Arne  Denesik

Arne Denesik

1603263600

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.

#programming #jupyter #jupyter-notebook #jupyterlab #data-science

What is GEEK

Buddha Community

Why switch to JupyterLab from jupyter-notebook?
Arne  Denesik

Arne Denesik

1603263600

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.

#programming #jupyter #jupyter-notebook #jupyterlab #data-science

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.

#alexandre oliva #anaconda #apache zeppelin #associação python brasil #azure notebooks #beakerx #binder #c++ #closure #colaboratory #donald knuth #fernando pérez #fortran #graphql #guido van rossum #ipython #java #javascript #json #jupyter kenels #jupyter notebooks #jupyterhub #jupyterlab #latex #lisp #literate programming #lua #matlab #perl #cinerdia #prêmio dorneles tremea #python #r #rodrigo senra #scala #spark notebook #tcl #typescript #zope

Self-contained reports from Jupyter Notebooks

Introduction

You’ve just written an amazing Jupyter Notebook, and you’d like to send it to your coworkers. Asking them to install Jupyter isn’t an option, and neither is asking IT for a server on which to host your page. What do you do?

I’ll show you how to export your notebook as a self-contained html report which anyone can open in their browser. I’ll start with the simplest possible example of how to export an html report, then I’ll show how to hide the input cells, and finally I’ll show how to toggle showing/hiding code cells.

Hello world example

Let’s start with the simplest possible example — we have a “hello world” notebook and we’d like to export it as a self-contained html report. We can do this by running

$ jupyter nbconvert notebooks/hello_world.ipynb --output-dir build

#python #jupyter-notebook #jupyterlab #report #jupyter

Grace  Lesch

Grace Lesch

1651996800

Jupyterlab: Support for Jupyter Notebook Templates in Jupyterlab

Support for jupyter notebook templates in jupyterlab

Install

pip install jupyterlab_templates
jupyter labextension install jupyterlab_templates
jupyter serverextension enable --py jupyterlab_templates

Adding templates

install the server extension, and add the following to jupyter_notebook_config.py

c.JupyterLabTemplates.template_dirs = ['list', 'of', 'template', 'directories']
c.JupyterLabTemplates.include_default = True
c.JupyterLabTemplates.include_core_paths = True

Templates for libraries

The extension will search subdirectories of each parent directory specified in template_dirs for templates. Note! Templates in the parent directories will be ignored. You must put the templates in subdirectories, in order to keep everything organized.

If include_default = True the notebook_templates directory under the jupyter data folder is one of the default parent directories. Thus, if you have tutorials or guides you'd like to install for users, simply copy them into your jupyter data folder inside the notebook_templates directory, e.g. /usr/local/share/jupyter/notebook_templates/bqplot for bqplot.

Flags

  • template_dirs: a list of absolute directory paths. All .ipynb files in any subdirectories of these paths will be listed as templates
  • include_default: include the default Sample template (default True)
  • include_core_paths: include jupyter core paths (see: jupyter --paths) (default True)

Development

See CONTRIBUTING.md for guidelines.


Author: jpmorganchase
Source Code: https://github.com/jpmorganchase/jupyterlab_templates
License: Apache-2.0 license

#jupyterlab #jupyter 

Jupyter Dash: Develop Dash Apps in The Jupyter Notebook and JupyterLab

Jupyter Dash

This library makes it easy to develop Plotly Dash apps interactively from within Jupyter environments (e.g. classic Notebook, JupyterLab, Visual Studio Code notebooks, nteract, PyCharm notebooks, etc.).

jupterlab example

See the notebooks/getting_started.ipynb for more information and example usage.

Installation

You can install the JupyterDash Python package using pip...

$ pip install jupyter-dash

or conda

$ conda install -c conda-forge -c plotly jupyter-dash

JupyterLab support

When used in JupyterLab, JupyterDash depends on the jupyterlab-dash JupyterLab extension, which requires JupyterLab version 2.0 or above.

This extension is included with the Python package, but in order to activate it JupyterLab must be rebuilt. JupyterLab should automatically produce a popup dialog asking for permission to rebuild, but the rebuild can also be performed manually from the command line using:

$ jupyter lab build

To check that the extension is installed properly, call jupyter labextension list.

Colab support

As of version 0.3.0, JupyterDash works in Colab with no additional configuration. Just install jupyter-dash using pip in a Colab notebook cell

!pip install jupyter-dash

Features

To learn more about the features of JupyterDash, check out the announcement post.

Development

To develop JupyterDash, first create and activate a virtual environment using virtualenv or conda.

Then clone the repository and change directory to the repository root:

$ git clone https://github.com/plotly/jupyter-dash.git
$ cd jupyter-dash

Then install the dependencies:

$ pip install -r requirements.txt -r requirements-dev.txt 

Then install the Python package in editable mode. Note: this will require nodejs to be installed.

$ pip install -e .

Then install the classic notebook extension in development mode:

$ jupyter nbextension install --sys-prefix --symlink --py jupyter_dash
$ jupyter nbextension enable --py jupyter_dash

Then install the JupyterLab extension in development mode:

$ jupyter labextension link extensions/jupyterlab

For release, build the JupyterLab extension to bundle with the Python package (see RELEASE.md for the full process):

$ python setup.py build_js

Author: plotly
Source Code: https://github.com/plotly/jupyter-dash
License: MIT License

#python #jupyter #jupyterlab