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In an ever-evolving 21st-century tech bubble overwhelmed with cutting-edge research scientists, full-stack developers, data scientists, and emerging entrepreneurs, finding the right programming language are almost like finding a needle in a haystack.
The search to find a high-level programming language while keeping end-to-end functionality, efficient runtime, scalability, and versatility in mind has been a challenge to almost every business starting their next big projects.
However, hitting that sweet spot of efficiency, functionality, and versatility has gotten a lot easier in the previous decade(s).
This is probably because of the emergence of Python – the modern holy grail of data science, machine learning, web scraping, and all of the above.
As a high-level and general-purpose programming language, Python has become a popular option for web app development, general software development, and data science/machine learning (ML).
This article will give an extensive overview and how Python is used, featuring ten examples of Python applications:
- Web Applications
- Console-Based Applications
- Data Science Implementations
- Desktop GUI Applications
- Software Development
- Business Applications
- Image Processing
- Game Development
- Web Scraping Applications
- Machine Learning and Artificial Intelligence
But first, you should understand that the fundamentals of Python programming and its roots in interpreted programming are essential.
What is Python?
Originally developed by Guido van Rossum in 1991, Python was released as an interpreted, high-level, general-purpose programming language with code readability and modularity in mind.
Each year, Python’s popularity reaches an all-time high with a current 31.73% share of popularity amongst modern development languages.
9 Traits of Python
You can understand the philosophy of Python through these nine traits of the developer-friendly language.
1. Code Readability
Python’s inherent syntax structure and seamless library/package imports allow for readability and conciseness.
The result – developers can structure comprehensible code and function blocks with logical organization and storytelling.
2. Cross-platform Versatility
Since Python uses internal C libraries and package installations, it can be used on various operating systems such as Mac, Windows, Linux, and Unix.
This cross-platform capability enhances software application development and development team collaboration.
3. Easy to Learn
Engineered as a high-level and expressive programming language, Python has a shallow learning curve with built-in functions and capabilities to call external third party dependencies and packages.
So long are the days of developing mammoth-sized algorithms in C.
With just under ten functional code blocks, beginner programmers can begin deploying baseline web applications and regression algorithms.
4. Open-Source and Community-Oriented
Since its early founding in 1991, Python has oriented itself with open-source community standards and long-term development frameworks.
With several community repositories open-sourced on Github, Python ensures that an inter-collaborative environment of developers works to release practical up-to-date libraries and packages.
5. Large Standard Library
As highlighted earlier, Python is full of built-in first and third party dependencies that enable application development and practical, high-level programming beyond manually implemented scripts made from scratch.
Python takes advantage of convenient code and functions that programmers can integrate into their scripting and app development processes.
The result is oftentimes over a thousand lines of conventional code broken down into under 10 functional lines of Python.
6. Free
Python and its associated standard libraries are free to download and implement.
More specifically, Python leads as a Free/Libre Open-Source Software (FLOSS) which allows users to freely distribute copies of the software, analyze its source code, and modify it accordingly.
7. Support for Exception Handling
Python supports exception handling, enabling you to write less error-prone code.
Exceptions are defined as events that can occur during program execution and disrupt the normal flow of a program.
Using exception handling also helps developers with debugging as they can test various scenarios that may or may not trigger exceptions.
8. Packaged with Advanced Features
Python supports list comprehensions and generator expressions.
This allows developers to use lists and generators efficiently with respect to memory and time consumption during program execution.
9. Automatic Memory Management
By effectively utilizing automatic memory management methods, Python clears and frees excess memory during and after execution.
For developers, this means less time clearing extraneous, residual program memory, and more time reserved for time and memory-sensitive programming.
Related reading: 12 Examples of Successful Companies Using React Native in 2021
What is Python Used For?
Imagine working tirelessly at a mega-corporate financial analytics firm, manually sifting through thousands upon thousands of quantitative revenue datasets.
That probably sounds gruesome, right?
But it gets worse.
After you’ve manually preprocessed data and deleted any remaining dataset anomalies and unnecessary financial variables, you have to perform predictive analytics and create a precise regression model that:
1) forecasts future revenue spikes and
2) uses global event patterns to anticipate critical stock movements and consumer behavior changes.
It is very difficult to create a predictive modeling architecture that both maintains accuracy on new and incoming variable data and efficiently forecasts revenue and stock movements in real-time.
Fortunately, by manipulating external Python machine learning frameworks such as Tensorflow/Keras/Pytorch and data visualization/linear algebra libraries such as Scipy and Matplotlib, you can automate the data preprocessing stage and engineer predictive ML models with out-of-the-box packages.
10 Examples of Python Applications
Hard-numbered data analytics is only one of many modern use cases where Python is helpful.
By strategically meshing Python package functions and libraries, developers and data scientists can create ML-driven web apps with frameworks such as Django or Flask or perform image processing scripts with Numpy.
In industry sectors such as business analytics, ML/Deep Learning models can be deployed to monitor changing customer behavior and automatically correspond those trends to product changes, advertisement strategies, etc.
On the other end of the aisle, emerging healthcare IT and biomedical engineering departments are actively leveraging deep learning/AI and Python frameworks to forecast the onset of terminal diseases and neurological/respiratory illnesses.
