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This includes features like routing, templating, request/response handling, a development server, unit testing support, and a few more. Even though Flask is one of the faster frameworks out there, there’s almost always scope for improving performance in web applications. Things might run fine when you serve minimal, static websites with ample compute resources. However, as things scale – in terms of the number of users, the amount of data you are working with, or just the complexity of your application, the same web app can be a sluggish nightmare. Django is a monolith, which means that it tries to be a complete, one-stop shop for your needs. By its own demanding nature, this can prove suboptimal for some developers.
No musician starts as an expert handler of their instrument. And even once they reach expertise in one topic, many python api design people return to the previous stage of the four stages of competence to develop their skills in another area.
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Python has many versatile ML specific libraries that include TensorFlow, Pytorch, Pycaret, and Keras. It also has packages for data manipulations and visualization like Numpy, Pandas, and Matplotlib. Since our software requires machine learning algorithms, big data processing and a backend server, Python seemed like the way to go. So, Flask is sufficient for almost all the machine learning models.
- Is a general purpose programming language that can be blazing fast and fun to work with.
- Lightweight frameworks often outperform larger frameworks.
- This code defines the icons_bp Flask Blueprint that exposes the files in the static directory under the /icons/ URL.
Given how popular it is, its community is very active for any problems that come up and is easy to hire for in the future. As it provides a lot of features out of the box and is extremely stable in performance.
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If you split this functionality into modules instead, then you could reuse complete modules across different projects. While you can start to write small scripts in Python after just a few days of study, you’ll probably spend around four months gaining an essential ability in programming with Python. You’ll have to spend years and build many projects to become a Python expert in even just one field. Learning Python means more than just learning the Python programming language. You’ll need to know more than just the specifics of a single programming language to do something useful with your programming skills. At the same time, you don’t need to understand every single aspect of Python to be productive. All of these are great reasons to get into programming!
What if you want a set of URLS, based on some pattern to go to a function? In fact, Flask will also parse the URL according to the pattern, giving you the pieces as arguments to your function.
Visiting Your Flask Server Endpoint
The view function index() is linked to the route / using the app.route decorator. To learn more about decorators, check out Primer on Python Decorators and Python Decorators 101. If you read online that someone learned Python quickly, then they were probably talking about this phase. Depending on your prior experience, you can learn enough Python relatively quickly that you’ll have the ability to work with the language. After you’ve gained this ability, you’ll step into the next phase, which is where most programmers spend most of their time. Start by learning Python’s syntax and fundamental programming concepts, then focus on a specific library that you need to solve your challenge.
When I was evaluating languages to write this app in, I considered either Python or JavaScript at the time. I find Ruby very pleasant to read and write, and the Ruby community has built out a wide variety of test tools and approaches, helping e deliver better software faster. Along with Rails, and the Ruby-first Heroku support, this was an easy decision. If you compare lines of code in the application written in Django as compared to the Flask, Django will always have more lines.