Bottle
Django
Flask
Node.js
Ruby on Rails
Laravel
Meteor
ASP.NET
NumPy
Pandas
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
WEKA
BottleBased on our record, NumPy should be more popular than Bottle. It has been mentiond 107 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
We will use Bottle a lightweight web framework for python. This is the first time I use python to build a web server and it was a very positif experience. With Bottle.py, all you need is:. - Source: dev.to / 9 months ago
Flask is simple and lightweight and as you said it give you flexibility. But if you want to have something that give you more flexibility and control over everything else besides the routing and server loop I would suggest bottle. It is a microframework, it is faster than flask and it is even more lightweight compared the other two I mentioned. But bare in mind that using bottle you have to proper select other... Source: over 1 year ago
If you want an even more trimmed down Flask, you can use Bottle. Source: over 1 year ago
Ok. Switch to python web framework and be happy. For example look at the Bottle - https://bottlepy.org/docs/dev/. Source: over 1 year ago
You should also take a look at Bottle. It's like mini-Flask, except the library is a single .py file. Great for doing one-off web dashboards, embedded web UI etc. https://bottlepy.org/docs/dev/. - Source: Hacker News / almost 2 years ago
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / about 2 months ago
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / about 2 months ago
Numpy: A library for scientific computing in Python. - Source: dev.to / 5 months ago
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy. - Source: dev.to / 6 months ago
A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example... - Source: dev.to / 7 months ago
Django - The Web framework for perfectionists with deadlines
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Flask - a microframework for Python based on Werkzeug, Jinja 2 and good intentions.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Node.js - Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications
OpenCV - OpenCV is the world's biggest computer vision library
