10 Must-Have Python Libraries for Every Developer
Python is a powerful and versatile programming language that is used for a wide range of tasks, including web development, data science, machine learning, and more. To get the most out of Python, it is important to know the essential libraries.
what is Python?
Python is a general-purpose programming language that is used for a wide variety of tasks, including web development, data science, machine learning, and artificial intelligence. It is a high-level, interpreted language, which means that it is easy to learn and use, even for beginners.
Python is known for its simple and elegant syntax, which makes it easy to read and write code. It also has a large and active community of developers, which means that there are many resources available to help you learn Python and get help with any problems you may encounter.
Here are some of the key features of Python:
- Simple syntax: Python has a simple and elegant syntax, which makes it easy to read and write code.
- Interpreted language: Python is an interpreted language, which means that it does not need to be compiled before it can be executed. This makes it quick and easy to start programming in Python.
- Object-oriented: Python is an object-oriented language, which means that it supports the concepts of classes and objects. This makes Python code more modular and reusable.
- Powerful libraries: Python has a large library of standard libraries, which provide a wide range of functionality. This means that you can often find a library that already does what you need, without having to write the code yourself.
- Free and open-source: Python is a free and open-source language, which means that it is free to use and distribute. This makes Python a popular choice for academic and research projects.
10 Must-Have Python Libraries for Every Developer
1. Scikit- learn
It is a free software machine learning library for the Python programming language. It 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).
Spotify uses Scikit-learn for its music recommendations and Evernote for building its classifiers. If you already have a working installation of NumPy and scipy, the easiest way to install scikit-learn is by using pip.
2. NuPIC
The Numenta Platform for Intelligent Computing (NuPIC) is a platform that aims to implement an HTM learning algorithm and make them a 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 that is used for the rapid prototyping of machine learning models. Ramp provides a simple, declarative syntax for exploring features, algorithms, and transformations. 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. NumPy relies on BLAS and LAPACK for efficient linear algebra computations. NumPy can also be used 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. The cardinal purpose is to provide users with a working environment that 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
TensorFlow’s most popular deep learning framework is an open-source software library for high-performance numerical computation. It is an iconic math library and is also used for Python in machine learning and deep learning algorithms. Tensorflow was developed by the researchers at the Google Brain team within the Google AI organization. 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 and TensorFlow python. 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. 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 that gives the user a blend of 2 high-level features – Tensor computation (like NumPy) with strong GPU acceleration and the development of Deep Neural Networks on a tape-based auto diff system. PyTorch provides a great platform to execute Deep Learning models with increased flexibility and speed built to be integrated deeply with Python.
Looking to get started with PyTorch? Check out these PyTorch courses to help you get started quickly and easily.
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. 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 I can 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, and decision trees. A range of combinations 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.
