What is Python?
Python is a programming language that is widely used in web applications, software development, data science, and machine learning (ML). Developers use Python because it is efficient and easy to learn and can run on many different platforms. Python software is free to download, integrates well with all types of systems, and increases development speed.
What are the benefits of Python?
Benefits of Python include:
- Developers can easily read and understand a Python program because it has basic, English-like syntax.
- Python makes developers more productive because they can write a Python program using fewer lines of code compared to many other languages.
- Python has a large standard library that contains reusable codes for almost any task. As a result, developers do not have to write code from scratch.
- Developers can easily use Python with other popular programming languages such as Java, C, and C++.
- The active Python community includes millions of supportive developers around the globe. If you face an issue, you can get quick support from the community.
- Plenty of helpful resources are available on the internet if you want to learn Python. For example, you can easily find videos, tutorials, documentation, and developer guides.
- Python is portable across different computer operating systems such as Windows, macOS, Linux, and Unix.
How is Python used?
The Python language has several use cases in application development, including the following examples:
Server-side web development
Server-side web development includes the complex backend functions that websites perform to display information to the user. For example, websites must interact with databases, talk to other websites, and protect data when sending it over the network.
Python is useful for writing server-side code because it offers many libraries that consist of prewritten code for complex backend functions. Developers also use a wide range of Python frameworks that provide all the necessary tools to build web applications faster and more easily. For example, developers can create the skeleton web application in seconds because they don’t need to write it from scratch. They can then test it using the framework’s testing tools, without depending on external testing tools.
Automation with Python scripts
A scripting language is a programming language that automates tasks that humans normally perform. Programmers widely use Python scripts to automate many day-to-day tasks such as the following:
- Renaming a large number of files at once
- Converting a file to another file type
- Removing duplicate words in a text file
- Performing basic mathematical operations
- Sending email messages
- Downloading content
- Performing basic log analysis
- Finding errors in multiple files
Data science and machine learning
Data science is extracting valuable knowledge from data, and machine learning (ML) teaches computers to automatically learn from the data and make accurate predictions. Data scientists use Python for data science tasks such as the following:
- Fixing and removing incorrect data, which is known as data cleaning
- Extracting and selecting various features of data
- Data labeling, which is adding meaningful names for the data
- Finding different statistics from data
- Visualizing data by using charts and graphs such as line charts, bar graphs, histograms, and pie charts
Software developers often use Python for different development tasks and software applications such as the following:
- Keeping track of bugs in the software code
- Automatically building the software
- Handling software project management
- Developing software prototypes
- Developing desktop applications using Graphical User Interface (GUI) libraries
- Developing simple text-based games to more complex video games
Software test automation
Software testing is the process of checking whether the actual results from the software match the expected results to ensure that the software is error-free.
- Developers use Python unit test frameworks, such as Unittest, Robot, and PyUnit, to test the functions they write.
- Software testers use Python to write test cases for various test scenarios. For example, they use it to test the user interface of a web application, multiple software components, and new features.
Developers can use several tools to automatically run test scripts. These tools are known as Continuous Integration/Continuous Deployment (CI/CD) tools. Software testers and developers use CI/CD tools such as Travis CI and Jenkins to automate tests. The CI/CD tool automatically runs the Python test scripts and reports the test results whenever developers introduce new code changes.
What is the history of Python?
Guido Van Rossum, a computer programmer in the Netherlands, created Python. He started it in 1989 at Centrum Wiskunde & Informatica (CWI), initially as a hobby project to stay busy during Christmastime. The name for the language was inspired by the BBC TV show Monty Python’s Flying Circus because Guido Van Rossum was a big fan of the show.
History of Python releases
- Guido Van Rossum published the first version of the Python code (version 0.9.0) in 1991. It already included good features such as some data types and functions for error handling.
- Python 1.0 was released in 1994 with new functions to easily process a list of data, such as map, filter, and reduce.
- Python 2.0 was released on October 16, 2000, with new useful features for programmers, such as support for Unicode characters and a shorter way to loop through a list.
- On December 3, 2008, Python 3.0 was released. It included features such as the print function and more support for number division and error handling.
What are the features of Python?
Following features of the Python programming language make it unique:
An interpreted language
Python is an interpreted language, which means it directly runs the code line by line. If there are errors in the program code, it will stop running. Therefore, programmers can quickly find errors in the code.
An easy-to-use language
Python uses English-like words. Unlike other programming languages, Python doesn’t use curly brackets. Instead, it uses indentation.
A dynamically typed language
Programmers do not have to declare variable types when writing code because Python determines them at runtime. Because of this, you can write Python programs more quickly.
A high-level language
Python is closer to human languages than some other programming languages. Therefore, programmers do not have to worry about its underlying functionalities such as architecture and memory management.
An object-oriented language
Python considers everything to be an object, but it also supports other types of programming such as structured and functional programming.
What are Python libraries?
A library is a collection of frequently used codes that developers can include in their Python programs to avoid writing code from scratch. By default, Python comes with the Standard Library, which contains a lot of reusable functions. In addition, more than 137,000 Python libraries are available for various applications, including web development, data science, and machine learning (ML).
What are the most popular Python libraries?
