Saravanan666/pypi-org huggingface dataset:
Package: dash-spinner
Dash SpinnerThis library is designed for use withPlotly Dash. The components have all been
designed to provide functionality similar to Dash's coreLoadingcomponent,
and will display a loading spinner whilst the underlying children are re-rendering.The spinners in it have been adapted for use from a number of other existing
libraries:react-spinners-kitThe majority of spinner names have been retained from the originals, but some have
been amended where there were name clashes.InstallationDash Spinners is available throughPyPI, and can be installed with pip:pipinstalldash-spinnerBasic UsageOnce installed, you can make use of the components as follows:importdash_spinnerfromdashimportDashapp=Dash(__name__)app.layout=dash_spinner.DashSpinner(Size=30,Color='#00ff89',spinner_type="PushSpinner",loading=True)if__name__=='__main__':app.run_server(debug=True)Using@callbacksimportdash_spinnerfromdashimportDash,html,callback,Input,Outputimporttimeapp=Dash(__name__)app.layout=html.Div(children=[dash_spinner.DashSpinner(id='dash-spinner',Size=30,Color='#00ff89',spinner_type="PushSpinner",loading=True),html.Div(id="input")])@callback(Output('dash-spinner','loading'),Input('input','children'))defdisplay_output(value):time.sleep(10)returnFalseif__name__=='__main__':app.run_server(debug=True)All Dash SpinnerDash Spinner AttributesThere are a number of attributes which are common across all spinners. These are:id(string; optional):
The ID of this component, used to identify dash components in
callbacks. The ID needs to be unique across all of the components
in an app.Size(number; required): Number RequiredColor(string; required): CSS RGB and Hex Color codesspinner_type(string; required):Property that determines which spinner to show one ofBallSpinnerClapSpinnerBarsSpinnerCircleSpinnerClassicSpinnerCombSpinnerCubeSpinnerDominoSpinnerFillSpinnerFireworkSpinnerFlagSpinnerFlapperSpinnerGooSpinnerGridSpinnerGuardSpinnerHeartSpinnerHoopSpinnerImpulseSpinnerJellyfishSpinnerMagicSpinnerMetroSpinnerPongSpinnerPulseSpinnerPushSpinnerRainbowSpinnerRingSpinnerRotateSpinnerSequenceSpinnerSphereSpinnerSpiralSpinnerSwapSpinnerSwishSpinnerTraceSpinnerWaveSpinnerWhisperSpinnerloading(boolean; requiredTrueorFalse):
Whether the Spinner should show on app start-up before the loading
state has been determined.
Package: dash-split
dash-splitdash-split is a Dash component library that wrapsSplit.jsso it is easy to use from within python.UsageSimple example:importdashfromdash_splitimportSplitfromdashimporthtmlapp=dash.Dash(__name__)style={"height":"90vh","display":"flex","justifyContent":"center","alignItems":"center"}app.layout=html.Div([Split(id='split',children=[html.Div(id='1',children="a",style=style),html.Div(id='2',children="b",style=style),html.Div(id='3',children="c",style=style),],)])if__name__=='__main__':app.run_server(debug=True)Also see theSplit.js react docs,
all relevant props are passed to SplitJS directly from dash.ContributingSeeCONTRIBUTING.md
Package: dash-split-pane
Dash Split paneA Dash Split-Pane component, can be nested or split vertically or horizontally!This is based on React Split Pane:https://github.com/tomkp/react-split-paneUsageimportdash_split_panedash_split_pane.DashSplitPane(children=[html.Div("LEFT PANE"),html.Div("RIGHT PANE")],id="splitter",split="vertical",size=50,)All the props fromreact-split-paneare exposed, except fordefaultSize(onlysizeneeds to be used).Thesizeproperty will be updated to dash when a drag is complete.For more information seereact-split-pane
Package: dash-svg
dash-svgScalable Vector Graphics (SVG) library forPlotly/DashDash clone of the ubiquitouscreate-react-appdefault project,import dash
from dash import html
from dash_svg import Svg, G, Path, Circle


app = dash.Dash(__name__)

app.layout = html.Div([
    html.Header([
        Svg([
            G([
                Path(d='...'),
                Circle(cx="420.9", cy="296.5", r='45.7'),
                Path(d='M520.5 78.1z')
            ], fill='#61DAFB')
        ], viewBox='0 0 841.9 595.3', className="App-logo", alt="logo"),
        html.P(["Edit ", html.Code("usage.py"), " and save to reload."]),
        html.A("Learn Dash", className="App-link", href="https://dash.plotly.com/",  target="_blank", rel="noopener noreferrer")
    ], className="App-header")
], className="App")


if __name__ == '__main__':
    app.run_server(debug=True)InstallationYou can installdash-svgwithpip:pip install dash-svgDocumentationHead over to theREADMEfor more details.ContributingThe source code fordash-svgis availableon GitHub. If you find a bug or something is unclear, we encourage
you to raise an issue. We also welcome contributions, to contribute, fork the
repository and open apull request.