Python-driven applications span ‘coast to coast’ in terms of their ability to generate meaningful insights and impacts, sometimes in only a couple dozen lines of code.
Whether you’re manipulating data visualizations with data science techniques or extracting key insights with ML, Python could revolutionize how you extract meaning from information and develop new applications.
1. Web Applications
By taking advantage of Python web frameworks (e.g. Flask and Django), Python can be manipulated in the backend to build effective server-side web applications.
Although web frameworks are not required to construct a sustainable backbone for a web application, it’s rare that existing open-source libraries wouldn’t be implemented in conjunction with server-side Python development to speed up the app deployment process.
However, you should note that Python is not directly compiled and interpreted in the web browser.
Although projects such as pyjs can compile Python to JavaScript, Python developers typically interlace Python and JavaScript to create both server and client-side applications.
In other words, Python is solely executed on the server-side while JavaScript is downloaded to the client-side of the web browser and executed accordingly.
Although a mountainous number of Python web frameworks and micro-frameworks exist out there, Django, Flask, and Pyramid take the cake.
Let’s briefly explore Django and Flask below with consideration for their distinct complexity levels and learning curves.
Django
Established as an open-source Python framework, Django is notorious for building complex data-driven web application instances.
The framework is packaged with a variety of templates, libraries, and application programming interfaces (APIs) that allow for scalable web development projects.
It is the most popular large-scale framework, integrated onto leading websites, including Instagram, Pinterest, Bitbucket, The Washington Times, and Nextdoor.
Related reading: 7 Global Websites That Use PHP in 2021
Flask
Although Flask is better characterized as a micro-framework for Python-driven web applications, it still leads as an up and coming leader in concise, yet complex, data-driven web app programming.
As a micro-framework, it possesses built-in development servers and support for convenient unit testing.
Additionally, it is prominent as a Unicode-based Python micro-framework that supports RESTFUL request dispatching.
Flask also has extensive documentation for programmers to get a head start.
Now you understand how each of these frameworks differs, you can better understand the following example applications of these frameworks in action.
Since Flask has risen to prominence as a scalable, yet compact and efficient, mini-framework, an array of machine learning applications have been deployed.
Some stand-out examples include computer vision algorithms that can predict gender and diabetic retinopathy detection based on eye fundus photographs.
Now see what Django – the more complex of the two frameworks – can do for you.
The modern websites below have used Django, along with Sentry, to scalably deploy large-structured web and mobile applications:
- Disqus
- Spotify
- Dropbox
- The Washington Post
- Mozilla
- National Geographic
- The Onion
- National Aeronautics and Space Administration (NASA)
2. Console-Based Applications
Otherwise known as terminal applications, console-based applications are broadly defined as any Python-associated app that runs inside the terminal.
In the grand scheme of casual Python development, console-based Python deployments have the following advantages:
- They’re much more fun and fulfilling to write than simple script-based applications.
- Terminal apps enable you and involved users to play with complex code without ambiguous layers of abstraction between you and the user.
- You’ll learn about fundamental user interaction issues at a primitive level that doesn’t include wishy-washy UI/UX distractions.
Although console-based applications and command-line interfaces (CLIs) primarily exist to support convenient user interaction during more complex command-line processes, fun and practical use cases include:
- Command-line Python applications that consume the News API and return up-to-date world and national events.
- A simple terminal-based chatbot application built with ML/Tensorflow.
3. Data Science Implementations
Data science might be the most salient Python implementation of them all.
By using already-integrated code dependencies, such as Pandas, NumPy, or Matplotlib, amateur and expert-level Python programmers can seamlessly visualize and manually extract actionable insights from massive public and private datasets – both off and on the cloud.
At a high level, developers can even synthesize data science manipulations with ML models to extract, visualize, and predict features from unstructured data (e.g. imaging data, time-series stock market data, or house price predictions).
What’s more, open-source Github repositories are filled to the brim with business-level Python applications such as NLP-driven sentiment and toxic comment classification or future sales predictions.
Additionally, data science communities such as Kaggle provide that extra bit of inspiration for you to get started on novel data science solutions to big corporate problems.
4. Desktop GUI Applications
Admittedly, Python isn’t fully optimized for deploying business-grade desktop GUIapplications, at least not when you compare it to C and Java libraries.
However, developers can get started with official Python documentation and implement toolkits/frameworks such as PyQT, Tkinter, Kivy, WxPython, and PyGUI to create lightweight user interfaces with full software functionality in mind.
For example, this Github repository boasts 15 distinct PyQT-driven projects that use Python-driven UI and backend components to create responsive and functional Python applications.
A preview of these applications include:
- Untabbed Web Browser
- Tabbed Web Browser
- Minesweeper
- Notepad
- Calculator
- Word Processor
- Webcam/Snapshot
- Media Player
- Post-it Note
- Paint Software
- Unzip
- Translator
- Weather
- Currency converter GUI
- And Solitaire
5. Software Development
As Python rises to new and unforeseen popularity, product/project development pipelines have gradually adapted to Python’s set of frameworks and best practices.
