The great fascination with Python
Did you notice how everyone’s talking about Python these days? It’s not surprising, since it’s the fastest growing and the most loved programming language.
It’s versatile, which means it can be used in a variety of projects and across multiple industries, including data science, machine learning, blockchain, etc. These attract a lot of interest currently, which partly explains Python’s growing popularity.
What is the Python programming language?
Python is an interpreted, general-purpose programming language, which is one of the most popular choices for modern software development. Why?
Firstly, it’s modular — i.e., it can be easily integrated with other technologies and solutions.
Secondly, it’s open-source. There’s a vibrant community of developers who contribute to the development of the technology, and the Python Software Foundation oversees the quality and the direction in which the language is going.
Thirdly, Python is ‘interpreted’, which means it’s translated to machine code right before the program is launched. This facilitates writing portable and universal programs, which are easier to use on different operating systems.
These are just some of the reasons which we dive into in this post.
A short history of Python
Python was created in the early 1990s by Dutch programmer Guido van Rossum who happened to be a massive fan of the Monty Python’s Flying Circus and a British sketch comedy series created by Monty Python also known as “the Pythons”. Guido named the programming language “Python” as a tribute to his favorite comedians.
It is the fourth most used language — according to Stack Overflow’s 2020 survey, it’s used by 44.1% of developers. It’s also the third “most loved” language with 66.7% of developers admitting that it’s their favorite language.
It’s becoming increasingly popular in Machine Learning and Big Data — two emerging technologies that attract a lot of global interest. This further explains Python’s growing popularity.
Why is Python so popular?
Python is popular for a number of reasons. Here’s a deeper look at what makes it so versatile and easy to use for coders.
· It has a simple syntax that mimics natural language, so it’s easier to read and understand. This makes it quicker to build projects, and faster to improve on them.
· It’s versatile. Python can be used for many different tasks, from web development to machine learning.
· It’s beginner friendly, making it popular for entry-level coders.
· It’s open source, which means it’s free to use and distribute, even for commercial purposes.
· Python’s archive of modules and libraries — bundles of code that third-party users have created to expand Python’s capabilities — is vast and growing.
· Python has a large and active community that contributes to Python’s pool of modules and libraries, and acts as a helpful resource for other programmers. The vast support community means that if coders run into a stumbling block, finding a solution is relatively easy; somebody is bound to have run into the same problem before.
Python terms worth knowing
There are many Python terms that can, at first, seem quite intimidating to a non-technical person. Many of the popular phrases you’ll hear in reference to Python relate to its frameworks, libraries, objects, and classes.
A framework is a wireframe that serves as the foundation for software development. Developers use them to build programs for a specific platform. As a result, they don’t have to “reinvent the wheel” every time they work on a project.
1. Popular Python frameworks
Django is by far the most popular Python framework — in fact, you’ll see it’s a requirement in many Python developer job openings.
Django offers multiple out-of-the-box functionalities, for example an Admin Panel, Generic Forms, and ready-to-use components.
As the framework is full-stack (i.e. works for both the frontend and backend), it’s a popular choice for big software projects.
While powerful, Django enforces its own rules and coding style on developers, which makes it the best solution for projects where code flexibility isn’t a top priority.
Tornado
Tornado is a Python web framework and an asynchronous networking library which was originally developed by FriendFeed, a company acquired by Facebook in 2009.
While, in many ways, it’s similar to other Python frameworks (including the above-mentioned Django), what makes it stand out is its focus on speed and the ability to handle large amounts of web traffic.
As explained on the Facebook for Developers site, the framework also provides support for templates, security, user authentication, and localization. Tornado boasts an active user community and is considered one of the most modern Python frameworks.
Fast API
Fast API is the perfect framework for building APIs with Python 3.6+ — it’s modern and guarantees fast performance. Its main benefits include:
· Speed — it speeds up development time by 200% to 300%;
· Reduced bugs — eliminates about 40% of human-generated errors;
· Easy — it’s simple to use and learn;
· Short — cuts code duplication to minimum, which also results in fewer bugs;
· Robustness — you get production-ready code with automatic interactive documentation.
Flask
Flask is one of the most popular Python microframeworks. It’s lauded in the Python developer community for ease of use, speed, and reliability.
Flask is a popular choice for small and medium projects, where the implementation of Django would be too complex. Importantly, while it offers a variety of third-party modules, it’s also compatible with native, custom-made solutions.
2.Python libraries
Like frameworks, libraries are an extremely useful resource for software developers. They provide ready-to-use modules and functions that save developers tons of time, as they can be embedded directly into the code.One of the reasons behind Python’s increasing popularity is the wealth of libraries to choose from.
Examples:
· TensorFlow
TensorFlow is an open-source library developed by Google — in fact, many of us interact with the library daily via TensorFlow-powered services like Google Voice Search or Google Photos. The library is an extremely popular choice among Python developers working on machine learning projects.
One of its key functionalities are so-called “dataflow graphs” — i.e. structures that show the movement of data through a graph. The library is also easily trainable and flexible (i.e. you can select the parts you’d like to run with alone).
· NumPy
NumPy is another popular library leveraged for machine learning projects. It is valued by developers for its interactivity and simplicity, as well as the ability to implement complex mathematical processes quickly and easily.
