Python Libraries are a set of useful functions that eliminate the need for writing codes from scratch.There are over 137,000 python libraries present today.
Python libraries play a vital role in developing machine learning, data science, data visualization, image and data manipulation applications and more. Let us start with a brief introduction to Python Programming Language and then directly dive into the most popular Python libraries.
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The probability that you must have heard of ‘Python’ is outright. Guido Van Rossum’s brainchild – Python, which dates back to the ’80s has become an avid game changer. It is one of the most popular coding languges today and is widely used for a gamut of applications. In this article, we have listed 34 Open Source Python Libraries you should know about.
What is a Library?
A library is a collection of pre-combined codes that can be used iteratively to reduce the time required to code. They are particularly useful for accessing the pre-written frequently used codes, instead of writing them from scratch every single time. Similar to the physical libraries, these are a collection of reusable resources, which means every library has a root source. This is the foundation behind the numerous open-source libraries available in Python.
Before getting started, If you are a newbie to the domain of Python I recommend you to watch this video on How to use Python for data visualization and data analytics techniques.
Let’s Get Started!
1. Scikit- learn
It is a free software machine learning library for the Python programming language and can be effectively used for a variety of applications which include classification, regression, clustering, model selection, naive Bayes’, grade boosting, K-means, and preprocessing.
Scikit-learn requires:
Python (>= 2.7 or >= 3.3),
NumPy (>= 1.8.2),
SciPy (>= 0.13.3).
Also spotify uses Scikit-learn for its music recommendations and Evernote for building their classifiers. If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip.
2. NuPIC
The Numenta Platform for Intelligent Computing (NuPIC) is a platform which aims to implement an HTM learning algorithm and make them public source as well. It is the foundation for future machine learning algorithms based on the biology of the neocortex. Click here to check their code on GitHub.
3. Ramp
It is a Python library which is used for rapid prototyping of machine learning models. Ramp provides a simple, declarative syntax for exploring features, algorithms, and transformations. In addition it is a lightweight pandas-based machine learning framework and can be used seamlessly with existing python machine learning and statistics tools.
4. NumPy
When it comes to scientific computing, NumPy is one of the fundamental packages for Python providing support for large multidimensional arrays and matrices along with a collection of high-level mathematical functions to execute these functions swiftly. So numPy relies on BLAS and LAPACK for efficient linear algebra computations. Moreover numPy can also be useful as an efficient multi-dimensional container of generic data.
The various NumPy installation packages can be found here.
5. Pipenv
The officially recommended tool for Python in 2017 – Pipenv is a production-ready tool that aims to bring the best of all packaging worlds to the Python world. Also the cardinal purpose is to provide users with a working environment which is easy to set up. Pipenv, the “Python Development Workflow for Humans” was created by Kenneth Reitz for managing package discrepancies. The instructions to install Pipenv can be found here.
6. TensorFlow
The most popular deep learning framework, TensorFlow is an open-source software library for high-performance numerical computation. Then it is an iconic math library and is also useful for machine learning and deep learning algorithms. Tensorflow was developed by the researchers at the Google Brain team within Google AI organisation, and today it is being used by researchers for machine learning algorithms, and by physicists for complex mathematical computations. The following operating systems support TensorFlow: macOS 10.12.6 (Sierra) or later; Ubuntu 16.04 or later; Windows 7 or above; Raspbian 9.0 or later.
Do check out our Free Course on Tensorflow and Keras. This course will introduce you to these two frameworks and will also walk you through a demo of how to use these frameworks.
7. Bob
Developed at Idiap Research Institute in Switzerland, Bob is a free signal processing and machine learning toolbox. However the toolbox is written in a mix of Python and C++. From image recognition to image and video processing using machine learning algorithms, a large number of packages are available in Bob to make all of this happen with great efficiency in a short time.
8. PyTorch
Introduced by Facebook in 2017, PyTorch is a Python package which gives the user a blend of 2 high-level features – Tensor computation (like NumPy) with strong GPU acceleration and developing Deep Neural Networks on a tape-based auto diff system. Also pyTorch provides a great platform to execute Deep Learning models with increased flexibility and speed built to be integrated deeply with Python.