Although software development with Python covers the technical trails of ML/Deep Learning, scripting, and web/OS-level application development, Python’s most advantageous contribution to software development is its ability to accommodate project ideation and development and advance applications from insecure to robust.
With Python, rising software developers can effectively fulfill the following development-focused goals:
- Advancing Python applications from conception through full-stack execution and app deployment by integrating Python frameworks and development tools.
- Contributing to private and open-source Python applications by regulating version-controlled workflows and collaboration with your development team.
- Understanding interactions between raw code and the client-side facet of your web apps/Python applications
- Writing secure apps that blockade and deter cybercriminals from exploiting common open patches and vulnerabilities.
- Adopting best practices in code review and portfolio development with your Python applications
6. Business Applications
With modern business applications covering the spectrum of e-commerce, ERP, and much more, having a scalable and open-source language is crucial.
Python allows businesses to develop scalable and fully functional applications right out of the box.
Platforms such as Tryton can be used by business analysts to develop and deploy Python applications.
The umbrella of business development in Python can encompass many tools including:
- Web development (e.g. Instagram)
- Data science solutions (e.g. Spotify w/ their Discovery Playlists)
- ML frameworks (e.g. Uber’s driver-rider algorithms)
- Natural Language Processing (NLP) (e.g. Google Home Assistant, Amazon Alexa, and chatbots for customer service).
- Video game development (e.g. Sims 4)
- Entertainment (e.g. Light and Magic studio)
- Blockchain-enabled contracts (e.g. Pyethereum)
- Fintech platforms (e. g. Citigroup and JP Morgan Chase)
7. Image Processing
Python is a useful tool for image manipulation and analysis. With its open-source dependencies, which can oftentimes be installed on your command line, you can extract and manipulate features with a mere 8-10 lines of code.
Whether it’s research, education, or industry-level applications, these following Python libraries provide you with convenient image analysis tools.
8. Game Development
In addition to research and business applications, Python has a critical role in backend game development with logic and mods.
Perhaps the most memorable Python applications in game development include the following:
- Civilization IV
- Battlefield 2
- Sims 4
- Toontown Online
- World of Tanks
- EVE Online
- Frets on Fire
Additionally, by using open-source game development libraries such as pygame, programmers can build simple games such as a Dungeon Game and Guessing Game.
9. Web Scraping Applications
By using prebuilt Python libraries such as Beautiful Soup, developers with minimal hands-on Python experience can extract specific, purposeful data from virtually any webpage.
At a higher level, Python web scraping libraries can be combined with NLP models to effectively scrape the surface web for textual and visual information associated with specific words, intentions, or emotions.
Examples include:
- Extracting table from Nepia.com’s shipping impact page
- Scraping dates and times that governmental safety orders were imposed
- Scraping information about trending Github repositories, such as name, stars, links, etc.
10. Machine Learning and Artificial Intelligence
In nearly every corporate and research-focused department, massive machine learning/deep learning models and architectures are beginning to be used to solve big data problems.
Whether it’s an ML algorithm that can generate new cancer drugs or a model that detects dangerous highway activity for an autonomous car, ML is bound to revolutionize every industry sector.
In short, machine learning can be divided into visual and non-visual tasks that are supervised, semi-supervised, or unsupervised. Examples of business and research-level ML python applications include:
- Classifying toxic and insincere questions on Quora with NLP
- Retrieving Google landmarks images with computer vision
- Generating images and transferring image styles
ML frameworks such as Tensorflow/Keras/Pytorch can be used to develop and deploy predictive and generative models.
Conclusion
Python is oftentimes underestimated as a rather simple programming language that wasn’t engineered with versatility in mind.
However, Python has emerged amongst the stars as one of the most popular programming languages.
Whether you’re building web applications with convenient Python frameworks or using data science and machine learning to optimize your business outcomes and strategies, Python produces actionable benefits at both a low and high level.
Trio has senior-level Python developers familiar with advanced libraries and frameworks to get the backend of your project up and running.
If you’re starting a Python application project, consider hiring Python developers at Trio!
Daniel Fleury
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The fintech industry is powered by a venerable tech stack behind the scenes. These technologies are responsible for the billions of dollars flowing into fintech companies in the past 3-4 years.
Python is currently one of the most loved programming languages according to the Stack Overflow Developer Survey 2022. It also makes for a versatile fintech development tool owing to its vast ecosystem of financial libraries. Plus, it's simple syntax and high-level language tenets make it easy to learn for non-technical people.
Read on to learn how to leverage Python in finance and the libraries to help you do it.
Why Is Python Widely Used by the Financial Sector?
The finance sector and fintech in particular operate in a fast, competitive, and heavily-regulated environment. Time-to-market is key and efficiency and ease of use are highly desired in any technological tool.
Python lends itself well to fintechs by scoring high on the above metrics and more. The most significant advantages it brings to the fintech sector are:
Accelerated Time-To-Market
The fintech space is marked by hundreds, if not thousands of players vying to crack the next big solution and get funded. There really is no place for laggards or technologically hesitant companies. Python’s flexible and scalable code enables developers to quickly build a minimum viable product (MVP) and test its efficacy on an existing market problem.
Companies no longer need months of work to test the waters with a new idea. Once they’ve validated their MVP, they can easily modify and refine it, thanks to Python’s performant code.