Developers use Matplotlib to plot data in high-quality two- and three-dimensional (2D and 3D) graphics. It is often used in scientific applications. With Matplotlib, you can visualize data by displaying it in different charts such as bar charts and line charts. You can also plot multiple charts at once, and the graphics are portable across all platforms.
Pandas provides optimized and flexible data structures that you can use to manipulate time-series data and structured data, such as tables and arrays. For example, you can use Pandas to read, write, merge, filter, and group data. Many people use it for data science, data analysis, and ML tasks.
NumPy is a popular library that developers use to easily create and manage arrays, manipulate logical shapes, and perform linear algebra operations. NumPy supports integration with many languages such as C and C++.
The Requests library provides useful functions that are required for web development. You can use it to send HTTP requests, add headers, add URL parameters, add data, and perform many more tasks when communicating with web applications.
OpenCV-Python is a library that developers use to process images for computer vision applications. It provides many functions for image processing tasks such as reading and writing images simultaneously, building a 3D environment from a 2D one, and capturing and analyzing images from video.
Keras is Python's deep neural network library with excellent support for data processing, visualization and much more. Keras supports many neural networks. It has a modular structure that offers flexibility in writing innovation applications.
What are Python frameworks?
A Python framework is a collection of packages and modules. A module is a set of related code, and a package is a set of modules. Developers can use Python frameworks to build Python applications more quickly because they do not have to worry about low-level details such as how communications happen in the web application or how Python will make the program faster. Python has two types of frameworks:
- A full-stack framework includes almost everything that is required to build a large application.
- A microframework is a basic framework that provides minimal functionalities for building simple Python applications. It also provides extensions if applications need more sophisticated functions.
What are the most popular Python frameworks?
Developers can use multiple Python frameworks to make their development efficient, including the following frameworks:
Django is one of the most widely used full-stack Python web frameworks for developing large-scale web applications. It provides several useful features, including a web server for development and testing, a template engine to build the website frontend, and various security mechanisms.
Flask is a micro-framework for developing small web applications. Its features include strong community support, well-written documentation, a template engine, unit testing, and a built-in web server. It also provides extensions for validation support, database mapping layers, and web security.
TurboGears is a framework designed to build web applications faster and easier. These are some of its popular features:
- A specific database table structure
- Tools for creating and managing projects
- A template engine to build the databases
- A template engine to build the frontend
- Mechanisms to handle web security
Apache MXNet is a fast, flexible, and scalable deep learning framework that developers use to build research prototypes and deep learning applications. It supports multiple programming languages, including Java, C++, R, and Perl. It provides a rich set of tools and libraries to support development. For example, you can find an interactive machine learning (ML) book, computer vision toolkits, and deep learning models for Natural Language Processing (NLP), which processes natural language such as text and speech.
PyTorch is a framework for ML that has been built on top of the Torch library, which is another open-source ML library. Developers use it for applications such as NLP, robotics, and computer vision, finding meaningful information in images and videos. They also use it to run those applications in CPUs and GPUs.
What are Python IDEs?
An integrated development environment (IDE) is software that gives developers the tools they need to write, edit, test, and debug code in one place.
What are the most popular Python IDEs?
JetBrains, a Czech company that develops software tools, created PyCharm. It has a free community edition that is suitable for small Python applications and a paid professional edition that is suitable for building large-scale Python applications, with the following full set of features:
- Automatic code completion and code inspection
- Error handling and quick fixes
- Code cleaning without changing the functionality
- Support for web application frameworks such as Django and Flask
- Scientific tools and libraries such as Matplotlib and NumPy
- Ability to run, debug, test, and deploy applications in remote virtual machines
- A debugger to find errors in the code, a profiler to identify performance issues in the code, and a test runner for running unit tests
- Support for databases
- Works across many operating systems such as Windows, Unix, and macOS
- Provides a shell window to run commands and display the output
- Offers a multiple-window text editor that provides code syntax highlighting and automatic code completion
- Has its own debugger
Spyder is an open-source IDE that many scientists and data analysts use. It provides a comprehensive development experience with features for advanced data analysis, data visualization, and debugging. It also includes the following features:
- A rich code editor that supports multiple languages
- An interactive IPython console
- A basic debugger
- Scientific libraries such as Matplotlib, SciPy, and NumPy
- Ability to explore variables in the code
- Ability to view documentation in real time
Atom is a free editor developed by GitHub that supports coding in many programming languages, including Python. Using Atom, developers can directly work with GitHub, the website where you can save your code centrally. Atom offers the following features:
- Ability to use with many operating systems
- Easy installation or creation of new packages
- Faster automatic code completion
- Ability to search files and projects
- Easy customization of the interface
What are Python SDKs?
A software development kit (SDK) is a collection of software tools that developers can use to create software applications in a particular language. Most SDKs are specific to different hardware platforms and operating systems. Python SDKs include many tools such as libraries, code samples, and developer guides that developers find helpful when writing applications.
What is Boto3 in Python?
What is AWS PyCharm?
The AWS Toolkit for PyCharm is the plug-in for the PyCharm IDE that makes it easier to create, debug, and deploy Python applications on AWS. Using the AWS Toolkit for PyCharm, developers can easily get started with Python development. It provides several useful features for developers, including start guides, step-through debugging, and IDE deployment.