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Django vs. Flask compares two Python frameworks that can help you jumpstart your next web development project.
With Python placing in the top 15 of the most popular programming languages for over a decade, it is a frequent favorite for software developers. But this means even more scrutiny when it comes to choosing Python frameworks; such is the case with Django vs. Flask.
Through this Django vs. Flask side-by-side you will find that Django carries many advantages over Flask when you analyze their performance in the following nine categories:
- Admin Panel
- Web Framework
- Database
- Performance
- Security
- Flexibility
- Usage & Community
- Template Engine
- Reusable Components
Learning more important details about each framework will guide you along in making the decision that will best suit your business for the long term. Keep reading just below!
Django vs. Flask: Side-by-Side Comparison
The following side-by-side compares and contrasts Django and Flask. Both are Python-based, free, open-source web frameworks.
Admin Panel
A framework’s admin panel is important when giving different professionals on your software development team access to certain privileges.
Django comes with a ready-to-use admin panel. Developers can customize their admin interface and perform tasks related to user management and website control.
The equivalent of this is not readily equipped for Flask, although there is a Flask-Admin extension. This look-a-like extends much less configuration than its Django counterpart.
→ Django vs. Flask for admin panels: Django wins.
Web Framework
Note that Flask and Django are both Python frameworks, but have very different intentions.
Flask is a microframework with little to nothing in its box besides the framework itself. The benefit of this is the framework’s lightweight build. It does not require tools or libraries, encouraging the development of simple web applications.
Django is more or less Flask’s opposite. The design of Django emphasizes a batteries-included approach where you get everything you need right out the box. Resulting from this approach is the rapid development of complex web applications.
Evidently, Django and Flask differ, but their primary motivations do not make one framework better or worse in this regard. The most important factor is figuring out what works for you and your business.
→ Django vs. Flask for web framework: It’s a tie.
Database
Databases store information for your web application or website. No doubt, this is an important part of managing a website, from collecting inputs to saving user data.
Django uses object-relational mapping (ORM), a programming technique that allows developers to query and manipulate data via an object-oriented paradigm.
In Django, you can use its ORM systems with relational database management systems (RDBMS), namely Structured Query Language (SQL) and its posterity such as PostgreSQL or MySQL.
Flask, in its effort to remain light, does not come with a database system in its original packaging. However, this leaves developers with the flexibility to use whatever extensions and libraries they may need to accomplish any data-related tasks.
The downside to Flask’s way of doing things is that there’s a high learning curve involved in having to set up and manage your own databases. Flexibility comes at the price of knowledge.
→ Django vs. Flask for databases: Django wins.
Performance
Lightweight frameworks often outperform larger frameworks. Because of their size, speed is a natural perk. In essence, this means Flask is faster than Django, though the difference is negligible.
Both frameworks are deployed on high-traffic websites. And Django has specified capabilities promoting rapid development.
→ Django vs. Flask for performance: It’s a tie.
Security
Django has built-in protection against the most common attack vectors: cross-site request forgery (CSRF), cross-site scripting (XSS), and SQL injection.
CSRF is an attack that coerces end-users into performing unwanted actions on an authenticated website. XSS is a similar attack that injects malicious code into trusted websites. SQL injection does the same, but with databases.
Flask relies on third-party extensions, giving developers the freedom to set up their own security protocols. Flask-Security library provides the same protections as Django, but it isn’t standard to the framework, meaning more work for the developers who set it up.
→ Django vs. Flask for security: Django wins.
Flexibility
Flask is more flexible by design. There’s not much to contest here. The framework is meant to be extensible and leverages control through its simplicity. By design, Django has directly opposing intentions.
→ Django vs. Flask for flexibility: Flask wins.
Usage & Community
Django is older than Flask. Developed between the years 2003 and 2005, Django has at least a half-decade head start on Flask, which was released publicly in 2010.
Naturally, this means Django has a larger community, as it has several years of history behind its back.
As of March 2021, Django has about 260,000 Stack Overflow questions whereas Flask has 45,000 questions with its tag. The ratio of Django’s popularity to Flask’s in this instance is nearly 6 to 1. These numbers also indicate that more people are using Django.