Increased Simplicity
Python was designed with readability in mind. Its syntax resembles plain English and is fairly easy to learn and master which is especially useful for designing complicated financial models. It’s also known for its concise code and is hence perfect for deploying large financial applications.
Its simplicity also minimizes potential errors and can save organizations time and money by avoiding penalties in a heavily regulated industry.
Unprecedented Functionality
Fintech developers don’t need to design their applications from the ground up. Python makes the job easy with its rich collection of standard and third-party libraries and tools.
Financial software involves analysis, algorithms, models, and other complex mathematical concepts. By providing these solutions built-in using various libraries and third-party integrations, Python surpasses other programming languages.
Rising Popularity
In terms of popularity, Python sits well above Java and C++ which are also commonly used by the financial sector.
Python provides stellar support to fintech developers, due to its open-source codebase and excellent support from a passionate community. These factors naturally provide fintechs with ample Python talent to choose from.
Better Versatility
Economists and other finance professionals don’t need to get their MATLAB models translated into Python code. Python supports end-to-end development of financial models and solutions in a single environment. It’s a versatile tool for prototyping and development for both small and large applications.
4 Applications of Python in Fintech
Fintech applications encompass a wide range of sub-verticals and mathematical disciplines. The beauty of Python is that its multiple libraries give developers access to countless pre-built instances of financial models and solutions.
Here are four major applications of Python in finance.
1. Data Analysis & Visualization
Making sense of large and complex datasets and visualizing them for further predictive analytics is at the core of many fintech solutions. Python libraries are equipped with robust data visualization, statistical analysis, and machine learning capabilities. Pandas, PyBrain, and Scikit are some examples of such libraries.
2. Banking Software
Banking applications, both traditional and modern, make use of Python’s simplicity and scalability. ATM software uses Python. So does Venmo for its payment services. Many other fintech companies like the payment processor Stripe and the trading platform Robinhood utilize Python for their platforms as well.
3. Cryptocurrency
With new players entering the cryptocurrency space almost every day, the demand for market analysis programs is also increasing. Companies dependent on analyzing cryptocurrency prices and offering predictive insights need these programs on a daily basis.
Data analysis tools like Anaconda and many decentralized platforms on the blockchain utilize Python and its libraries to empower their code.
4. Stock Trading
Python’s impressive data analysis capabilities are a great fit for designing stock trading strategies and offering predictive insights into the markets. Algorithmic trading is no longer limited to large corporations. Anyone can use Python’s libraries to design their own predictive algorithms for stock trading.
What Are the Advantages of Python Over Excel?
Microsoft Excel is one of the most preferred tools for data analysis and visualization. It’s easy to pick up and offers a decent degree of functionality. But Python leaves Excel in the dust when it comes to working with data. Here’s why:
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Better Data Import & Processing: Cleaning multiple large datasets in Excel isn’t really what you’d call simple. It’s tedious and time-consuming. Python can easily recognize and clean both structured and unstructured data much faster than Excel.
Plus, performing tasks like merging or recoding data are also much simpler in Python.
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Powerful Automation: Excel is many things but it isn’t an automation powerhouse. If you wanted to run the same analysis week after week with different data, you’d have to do it manually in Excel.
But in Python, you can write a simple script that imports the new data every week and performs the analysis automatically, while also outputting the results in your desired format.
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Easier Debugging: When something doesn’t go according to plan in Excel, you’re given an error notation and left to fend for yourselves. In Python, however, you'll get an error message explaining what exactly went wrong.
Moreover, if you’ve left comments in your code, you’ll have additional information to help you fix the source of the error.
-
Open-Source Accessibility: Excel might help you do a lot with your data, but you’re ultimately dependent on Microsoft for feature updates and support. Whereas Python is a free, open-source language which can anyone can update and add functionality to.
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Superior Statistics & ML capabilities: Excel isn’t suited to building advanced statistical and machine learning models. Python, with its extensive third-party libraries, has superior tools to easily create advanced data analysis models.
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Advanced Data Visualization: Data visualization is essential for effective communication with stakeholders. Although Excel has a robust data visualization interface with its charts and graphs, Python lets you do much more with your data.
You can create more customizable graphs and charts and use techniques that are simply much more flexible as compared to Excel.
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Cross-Platform Portability: Python’s scripts are portable across platforms like Windows, MacOS, Linux, and more. This isn’t common with other preferred technologies in the fintech industry, including Excel.
The 10 Most Useful Python Packages for Fintech
Now that you’re aware of Python’s incredible functionality for building fintech applications, it’s time to learn the most useful packages or libraries that will actually help you do it.
1. NumPy
NumPy (pronounced num-pi) is one of the most basic yet essential libraries in Python. It brings several mathematical and scientific computing capabilities to the language, which other libraries on this list build on.
It introduces concepts like n-dimensional arrays and matrices into Python and contains basic functions to manipulate such data structures.
2. SciPy
SciPy introduces the tools to build sophisticated data models from the basic mathematical structures provided by NumPy. Any statistical model requires algorithms for tasks like clustering, interpolation, transformation, integration, and more.
