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About Useful Python Tools

Among the developers, Python is one of the most popular languages in today’s world. There are numerous reasons behind the popularity of Python but primarily two reasons are easy syntax writing and an exclusive range of libraries, tools for data science, and scientific computing that Python offers to its users.
Today, we will discuss the most widely used Python tools by data scientists and developers across the world. If you know how to use them then these tools are very convenient for many different purposes. Let’s divide these tools into some categories that are mentioned below:

  • Data Science Python tools
    • Keras
    • Scikit learn
    • Theano
    • SciPy
  • Automation Testing Python tools
    • Selenium
    • Robot Framework
    • TestComplete
  • Web Scraping Python tools
    • Beautiful Soup
    • LXML
    • Scrapy
    • urllib

Data Science Python Tools

  1. Keras
    Keras is an open-source, high-level neural network API written in Python. It supports multiple back-ends, neural network models. It is highly convenient for Machine Learning and Deep Learning field. Keras library is user-friendly, easy to extend, modular, and working with Python. It is easy to introduce your new neural network with the help of modules that Keras provides like neural layers, activation function, cost functions, optimizer, etc. Kears supports a wide range of production deployment, integration with back-end engines like Tensorflow, CNTK, Theano, LXNet, and PlaidML.
  2. Scikit learn
    It is an open-source for data science and machine learning used by developers and data scientists for data mining and predictive data analysis operations including classification, regression, clustering, and dimensionality reduction. It includes features like supervised learning algorithm (Support Vector Machines (SVM), Decision Trees to Bayesian methods ), cross-validation, unsupervised learning algorithm clustering, factor analysis, principal component analysis to unsupervised neural networks, feature extraction, and a lot of more.
  3. Theano
    Theano is a completely Python-based library that allows optimizing, defining and evaluating any mathematical expressions of the multidimensional array in a very efficient way. Some key features that make this library very efficient are; integration with NumPy, transparent use of GPU, allow efficient symbolic differentiation, stability optimization, speed, and expensive unit testing.
  4. Scipy
    It is an open-source library ecosystem used for technical and scientific computing. Engineering, Science, and mathematics is the main domain in which this library is extensively used. It leverages other Python packages, including pandas, ipython, and NumPy to make it more general for science and math-oriented programming tasks.

Automation Testing Python Tools

  1. Selenium
    This is one of the best Python open-source testing frameworks. With selenium, test scripts are written in many other programming languages including Python, Java, C#, PHP, Ruby, etc. Every browser is compatible with selenium so testing could be performed by any web browser firefox, GoogleChrome, Internet Explorer, Safari, etc. at all three major OS – LINUX, Windows, Mac. Selenium can be integrated with other tools like JMeter, JUnit, TestNG for generating reports, and managing the test cases.
  2. Robot framework
    It’s another open-source test framework designed for acceptance testing and acceptance test-driven development. It is keyword-driven and uses tabular test data syntax. It integrated many frameworks for different test automation requirements. You can also integrate it with Python or java libraries to expand its abilities. It is used for web testing along with android and iOS test automation.
  3. Test Complete
    TestComplete is an automation testing tool that supports web, desktop, and mobile automation testing. It is not open-source, you must acquire a commercial license to be able to use it. Just like Robot Framework, TestComplete allows performing keyword-driven testing. It comes with an easy-to-use record and playback feature. GUI object recognition abilities make it a more useful tool for both detecting and updating UI objects.

Web Scraping Tools

  1. Beautiful Soup
    This Python library is used to pull data from HTML and XML files. Some key features of beautiful soup are:

    • It provides a few simple functions and pythonic idioms for searching, navigating, and modifying the parse-tree.
    • It converts incoming documents to Unicode and outgoing documents to UTF-8 automatically.
    • It works on top of Python parsers like html5lib and lxml for different parsing strategies and flexibility.
  2. LXML
    It is the most easy-to-use library for processing HTML and XML in Python. The key features of this library are its extremely fast while parsing a large document, well documented, and easy conversion of data into Python data types, easier file manipulation.
  3. Scrapy
    It’s an open-source Python library used for developing web spiders that crawl websites and extract data from them. Scrapy is a fast web crawling and scraping framework used for many tasks like data mining and automated testing.
  4. urllib
    This Python module is designed for collecting and opening URLs. It has many functions to work with URLs. It uses ‘urllib.request’ for opening and reading url most of the time it is HTTP, ‘urllib.error’ for defining the exception class for exceptions that are raised by ‘urllib.request’, ‘urllib.parse’ used to define a standard interface to fragment URL string.

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