→ Django vs. Flask for usage and community: Django wins.
Template Engine
Django has its own template engine which is said to save developers lots of time in development. Flask uses Jinja2 which ironically is based on Django’s template engine.
→ Django vs. Flask for Template Engines: It’s a tie.
Reusable Components
Both Django and Flask have reusable components. Django’s interpretation of reusable components is called apps; Flask’s is called blueprints.
As should be expected, Django apps are more complex than Flask blueprints. But once they’re set up they are often easy to use and re-use. Flask blueprints are easier to set up in the first place.
Given these nuances, neither Django nor Flask stand out for how they tackle reusable components.
→ Django vs. Flask for Reusable Components: It’s a tie.
Now that you know how Django vs. Flask compare in several categories, perhaps it’s time to see how each framework maps out without the burden of contrast.
Read on to find out more about Django and Flask.
Related reading: Python vs. Java - Side-By-Side Comparison
What Is Django?
Django is a web framework for fast-moving application building. Its origin story starts in 2003 when Adrian Holovaty and Simon Willison — two programmers at the Lawrence Journal-World newspaper — got tired of PHP and switched to Python.
Originally intended to be merely a content management system (CMS), Django was an attempt at making Python more scalable for the long term. They called the project Django after guitarist Django Rheinhart, and you know the rest.
Django utilizes a model-template-views (MTV) architectural pattern. The pattern prompts lots of questioning from developers who recognize the architecture as more or less the same as the popular model-view-controller (MVC).
Except in this configuration, the template of MVC corresponds to the views of MVC, and Django’s views correlate to the controllers.
Django designers simply explain this as a matter of perspective and most objective sources hold that Django’s architectural pattern is close enough to MVC to be categorized as such.
So how does MTV work? Essentially, it’s a software design separating the business logic from the graphical user interface (UI). The model handles data while the template covers the UI. The view executes the logic, working with the model to carry data and rendering the template.
Other than its architecture, Django prioritizes the reusability and pluggability of components, less code, low coupling, rapid development, and the principle of ‘don’t repeat yourself' (DRY).
Low coupling ensures that developers can make changes to modules without impacting related modules. And DRY focuses on eliminating repetition in logic by the means of automation and abstraction.
It’s also important to note that Django is based on Python, a language founded on the philosophy of simplicity and code readability.
What Are the Advantages of Django?
The Lawrence Journal-World newspaper is still up and running online, but since its development others have decided to use Django too. Here's why.
Scalable
Django brings a lot of high-traffic websites to its doors, and for good reason. There are many actions developers can take in regard to scalability. This includes running separate database servers, clustering, and load balancing.
Clustering allows developers to configure servers as a group providing extra availability and preventing data loss. From clustering, developers beget load balancing, where workloads are distributed evenly amongst multiple servers.
Popular
Like most open-source and free technologies, Django has a dedicated and enthusiastic community. Being active for over a decade, in consequence, Django is mighty popular. Use one of several vessels to learn and discuss Django and related topics like its mailing list, IRC channel, blog posts, and Stack Overflow.
Secure
Django protects against a number of attack types — XSS, CSRF, SQL injections, and more. The framework’s authentication system is also noticeably useful for developers.
Comprehensive
Again, Django is a batteries-included framework. In software, this means that you get almost everything you need right out of the box. This convenience is based on another popular software principle — ‘convention over configuration’ (CoC).
The CoC methodology works by including common conventions in software frameworks like Django so you can worry less about the configuration process and more about actual development.
For Django, the result is complimentary perks such as packages for auth, admin, Sessions, Messages, Postgres, and Sitemaps.
When Is the Use of Django Recommended?
Django enables rapid development for your web applications. Using Django, you can speed up complex web development for your next project. The Stack Overflow 2020 Developer Survey even found that Django was among the top ten frameworks for web development.
Python, in particular, is suitable on both the back-end and the front-end, meaning it is effectively a full-stack framework. Developers can build UIs with Django as well as work on back-end processes like server-side scripting, database querying, and application programming interface (API) building.
What Companies Use Django?
To better understand Django vs. Flask, you may want to know how other companies are using each framework. The companies using Django, for instance, are quite well-known.