Package: dashtable
dashtable has functions for converting data to reStructuredText
tables and Markdown tables, as well as methods for generating data from
these text-tables. It can quickly convert html tables to rst, but you
may have better results with pandoc.Methodshtml2rst:Convert html table toRST grid tablehtml2md:Convert html table to Markdown tabledata2md:Convert a list of lists of strings to Markdown Tabledata2rst:Convert a list of lists of strings toRST grid Tabledata2simplerst:Convert a list of lists of strings to asimple RST
Tablegrid2data:Convert anRST grid tableto datasimple2data:Convert asimple RST tableto dataInstallationdashtable can be installed using pip:sudo pip install dashtableDepending on how you’d like to use dashtable, you may need to install
some dependencies.DependenciesSeveral of the functions in dashtable have no outside requirements.
However, for the following functions you will need to install certain
dependencies:html2rst:BeautifulSoup,html2md:BeautifulSouphtml2data:BeautifulSoup,grid2data:docutilssimple2data:docutilsUsageComplete documentation on usage can be found onRead the Docs.
Package: dash-table
NoticeAs of Dash 2, the development of Dash Table has been moved to themain Dash repoThis package exists for backward compatibilityDash TableAn interactiveDataTableforDash.:point_right:DocumentationQuickstartpip install dash-tableimportdashimportdash_tableimportpandasaspddf=pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/solar.csv')app=dash.Dash(__name__)app.layout=dash_table.DataTable(id='table',columns=[{"name":i,"id":i}foriindf.columns],data=df.to_dict('records'),)if__name__=='__main__':app.run_server(debug=True)BackgroundDash DataTable is an interactive table component designed for viewing, editing, and exploring large datasets.DataTable is rendered with standard, semantic HTML<table/>markup, which makes it accessible, responsive, and easy to style.This component was written from scratch in React.js and Typescript specifically for the Dash community. Its API was designed to be ergonomic and its behavior is completely customizable through its properties.DataTable was designed with a featureset that allows that Dash users to create complex, spreadsheet driven applications with no compromises. We're excited to continue to work with users and companies thatinvest in DataTable's future.Please subscribe todash-table#207and theCHANGELOG.mdto stay up-to-date with any breaking changes. Note: DataTable is currently supported in Chrome, Firefox, Safari, Edge (version 15+), and Internet Explorer 11.Share your DataTable Dash apps on thecommunity forum!ContributingSeeCONTRIBUTING.md
Package: dashtable-ext
dashtable-extShitty Extension to doakey3/DashTableInstallation$pipinstall-Udashtable-extRST 2 ASCII:+-----------+-----------+-----------------------+|Heading 1 | Heading 2 | Heading 3             ||+-----------+-----------+-----------+|| Heading 4 | Heading 5 | Heading 6 |
+===========+-----------+-----------+-----------+|A         | B         | C                     ||+-----------+                       ||| D         |                       ||+-----------+-----------+-----------+|| E         | F         | G         |
+-----------+-----------+-----------+-----------+|H                     | I         | J         |
+-----------------------+-----------+           ||K                     | L         |           ||+-----------+-----------+|| M         | N         |
+-----------------------+-----------+-----------+fromdashtable.extimportrst2ascii,PresetStyleimportdashtablewithopen("table.html","r")asf:html=f.read()table=dashtable.html2rst(html)print(rst2ascii(table=table,preset=PresetStyle.thin_thick_rounded))Output:╭───────────┬───────────┬───────────────────────╮
│Heading1│Heading2│Heading3│
│├───────────┼───────────┬───────────┤
││Heading4│Heading5│Heading6│
├━━━━━━━━━━━┼───────────┼───────────┴───────────┤
│A│B│C│
│├───────────┤│
││D││
│├───────────┼───────────┬───────────┤
││E│F│G│
├───────────┴───────────┼───────────┼───────────┤
│H│I│J│
├───────────────────────┼───────────┤│
│K│L││
│├───────────┼───────────┤
││M│N│
╰───────────────────────┴───────────┴───────────╯
Package: dash-text-to-speech
dash-text-to-speechdash-text-to-speech is a Dash component library.my dash mic recorderGet started with:Install Dash and its dependencies:https://dash.plotly.com/installationRunpython usage.pyVisithttp://localhost:8050in your web browserContributingSeeCONTRIBUTING.mdInstall dependenciesIf you have selected install_dependencies during the prompt, you can skip this part.Install npm packages$ npm installCreate a virtual env and activate.$ virtualenv venv