Hence, SciPy is the library providing developers with the advanced techniques to build predictive data models, a mainstay of many fintech applications.
3. Pandas
Pandas is a popular Python library known for its ‘DataFrame’ and ‘Series’ structures that are specially designed for data analysis and model building. Pandas is optimized for multiple kinds of data like tabular, multidimensional, and heterogeneous. You can also create basic visualization plots using this library.
It also provides concise and powerful functions for importing and manipulating data.
4. statsmodels
While SciPy provides advanced statistical tools and Pandas helps implement them, statsmodels introduces more thorough testing of different statistical models. Diagnostics and results statistics for every model are available and the results are tested against statistical packages to ensure accuracy.
5. Quandl
Quandl allows you to access financial datasets from central banks, government websites, multinational companies, and stock markets to help you build the financial model you desire. You simply need an API key to access most of the raw datasets for free, with more in-depth ones available for a fee.
6. Zipline
Zipline brings many of the above libraries together to become an algorithmic trading library. Quantopian, a popular platform for building trading strategies, is powered by Zipline.
It imports data from Quandl, and helps you design and implement custom trading algorithms. It also includes backtesting of algorithms and live trading.
7. Pyfolio
Pyfolio enables fintech developers to generate tearsheets containing performance statistics pertaining to the algorithms they designed with Zipline. Stats like annual returns, Sharpe ratios, portfolio turnover, and more are easily available through Pyfolio.
8. TA-Lib
Technical Analysis Library or TA-Lib can be used as an alternative to Zipline and Pyflio. It’s a C++ library with a Python wrapper available. TA-Lib provides common financial tools like momentum indicators, volatility indicators, cycle indicators, pattern recognition, and pure statistical functions.
9. QuantLib
QuantLib is another alternative to Zipline and Pyflio. It is also written in C++ and then exported to Python. Quantlib is optimized for building tools related to modeling, trading, and risk management.
Its algorithms include features like yield curve models, solvers, Monte Carlo analysis, market conventions, and more.
10. Matplotlib
While Pandas offers some basic visualization tools, it’s not a data visualization library like Matplotlib. It is one of the easiest packages to implement for financial modeling. It has a simple syntax and extensive documentation.
Supercharge Your Python Project With Trio
Python is primed for fintech development but excellent results require excellent developers, If you’re in the market for hiring Python developers for your project, Trio can help make your job easier.
Trio developers are world-class professionals and are thoroughly vetted to guarantee you get the best results for your projects. Partnering with Trio also means you don’t need to pay inflated hiring costs or get bogged down by HR processes. Trio handles everything for you, end to end. Contact Trio today to learn more about connecting with top-tier Python talent from around the world.
Python is known as a versatile, general-purpose programming language. It is an open-source high-level language that’s easy to learn due to its use of plain English syntax. Due to numerous advantages (that we’ll address later), the multi-paradigm programming language lends itself well to varied use cases.
From its initial days in 2000, Python 2 came a long way from being a new kid on the block to becoming a favorite of programmers. With Python 2.7 being the last major upgrade back in 2010, Python 2’s end-of-life date was January 1, 2020.
Python 3 was released as an upgrade to Python 2 in 2008 and the two versions co-existed for around a decade. Today, Python 3 is used by 95% of Python developers. If you’re in the market for Python developers, you might favor Python 3 over Python 2.
But, what exactly is the debate when it comes to Python 2 vs. Python 3? And is Python 2 still relevant? Read on and you might be surprised by the answer.
What Is Python 2?
Python 2.0 was meant to bring more inclusivity and coding literacy to the masses. Before its release in 2010, Python was largely supported by its creator Guido van Rossum who developed the original version of the language in 1991.
With Python 2, Rossum opened up Python's development and improvement to the larger developer community. As the community contributed to future releases and improved upon its codebase, Python 2 grew to become one of the most commonly used languages in the world.
Python 2 took the language’s original objective of spreading coding literacy to another level due to its array of improvements over previous versions.
It has also played a major role in the development of languages like Perl and Ruby and is used by organizations like Netflix, Spotify, Reddit, Uber, and Instagram today.
What Is Python 3?
Python 3 was released in 2008. It sought to improve upon Python 2 without making radical changes with a new 2.x release. Hence, Python 3 came about as a separate branch of the overall Python ecosystem. By design, Python 3 is backward incompatible with Python 2.
The user base of Python is split between Python 2 and 3 depending on differences in use cases and their willingness to undertake complex migrations.
From web development and computer graphics to machine learning and data analytics, the two versions of Python were suited to different use cases. Even now, Python 2 is preferred over Python 3 when coding for computer graphics, games, and mobile development.
Although some features of Python 3 have been brought over to Python 2 to facilitate easy migration to Python 3, the process still requires considerable effort.
What Are the Differences Between Python 2 and Python 3?
With Python 3 being such a major departure from Python 2, there are bound to be major differences between the two versions.
- Backwards compatibility: While Python 2 code can be ported to Python 3 with some effort, Python 3 is not backwards compatible with Python 2.
- Syntax: Python 2 has a more complicated syntax and is more difficult to understand compared to Python 3, though the syntaxes are similar.
