Top 7 Python Developer Tools
Last Updated : 14 Oct, 2024
Believe it or not, today python is considered one of the most powerful programming languages, and it’s spreading at a mass level. We have witnessed a surge of Python developers in the past couple of years at a whopping rate of 27% YoY (Year on Year). Last year python marked 30 years of success and it is clearly a sign that it is going to disrupt the market in the upcoming few years.
With Python, you can do a lot, Data Visualization, Artificial Intelligence, Machine Learning, Deep Learning, Task Automation, Analysis of Data, Developing software and websites, and whatnot. Moreover, being simple by nature has attracted many developers to switch their careers and pave their way toward Python Programming. Today, we’ve come up to provide you with a list of hand-picked top 7 Python developer tools that might be helpful for you, and should be well aware of these tools for making your journey easy during programming.
Since we are discussing Python and its developer tools, it is necessary to get hands-on practice to get used to it. Python Programming Foundation – Self-Paced will help you to get an in-depth knowledge of Functions, Loops, Strings, and Lists and learn how to solve coding problems efficiently in python.
List of top 7 python developer tools that you must try:
1. SciPy
It’s a free and open-source tool that can be accessed by anyone built to solve mathematical, scientific, and engineering calculations. Travis Oliphant, Eric Jones, and Pearu Peterson introduced it first in 2001. Since it’s mostly used for data processing and built on the Numpy extension is more or less like MATLAB. Being an open-source allows it to have a large community group all across the world. Since it supports all major operating systems so below are the methods of installing the same in the system.
For Windows Python3 -m pip install –user numpy scipy
For Linux sudo apt-get install python-scipy python-numpy
For Mac sudo port install py35-scipy py35-numpy
Although we use SciPy in Python and major parts are covered in the same language but there are some elements that are written in C Programming Language.
2. Scikit-learn
A well-known, very popular, highly used, open-source tool that was also known as scikit-learn and sk-learn was introduced back in 2007 by David Cournapeau during Google Summer of Code (GSoC) and was published in 2010. Besides this, a large part of scikit-learn is written in python only but there are certain core algorithms that were backed up by Cython (a superset of python) to enhance its speed. Being a demanding tool it offers a variety of features that includes classification, regression, clustering, etc. that are extensively used for machine learning and statistical modeling. Below are the methods to install them in any of your operating systems.
For Windows pip install -U scikit-learn
For Mac pip install -U numpy scipy scikit-learn
*Note: Currently, scikit-learn does not provide any official packages for Linux so you will be required to build the same from the source.
Besides this, scikit-learn is built on some of the most notable tools/technology that you must have heard of: Pandas, NumPy, and matplotlib.
3. Keras
Another open-source neural network library was introduced first by François Chollet (Google Engineer) in 2015. Keras was written in python and boats on the top of some of the most popular libraries such as theano, and TensorFlow. The concept of designing Keras was to offer reliability, fast and should be easy to use. In fact, it also uses a special library called “Backend” to handle low-level computations. This library “Backend” enables it to act as a high-level API wrapper (for low-level API) which enables it to run independently on theano or TensorFlow.
Today, tech giants like Google, Amazon, etc. are actively implementing keras and have a significant contribution towards it. The two major reasons for Keras being popular are:
- The production is far easy and can also perform fluently on CPU as well as GPU.
- Keras offers independence to design any architecture, which then later is utilized as an API for any project.
4. Selenium
If you want to write test scripts in a certain language like Java, C# or PHP, and many more then selenium is for you. With selenium, you can combine it with multiple languages and can also perform tests from literally any web browser (like chrome, explorer, safari, etc.) Moreover, selenium is one of the widely used and best development used that we use with python programming. It was first introduced by Simon Stewart in 2004 and it’s open to using an automation framework tool that can be accessed by anyone. For testing, you can use all of the major operating systems (Windows, macOS, and Linux).
Moreover, if anyone wants to integrate the same with other tools to manage test cases then the same can be done by using Junit and TestNG.
5. Pip Package
Every action that you wish to perform in python programming requires a certain set of packages for finance there are different tools and algorithms, likewise, data science tools are different. That’s where we need to have a tool that can handle such complexity and provide a bundle of tools as per requirement and that’s what a smart tool can be called Pip in python. It was first introduced by Ian Bicking in 2011 and since then there have been several updates and releases. When you’ll start working with the python programming language, you’ll realize the importance of this tool and why every developer should have knowledge of pip from the beginning of their journey. The procedure to start with it is also very simple, all you need is just to have a stable internet connection and download the Pip file, and see the difference in handling tools by yourself.
6. Sublime Text
It is one of the most fascinating, lightweight, and cross-platform code editor tools that was introduced first in 2008 by Jon Skinner at Sublime HQ. It is well known for its speed, accuracy, and ease of use, and is backed by a strong community group. The best part about sublime is that it can be used with almost every operating system and it’s often one of the first editor tools that are used in python and was also written in python and some of the elements were integrated with C++. The usability in the menu and options of sublime is so easy that it can be accessed by just using a mouse. The latest version of sublime text i.e. Sublime Text V.4 was launched last year with more updated features. Some of them are listed below:
- Auto-complete: Since sublime text offers an auto-complete function so the latest version has been improvised to offer smart completions of any existing code in any project.
- Updated UI: The UI has been updated along with the new tab styles, themes, and color schemes for auto dark-mode switching mode.
- GPU enhancement: With its new feature, GPU on various operating systems such as windows, mac, etc. can be utilized within it which can result in a smooth UI (that can extend up to 8k resolution) and will consume less power simultaneously.
7. Jupyter Notebook
The last one on this list is a Jupyter Notebook which is one of the most elegant documentation tools that was introduced back in 2014 by Fernando Pérez and Brian Granger (under the BSD license). The objective of Jupyter in python was to create project documentation in a variety of languages. It can produce python documentation in LaTex, JSON, HTML, and certain other formats. It enables a feature that helps developers to set up automatic, language-specific indices and test for code snippets of any projects.
Note: It allows developers to work on data cleaning, visualization, numerical simulation, machine learning, and many more.
How to get started?
Anaconda
- Download Anaconda first
- Follow the instructions and proceed to complete the setup
pip
- Install package manager
- pip3 install –upgrade pip
- Install Jupyter using -> pip3 install jupyter
*Also, ensure that you’ve downloaded the latest Python Version (3.10)
Bottom Line
The best way of getting a good grip on any programming language is by getting under its skin. Whether you’re at an expert level or just started working on Python, these are some of the most used developer tools that you must know. Although the list is not limited to this, you may find a number of other developer tools but these were the Top 7 Python Developer Tools that will make your everyday work more smooth.
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