Instagram is a massive social media network and app connecting people through photos, video clips, and daily stories featuring either medium. The platform is responsible for Django’s largest deployment.
After choosing Python because of its practicality and simplicity, Django was one of the only technologies that could increase the efficiency of the app once Instagram needed to scale.
National Geographic
National Geographic is an educational television network with a corresponding magazine spanning subject matter from history to science.
Their website uses Django to manage their website with their custom-built CMS. The CMS organizes images, advertisements, modules, and whatever else comprises the website.
Pinterest is another social network except users share pins emphasizing aesthetics and inspiration. The content users share is less personal but related to niche topics like cooking or fashion.
Django emboldens Pinterest to operate at a highly performant level with the capacity to scale where necessary.
What Is Flask?
Flask is a micro web framework. Like Django, the framework is written in Python. The gist of the framework is that if a third-party library can provide a specific function, then that function or library is not pre-equipped in Flask.
Consequently, Flask has no database abstraction layer, no form validation, or other standard functions. Armin Ronacher was a member of Pocoo, an international group of Python enthusiasts, when Flask was first created.
Initially, Flask was developed as an April Fool’s joke called deny.py. It was a single file with no installation or configuration required.
The fact that the project got traction, however, proved that there was merit in a framework that sported little to no dependencies. Hence Flask emerged.
What Are the Advantages of Flask?
In 2018, Flask was voted the most popular web framework among all Python frameworks in the Python Developers Survey. Then the question is, when we compare Django vs. Flask, what makes Flask so great?
Easy
Being built with Python, it goes without saying that Flask is easy to understand and use. But the best part is that it can get even simpler than that. The point of Flask is to be non-complex. Thus, the framework itself enables developers to navigate and create web applications with ease.
Flexible
Because Flask is made to be extensive, this offers more control to developers to build their website or application exactly the way they want. Every part of your configuration and application is open for change, making for the ultimate of customizability.
Small
Flask is uber lightweight. Smaller frameworks generally run faster and perform better than bulkier frameworks with similar functionality. Its minimalistic design makes it a great choice for developing a minimum viable product (MVP).
Test-Driven
Ronacher built Flask keeping in mind that testing is paramount. Leading with the tenet, “Something that is untested is broken,” Flask permits developers to write unit tests for their applications using a pytest fixture named client. Flask also has a fast debugger.
When Is the Use of Flask Recommended?
Flask is a framework for back-end web development. Therefore developers should use Flask for building server-side software.
The main advantage of Flask is having the opportunity to build from the ground up. The modularity that follows makes development and testing more efficient.
Reduced time spent in development, not to mention faster-run speeds, are just a few of the benefits a micro framework like Flask can promise.
Which Companies Use Flask?
Having an idea of what type of companies use Flask could make choosing between Django vs. Flask a little bit easier.
Netflix
You probably already know what Netflix is but just in case, here’s a review. Netflix is a streaming service proffering a wide variety of TV shows and movies to its subscribers. Much of the more analytical and infrastructure side of Netflix depends on Flask APIs.
Reddit is a bit harder to explain. It is a site hosting thousands of communities for conversation and connection between internet strangers. The concept is simple enough for Flask to be of use yet expansive enough that Flask still makes sense.
Lyft
Lyft is a ride-sharing app getting people from point A to point B whereas available drivers can be found in minutes. Flask is part of Lyft’s tech stack, embedding its microservices architecture.
Microservices is a software build that employs a suite of small services to deliver a more complex, singular app.
Conclusion
Python is a programming language delivering smooth sailing for developers who want ease and simplicity. But even within Python, certain frameworks can lay a foundation for your application which can lead to an increase in productivity and expediency.
Django and Flask are two sides of the same coin, and yet they face comparison regularly. If you’ve learned anything today, it’s that the best framework lies entirely in the goals for your project; to be sure, it’s about whether you want something small or complex.
That said, if you really can’t decide, the information here should be of use. The Django vs. Flask side-by-side comparison draws a lot of conclusions. Namely, Django does better than Flask in more categories than otherwise.
Want to hire Django developers? Well, Trio knows where to find them. Hire qualified Django developers for your project right now!
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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.
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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.
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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.
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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!