The Most Important Libraries For Python 🐍

As such, the programming language has numerous applications and has been widely adopted by all sorts of communities, from data science to business.

These communities value Python for its precise and efficient syntax, relatively flat learning curve, and good integration with other languages (e.g. C/C++).

The language’s popularity has resulted in a wide range of Python packages being produced for data visualisation, machine learning, natural language processing, complex data analysis, and more.

Why should you use Python libraries for data science?

Python has become the go-to language in data science and it’s one of the first things recruiters will probably search for in a data scientist’s skill set.

It consistently ranks top in the global data science surveys and its widespread popularity keeps on increasing. As a matter of fact, a recent survey revealed that roughly 65.8% of machine learning engineers and data scientists use Python regularly—way more often than SQL (44%) and R (31%).

7 essential Python libraries for data science, machine learning, and more

1. Astropy Astropy is a collection of packages designed for use in astronomy. The core Astropy package contains functionality aimed at professional astronomers and astrophysicists, but may be useful to anyone developing software for astronomy.

2. Biopython Biopython is a collection of non-commercial Python tools for computational biology and bioinformatics. It contains classes to represent biological sequences and sequence annotations. The library can also read and write to a variety of file formats.

3. Bokeh Bokeh is a Python interactive visualisation library that targets modern web browsers for presentation. It can help anyone who wishes to quickly and easily create interactive plots, dashboards, and data applications. The purpose of Bokeh is to provide elegant, concise construction of novel graphics in the style of D3.js, but also deliver this capability with high-performance interactivity over very large or streaming datasets.

4. Cubes 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.

5. Dask Dask is a flexible parallel computing library for analytic computing, composed of two components:

1. dynamic task scheduling optimised for computation and interactive computational workloads;

2. Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces such as NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments.

6. Mahotas Mahotas is a computer vision library designed for image processing. It uses algorithms implemented in C++ and operates on top of NumPy for an easy-to-use, clean, and fast Python interface. Mahotas provides various image processing functions like thresholding, convolution, and Sobel edge detections.

7. Statsmodels Statsmodels is a part of the Python scientific stack oriented toward data science, data analysis, and statistics. It is built on top of NumPy and SciPy, and integrates with Pandas for data handling. Statsmodels supports users in exploring data, estimating statistical models, and performing statistical tests.

We hope this article made finding the right Python library for data science a lot easier for you. However, you can always reach out to us if you have any questions—we’ll be glad to answer them.