The programming world is incomplete without python, as it is the powerhouse of diverse applications across various domains. Fueling its prowess are the countless libraries that enhance its functionality and allows developers to create robust and efficient solutions.
Python Libraries provides useful functions that eliminate the need for writing codes from scratch. According to sources there are 137,000 python libraries available today. Because these top python libraries are useful, they play a vital role in developing machine learning, data science, data visualisation, image and data manipulation applications etc. In this Kodehash blog, we are exploring what python is, why it is so popular, and so on.
Python, Guido Van Rossum’s brain child, is a popular coding language that emerged in the 80s. It is widely used for a gamut of applications. Let’s find out how to make an app using python. But before that, understand what a library is in a programming language, so that we can use it better.
What is a library?
Library is a collection of pre-combined codes and is utilised to reduce the code required time. They are useful for accessing the pre-written frequently used codes instead of writing them from scratch every time. These libraries’ functionalities are the same as the physical libraries, to be the source of collection of reusable resources.
A Python library is a collection of modules containing functions and classes which are used by other programs for doing vivid tasks. Here find the top Python libraries because of their recent statistics:
NumPy:
Kicking off our list is NumPy, a fundamental library for scientific computing in Python. Boasting powerful N-dimensional array objects, it provides tools for integrating C/C++ and Fortran code. Because of the vibrant community and extensive documentation, NumPy has become the go-to choose for numerical operations.
Pandas:
Data manipulation and analysis are streamlined with Pandas, a versatile library that offers data structures like DataFrames and Series. Pandas simplifies tasks such as data cleaning, exploration, transformation, and so on, makes it an indispensable tool for data scientists and analysts.
Matplotlib:
Visualisation is key, and Matplotlib excels at it. This library enables the creation of a wide array of static, animated, and interactive plots in Python. From simple line charts to complex 3D visualisations and so on, Matplotlib empowers developers to communicate data effectively.
TensorFlow:
In the realm of machine learning, TensorFlow is one of the top Python libraries that stands out of all. Google developed this open source library to facilitate the creation and training of deep neural networks. This library is widely used because of its flexibility, scalability, and compatibility. TensorFlow is a go-to choice for building and deploying machine learning models.
Scikit-learn:
Scikit-learn is known for its simplifying development of predictive data analysis. With an extensive set of tools for classification, regression, clustering,and more, this library makes machine learning accessible to developers at various skill levels.
Flask:
Flask is one of the top Python libraries known for web development. It is a lightweight yet powerful choice. Known for its simplicity and ease of use. Flask is widely adopted for building web applications and APIs. With a thriving community and extensive documentation, Flask empowers developers so that they can create robust web solutions effortlessly.
Requests:
Managing HTTP requests becomes seamless with the requests library. Simplifying communication with APIs, this library allows developers to send HTTP/1.1 requests effortlessly, making it an essential tool for web scraping, data retrieval, and integration with external services.
SQLAlchemy:
Database interactions are made elegant with SQLAlchemy, one of the top Python libraries. This SQL toolkit and Object-Relational Mapping (ORM) library simplify database operations, providing a seamless bridge between Python applications and relational databases.
Scikit-learn
This free software machine learning library is known for its python programming language. It is effectively used for a variety of applications that include classification, regression, clustering, model selection, naive Bayes’, grade boosting, K-means, and preprocessing. Spotify uses this free machine learning library for its music recommendations and Evernote for building its classifiers. Using pip is the easiest way to install scikit-learn if you already have NumPy and Scipy installed.
Kerras
This is one of the top Python libraries known for its open-source neural network. This library is written in Python designed to enable fast experimentation with deep neural networks. Keras becomes one of the best choices as it is an API designed for humans not machines. As per resources, with over 200,000 users as of November 2017, Keras has stronger adoption in both the industry and the research community.
PyTorch:
Facebook had developed this graph-based machine learning library to address the needs for model development and deployment purposes. The variables add layers can be changed during the iterations because the neural networks are easy to debug, so provide more flexibility. This library offers a simple flag to switch between GPU and CPU, which makes the life of programmers extremely easy by making the code portable.
BeautifulSoup:
This parsing library in Python enables web scraping from HTML and XML documents. It is useful detecting encodings and managing HTML documents with special characters.
Scipy:
This open-source software is used for scientific computing in Python. Data computation, productivity, high-performance computing, and quality assurance are some of its benefits.
LightGMB:
It is used in data science projects to deploy gradient-boosting algorithms. It provides a high-performance implementation of gradient boosting that can handle large datasets and high dimensional feature spaces. The overall model performance for specific datasets and use cases improves alot.
Theano:
Theano is a Python library for numerical computation useful for deep learning and machine learning applications. It allows users to define, optimise, and gauge mathematical expressions, which includes multi-dimensional arrays-the fundamental building blocks of many machine learning algorithms.
Final Thoughts
As Python continues its reign as one of the most popular programming languages, the libraries that complement it play a pivotal role in shaping its versatility. The ones mentioned here represent just a fraction of the vast Python libraries ecosystem. Whether you are diving into data science, machine learning, and web development, these top Python libraries offer a robust foundation for your projects. Stay tuned for updates, as the Python landscape is ever-evolving, and new libraries are continually pushing the boundaries of what is possible in the world of programming. As Python’s dominance persists, so the libraries shape its versatility. This list is a glimpse; explore more with Kodehash for comprehensive coding solutions and continuous evolution.