A ReactJS Component Extension for Jupyter Notebooks

jupyter-react

This repo actually has nothing to do with React, but rather is a base class meant for pairing up with JS based front-end UI components (see https://github.com/timbr-io/jupyter-react-js). The only thing in this module is a “Component” class. This class can be created with a “module” name that matches the name of a JS UI component and opens up a line of commuination called an “IPython Comm”. Using the comm messages can be pased back and forth and property and actions can be taken as a result of UI interaction.

Example

# Create a custom module in python 

from jupyter_react import Component 

class MyThing(Component):
    module = 'AnyJSClassName'
    comm_channel = 'custom.name'

    def __init__(self, **kwargs):
        super(MyThing, self).__init__(target_name='custom.name', **kwargs)
        self.on_msg(self._handle_msg)

    def _handle_msg(self, msg):
        print msg      

# In Jupyter / IPython instantiate the class and display it

from mything import MyThing
from IPython.display import display

mything = MyThing(props={})
display(mything)

Once a component is “displayed” a message is passed over the comm to the front-end. If the front-end has registered a handler for the same comm target\_name then it will be called when the class is created. You can see more about how to build the front-end code here: https://github.com/timbr-io/jupyter-react-js.

Methods

open(props)

@param props - dict of props to be passed to the front-end js when a comm is opened.

Called immediately when a class is instantiated, this method opens a comm to the front-end and passes the props to the JS.

close

Closes the comm.

send(data)

@param data - dict of data to send over as a message comm

Sends a message over the comm.

on_msg(callback)

@param callback - method to be called when a message is received over the comm

Download Details:

Author: timbr-io

Source Code: https://github.com/timbr-io/jupyter-react

#react #reactjs #javascript

What is GEEK

Buddha Community

Rodrigo Senra - Jupyter Notebooks

Nosso convidado de hoje é diretor técnico na Work & Co, PhD em Ciências da Computação, já contribuiu com inúmeros projetos open source em Python, ajudou a fundar a Associação Python Brasil e já foi premiado com o Prêmio Dorneles Tremea por contribuições para a comunidade Python Brasil.

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A ReactJS Component Extension for Jupyter Notebooks

jupyter-react

This repo actually has nothing to do with React, but rather is a base class meant for pairing up with JS based front-end UI components (see https://github.com/timbr-io/jupyter-react-js). The only thing in this module is a “Component” class. This class can be created with a “module” name that matches the name of a JS UI component and opens up a line of commuination called an “IPython Comm”. Using the comm messages can be pased back and forth and property and actions can be taken as a result of UI interaction.

Example

# Create a custom module in python 

from jupyter_react import Component 

class MyThing(Component):
    module = 'AnyJSClassName'
    comm_channel = 'custom.name'

    def __init__(self, **kwargs):
        super(MyThing, self).__init__(target_name='custom.name', **kwargs)
        self.on_msg(self._handle_msg)

    def _handle_msg(self, msg):
        print msg      

# In Jupyter / IPython instantiate the class and display it

from mything import MyThing
from IPython.display import display

mything = MyThing(props={})
display(mything)

Once a component is “displayed” a message is passed over the comm to the front-end. If the front-end has registered a handler for the same comm target\_name then it will be called when the class is created. You can see more about how to build the front-end code here: https://github.com/timbr-io/jupyter-react-js.

Methods

open(props)

@param props - dict of props to be passed to the front-end js when a comm is opened.

Called immediately when a class is instantiated, this method opens a comm to the front-end and passes the props to the JS.

close

Closes the comm.

send(data)

@param data - dict of data to send over as a message comm

Sends a message over the comm.

on_msg(callback)

@param callback - method to be called when a message is received over the comm

Download Details:

Author: timbr-io

Source Code: https://github.com/timbr-io/jupyter-react

#react #reactjs #javascript

Get started with Jupyter Notebook

Jupyter Notebook is an online computational notebook that allows you to combine code, comments, media, and visualizations in interactive documents. It has quickly become one of the most popular online computational notebooks, used by top companies such as Google, Microsoft, and NASA. Today, we’re going to explore Jupyter Notebooks and discuss their benefits and how to get started.

We’ll cover:

• What is Jupyter Notebook?
• Why use Jupyter Notebook?
• Get started with Jupyter
• Create a new notebook
• Add content to your notebook
• Share your notebook
• What to learn next

#jupyter #python #jupyter-notebook

Why switch to JupyterLab from jupyter-notebook?

First, let’s talk about both Lab and Notebook separately and then will talk about the differences.

Jupyter Notebook is a web-based interactive computational environment for creating Jupyter notebook documents. It supports several languages like Python (IPython), Julia, R, etc. and is mostly used for data analysis, data visualization, and other interactive, exploratory computing. For beginners in data science, jupyter notebook is more preferred; it only consists of a file browser and a (notebook) editor view, which is easier to use. When you get familiar with it and need more features(which we will talk about later), you can then definitely switch to JupyterLab.

JupyterLab is the next-generation user interface, including notebooks. It has a modular structure, where you can open several notebooks or files (e.g., HTML, Text, Markdowns, etc.) as tabs in the same window. It offers more of an IDE-like experience. JupyterLab uses the same Notebook server and file format as the classic Jupyter Notebook to be fully compatible with the existing notebooks and kernels. The Classic Notebook and Jupyterlab can run side to side on the same computer. One can easily switch between the two interfaces. The interface of both Lab and notebook are similar, except the panel of the file system on the left side in Jupyter lab. You can see that in the images below.

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