6 Essential Python Libraries for Python Programming

Python is a high-level, general-purpose programming language that has become one of the leading names in the programming community. It ranges in the ability from developing simplistic applications to carrying out complex, mathematical calculations with an equal level of ease.

Being one of the leading programming languages means that there is no scarcity of great frameworks and libraries available to toy with. A programming language library is simply a set of modules and functions that eases some specific operations using the programming language.

So, here are 6 essential Python libraries for Python programming that every Python developer or aspirant must know about:

1. Keras

Type – Neural Networks Library

Initial Release – March 2015

Written in Python, Keras is an open-source neural-network library. Designed especially for enabling fast experimentation with deep neural networks, Keras prioritizes for being user-friendly, extensible, and modular.

In addition to providing an easier mechanism for expressing neural networks, Keras also offers some of the best features for compiling models, processing datasets, and visualizing graphs. On the backend, Keras makes use of either Theano or TensorFlow.

Due to the fact that Keras creates a computation graph by using backend infrastructure and then uses it to perform operations, it is slower than other machine learning libraries. Nonetheless, all models in Keras are portable.

Highlights:

• Easy to debug and explore as it is completely written in Python

• Features several implementations of the commonly used neural network building blocks such as activation functions, layers, objectives, and optimizers

• Incredible expressiveness and flexibility makes it ideal for innovative research

• Offers several pre-processed datasets and pre-trained models like Inception, MNIST, ResNet, SqueezeNet, and VGG

• Provides support for almost all neural networks models, including convolutional, embedding, fully connected, pooling, and recurrent. Moreover, these models can be combined to develop even more complex models

• Runs smoothly on both CPU as well as GPU

Applications:

• Already used by Netflix, Square, Uber, and Yelp

• For deep learning research. Adopted by researchers at CERN and NASA

• Popular among startups developing products based on deep learning

2. NumPy

Type – Technical Computing Library

Initial Release – 1995 (As Numeric)

2006 (As NumPy)

NumPy was created by Travis Oliphant in 2005 by incorporating features of the rival Numarray library into the Numeric library and applying extensive modifications. The free and open-source library has several contributors from all over the globe.

One of the most popular machine learning libraries in Python, TensorFlow and several other libraries make use of the NumPy Python library internally in order to perform multiple operations on tensors.

Highlights:

• Active community support

• Completely free and open source

• Complex matrix operations, such as matrix multiplication

• Interactive and super easy to use

• Eases complex mathematical implementations

• Easy to code with digestible concepts

Applications:

• For carrying out complex mathematical computations

• For expressing images, sound waves, and other forms of binary raw streams as an array of real numbers in N-dimensional

• For machine learning projects

3. Pillow

Type – Image Processing and Manipulation Library

Initial Release – 1995 (As Python Imaging Library or PIL)

2011 (As Pillow)

Pillow is a Python library that is almost as old as the programming language for which it was developed. In reality, Pillow is a fork for the PIL (Python Imaging Library). The free to use Python library is a must-have for opening, manipulating, and saving a diverse range of image files.

Pillow has been adopted as the replacement for the original PIL in several Linux distributions, most notably Debian and Ubuntu. Nonetheless, it is available for MacOS and Windows too.

Highlights:

• Adds text to images

• Image enhancing and filtering, including blurring, brightness adjustment, contouring, and sharpening

• Masking and transparency handling

• Per-pixel manipulations

• Provides support for a galore of image file formats, including BMP, GIF, JPEG, PNG, PPM, and TIFF. Provides support for creating new file decoders in order to expand the library of file formats accessible

Applications:

• For image manipulation and processing

4. PYGLET

Type – Game Development Library

Initial Release – April 2015

A multi-platform windowing and multimedia library for Python, PYGLET is a popular name when it comes to game development using Python. In addition to games, the library is developed for crafting visually rich applications.

In addition to supporting windowing, PYGLET provides support for loading images and videos, playing sounds and music, OpenGL graphics, and user interface event handling.

Highlights:

• Leverage multiple windows and multi-monitor desktops

• Load images, sound, and video in almost all formats

• No external dependencies and installation requirements

• Provided under the BSD open-source license, therefore free to be used for personal as well as commercial uses

• Provides support for both Python 2 and Python 3

Applications:

• For developing visually rich applications

• For game development

5. Requests

Type – HTTP Library

Initial Release – February 2011

A Python HTTP library, Requests is aimed at making HTTP requests simpler and more human-friendly. Developed by Kenneth Reitz and a few other contributors, Requests allows sending HTTP/1.1 requests without requiring human intervention.

From Nike and Spotify to Amazon and Microsoft, dozens of big organizations make use of Requests internally to better deal with the HTTP. Written completely in Python, Requests is available as a free open-source library under the Apache2 License.

Highlights:

• Automatic content decoding

• Basic/digest authentication

• Browser-style SSL verification

• Chunked requests and connection timeouts

• Provides support for .netrc and HTTP(S) proxy

• Sessions with cookie persistence

• Unicode response bodies

Applications:

• Allows sending HTTP/1.1 requests using Python and add content such as headers, form data, and multipart files

• For automatically adding query strings to URLs

• For automatically form-encode the POST data

6. TensorFlow

Type – Machine Learning Library

Initial Release – November 2015

TensorFlow is a free and open-source Python library meant to accomplish a range of dataflow and differentiable programming tasks. Although a symbolic math library, TensorFlow is one of the most widely used Python machine learning libraries.

Developed by Google Brain for internal use, the library is used for both commercial and research purposes by the tech mogul.

Tensors are N-dimensional matrices that represent data. The TensorFlow library allows writing new algorithms involving a grand number of tensor operations.

Because neural networks can be expressed as computational graphs, they can be easily implemented using the TensorFlow library as a series of operations on tensors.

Highlights:

• Allows visualizing each and every part of the graph

• Completely free and open source

• Easily trainable on CPU and GPU for distributed computing

• Humongous community support

• Offers flexibility in its operability. Parts that are required the most can be made standalone

• Supports training multiple neural networks and multiple GPUs to make efficient models on large-scale systems

• Uses techniques to the likes of XLA for hastening linear algebra operations

Applications:

• For machine learning projects

• For neural networks projects

• In automated image-captioning software like DeepDream

• Machine learning in Google products, such as Google Photos and Google Voice Search

That finishes the list of the 6 essential Python libraries for Python programming. Which libraries should/shouldn’t have made it to the list? Let us know in your comments.

Check out some of the best Python tutorials now!