As we all know, Python is the fastest-growing programming language of modern times. Python developers are increasing every year by 27% as per the latest data. Python is all set to rule the market in 2023 and the near future because of its multiple uses. To hire python developers for future projects isa current technological trend today.
As we know, with Python we can do wonders ranging from Data Visualization, Data Analysis, Deep and Machine Learning, Data Automation tasks, and Software development for creating websites. It has come over as a fundamental platform with a variety of applications. Many people are learning and growing with Python programming in their careers.
At Internet Soft, a leading software development company in California, we have a pool of Python development teams. You can hire Python developers. for your Python development project requirements. You can also hire Python development services from our website.
There are many Python development tools available. In this article, let us see the top 7 Python tools that Python developers will love to refer to and learn about to conquer their career and project goals.
1) SciPy
SciPy is a free, open-source tool that is accessible to anyone who wants to solve mathematical, scientific, and engineering calculations. Travis Oliphant, Eric Jones, and Pearu Peterson created it in 2001. It is mostly for data processing. It is built on the Numpy extension. SciPy is similar to MATLAB. As an open-source, it allows it to have a large community group across the world. SciPy supports all main operating systems. Some elements of SciPy are available in C language.
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2) Keras
This is another open-source neural network library. François Chollet (a Google Engineer) developed Keras in 2015. Keras is written in Python. The idea behind designing Keras was to be a reliable, safe, and easy-to-use tool. It uses a special library called “Backend” for low-level computations. This “Backend” library allows it to act as a high-level API wrapper (for low-level API), which permits it to run alone on theano or TensorFlow.
Today, big companies like Google, Amazon, etc. are also implementing keras and have made a significant contribution towards it. The main reasons for Keras being popular are production is much easier, and also perform fluently on CPU and GPU. Another reason is Keras offers independence to design any architecture, which is useful as an API for any project.
3) Selenium
Selenium is one of the most extensively used and best development cases with Python programming. It was first introduced by Simon Stewart in 2004 .It’s an open automation framework tool that can be accessed for use by anyone. In Selenium, you can write test scripts in many languages. For testing, all browsers are compatible. We can use Selenium on main operating systems such as Windows, macOS, and Linux. Integration of Selenium with other tools is possible with Junit and TestNG to manage test cases.
4) Sublime Text
Sublime Text is one of the most interesting, lightweight, and cross-platform code editor tools. It was introduced first in 2008 by Jon Skinner. It is well-backed by a strong community group. It has speed, accuracy, and easy usage. Sublime can be used with every operating system.it is one of the first editor tools used in Python and was also written in Python with some of the elements being integrated with C++. The usability of the menu and options of the sublime is easier with a mouse. Sublime Text V.4, the latest version of Sublime Text, launched last year has some exciting features like Auto-complete, Updated UI, and GPU enhancement.
5) Jupyter Notebook
Jupyter Notebook is one of the most stylish documentation tools. It was introduced in 2014 by Fernando Pérez and Brian Granger (under the BSD license). Jupyter Notebook can create Python documentation in LaTex, JSON, HTML, and some other formats. It has a feature to set up automatic, language-specific indices and test for project code snippets for developers. It gives freedom to the developers to work on data visualization, data cleaning, Deep Learning, Machine learning, Data Analysis, etc.
7) Pip Package
Every step we wish to perform in Python programming needs a fixed set of packages. There are different tools and algorithms available. Also, data science tools are different. Therefore, we need a tool that would handle complexity and offer a bunch of tools as per requirement. It is a smart tool called Pip in Python. It was introduced by Ian Bicking in 2011. After this, there have been many updates and releases. This Pip tool is very much important in Python. Every programmer must learn this tool right from the beginning. Installation of Pip in Python is very easy. You can download the zip file and start using it or see what it offers.
7) Scikit-learn
Scikit-learn is a famous, open-source tool that was also known as scikit-learn and sk-learn was introduced in 2007 by David Cournapeau during Google Summer of Code (GSoC) and it was published in 2010. Largely it is Python written but certain core algorithms were backed up by Cython- a superset of Python. This is for boosting the speed. This tool is very demanding carrying various features that include classification, regression, clustering, etc. It is mainly used for machine learning and statistical modeling.
Hire a Dedicated Python Developer
For your Python development services, you can hire a dedicated Python developer from Internet Soft, a company with core experience in Python projects across many verticals. As a Python development company in California, our core experience is sure and we have completed successful projects across industries.
Closing Summary
The above 7 Python tools give the best features and outcomes upon their use in Python programming for project development. There are many other tools and libraries, but these top 7 Python development tools are more efficient and scalable.
To hire Python developers for your projects, gives you a base structure for the further development. The Python platform in itself is widely acceptable and usable for variety of end users.
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