9. PyBrain
PyBrain contains algorithms for neural networks that can be used by entry-level students yet can be used for state-of-the-art research. In addition the goal is to offer simple, flexible yet sophisticated and powerful algorithms for machine learning with many pre-determined environments to test and compare your algorithms. Researchers, students, developers, lecturers, you and me – we can all use PyBrain.
10. MILK
This machine learning toolkit in Python focuses on supervised classification with a gamut of classifiers available: SVM, k-NN, random forests, decision trees. A range of combination of these classifiers gives different classification systems. For unsupervised learning, one can use k-means clustering and affinity propagation. There is a strong emphasis on speed and low memory usage. Therefore, most of the performance-sensitive code is in C++. Read more about it here.
11. Keras
It is an open-source neural network library write in Python and it’s there to enable fast experimentation with deep neural networks. With deep learning becoming ubiquitous, Keras becomes the ideal choice as it is API for humans and not machines according to the creators. Besides with over 200,000 users as of November 2017, Keras has stronger adoption in both the industry and the research community even over TensorFlow or Theano. Before installing Keras, it is better to install TensorFlow backend engine.
12. Dash
From exploring data to monitoring your experiments, Dash is like the frontend to the analytical Python backend. Besides this productive Python framework is ideal for data visualization apps particularly suitable for every Python user. The ease which we experience is a result of extensive and exhaustive effort.
13. Pandas
It is an open-source, BSD licensed library. Pandas enable the provision of easy data structure and quicker data analysis for Python. Also for operations like data analysis and modelling, Pandas makes it possible to carry these out without needing to switch to more domain-specific language like R. And the best way to install Pandas is by Conda installation.
14. Scipy
This is yet another open-source software for scientific computing in Python. Apart from that, Scipy is also for Data Computation, productivity, and high-performance computing and quality assurance. Also you can find the various installation packages here. The core Scipy packages are Numpy, SciPy library, Matplotlib, IPython, Sympy, and Pandas.
15. Matplotlib
All the libraries that we have discussed are capable of a gamut of numeric operations but when it comes to dimensional plotting, Matplotlib steals the show. Also this open-source library in Python is widely useful for publication of quality figures in a variety of hard copy formats and interactive environments across platforms. You can design charts, graphs, pie charts, scatterplots, histograms, error charts, etc. with just a few lines of code.
Also you can find The various installation packages here.
16. Theano
This open-source library enables you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. In addition for a humongous volume of data, handcrafted C codes become slower. Also theano enables swift implementations of code. Theano can recognise unstable expressions and yet compute them with stable algorithms which gives it an upper hand over NumPy. Follow the link to read more about Theano. The closest Python package to Theano is Sympy. So let us talk about it.
17. SymPy
For all the symbolic mathematics, SymPy is the answer. This Python library for symbolic mathematics is an effective aid for computer algebra system (CAS) while keeping the code as simple as possible to be comprehensible and easily extensible. SimPy is write in Python only and can embedd in other applications and extend with custom functions. You can find the source code on GitHub.
18. Caffe2
The new boy in town – Caffe2 is a Lightweight, Modular, and Scalable Deep Learning Framework. Also it aims to provide an easy and straightforward way for you to experiment with deep learning. Thanks to Python and C++ API’s in Caffe2, we can create our prototype now and optimize later. You can start with Caffe2 now with this step-by-step installation guide.
19. Seaborn
When it comes to visualisation of statistical models like heat maps, Seaborn is among the reliable sources. Although this Python library is derive from Matplotlib and closely integrated with Pandas data structures. Further you can visit the installation page to see how you can install this package.
20. Hebel
This Python library is a tool for deep learning with neural networks using GPU acceleration with CUDA through pyCUDA. Moreover right now, Hebel implements feed-forward neural networks for classification and regression on one or multiple tasks. Also other models such as Autoencoder, Convolutional neural nets, and Restrict Boltzman machines are planning for the future. So follow the link to explore Hebel.