$ . venv/bin/activateNote: venv\Scripts\activate for windowsInstall python packages required to build components.$ pip install -r requirements.txtInstall the python packages for testing (optional)$ pip install -r tests/requirements.txtWrite your component code insrc/lib/components/DashTextToSpeech.react.js.The demo app is insrc/demoand you will import your example component code into your demo app.Test your code in a Python environment:Build your code$ npm run buildRun and modify theusage.pysample dash app:$ python usage.pyWrite tests for your component.A sample test is available intests/test_usage.py, it will loadusage.pyand you can then automate interactions with selenium.Run the tests with$ pytest tests.The Dash team uses these types of integration tests extensively. Browse the Dash component code on GitHub for more examples of testing (e.g.https://github.com/plotly/dash-core-components)Add custom styles to your component by putting your custom CSS files into your distribution folder (dash_text_to_speech).Make sure that they are referenced inMANIFEST.inso that they get properly included when you're ready to publish your component.Make sure the stylesheets are added to the_css_distdict indash_text_to_speech/__init__.pyso dash will serve them automatically when the component suite is requested.Review your codeCreate a production build and publish:Build your code:$ npm run buildCreate a Python distribution$ python setup.py sdist bdist_wheelThis will create source and wheel distribution in the generated thedist/folder.
SeePyPAfor more information.Test your tarball by copying it into a new environment and installing it locally:$ pip install dash_text_to_speech-0.0.1.tar.gzIf it works, then you can publish the component to NPM and PyPI:Publish on PyPI$ twine upload dist/*Cleanup the dist folder (optional)$ rm -rf distPublish on NPM (Optional if chosen False inpublish_on_npm)$ npm publishPublishing your component to NPM will make the JavaScript bundles available on the unpkg CDN. By default, Dash serves the component library's CSS and JS locally, but if you choose to publish the package to NPM you can setserve_locallytoFalseand you may see faster load times.Share your component with the community!https://community.plotly.com/c/dashPublish this repository to GitHubTag your GitHub repository with the plotly-dash tag so that it appears here:https://github.com/topics/plotly-dashCreate a post in the Dash community forum:https://community.plotly.com/c/dash
Package: dash-tokamak
DashDash is a Python framework for building analytical web applications. No JavaScript required.Built on top of Plotly.js, React and Flask, Dash ties modern UI elements like dropdowns, sliders, and graphs directly to your analytical Python code. Read our tutorial proudly crafted ❤️ by Dash itself.User GuideOffline (PDF) DocumentationDash Docs on Heroku(for corporate network that cannot access plot.ly)App SamplesAppDescriptionHere’s a simple example of a Dash App that ties a Dropdown to a D3.js Plotly Graph. As the user selects a value in the Dropdown, the application code dynamically exports data from Google Finance into a Pandas DataFrame. This app was written in just43lines of code (view the source).Dash app code is declarative and reactive, which makes it easy to build complex apps that contain many interactive elements. Here’s an example with 5 inputs, 3 outputs, and cross filtering. This app was composed in just 160 lines of code, all of which were Python.Dash usesPlotly.jsfor charting. Over 35 chart types are supported, including maps.Dash isn't just for dashboards. You have full control over the look and feel of your applications. Here's a Dash App that's styled to look like a PDF report.To learn more about Dash, read theextensive announcement letterorjump in with the user guide.Contact and SupportFor companies with software budgets, Plotly offersDash Deployment Serverspeeds your time-to-delivery while providing the right resources, security, and scalability you need to deliver production-quality appsDash Design Kitmakes your internal dashboard awesome without expertise in JavaScript & CSS.Snapshot Engineseamlessly links your analytics and reporting workflows together, giving you a fast way to generate interactive reports of just the data you needSeehttps://plot.ly/dash/supportfor ways to get in touch.
