Best libraries for Python 3

Python is Here to Stay!

Python has become one of the most popular programming languages on the planet. Why? Let us count the ways. First off, Python is one of the easiest languages to learn. With strict adherence to simplicity and reusability, Python makes it possible to create more with less. Python is less verbose than most other languages and requires less understanding of programming and more understanding of the English language.

Python is also a high-level language, meaning you don’t have to deal with the low-level bits. And since it’s a very flexible language, it’s possible to use it for standard programming or even scripting, which makes it possible to integrate the language into a number of different projects, from web applications to backend servers, standard apps, and scripted services.

But one very important reason that makes Python stand out is the vast amount of libraries that are available for the language. These libraries make it such that Python is capable of doing just about anything you need.

Let’s take a look at some of the best libraries available for Python 3.

But, before we do so, let’s define exactly what a library is.

Python Software Development by the Best Python Developers

What is a Library?

A library is a collection of objects (such as functions) that can be added to an application and called from within the application code when necessary. Think of libraries as supplying reusable chunks of code. This relieves engineers from having to constantly rewrite something that was already written and functions well.

Write Less, Create More.

Unlike other programming languages, such as C and C++, Python libraries do not pertain to any specific context. Instead, Python libraries describe a collection of core modules that extend the functionality of the language. 

It’s also important to know that libraries have to be installed and imported into Python. This is done from the command line like so:

• To install a Python 3 library on a Debian-based Linux distribution, you'd issue a command like:

sudo apt-get install python3-LIBRARY -y:
Where LIBRARY is the name of the library to be installed.

• To import the library, you'd first have to access the Python console with the command:

python3

• Once at the console, you can import the library with a command like:

import LIBRARY
Where LIBRARY is the name of the library to be imported.

But what are the more important Python libraries you should know about (and be prepared to use)? Let’s take a look at some of the more popular Python libraries.

Python Libraries

Matplotlib

Matplotlib makes it possible for Python to create static, animated, and interactive visualizations for data. This library is mostly used for mathematical and scientific applications that require more than a single axis to represent data. With this module, you can build multiple plots at once and manipulate different data characteristics. Other features of Matplotlib include:

  • Create publication-ready figures.
  • Can be used with other Python toolkits (such as Python Scripts, IPython Shells, and Jupyter Notebook).
  • Can be integrated with a number of third-party applications.
  • Can be used with web application servers.

Pillow

Pillow is a fork of PIL, the Python Image Library. This library adds image processing capabilities to the Python interpreter. Pillow was originally based on the code-structure of PIL but eventually migrated to a more improved and user-friendly tool. Anytime you need to work with images in Python, you’ll use Pillow. This library features:

  • Support for a number of file formats, such as PDF, WebP, PCX, PNG, JPEG, GIF, PSD, WebP, PCX, GIF, IM, EPS, ICO, and BMP.
  • Thumbnail creation.
  • Numerous image filters, such as FIND_EDGES, DETAIL, SMOOTH, BLUR, CONTOUR, SHARPEN, and SMOOTH_MORE.
  • A robust and supportive community.

NumPy

NumPy is a library that adds support for large, multi-dimensional arrays and matrices. If you’re looking to use Python with big data, you’ll need this library. NumPy uses a high-level syntax, so it’s accessible to any Python programmer, regardless of experience level. This library features:

  • Supports massive amounts of data.
  • Supports general and masked arrays.
  • Includes functions for the manipulation of logical shapes, discrete Fourier transform, and general linear algebra.
  • Can be integrated with other programming languages.
  • Supports a wide range of hardware and platforms.
  • The de facto standard for array compute.

OpenCV Python

OpenCV Python is a massive library for computer vision, machine learning, and image processing. This library monitors all functions that are required to read and write images. Most Python programmers will agree that OpenCV is one of the most challenging libraries to learn. Part of the reason for that is because OpenCV can do so much. If you’re looking to use Python in Artificial Intelligence, you’ll be heavily using OpenCV Python. This library features:

  • Ability to rebuild, interpret, and comprehend a 3D environment from a respective 2D environment.
  • Feature and object detection.
  • Video analysis.
  • Support for machine learning.
  • Support for computational photography.
  • Save and capture any moment from a video and analyze its properties.

Pandas

Pandas is a powerful, flexible, data analysis, and manipulation tool. Because of its ability to translate complex operations with data, this library is in heavy rotation for Machine Learning, though it can be used for other fields, too. In fact, most consider Pandas a must-use for data science. This library features:

  • Fast, expressive, flexible data structures.
  • Support for structured and time-series data.
  • Support for re-indexing, Iteration, Sorting, Aggregations, Concatenations, and Visualizations.
  • Provides Series and DataFrames.
  • Smart alignment and indexing.
  • Support for both reading and writing data in different web services, data-structures, and databases.
  • Supports file formats such as JSON, Excel, CVS, and HDF5.

SciPy

SciPy is one of the core packages that make up the SciPy stack (the Python library used for mathematics, science, and engineering). The SciPy library provides user-friendly and efficient routines for numerical integration, interpolation, optimization, linear algebra, and statistics. This library features:

  • Supports NumPy arrays for general data structure.
  • Support for 1-d polynomials.
  • Supports co-efficient arrays.  

TensorFlow

TensorFlow is one of the main libraries used in Machine Learning. In fact, the TensorFlow library is used in almost every Google app focused on machine learning. TensorFlow is a symbolic math library with the main purpose of training and inference of deep neural networks. Features of TensorFlow include:

  • Easy visualization of every part of a graph.
  • Capable of handling large numbers of tensors.
  • Modular, so you can only use the parts you need.
  • Easily trainable on CPUs, GPUs, and neural networks.

Conclusion

This only scratches the surface of the available Python libraries. In fact, you’ll find libraries for all types of functionality, such as cryptography, databases, game development, audio, ID3 handling, Java, networking, plotting, web development, and XML processing. To find out more about the available libraries, check out the Python Useful Modules page.



Related Pages

Get a Dedicated Team Powered By Technology and Driven By Talent.

Clients' Experiences

Ready to work with the Top 1% IT Talent of the market and access a world-class Software Development Team?

Scroll to Top

By continuing to use this site, you agree to our cookie policy.