Shiny for Python :: Cheatsheet

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Build an App

A Shiny app is an interactive web page (ui) powered by a live Python session run by a server (or by a browser with Shinylive).

Users can manipulate the UI, which will cause the server to update the UI’s displays (by running Python code).

Save your app as app.py in a directory with the files it uses.

app-name: The directory name is the app name

app.py
www/: Place images, CSS, etc. to share with the browser in a folder named “www”
Include any other scripts, data sets, or assets used by the app in the same directory.

Run shiny create . in the terminal to generate a template app.py file

Launch apps with shiny run app.py --reload

from shiny import App, render, ui
import matplotlib.pyplot as plt
import numpy as np

# Nest Python functions to build an HTML interface
app_ui = ui.page_fluid( # Layout the UI with Layout Functions
 
  #  Add Inputs with ui.input_*() functions 
  ui.input_slider(
    "n", "Sample Size", 0, 1000, 20
  ),
  # Add Outputs with ui.ouput_*() functions
  ui.output_plot("dist")
)

def server(input, output, session):
  
  # For each output, define a function that generates the output
  @output # Designate output functions with the @output decorator
  @render.plot # Specify the type of output with a @render. decorator
  def dist(): # Use the output id as the function name
    
    # Call the values of UI inputs with input.<id>()
    x = np.random.randn(input.n()) 
    plt.hist(x, range=[-3, 3])

# Call App() to combine app_ui and server() into an interactive app
app = App(app_ui, server)

Shinylive

Shinylive apps use WebAssembly to run entirely in a browser–no need for a special server to run Python.

Edit and/or host Shinylive apps at shinylive.io
Create a Shinylive version of an app to deploy with shinylive export myapp site. Then deploy to a hosting site like Github or Netifly.
Embed Shinylive apps in Quarto sites, blogs, etc

To embed a Shinylive app in a Quarto doc, include the below syntax.

---
filters:
- shinylive
---
  
An embedded Shinylive app:
  
```{r}
#| standalone: true
# [App.py code here...]
```

Outputs

Match ui.output_* functions to @render.* decorators

Table of `@render.*()` decorators and their associated `ui.output_*()` functions.
decorator	function
`@render.data_frame`	`ui.output_data_frame(id)`
`@render.image`	`ui.output_image(id, width, height, click, dblclick, hover, brush, inline)`
`@render.plot`	`ui.output_plot(id, width, height, click, dblclick, hover, brush, inline)`
`@render.table`	`ui.output_table(id)`
`@render.text`	`ui.output_text(id, container, inline)` also `ui.output_text_verbatim()`
`@render.ui`	`ui.output_ui(id, inline, container, ...)` also `ui.output_html()`
`@sessiom.download`	`ui.output_download_button(id, label, icon,...)`

Inputs

Use a ui.input_*() function to make an input widget that saves a value as input.<id>. Input values are reactive and need to be called as input.<id>().

ui.input_action_button(id, label, icon, width, ...)
ui.input_action_link(id, label, icon, ...)
ui.input_checkbox(id, label, value, width)
ui.input_checkbox_group(id, label, choices, selected, inline, width)
ui.input_date(id, label, value, min, max, format, startview, weekstart, language, width, autoclose, datesdisabled, daysofweekdisabled)
ui.input_date_range(id, label, start, end, min, max, format, startview, weekstart, language, separator, width, autoclose)
ui.input_file(id, label, multiple, accept, width, buttonLabel, placeholder, capture)
ui.input_numeric(id, label, value, min, max, step, width)
ui.input_password(id, label, value, width, placeholder)
ui.input_radio_buttons(id, label, choices, selected, inline, width)
ui.input_select(id, label, choices, selected, multiple, selectize, width, size) Also ui.input_selectize()
ui.input_slider(id, label, min, max, value, step, ticks, animate, width, sep, pre, post, timeFormat, timezone, dragRange)
ui.input_switch(id, label, value, width)
ui.input_text(id, label, value, width, placeholder, autocomplete, spellcheck) Also ui.input_text_area()

Reactivity

Reactive values work together with reactive functions. Call a reactive value from within the arguments of one of these functions to avoid the error No current reactive context.

Expand to read about the reactivity diagram

Phases in the reactivity diagram

Create your own reactive values
- ui.input_*()
- `reactive.Value()``
- @reactive.file_reader()
- @reactive.poll()
Perform side effects
- @reactive.Effect
Schedule updates
- reactive.invalidate_later()
Create reactive expressions
- reactive.Calc()
Remove reactivity
- reactive.isolate()
React based on event
- @reactive.event()
Render reactive output
- @output
- @render*

Create Your own Reactive Values

ui.input_*() makes an input widget that saves a reactive value as input.<id>().

reactive.value( ) Creates an object whose value you can set.

#...
app_ui = ui.page_fluid(
    ui.input_text("a", "A")
)

def server(input, output, session):
    rv = reactive.Value()
    rv.set(5) 
#...