Package: dash-tools
Create, Run and Deploy Plotly Dash Apps from Terminal|||👉 Visit the Official DashTools Docs Site 👈DashTools is an open-source command line toolchain for Plotly Dash that makes creating and deploying dash projects to Heroku intuitive and easy.Use dashtools to...Create templated dash appswith one commandDeploy your appto Heroku and Render.com in under a minuteGenerate Procfile, requirements.txt and runtime.txtautomatically on deployMany boilerplate templatesfor creating appsDockerize your appin a single stepGet startedInstall with:pipinstalldash-toolsRun the dashboard with:dashtoolsrunCreate projects with:dashtoolsinit<appname>👉 Get Started With DashTools 👈
Package: dash-trich-components
dash-trich-componentsTo use the Dash Trich Components library, read thedocumentationDocumentation:https://romanonatacha.github.io/dash_trich_components/dash-trich-components is a Dash component library.Get startedTo use dash_trich_components install the package on your project's terminal.$ pip install dash-trich-componentsHow to useTo use the Dash Trich Components library, read thedocumentationDocumentation:https://romanonatacha.github.io/dash_trich_components/UpdatingStay tuned and update our package to get new components released, bug fixes and improvements.To uptade just run in your project's terminal the command bellow.$ pip install --upgrade dash-trich-componentsAuthorsThis library is developed bytrich.aiteam.Leonardo FerreiraNatacha Romano
Package: dash-tvlwc
# 🎛 Dash Tradingview Lightweight Charts Component 📊

Dash Tradingview Lightweight Charts Components is a Dash component library. This component wraps the popular [TradingView Lightweight Charts by TradingView](https://github.com/tradingview/lightweight-charts) written in Typescript/Javascript, and renders it for use in Python [Dash](https://dash.plotly.com/) apps.
Package: dash-uploader
📤 dash-uploaderThe upload package forDashapplications using large data files.🏠 Homepage & Documentationhttps://github.com/np-8/dash-uploaderShort summary💾 Data file size has no limits. (Except the hard disk size)☎ Call easily a callback after uploading is finished.📦 Upload files usingresumable.js✅ Works with Dash 1.1.0.+ & Python 3.6+. (Possibly with other versions, too)Installingpip install dash-uploaderUsageSimple exampleimportdashimportdash_html_componentsashtmlimportdash_uploaderasduapp=dash.Dash(__name__)# 1) configure the upload folderdu.configure_upload(app,r"C:\tmp\Uploads")# 2) Use the Upload componentapp.layout=html.Div([du.Upload(),])if__name__=='__main__':app.run_server(debug=True)
Package: dash-useful-components
dash useful componentsdash useful components is a Dash component library.
