Python is one of the most used general-purpose high-level programming languages created by Guido Van Rossum released in 1991. The name is inspired by the famous ‘Monty Python’s Flying Circus’ series. This is mainly used for web development, software development, mathematics, and certain scripting activities. When it comes to Python, there are several Python Development Tools. The major versions of python are Python 2 and python 3.
It emphasizes code readability and simple syntax that allows the programmers to express the concept in fewer lines of code. Python code is compatible in a Jupyter notebook.
Python has a wide variety of packages including databases, networking, Web scraping, Documentation, Text processing, Image processing, scientific computing, etc
Tools Python Developer needs to know
Here are the top 15 Python development tools that every Python developer must know:
Jupyter Notebook: Open source web application that is useful to create, and share the codes, and contains visualization and texts. Jupyter is useful as an IDE for python. Its flexible interface allows one to configure and arrange data in data science, scientific computing activities, etc. https://try.jupyter.org is a site for trying out the jupyter in the browser without installation.
Keras: Keras is an API for reducing cognitive loads. It minimizes the number of user actions required for common use cases and it provides error messages. It is a high-level, deep learning API developed by Google mainly for implementing neural networks. This will be integrated with the Tensor flow, which is used to build the machine learning models. Keras empowers users to take full advantage of the scalability and cross-platform capabilities of TensorFlow.The core data structure of Keras will have layers and models. The simplest type of model is the sequential model, a linear stack of layers. Companies like Netflix used Keras to build recommender systems that predict user preferences given past data.
Scikit Learn: Open source commercially usable-BSD license for machine learning in python. It provides efficient tools for machine learning and statistical modeling. This library is written in Python and is built on numpy, Scipy, and Matplotlib. This is vastly used by ML Engineers and Data scientists for mining and data analysis. This is useful for predictive analysis as well. The tool is useful in classification, regression, clustering, dimensionality reduction, and pre-processing.
Scipy: Free and open-source python library used for scientific computing. It contains modules for optimization, linear algebra, integration, interpolation, etc. Scipy stands for scientific python. It is built on top of the Numpy extension(if we import scipy no need to import numpy). Scipy is a data processing and system prototyping environment similar to MATLAB. Scipy contains significant mathematical algorithms that provide easiness to developing sophisticated and dedicated applications.
Tensorflow: First python library for data science. It is a framework for building and executing tensor-based calculations mainly used in deep learning models. TensorFlow is from Google. It is useful in both research and development and production systems.
Installation: TensorFlow works with Python 3.3+..pip command is useful for Linux or Mac OS platforms.
pip install TensorFlow
For the new mac os with Apple Silicon CPU,
pip install TensorFlow-mac os
Selenium: It is an open-source web application automation framework. We can use selenium to create test scripts in a variety of languages including Java, Python, PHP, and Ruby, and a tool for agile testing in python. It supports Windows,macOS, and Linux platforms. Through Selenium Python API you can access all the functionalities of Selenium Webdriver in an intuitive way.
Installation -Installing python bindings: pip install selenium
Beautiful Soup: Python package that is used for processing HTML and XML files and used for extracting data. Input documents are converted to Unicode and output is converted to UTF-8 format. It is a web scraping framework that comes in handy when accessing or managing Python web app data.
Installation- Use the pip command for installation
pip install beautifulsoup4
Other frameworks we need in the future to work with the different parsers and other frameworks are:
- pip install selenium
- pip install requests
- pip install lxml
- pip install html5lib
Urllib: Used to open URL s.Modules such as urllib..request are useful to open and read the HTTP URLs. The parse module exposes a standard interface for decomposing Uniform Resource Locator strings into components and urllib. It uses the urlopen function and can fetch URLs using a variety of different protocols. Some other functions are:
- urllib request for opening and reading URLs.
- urllib.parse for parsing URLs
- urllib.error for exceptions
- urllib.robotparser for parsing robot.txt files
Installation- Below command for the installation
pip install urllib
Theano: This tool is for multi-dimensional arrays. It helps to develop, optimize, and is useful for the optimization of array-based mathematical operations. It is useful in building deep learning projects. Theano is actually a hybrid of numpy and sympy, needing to combine the two into one powerful library.
Advantages of theano are:
- Stability Optimization: The tool can find some unstable expressions and can use stable means to evaluate them.
- Execution speed optimization: Theano can use GPU and execute parts of expressions in your CPU or GPU.
- Symbolic Differentiation: It is used to create graphs for computing gradients.
Installation : Command for installation
pip install theano
Python Requests: It is a simple and elegant python HTTP library created by Kenneth Reitz, which provides the methods for accessing Web resources. It also supports a variety of features such as browser-style SSL verification, automated decompression, content decoding, and connection pooling. There is no need to manually add query strings to URLs. Here it can be possible using JSON methods.
Installation: Command for installation
pip install requests
pip install requests
pip install requests
Pip Package: This is one of the most powerful python tools every developer should have used to install the python package. The syntax will be to type pip and type the name of the package (pip install package name), and this tool will download and install. We can use pip to install the package by command pip uninstall package name.
Sublime text: Text editor that creates clean python code. As it is beginner friendly, it has excessive Python API documentation. Other features such as multi-tab selection, and quick navigation to lines, files, and symbols are there. It supports many languages as well. The editor also supports the plugins that can be useful to enhance its capabilities. The latest version was Version 4 released on 20 May 2021. Some other features are:
- Autosave, attempts to prevent users from losing their work.
- Repeat the last session
- Editing commands, indentation correction
- Cross-platform -Windows, MACOS, and supportive plugins as well
- Python-based plugin API.
Scrapy: Python framework used for large-scale web scraping. It gives you a tool to efficiently extract data from websites, and process and store them in a preferred structure and format. This was originally designed for web scraping and it can be also used to extract data using API or as a general-purpose crawler. Examples like crawling to a website, extracting the title of the post, vote counts, and a number of comments.
- Python 3.7+
- Works on Linux, Windows, BSD
Installation: pip install scrapy
LXML: Python utility for libxml2 and libxslt designed for C libraries. It is usefulfor processing XML and HTML. LXML combines the speed and XML features of these libraries with the simplicity of native python. It extends the ElementTree API to provide support for XPath, RelaxNG, and XSLT.
All the adobe mentioned python development tools cover all the needs, features, and functionality. Whether you are a beginner or an experienced python developer, the above-mentioned python tools will help you to improve your efficiency and productivity in development.