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Top Python Tools to get Started with | Best Python Tools

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Introduction

Every once in a while, there comes a new programming language and along with that great community to support that. It is a fact that by the time you are getting good at something, there is a new technology out there and you are outdated from the market. It is necessary to cope up with the industry to make living. Python is that new thing that you should learn in order to stay to the course. Python is a general-purpose language which can be used strongly in Data Science. However, opinions may change from person to person, but here are the tools that will help you get started and if you already have, this might boost you up.

Python tools for Visual Studio

For those who don’t know what this is, Python tools for Visual Studio is open source plugin that turns Visual Studio into a Python IDE, official web page says. Now if you are just like me, you’ll be asking yourself a question. First, why? And then How? Is that correct? Don’t worry, I will answer those both questions. It supports from CPython and IronPython to IPython. And other features such as IntelliSense, profiling, debugging, mixed C++/Python debugging, and more. About how to install Python tools for Visual Studio, Stay with me.

Get Python Tools Window

First, open Visual Studio and start new project from File>New Project and under Python you will see Get Python Tools for Visual Studio. Click on that and follow the instructions. As you download python tools for visual studio and install it, restart Visual Studio and go to the same location as before and now you will see number of different project types that you can select instead of the link to get python tools.

Project Types in Python

Now let’s talk about features of Python tools for Visual Studio. It has interactive command line right below the editor which comes really hand when you want to check certain one liners. And the important thing about interactive window is you can have multi line history. Didn’t get it? Look below, first I wrote the function f() and then I don’t have to write new commands for function G(), I just went up and changed the content of function accordingly.

Python Interactive Part 1

Python Interactive Part 2

With only one click you can find the definition of the called function or while at function you can find with one click all the reference to that function. This is just the tip of the iceberg but there are other many important features such as code understanding and IntelliSense which will really help you if you are working on large Python projects.

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Best Python tools for automation testing

Every Software Development group tests its products, yet however, software which is being delivered always has flaws. It is the fact. Let’s face it. Test Engineers try to catch as many as bugs as possible and even after fixing it they often reappear, even if the testing was done by best manual testing processes. That’s why Test Automation Software is the robust way to increase productivity and coverage of your software. Because of course, machines have patience when it comes to repeating same task thousand times, while we humans haven’t. Let’s look at some of the best Python Automation Tools or as some may like to call it best automation testing tool using Python (by that I mean their scripting can be done using Python along side with other programming languages.)

Selenium

When it comes to best Python Development tool, Selenium is presumably the best automation framework for Web Applications out there, and it is open-source, what’s not to like? Other reasons behind it’s emerging popularity are: First, you can write test scripts in any of these languages which includes Java, Python, C#, PHP, Ruby, Perl and [dot]net. Second, you can carry out test in either of the three major operating systems: Windows, macOS or Linux. Third, tests can be carried out from any browser, which includes Firefox, Safari, Internet Explorer (like who’s going to use IE, but anyway), Chrome and Opera. Fourth, you can integrate Selenium with tools such as JUnit and TestNG to manage test cases and generate report. And finally, if you want to perform continuous testing, it can be integrated with Maven, Jenkins and Docker. Phew!

Although, while testers have flexibility and they can write advance test scripts to meet various levels of complexity, it demands advanced programming skills and highly dedicated efforts to make automation framework for specific testing needs.

Robot Framework

Another open-source framework is Robot framework, initially developed in 2005 by Nokia Networks, is a generic test automation framework for acceptance testing and acceptance test-driven development (ATTD). This testing framework is keyword-driven and uses tabular test data syntax. It provides several frameworks for different test automation need. But it’s capabilities can be further extended by implementing additional libraries using Python or Java.

Not only is that engineers can leverage this as an automation framework for web, but also it can also be used for Android and iOS test automation. Isn’t it amazing? In addition to this, it is easy to learn for those who already know keyword-driven testing.