It provides the following components:a checkbox treea tree tablea collapsible table bodyGet started with:Install Dash and its dependencies:https://dash.plotly.com/installationRunpython usage.pyVisithttp://localhost:8050in your web browserContributingSeeCONTRIBUTING.mdInstall dependenciesIf you have selected install_dependencies during the prompt, you can skip this part.Install npm packages$ npm installCreate a virtual env and activate.$ virtualenv venv
$ . venv/bin/activateNote: venv\Scripts\activate for windowsInstall python packages required to build components.$ pip install -r requirements.txtInstall the python packages for testing (optional)$ pip install -r tests/requirements.txtWrite your component code insrc/lib/components/DashUsefulComponents.react.js.The demo app is insrc/demoand you will import your example component code into your demo app.Test your code in a Python environment:Build your code$ npm run buildRun and modify theusage.pysample dash app:$ python usage.pyWrite tests for your component.A sample test is available intests/test_usage.py, it will loadusage.pyand you can then automate interactions with selenium.Run the tests with$ pytest tests.The Dash team uses these types of integration tests extensively. Browse the Dash component code on GitHub for more examples of testing (e.g.https://github.com/plotly/dash-core-components)Add custom styles to your component by putting your custom CSS files into your distribution folder (dash_useful_components).Make sure that they are referenced inMANIFEST.inso that they get properly included when you're ready to publish your component.Make sure the stylesheets are added to the_css_distdict indash_useful_components/__init__.pyso dash will serve them automatically when the component suite is requested.Review your codeCreate a production build and publish:Build your code:$ npm run buildCreate a Python tarball$ python setup.py sdistThis distribution tarball will get generated in thedist/folderTest your tarball by copying it into a new environment and installing it locally:$ pip install dash_useful_components-0.1.0.tar.gzIf it works, then you can publish the component to NPM and PyPI:Publish on PyPI$ twine upload dist/*Cleanup the dist folder (optional)$ rm -rf distPublish on NPM (Optional if chosen False inpublish_on_npm)$ npm publishPublishing your component to NPM will make the JavaScript bundles available on the unpkg CDN. By default, Dash serves the component library's CSS and JS locally, but if you choose to publish the package to NPM you can setserve_locallytoFalseand you may see faster load times.Share your component with the community!https://community.plotly.com/c/dashPublish this repository to GitHubTag your GitHub repository with the plotly-dash tag so that it appears here:https://github.com/topics/plotly-dashCreate a post in the Dash community forum:https://community.plotly.com/c/dash
Package: dash-user-analytics
dash-user-analyticsdash-user-analytics is a part ofDash Enterprise,
Plotly's end-to-end Dash application platform. This public copy of the package
is a stub published on pypi.org designed to shield Dash Enterprise users fromdependency confusion,
a remote code execution attack publicly disclosed in 2021 by Alex Birsan.If you are a Dash Enterprise user, you will only see this public package if your
app is not properly configured to find the local package repository built into
Dash Enterprise. Visit the docs for your Dash Enterprise installation for details.If you never see this public package, it's still doing its job of protecting you
from the dependency confusion attack!
Package: dashvis
DashVis ComponentFull implementation of vis.js network framework for Plotly DashExplore the documentation·Report a bug·Request a featureA full implementation ofvis.jsNetwork component forDash Plotly. Useful for displaying dynamic, automatically organised, customizable network views.Table of contentsTable of contentsInstallationPyPIQuick startSee how it is usedAdvanced examplesLinking a stylesheetContributingFuture work 🔨InstallationPyPIYou can installdashviswithpip:pipinstalldashvisQuick startdashvisexposes a single component,DashNetwork. Simply create one and include it in your Dash layout. Simplest example:importdashfromdashimporthtmlfromdashvisimportDashNetworkapp=dash.Dash()app.layout=html.Div([DashNetwork()])if__name__=='__main__':app.run_server(debug=True)See how it is usedA simple usage example is provided inusage.py.Run:python-mvenvdashvis-venvsourcedashvis-venv/bin/activatepipinstall-rrequirements.txt
npminstall
npmrunbuild
pythonusage.pyVisithttp://localhost:8050in your web browserAdvanced examplesExamples which cover the entire API ofvis.jsare available inusage_examplesfolder.
Running them requires PYTHONPATH to be made aware ofdashvis.
Simply run:exportPYTHONPATH="${PYTHONPATH}:./dashvis"and then run any example from repo root directory of the repository:pythonusage_examples/<example_name>.pyLinking a stylesheetdashvis doesn't come with CSS included.
If you enable network manipulation or navigation features, you need to include a stylesheet to draw those components of
the network.For convenience, links to vis.js stylesheets are included for you and can be used as follows:importdashimportdashvis.stylesheetsapp=dash.Dash(external_stylesheets=[dashvis.stylesheets.VIS_NETWORK_STYLESHEET])ContributingSeeCONTRIBUTING.mdFuture work 🔨Updateusage.pyFix two disabledvis.jsfunction callsWrite testsAdd tests and code coverage badgesUpdate this README