Many developers also underline that it’s intuitive and easy to use, even for those who have no previous experience working with libraries.NumPy also has a large user community — the library gets updated regularly, with multiple open-source contributions.
· PyTorch
PyTorch is a Python-native library developed in 2017. Year after year, it has been gaining popularity among machine learning developers and researchers. In fact, it is currently the single biggest alternative to Google’s TensorFlow library, with its popularity skyrocketing from mid-2019 to mid-2020.
PyTorch is valued for its ease of use, intuitiveness, and simple API implementation. It’s also the more popular choice for new Python developers looking to learn a new library, as compared to TensorFlow.
3. Python objects and classes
Objects in Python are quite similar to the objects we surround ourselves with in real life. Namely, they have specific properties (in Python, it’s the data they store) and behaviors (i.e. methods). In Python, various objects which are created from classes can interact with each other to drive results.
In order for objects to deliver results, you need to provide them with the right data — and that’s where classes come in.
3. Python interpreter
Python is an interpreted language. This is done thanks to Python’s interpreter — i.e. a program that parses, processes, and executes code.
Interpreters are very helpful for running scripts — without it, source code would be simple plain text, not an executable program. For this reason, interpreters are leveraged for running scripts, small programs, and are often automatically installed on the majority of web servers due to their lightness.
What are the benefits of Python?
·Python is easy to use (and learn)
Python language was designed with simplicity in mind — the syntax is simple, and you can achieve a high level of proficiency faster than in the case of other backend languages.
· A big collection of libraries
There are many libraries for machine learning, game development, or for any other project. From a business perspective, this speeds up development time
· Fast development
A friendly, simple syntax and a variety of frameworks and libraries make Python development makes fast and productive.
.· Fast code review
Python offers a wide range of code review tools, including Flake8, Radon, and Pylint. we can quickly check the correctness and quality of your code.
· It’s portable
Python can run on major operating systems — Windows, Mac, and Linux. It can also work with some embedded systems like Raspberry Pi and Gumstix
· Flexible integrations
Python can be integrated with a whole range of other languages, including PHP, Java, and .NET.
Python’s disadvantages
· Speed limitations
Python is slower than other programming languages like Java or C++.
· Needs more testing
It doesn’t require programmers to define the type of variable as it uses Dynamic Typing, which makes coding easier and faster.
· It’s not native to the mobile environment
Since Python is not native to mobile, some developers perceive it as a poor option for mobile computing. Android and iOS don’t support Python as an official programming language.
· Relies on third-party frameworks and libraries
Python misses certain features that other modern programming languages have. These feature deficiencies are compensated with third-party frameworks and tools which enable the development of web and mobile apps.
· High memory consumption
Python is designed in a way that requires large memory consumption while processing, as opposed to other languages like C/C++.
When to use Python
Python in Machine Learning, Data Science and Deep Learning -Python includes virtual personal assistants, image recognition software, and product recommendations etc. It is a perfect solution for analytics, as it enables fast data processing and analysis. The whole process is simplified thanks to access to a variety of libraries like Panda, which helps with data visualization and statistical calculations.
Scikit and PyBrain provide Machine Learning algorithms, which are used in predictive analytics — valued by all financial service providers. Python’s simplicity, related to its easily readable code, gives programmers confidence in the projects they work on. Its simple syntax allows it to quickly test algorithms without actually implementing them
Cryptocurrency — businesses that sell cryptocurrency must regularly carry out cryptocurrency market analyses, for which they need the right tools. Python has access to a data science ecosystem, Anaconda, which enables developers to collect cryptocurrency pricing, analyze it, and create data visualizations. This is the main reason why web apps that deal with cryptocurrency analysis choose Python.
Python for Blockchain-It’s safe to say that Blockchain is considered one of the most disruptive technologies that came into existence recently. It’s advanced but easy to learn — it’s been around for a while and has a big community, which actively participates in the language’s development, making it stable and reliable. It has free packages for Blockchain specifically — this helps software developers write code faster and more efficiently. There are around 195 packages available just for Blockchain!
Data analytics and visualization-At a time when we’re creating more data than ever before, there is a need for those who can collect, manipulate and organize the information. Python has become a staple in data science, allowing data analysis and other professionals to use the language to conduct complex statistical calculations, create data visualizations, build machine learning algorithms, manipulate and analyze data, and complete other data-related tasks.Python can build a wide range of different data visualizations, like line and bar graphs, pie charts, histograms, and 3D plots. Python also has a number of libraries that enable coders to write programs for data analysis and machine learning more quickly and efficiently, like TensorFlow and Keras.
Examples of products and companies that use Python
· Netflix
Netflix uses Python extensively, starting with operations management and analysis, ending with security and networking.
· Spotify
Spotify relies heavily on analytics to provide suggestions and recommendations for their users. It uses a Python module, to interpret all the data.
It’s safe to say the lifestyle app used by millions of users worldwide would not be what it is today if not for Python. The app’s creators chose the language (more specifically, its Django framework) at the very core of the app’s backend.
It powers Google Voice Search, YouTube, libraries like TensorFlow with python
Facebook uses Python for a whole range of production engineering, configuration management, and service-related tasks. These include Facebook’s job engine, testing and repair management, and scheduling maintenance work.
