10 most useful python libraries
You have already know about the Python and its domination in our previous posts (e.g. Know more about Python, career in Python, mistakes by developers, Python online resources). In this geek story, we outline some of its most useful libraries. Hope this will help you to choose best libraries for your future projects in Python.
This is the foundational library of Python. There have many libraries that used NumPy as their basic inputs and outputs. It is very useful for linear algebra, Fourier transforms and random number capabilities. It also can be used as a multi-dimensional container of generic data.
This library is built on NumPy by adding a collection of algorithms and high-level commands for manipulating and visualizing data. It provides efficient numerical routines as numerical integration, optimization, and many others via its specific submodules.
It is used to make crawling programs for retrieval of the structured data, such as contact info or URLs from the web. It was originally designed strictly for scraping, but it has evolved in the full-fledged framework and act as general-purpose crawlers. ScraPy provides out of the box support for extracting data from HTML/XML using Xpath and export into Json, csv, xml, backend storage FTP, s3, local storage, cookies and session handling, compression, caching, authentication, user-agent spoofing, crawl depth restriction and much more.
This is a Python library that was designed to do the work with 'labeled' and 'relational' data simple and intuitive. It is a perfect tool for data wrangling. Pandas works well with incomplete, messy, and unlabeled data and designed for quick and easy data manipulation, aggregation, and visualization.
It is the standard Python library for creating 2D plots and graphs. However, the library is pretty low-level, and you need to write more code to reach the advanced levels of visualizations.
This library based on Matplotlib and mostly focused on the visualization of statistical models, such visualizations include heat maps. It summarizes the data, but still depict the overall distributions.
This is the additional packages of SciPy Stack designed for specific functionalities like image processing and machine learning. It was built on NumPy and SciPy by adding a set of algorithms for basic level machine learning and data mining tasks, including clustering, regression, and classification.
It is an open-source library from Google for data flow graph computations. It was designed to meet the high-demand of the Google environment for training Neural Networks and is a successor of DistBelief. The key feature of TensorFlow is their multi-layered node system that enables quick training of artificial neural networks on large data sets.
It's also an open source library that used for making multimedia applications including games. It was built on top of the powerful Simple Directmedia Library (SDL), and used to teach game development to kids. This library will help you to achieve your goal of developing a 2d game.
It is a Python library that enables users to conduct data exploration via the use of various methods of estimation of statistical models and performing statistical assertions and analysis. It offers many useful descriptive features and statistics result via the use of linear regression models, generalized linear models, discrete choice models, robust linear models, time series analysis models, various estimators.
The libraries are among those which are widely used and a small sample of the tools that available in Python. Hope, this article will make your development process easier, but don't forget to share your thoughts via comments.