TestComplete

We saw test automation software for web and mobile. But TestComplete goes beyond that, it supports web, mobile as well as desktop automation testing. However, to use this you have to acquire a commercial licence. It supports various scripting languages like Python, VBScript, and C++ script. Just like Robot Framework, software testers can perform keyword-driven testing. This tool also offers easy-to-use record & playback feature.

Another feature worth noticing is that, Its GUI object recognition capabilities can itself detect and update UI object. This helps in reducing the efforts to maintain the test scripts.

Python tools for Machine Learning

While others may debate over which language is better for Data Science: R or Python, I believe that Python is emerging as most favourable language for Data Science because of the libraries it has to offer. Let’s take a look at some of them.

Scikit-learn

Python for Data Science and Machine Learning mainly uses libraries to perform the operations. It is specifically designed for functionalities like image processing and Machine Learning. In the regard of Machine Learning it is one of the most prominent package till date. It includes packages for classification, clustering, regression, pre-processing, and many more. The icing on the cake is that it uses Scipy data structures under the hood and fits quite well with the rest of scientific computing in Python with Scipy, Numpy, Pandas and Matplotlib packages.

Statsmodels

It is another great library which mainly aims on statistical models and used for exploratory analysis. The Statistical tests it provides are quite comprehensive and cover most of the cases. If by any chance you are a R user, it also provides support for R syntax for some of its stats models. In addition to that, it also accepts Numpy arrays as well as Pandas data-frames for its models.

PyMC

If you ask a Bayesian, it is their go to library. It mainly includes Bayesian models but also supports by providing statistical distributions and diagnostic tools. It includes some hierarchical models as well.

Theano

Theano is a package that defines multi-dimensional arrays which is similar to NumPy, along with math operations and expressions. Originally developed by the Machine Learning group of Université de Montréal, it is primarily used for the needs of Machine Learning. Also, the library is compiled, making it run efficiently on all architectures. The important thing to note is that Theano tightly integrates with NumPy on low-level of its operations. The library also optimizes the use of GPU and CPU, making the performance of data-intensive computation even faster.

Best tools to Learn Python

The best way to learn any programming language starts with deciding what you want to build. Next thing you need to find is a course or some resources to help you guide through learning, you will find enormous number of python programming tools or any other programming language for that matter. Let’s go through some of the best tools or websites for learning python.

Real Python

If you are looking for learning both Python programming language as well as Web Development using Python, It is a good thing to stick with Real Python Course. This course starts with the basics of the Python programming language and moves on to web development using Django, Flask, and web2py. Not only is that you will learn these frameworks, but also you will learn about Git and Heroku which will actually build and ship your code once you are finished coding.

Learn Python the Hard Way

It is one classic in the python education space, it is mainly an ebook by Zed Shaw, a software developer and creator of the Mongrel web server for Ruby. It is a simple three step formula.

  • Go through each exercise
  • Type in each example exactly
  • Make it run

Yes, it is difficult to get it done. Yes, it will be a bit frustrating. But it is worth it.

Intro to Python for Data Science (Data Camp)

With as many people want to learn Python for web development, there are another bunch of same size want to learn Python for Data Science. For a career in Data Science you should learn libraries more than anything. This course will give you insights to that as well.

Python Anywhere

If you are completely new, and if you are struggling to install and configure Python on your system, start with Python Anywhere while you learn the basics and then return to the install process later on. It is a full-fledged Python environment that runs in your web browser.

Conclusion

In a nutshell, Python is great language to start with if you haven’t started any yet. These are the tools that can get you started but are not limited to. There are thousand more just like this, better than this or under development. If you have any other tool that is as important as these ones, do let us know in the comments and we’ll try to cover it in next relative post.

Happy Learning.

Guest Blogger (Data Science) at Digital Vidya. A Data passionate who loves reading and diving deeper into the Machine Learning and Data Science arts. Always eager to learn about new research and new ways to solve problems using ML and AI.

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