization mechanism such as multiprocessing and SCOOP.



DataMelt is software for numeric computation, mathematics, statistics, symbolic calculations, data analysis, and data visualization. It can be used with several scripting languages, including Python/Jython, BeanShell, Groovy, Ruby, and Java.

DMelt is a fully object-oriented Java virtual machine regardless of computer architecture. It uses the python language to call Java classes for numerical and statistical computation, and data and mathematical visualization.



Graph-tool is a python model used for manipulation and statistical analysis of graphs. It is contrary to most other python modules with similar functionality.



Matplotlib is a 2D plotting library for Python programming language. Matplotlib produces publication-quality figures in a variety of hard-copy formats and interactive cross-platform environment.

Matplotlib was written by John D Hunter. It allows to generate plots, histograms, power spectra, bar charts, error charts, scatter plots, etc.



Mlpy is a python module for machine learning build on top of NumPy/SciPy and the GNU Scientific Libraries. It provides a wide range of state-of-the-art machine learning methods supervised and unsupervised problems. Mlpy is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability, and efficiency.

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