Create Reactive Expressions

@reactive.Calc makes a function a reactive expression. Shiny notifies functions that use the expression when it becomes invalidated, triggering recomputation. Shiny caches the value of the expression while it is valid to avoid unnecessary computation.
```
#...
def server(input, output, session ):
    @reactive.Calc
    def re():
        return input.a() + input.b()
#...
```

React Based on Event

@reactive.event() Makes a function react only when a specified value is invalidated, here input.a.

#...
def server(input, output, session ):
    @reactive.Calc 
    @reactive.event(input.a) 
    def re():
        return input.b() 
#...

Display Reactive Output

ui.output_*() adds an output element to the UI.
@output, @render.* are decorators to identify and render outputs

def <id>(): A function to generate the output

app_ui = ui.page_fluid(
    ui.input_text("a", "A"), 
    ui.output_text("b"),
)

def server(input, output, session ):
    @output 
    @render.text 
    def b():
        return input.a()

Perform Side Effects

@reactive.Effect Reactively trigger a function with a side effect. Call a reactive value or use @reactive.event to specify when the function will rerun.

#...
def server(input, output, session ):
    @reactive.Effect
    @reactive.event(input.a) 
    def print():
        print("Hi") 
#...

Remove Reactivity

reactive.isolate()Create non-reactive context within a reactive function. Calling a reactive value within this context will not cause the calling function to re-execute should the value become invalid.
```
# ...
def server(input, output, session):
    @output 
    @render.text 
    def a():
        with reactive.isolate():
            return input.a() 
#...
```

Layouts

Combine multiple elements into a “single element” that has its own properties with a panel function:

ui.panel_absolute()
ui.panel_conditional()
ui.panel_fixed()
ui.panel_main()
ui.panel_sidebar()
ui.panel_title()
ui.panel_well()
ui.row() / ui.column()

ui.panel_well(
    ui.input_date(...),
    ui.input_action_button(...)
)

Layout panels with a layout function. Add elements as arguments of the layout functions.

ui.layout_sidebar()

app_ui = ui.page_fluid(
    ui.panel_title( ), 
    ui.layout_sidebar(
        ui.panel_sidebar( ),
    )
)

ui.row()

app_ui = ui.page_fluid(
    ui.row(
        ui.column(width = 4), ui.column(width = 2, offset = 3),
    ),
    ui.row(
        ui.column(width = 12)
    )
)

Layer ui.nav( )s on top of each other, and navigate between them, with:

# navset tab
ui.page_fluid(
    ui.navset_tab(
       ui.nav("tab 1", "contents"), 
       ui.nav("tab 2", "contents"), 
       ui.nav("tab 3", "contents")
    )
)

# navpill list
ui.page_fluid(
    ui.navset_pill_list(
        ui.nav("tab 1", "contents"), 
        ui.nav("tab 2", "contents"), 
        ui.nav("tab 3", "contents")
    )
)

# navbar page
ui.page_navbar(
    ui.nav("tab 1", "contents"), 
    ui.nav("tab 2", "contents"), 
    ui.nav("tab 3", "contents"), 
    title = "Page"
)

Themes

Use the shinyswatch package to add existing bootstrap themes to your Shiny app ui.

import shinyswatch

app_ui = ui.page_fluid(
    shinyswatch.theme.darkly(), 
    # ...
)

Shiny for R Comparison

Shiny for Python is quite similar to Shiny for R with a few important differences:

Call inputs as input.<id>()

R
```
input$x
```
Python
```
input.x()
```

Use decorators to create and render outputs. Define outputs as functions def <id>():

R
```
output$y <- 
  renderText(z())
```

Python

@output 
@renderText 
    def y():
        return z()

To create a reactive expression, use @reactive.Calc

R
```
z <- reactive({ 
  input$x + 1
})
```

Python

@reactive.Calc 
def z():
    return input.x()+1

To create an observer, use @reactive.Effect

R
```
a <- observe({ 
  print(input$x)
})
```

Python

@reactive.Effect 
def a():
    print(input.x())

Combine these with @reactive.event

b <- eventReactive(
  input$goCue,
  {input$x + 1}
)

Python

@reactive.Calc 
@reactive.event(input.go_cue )
def b():
    return input.x() + 1

Use reactive.Value() instead of reactiveVal()

R
```
reactiveVal(1)
```
Python
```
reactive.Value(1)
```

Use nav_*() instead of *Tab()

R
```
insertTab() 
appendTab() 
# etc.
```
Python
```
nav_insert() 
nav_append() 
# etc.
```

Functions are intuitively organized into submodules

R
```
dateInput() 
textInput() 
# etc.
```

Python

ui.input_date() 
ui.input_text() 
# etc.

CC BY SA Posit Software, PBC • info@posit.co • posit.co

Learn more at shiny.posit.co/py/

Updated: 2024-05.

packageVersion("shiny")

[1] '1.8.1.1'

Build an App

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Shinylive

Outputs

Inputs

Reactivity

Phases in the reactivity diagram

Create Your own Reactive Values

Create Reactive Expressions

React Based on Event

Display Reactive Output

Perform Side Effects

Remove Reactivity

Layouts

Themes

Shiny for R Comparison