It is no longer surprising to hear that Python is one of the most popular languages among Developers and in the Data Science community. While there are numerous reasons behind Python’s popularity, it is primarily because of two core reasons.
- Python has a very simple syntax – almost equivalent to the mathematical syntax – and hence, it can be easily understood and learned.
- Second, it offers extensive coverage (libraries, tools, etc.) for scientific computing and Data Science.
There are numerous reasons to use Python for data science. Today, we’ll talk about some of the most widely used Python tools by developers, coders, and Data Scientists across the world. If you are a beginner and interested to learn more about data science, check out our data science certification from top universities.
These Python tools can be convenient for many different purposes if you know how to use them right. So, without further delay, let’s look at the best Python tools out there!
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1) Scikit-Learn
Scikit-Learn is an open-source tool designed for Data Science and Machine Learning. It is extensively used by Developers, ML Engineers, and Data Scientists for data mining and data analysis. One of the greatest features of Scikit-Learn is its remarkable speed in performing different benchmarks on toy datasets.
The primary characteristics of this tool are classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. It offers a consistent and user-friendly API along with grid and random searches.
2) Keras
Keras is an open-source, high-level neural network library written in Python. It is highly suited for ML and Deep Learning. Keras is based on four core principles – user-friendliness, modularity, easy extensibility, and working with Python. It allows you to express neural networks in the easiest way possible. Since Keras is written in Python, it can run on top of popular neural network frameworks like TensorFlow, CNTK, and Theano.
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3) Theano
Theano is a Python library designed explicitly for expressing multi-dimensional arrays. It allows you to define, optimize, and evaluate mathematical computations comprising multi-dimensional arrays. Some of its most unique features include its tight integration with NumPy, transparent use of GPU, efficient symbolic differentiation, speed and stability optimizations, dynamic C code generation, and extensive unit-testing, to name a few.
4) SciPy
SciPy is an open-source Python-based library ecosystem used for scientific and technical computing. It is extensively used in the field of Mathematics, Science, and Engineering. SciPy leverages other Python packages, including NumPy, IPython, or Pandas, to create libraries for common math and science-oriented programming tasks. It is an excellent tool for manipulating numbers on a computer and generate visualized results as well.
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Automation Testing Python tools
5) Selenium
Selenium is undoubtedly one of the best Python development tools. It is an open-source automation framework for web applications. With Selenium, you can write test scripts in many other programming languages, including Java, C#, Python, PHP, Perl, Ruby, and .Net.
Furthermore, you can perform tests from any browser (Chrome, Firefox, Safari, Opera, and Internet Explorer) in all of the three major operating systems – Windows, macOS, and Linux. You can also integrate Selenium with tools like JUnit and TestNG for managing test cases and generate reports.
6) Robot Framework
Robot Framework is another open-source generic test automation framework designed for acceptance testing and acceptance test-driven development (ATTD). It uses tabular test data syntax and is keyword-driven. Robot Framework integrates many frameworks for different test automation requirements.
You can expand the framework’s abilities by further integrating it with Python or Java libraries. Robot Framework can be used not only for web app testing but also for Android and iOS test automation.
7) TestComplete
TestComplete is an automation testing software that supports web, mobile, and desktop automation testing. However, you must acquire a commercial license to be able to use it. TestComplete also allows you to perform keyword-driven testing, just like Robot Framework. It comes with an easy-to-use record and playback feature.
It supports many scripting languages, including Python, VBScript, and C++ script. Just like Robot Framework, software testers can perform keyword-driven testing. A noteworthy feature of this Python tool is that its GUI object recognition abilities can both detect and update UI objects. This helps reduce the efforts required to maintain test scripts.
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Web Scraping Python tools
8) Beautiful Soup
Beautiful Soup is a Python library for extracting data from HTML and XML files. You can integrate it with your preferred parser to leverage various Pythonic idioms for navigating, searching, and modifying a parse tree. The tool can automatically convert incoming documents to Unicode and outgoing documents to UTF-8 and is used for projects like screen-scraping. It is a great tool that can save you hours of work.
9) LXML
LXML is a Python-based tool designed for C libraries – libxml2 and libxslt. It is highly feature-rich and one of the most easy-to-use libraries for processing XML and HTML in Python. It facilitates safe and convenient access to libxml2 and libxslt libraries by using the ElementTree API.
What’s unique is that it combines the speed and XML features of these libraries with the simplicity of a native Python API. Furthermore, it extends the ElementTree API to provide support for XPath, RelaxNG, XML Schema, XSLT, and C14N.
10) Scrapy
Scrapy is an open-source and collaborative framework written in Python. Essentially, it is an application framework used for developing web spiders (the classes that a user defines) that crawl web sites and extract data from them. It is mainly used for extracting the data from websites.
Scrapy is a fast, high-level web crawling and scraping framework that can also be used for many other tasks like data mining, automated testing, etc. It can efficiently run on all three major operating systems, that is, Windows, macOS, and Linux.
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Bonus: 11) Urllib
Urllib is a Python package that is designed for collecting and opening URLs. It has various modules and functions to work with URLs. For instance, it uses “urllib.request” for opening and reading URLs that are mostly HTTP; “urllib.error” to define the exception classes for exceptions raised by urllib.request; “urllib.parse” to define a standard interface to fragment Uniform Resource Locator (URL) strings up in components, and “urllib.robotparser” function to create a single class.
Wrapping Up
These Python tools can cover a wide range of needs and functionalities, irrespective of who is using them. Whether a Data Scientist, or a Developer, or Software Engineer, these are some of the best Python tools that are used by tech professionals all around the world.
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Why do most data scientists prefer Python over other languages?
There are many languages like R and Julia that can be used for data science but Python is considered to be the best fit for it due to many reasons. Some of these reasons are mentioned below: Python is much more scalable than other languages like Scala and R. Its scalability lies in the flexibility that it provides to the programmers. It has a vast variety of data science libraries such as NumPy, Pandas, and Scikit-learn which gives it an upper hand over other languages. The large community of Python programmers constantly contributes to the language and helps the newbies to grow with Python.
What makes Python Anaconda so special?
Anaconda is a package manager for Python and R and is considered to be one of the most popular platforms for data science aspirants. The following are some of the reasons that get Anaconda way ahead of its competitors. Its robust distribution system helps in managing languages like Python which has over 300 libraries. It is a free and open-source platform. Its open-source community has many eligible developers that keep helping the newbies constantly. It has some AI and ML-based tools that can extract the data from different sources easily. Anaconda has over 1500 Python and R data science packages and is considered the industry standard for testing and training models.
Which Python libraries can be used for image processing?
Python is the most suitable language for image processing due to the feature-rich libraries that it provides. The following are some of the top Python libraries that make image processing very convenient. OpenCV is hands down the most popular and widely used Python library for vision tasks such as image processing and object and face detection. It is extremely fast and efficient since it is originally written in C++. The conversation over Python image processing libraries is incomplete without Sci-Kit Image. It is a simple and straightforward library that can be used for any computer vision task. SciPy is majorly used for mathematical computations but it is also capable of performing image processing. Face Detection, Convolution, and Image Segmentation are some of the features provided by SciPy.