- Modern usage: Python 2’s usage has virtually ceased while Python 3 is more popular than ever and is used by 95% of Python developers.
- Application: Python 2 is preferred for select use cases like mobile development and computer graphics while for everything else, Python 3 is still the version of choice.
- ‘Print’: While Python 2 considered ‘print’ a statement, Python 3 considers it a function.
- Storage of strings: Python 2 stores strings in ASCII by default while Python 3 stores them in Unicode.
- Integer division: Python 2 delivers an integral value when dividing integers whereas Python 3 delivers floating-point values,(e.g., 9 divided by 4 will yield 2 in Python 2 but 2.5 in Python 3).
- Exceptions: Python 2 encloses exceptions in notations whereas Python 3 encloses them in parentheses.
- Variable leakage: Variables are mutable in Python 2 (e.g., when used inside a for-loop). But in Python 3, variable values are not changeable.
- Iteration: In Python 2, the xrange() function is used for iterations but in Python 3, the new function Range() is used instead.
Why Move To Python 3?
Python 2.7, released in 2010, didn’t see any successor, and support for Python 2 was scheduled to end on January 1, 2020. That date has come and gone, and with it, Python 2 has officially become obsolete.
So, as an enterprise, should you move to Python 3? Yes, absolutely.
Python 3 is the modern version of the language and apart from enjoying the variety of benefits listed before, it’s the more secure and up-to-date version. If you’re still running Python 2, your systems are prone to security issues and bugs.
Python 3 also has a much larger community that regularly fixes bugs and works on newer releases to improve the functionality and stability of its codebase. You get access to better performance and always-updated features.
Python 3 was developed as a more readable and easier-to-comprehend version of the language.
Why Stay With Python 2?
While Python 2 is obsolete now and poses security and stability concerns if you're still running it on your systems, there might still be a reason to stay with it.
Many companies have applications written in Python 2 and don’t want to migrate to Python 3 for fear of breaking the applications. Migration does tend to be an effort-heavy task and requires not just time but also a significant monetary investment.
Maintaining such legacy applications means retention of Python 2 code alongside efforts to adopt Python 3 for newer development tasks.
Which Version Is Better: Python 2 or Python 3?
After going over the differences between the two versions of Python and the reasons to ‘stay or move’, one could wonder which one is the better version.
Advantages of Python 3
Python 3, by all means, is the better version of the two. The Python 2 vs. Python 3 debate doesn’t really hold water owing to the vast improvements Python 3 has over Python 2.
In addition to performance, syntax, and all other quality-of-life improvements, Python 3 is also the better option in terms of security and reliability. The ever-growing Python 3 community ensures bugs are readily fixed and newer features are introduced regularly.
Advantages of Python 2
Python 2 does lend itself better in some use cases, as we discussed above. If you’re working with computer graphics, games, or mobile development, it’s worth checking out Python 2.
For developers, perhaps the only other reason to learn or work with Python 2 is to get skilled in Python 3 migration. Moreover, companies require Python 2 developers for maintaining legacy Python 2 code.
How To Choose the Best Python Version To Use?
Python 3 might be the better version, objectively, but you still need to evaluate your needs and choose the best Python version for your project.
If you’re a fairly new organization, chances are that you’re not using Python 2. In that case, there’s fairly no reason to consider Python 2 unless you’re working with specific libraries that aren’t yet compatible with Python 3.
If you do have legacy Python 2 applications and don’t want to migrate to Python 3 yet, you can retain Python 2 and leverage developers for maintaining your codebase. But it’s recommended that you plan for migration to Python 3 as soon as possible. Also, try to choose Python 3 for all new development projects.
Regardless of your Python version, you’ll need to hire Python developers for development, migration, or simply maintenance projects.
Trio can help you hire world-class Python developers without investing thousands into a lengthy recruitment cycle.
When you partner with Trio, you don’t have to incur hiring costs, which can amount to a considerable percentage of a developer’s annual salary. Trio developers are well-trained, driven, and committed to the long run.
Moreover, you don’t need to handle HR functions like payroll, benefits, and compliance. Trio takes care of all of that for you.
Conclusion
Python is easy to pick up and learn, which also leads to a vast number of Python developers vying for in-demand Python roles. You need to thoroughly vet and interview the candidate pool to hire the right developers for your project. And that can sometimes take months.
Trio offers you plug-and-play recruitment and connects you with verified and senior Python developers from around the world. Contact us today to learn more about how we can help scale your next project to new heights.
Python frameworks provide the basic infrastructure for developing a robust Python software application. Depending on the software project your team is trying to build, there are better and worse Python frameworks suited for its purpose.
Sources demonstrate that Python's popularity has been steadily increasing in the past decade or so, with approximately 15% of Stack Overflow questions tagged “python” in 2021.
And as such, the corresponding technologies of Python gain similar popularity. It is common to see questions like “What is a Python framework?” or side-by-side Python framework comparisons.
Today, you’ll learn about numerous Python frameworks to use for your software development project. But first, what is a Python framework? Stick around to find out!
What Is a Python Framework?
A Python framework, like any framework, cushions your software development project with a foundation or substructure to build on top of. To be clear, software frameworks render the generic functionality of the program you’re trying to create, so your developers don’t have to start from scratch.
