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Panel is an open-source Python library that lets you create custom interactive web apps and dashboards by connecting user-defined widgets to plots, images, tables, or text.

Compared to other approaches, Panel is novel in that it supports nearly all plotting libraries, works just as well in a Jupyter notebook as on a standalone secure web server, uses the same code for both those cases, supports both Python-backed and static HTML/JavaScript exported applications, and can be used to develop rich interactive applications without tying your domain-specific code to any particular GUI or web tools.

Panel makes it simple to make:

Plots with user-defined controls
Property sheets for editing parameters of objects in a workflow
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Dashboards reporting key performance indicators (KPIs) and trends
Data-rich Python-backed web servers
and anything in between

Panel objects are reactive, immediately updating to reflect changes to their state, which makes it simple to compose viewable objects and link them into simple, one-off apps to do a specific exploratory task. The same objects can then be reused in more complex combinations to build more ambitious apps, while always sharing the same code that works well on its own.

Panel lets you move the same code freely between an interactive Jupyter Notebook prompt and a fully deployable standalone server. That way you can easily switch between exploring your data, building visualizations, adding custom interactivity, sharing with non-technical users, and back again at any point, using the same tools and the same code throughout. Panel thus helps support your entire workflow, so that you never have to commit to only one way of using your data and your analyses, and don’t have to rewrite your code just to make it usable in a different way. In many cases, using Panel can turn projects that used to take weeks or months into something you finish on the same day you started, creating a full Python-backed deployed web service for your visualized data in minutes or hours without having to run a software development project or hand your work over to another team.

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Panel can also be used with the separate Param project to create interactively configurable objects with or without associated visualizations, in a fully declarative way. With this approach, you declare your configurable object using the pure-Python, zero-dependency param library, annotating your code with parameter ranges, documentation, and dependencies between parameters and your code. Using this information, you can make all of your domain-specific code be optionally configurable in a GUI, with optional visual displays and debugging information if you like, all with just a few lines of declarations. With this approach, you don’t ever have to decide whether your code will eventually be used in a notebook, in a GUI app, or completely behind the scenes in batch processing, servers, or reports – the same code can support all of these cases equally well, once you declare the associated parameters and constraints. This approach lets you completely separate your domain-specific code from anything to do with web browsers, GUI toolkits, or other volatile technologies that would otherwise make your hard work become obsolete as they change over time.

The Getting Started will provide a quick introduction to the panel API and get you started while the User Guide provides a more detailed guide on how to use Panel.

For usage questions or technical assistance, please head over to Discourse. If you have any 免费加速器翻日本 or wish to contribute code, you can visit our GitHub site.

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Panel works with Python 2.7 and Python 3 on Linux, Windows, or Mac. The recommended way to install Panel is using the 免费加速器翻日本 command provided by Anaconda or Miniconda:

conda 手机怎样翻墙上p站 -c pyviz panel

or using PyPI:

pip install 免费加速器翻日本

Support for classic Jupyter Notebook is included with Panel. If you want to work with JupyterLab, you will also need to install the PyViz JupyterLab extension:

conda install -c conda-forge jupyterlab
jupyter labextension install @pyviz/免费加速器翻日本

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Once you’ve installed Panel, you can get your own copy of all the notebooks used to make this website by running the following commands on the commandline in your conda or pip environment:

panel 手机切换日本ip加速软件
cd panel-手机怎么挂香港ip

And then you can launch Jupyter to explore them yourself using either Jupyter Notebook or JupyterLab (having first installed the extension!):

jupyter 手机切换日本ip加速软件
jupyter lab

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Panel can be used in a wide range of development environments:

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You can edit your Panel code as a .py file in any text editor, marking the objects you want to render as .servable(), then launch a server with panel serve my_script.py –show to open a browser tab showing your app or dashboard and backed by a live Python process.

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In the classic Jupyter notebook environment and JupyterLab, first make sure to load the pn.extension(). Panel objects will then render themselves if they are the last item in a notebook cell.

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In Google Colaboratory, rendering for each notebook cell is isolated, which means that every cell must reload the Panel extension code separately. Panel can do this automatically when you first load the extension if you declare that you are running in Colab: 游戏加速器教程_手游加速器免费下载_biubiu加速器:2021-6-15 · biubiu加速器教程频道为各位玩家提供最新的游戏加速器免费版下载，热门手游免费加速教程，手机游戏加速器操作技巧等信息，找更多加速器内容就来biubiu加速器官网。. Otherwise you will need to put pn.extension() in each cell where you want to display Panel output. Either way, you should be able to have access to all of Panel’s functionality, though with a larger notebook size than with other notebook technologies that allow display code to be shared across cells.

VSCode¶

Visual Studio Code (VSCode) versions 2020.4.74986 and later support ipywidgets, and Panel objects can be used as ipywidgets since Panel 0.10 thanks to jupyter_bokeh, which means that you can now use Panel components interactively in VSCode. Ensure you install jupyter_bokeh with pip install jupyter_bokeh or 代理ip加速器_代理ip软件_http免费动态ip代理服务器-精灵 ...:2021-6-15 · 代理ip加速器选精灵代理,国内知名的代理ip软件,精灵代理专业提供免费动态ip,ip加速器,代理服务器,http代理,socks5代理等,在电脑ip修改器和手机ip转换器方面深受广大用户好评. and then enable the extension with pn.extension(comms=’vscode’).

nteract and other ipywidgets notebooks¶

In other notebook environments that support rendering ipywidgets interactively, such as nteract, you can use the same underlying ipywidgets support as for vscode: Install jupyter_bokeh and then use pn.extension(comms=’ipywidgets’).

Other environments¶

If your development environment offers embedded Python processes but does not support ipywidgets or Jupyter “comms” (communication channels), you will notice that some or all interactive functionality is missing. Some widgets that operate only in JavaScript will work fine, but others require communication channels between JavaScript and Python. In such cases you can either request ipywidgets or Panel support from the editor or environment, or else use the Editor + Server approach above.

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The Panel project is grateful for the sponsorship by the organizations and companies below: