Package: turbo-dash
turbo_dashautomated Dash framework with templatesQuickstartpip install turbo-dashGoalThe goal of theturbo_dashproject is to create a wrapper forplotly dashthat allows an
inexperienced python developer to quickly create a simple, clean, interactive, easy to manipulate dashboard.OKRsObjectivesKey ResultsStatus1. `turbo_dash` requires minimal python, plotly, or dash knowledge to create a fully functional 
                dashboard, as measured by:i. less than 10 lines of code required per objectgreyii. full documentation with examples for every developer-facing objectgreyiii. a suite of user-friendly templates that design the layout for the developergrey2. `turbo_dash` executes commands quickly and displays minimal lag between 
                input and output, as measured by:i. less than 1s load times for datasets up to 1M rows on a standard laptop CPUgrey3. `turbo_dash` doesn't break, as measured by:i. comprehensive test suitegreyii. full type-hinting with no errors shown by `mypy`greyExample app./app.pyimportturbo_dash# grab our datadf=turbo_dash.data.gapminder()# Here's where all the magic happens. This creates our dashboard.turbo_dashboard=turbo_dash.turbo_dashboard(# templatetemplate='turbo-dark',# dashboard pagesdashboard_page_list=[# App 1turbo_dash.turbo_dashboard_page(# page informationurl='/app1',name='App 1',# datadf=df,# setting our data at the page level allows us to use different datasets for each page# menu filters, i.e. dropdown, slider, etcmenu_filter_list=[turbo_dash.turbo_filter(filter_type='Dropdown-multi',column='country'),turbo_dash.turbo_filter(filter_type='RangeSlider',column='year'),],# outputs, i.e. graphs, images, etcoutput_list=[# bar graph of population vs yearturbo_dash.turbo_output(output_type='bar',x='year',y='pop',color='continent',hover_name='country',),# line graph of life expectancy vs year with an input to change the y axis to a different columnturbo_dash.turbo_output(output_type='line',x='year',y='lifeExp',color='country',chart_input_list=['y'],),],),# App 2turbo_dash.turbo_dashboard_page(# page informationurl='/app2',name='App 2',# datadf=df,# setting our data at the page level allows us to use different datasets for each page# menu filters, i.e. dropdown, slider, etcmenu_filter_list=[turbo_dash.turbo_filter(filter_type='Checklist',column='continent'),],# outputs, i.e. graphs, images, etcoutput_list=[# line graph of gdpPercap vs yearturbo_dash.turbo_output(output_type='line',x='year',y='gdpPercap',color='country',),],),# Playgroundturbo_dash.turbo_dashboard_page(# page informationurl='/playground',name='Playground',# datadf=df,# setting our data at the page level allows us to use different datasets for each page# menu filters, i.e. dropdown, slider, etcmenu_filter_list=[turbo_dash.turbo_filter(filter_type='Checklist',column='continent'),turbo_dash.turbo_filter(filter_type='Dropdown-multi',column='country'),turbo_dash.turbo_filter(filter_type='RangeSlider',column='year'),],# outputs, i.e. graphs, images, etcoutput_list=[# line graph of gdpPercap vs yearturbo_dash.turbo_output(output_type='line',x='year',y='gdpPercap',color='country',chart_input_list=['output_type','x','y','z','color','size','hover_name','hover_data','locations','locationmode','projection',],),],),],)# Execute the code in a development environment. For deploying in production, see the "Deploying in Production"#   section of the README here: https://github.com/turbo3136/turbo_dash/blob/master/README.mdif__name__=='__main__':server=turbo_dashboard.run_dashboard(app_name=__name__)Screenshotsapp1:playground:Deploying in ProductionreferenceWhat I did (probably unstable and stupid):