Python frameworks in particular, will automate standard application building steps using the Python programming language. As a result, Python developers will save time on development and your business will benefit overall.
Where Is the Use of Python Most Recommended?
Python is a general-purpose, interpreted, object-oriented programming language. The designers of the language prioritized code readability whilst building Python, making the language highly productive.
Readability being an important part of Python’s founding philosophy, Python’s most salient representations of this tenet include its intuitive use of the English language and easily navigable code blocks.
Python’s prime asset is its significant white space, which developers use to isolate code blocks. This tactic encourages easy-to-read code and gives a more friendly appearance to the program compared to other programming languages that use special characters such as brackets.
Released in 1991 by Guido van Rossum, today Python is known as a “batteries included” language because of its large standard library. Python libraries describe pluggable pieces of code that you can work into your program with a simple function call.
The Python Standard Library is what comes with the language right out of the box. This library is notably extensive, with high-level data structures like dictionaries and lists, turtle graphics, and more.
The Python programming language performs well on both the front-end and back-end of development. Moreover, developers use Python to build a variety of software, from web development to machine learning to data science.
Much of Python’s capacity for a diverse array of programming capabilities involves its simplicity and its standard as well as additional libraries.
Python Framework vs. Python Library
Some people wonder about the difference between Python frameworks vs. Python libraries. Although they both offer developers operational functionalities, their similarities end when it comes to specificity and complexity.
For example, Python libraries are filled with dozens of useful functions for specific purposes, but there is not much complexity. Python libraries perform a simple task and are ultimately a small piece of a large puzzle.
Because frameworks work off a specific comprehension of what your application might look like, even more specificity is permissible. Complexity is the name of the game. Thus, a Python framework will outline the puzzle; the more complex the framework, the more outlining of the internal pieces.
The 3 Main Types of Python Frameworks
Python frameworks come in all shapes and sizes. Factors that may affect which framework you choose include scalability, expertise, and your business-specific goals.
Full Stack Framework
Full-stack frameworks are suitable for both back-end and front-end development. Web development consists of front-end tools for graphic user interface (UI) design and back-end services like databases, security protocols, and business logic.
A full-stack Python framework will carry all the equipment you need to facilitate full-stack development. Django is a full-stack framework and is the second most popular Python framework.
Microframework
Microframeworks are by definition lightweight. In some ways, it is the opposite of a full stack framework. Python’s official Flask documentation explains that the “micro” in microframework signifies that the framework’s “core [is] simple, but extensible”.
The components that are fundamental to a full stack framework like a database management system and certain security measures do not come naturally to a microframework.
While this might seem like a bad thing, it actually encourages flexibility for developers who want to leverage control over their software, only adding in the relevant third-party libraries when they’re completely necessary.
Related reading: Ruby vs. Python - Which Programming Language To Choose?
Asynchronous Framework
Asynchronous programs are event-driven. Rather than line by line operational handling where one function runs after the other, asynchronous code is non-blocking and doesn’t wait for one event to execute before starting another.
Because of this parallel programming technique, asynchronous frameworks allow for a profusion of high-performance concurrent connections via running on an async-capable server.
10 Awesome Examples of Python Applications
25 Python Frameworks To Use In 2023
In total, there are dozens and dozens of Python frameworks. But since you care about your time, here are 25 Python frameworks that stand out the most.
1. CubicWeb
CubicWeb fits the definition of a full-stack framework. It is also a semantic framework. Semantic technologies rely on ontological and conceptual comprehension to understand data.
Though the typical model-view-controller (MVC) architecture is more commonly in use for developing UIs, CubicWeb uses the concept of cubes. Cubes join together to create an instance with configuration files, a database, and a web server.
2. AIOHTTP
AIOHTTP is an asynchronous framework as well as an HTTP client/server for asyncio. Though some of these words may seem foreign, the gist is that AIOHTTP allows your developers to build asynchronous clients and servers for your web application.
3. Dash
Dash is a microframework best suited for building analysis-oriented Python applications. The framework is a frequent choice for businesses that have stakes in machine learning and data science.
4. Bottle
Bottle is another microframework. Fast and simple, its original design was specified for building application programming interfaces (APIs).
Like the best of microframeworks, Bottle emphasizes its lightweight build, namely by implementing everything into a single source file and relying on few dependencies. Other important features of Bottle include routing, template, utilities, and a built-in HTTP server.
Bottle works closer to hardware than other frameworks. However, you should opt for a more complex framework if you think your program might span longer than 500 lines of code.
5. CherryPy
CherryPy calls itself a minimalist framework. With CherryPy, you can create an entire application using the principles of object-orienting programming. As a result, development is faster and easier.
6. Falcon
Yet another API-oriented framework is Falcon. Falcon is a minimalist microframework geared towards rapid application development. Developers can take advantage of Falcon to build RESTful APIs, app backends, and microservices without the burden of unnecessary abstractions.
7. Django
Django is one of Python’s more popular Python frameworks. Being full stack in nature, Django comes equipped with the tools to build large and complex web applications.
8. Flask
Flask is historically in competition with Django. Django and Flask are the top two most popular Python frameworks. That said, they are wildly different.
Unlike Django, Flask is a microframework and does not hold any tools or functions that a third-party library can fulfill as essential to its packaging.
9. Giotto
Giotto is a Python framework stressing the separation of concerns that the classic MVC architecture elicits. Because of Giotto’s generic implementation of views, models, and multiple pluggable controllers, the framework is a vessel for simplifying and shortening development.
10. Growler
Growler is both an asynchronous framework and a microframework. The framework uses Python’s asynchronous library, asyncio, along with middleware to process HTTP requests.
11. Pylons
Pylons borrows the finest ingredients of Perl, Python, and Ruby to bring to life a full stack framework with a focus on rapid development. Similar to Flask, Pylons is lightweight and flexible.
12. Pyramid
Pyramid is a leading full stack Python framework. Scalability is its biggest asset as small and full-scale applications can depend on Pyramid for their development.
On its official website, Pyramid calls itself a “Goldilocks Solution: not too small, not too big, just right.”
13. Sanic
Sanic shares similarities to Flask, but its asynchronicity makes it even faster than Flask. Benchmark tests show that Sanic can handle tens of thousands of requests per second.
14. Hug
Hug is a microframework for developing APIs. Labeled the fastest framework for Python 3, Hug is paving the way for the technology of the future.
15. Morepath
Dubbed the “Super Powered Python Web Framework”, Morepath is an expressive model-driven microframework providing routing to its models.
Like with MVC patterns, models represent data and views represent the UI. In Morepath, an application consists of models which are routed to a URL path. Content is expressed to the web through views.
16. Pycnic
Pycnic is an object-oriented microframework empowering developers to build APIs based on JSON. JavaScript Object Notation (JSON) is a syntax for manipulating data stored in objects.
17. Tornado
Initially developed to support a social media company named FriendFeed that has since been acquired by Facebook, Tornado is an asynchronous Python framework that can handle several thousand concurrent users at once with a non-blocking network I/O.
18. TurboGears
TurboGears has the versatility for use as a full stack framework or a microframework. The framework is data-driven, user-friendly, and extends a templating engine and an object-relational mapper (ORM).
19. Web2py
Web2py was originally built as a teaching tool, but as a full stack framework, it is loaded with tons of features to do so much more. For instance, there are no requirements for installation and configuration; web2py is cross-platform; and it comes with its own integrated development environment (IDE).
20. FastAPI
FastAPI is a bit self-explanatory. It’s a modern Python framework for building APIs with features to speed up the development process. Much of its design owes it credit to Flask.
21. Grok
Grok leverages important programming principles such as ‘don’t repeat yourself’ (DRY) and ‘convention over configuration’ (CoC) to promote an agile development experience.
DRY strives to reduce repetition by standardizing abstractions. Likewise, CoC aims to decrease the grueling decision-making process for developers by starting them off with a conventional foundation to build up from so they can save time on configuration.
22. BlueDream
BlueBream is a Python framework that is most optimal for building medium and large applications. More than a framework, BlueBream is a server and library too. Some of its best features are its component architecture, transactional object database, and integrated security protocols.
Related reading: Python vs. Java - Side-By-Side Comparison
23. Quixote
Quixote is a simple and flexible Python framework for designing web applications. Anyone with a good knowledge base in Python should thrive with Quixote.
24. Weppy
Weppy — stylized as the lowercased “weppy” — is a full stack Python framework that centers simplicity. Its elegant syntax encourages developers to focus on the product the’re building rather than struggling to navigate the framework itself.
25. Zope
Zope is an application server and web framework. Its primary use case is for the development of content management systems (CMS).
How To Choose the Best Python Framework To Use
Using a Python framework is a sure-fire way to speed up and optimize development. Python frameworks are not a must, but it would also be unwise to neglect the opportunity to enhance your development process where at all possible.
To figure out which Python framework is best for your software project, start by evaluating the different types of frameworks you can choose from. Three major categories of frameworks stand out: full stack frameworks, microframeworks, and asynchronous frameworks.
Microframeworks are small yet flexible; full stack frameworks are complex yet scalable; and asynchronous frameworks amplify Python’s capacity for concurrency.
Once you pick which class of Python frameworks is a good fit, you can narrow your choices down by focusing on the frameworks themselves. Listed above are 25 different frameworks with different capabilities.
You should choose a Python framework based on scalability, speed, your developers’ expertise in deploying framework-specific skills, and a number of other considerations that may be conducive to your business.
Going through each and every Python framework probably isn’t worth your time. But what you’ve learned here can better help you pinpoint exactly what you need.
Conclusion
What is a Python framework? It’s a tool to empower your next Python project, but only if you use the right one.
There are a diverse bunch of Python frameworks out there for you that will give you and your software development team a head start on your 2022 project. The Python framework world is your oyster.
Having a good understanding of how your next project will work, even if you're not tech-savvy, will be crucial to choosing the optimal tech stack to support your development.
Research is never a bad thing and if you need to consult with some of the more qualified minds in the industry, there’s no shame in it. That’s why Trio is here to help.
Trio can guide you in choosing the best Python framework for your 2022 project as well as build the project itself using the parameters you set.
Contact Trio now to hire the best Senior Python Developers and start your project today!