This is the 2nd chapter of the Dash Tutorial.
The previous chapter covered installation
and the next chapter covers Dash callbacks.

This tutorial will walk you through a fundamental aspect of Dash apps, the
app layout, through 6 self-contained apps.

For production Dash apps, we recommend styling the app layout with
Dash Enterprise Design Kit.

Dash apps are composed of two parts. The first part is the “layout” of
the app and it describes what the application looks like.
The second part describes the interactivity of the application and will be
covered in the next chapter.

Dash provides Python classes for all of the visual components of
the application. We maintain a set of components in the
dash_core_components and the dash_html_components library
but you can also build your own
with JavaScript and React.js.

Note: Throughout this documentation, Python code examples are meant to
be saved as files and executed using python app.py.
You can also use Jupyter with the JupyterDash library.

If you’re using Dash Enterprise’s Data Science Workspaces,
copy & paste the below code into your Workspace (see video).

Find out if your company is using Dash Enterprise

To get started, create a file named app.py with the following code.

Then, run the app with

$ python app.py
...Running on <a href="http://127.0.0.1:8050/">http://127.0.0.1:8050/</a> (Press CTRL+C to quit)

and visit http://127.0.0.1:8050/
in your web browser. You should see an app that looks like this.

Note:

The layout is composed of a tree of “components” like html.Div
and dcc.Graph.
The dash_html_components library has a component for every HTML
tag. The html.H1(children='Hello Dash') component generates
a <h1>Hello Dash<h1> HTML element in your application.
Not all components are pure HTML. The dash_core_components describe
higher-level components that are interactive and are generated with
JavaScript, HTML, and CSS through the React.js library.
Each component is described entirely through keyword attributes.
Dash is declarative: you will primarily describe your application
through these attributes.
The children property is special. By convention, it’s always the
first attribute which means that you can omit it:
html.H1(children='Hello Dash') is the same as html.H1('Hello Dash').
Also, it can contain a string, a number, a single component, or a
list of components.
The fonts in your application will look a little bit different than
what is displayed here. This application is using a
custom CSS stylesheet to modify the default styles of the elements.
You can learn more in the css tutorial,
but for now you can initialize your app with

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

to get the same look and feel of these examples.

Making your first change

New in dash 0.30.0 and dash-renderer 0.15.0

Dash includes “hot-reloading”, this features is activated by default when
you run your app with app.run_server(debug=True). This means that Dash
will automatically refresh your browser when you make a change in your code.

Give it a try: change the title “Hello Dash” in your application or change the x or the y data. Your app should auto-refresh with your change.

Don’t like hot-reloading? You can turn this off with app.run_server(dev_tools_hot_reload=False).

Learn more in Dash Dev Tools documentation Questions? See the community forum hot reloading discussion.

More about HTML

The dash_html_components library contains a component class for every
HTML tag as well as keyword arguments for all of the HTML arguments.

Let's customize the text in our app by modifying the inline styles of the components. Create a file named `app.py` with the following code:

# -*- coding: utf-8 -*-

# Run this app with `python app.py` and
# visit <a href="http://127.0.0.1:8050/">http://127.0.0.1:8050/</a> in your web browser.

import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.express as px
import pandas as pd

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

colors = {
    'background': '#111111',
    'text': '#7FDBFF'
}

# assume you have a "long-form" data frame
# see <a href="https://plotly.com/python/px-arguments/">https://plotly.com/python/px-arguments/</a> for more options
df = pd.DataFrame({
    "Fruit": ["Apples", "Oranges", "Bananas", "Apples", "Oranges", "Bananas"],
    "Amount": [4, 1, 2, 2, 4, 5],
    "City": ["SF", "SF", "SF", "Montreal", "Montreal", "Montreal"]
})

fig = px.bar(df, x="Fruit", y="Amount", color="City", barmode="group")

fig.update_layout(
    plot_bgcolor=colors['background'],
    paper_bgcolor=colors['background'],
    font_color=colors['text']
)

app.layout = html.Div(style={'backgroundColor': colors['background']}, children=[
    html.H1(
        children='Hello Dash',
        style={
            'textAlign': 'center',
            'color': colors['text']
        }
    ),

    html.Div(children='Dash: A web application framework for Python.', style={
        'textAlign': 'center',
        'color': colors['text']
    }),

    dcc.Graph(
        id='example-graph-2',
        figure=fig
    )
])

if __name__ == '__main__':
    app.run_server(debug=True)

Hello Dash

Dash: A web application framework for Python.

In this example, we modified the inline styles of the html.Div
and html.H1 components with the style property.

html.H1('Hello Dash', style={'textAlign': 'center', 'color': '#7FDBFF'})
is rendered in the Dash application as
<h1>Hello Dash<h1>.

There are a few important differences between the dash_html_components
and the HTML attributes:

The style property in HTML is a semicolon-separated string. In Dash,
you can just supply a dictionary.
The keys in the style dictionary are camelCased.
So, instead of text-align, it’s textAlign.
The HTML class attribute is className in Dash.
The children of the HTML tag is specified through the children keyword
argument. By convention, this is always the first argument and
so it is often omitted.

Besides that, all of the available HTML attributes and tags are available
to you within your Python context.

Reusable Components

By writing our markup in Python, we can create complex reusable
components like tables without switching contexts or languages.

Here's a quick example that generates a `Table` from a Pandas dataframe. Create a file named `app.py` with the following code:

# Run this app with `python app.py` and
# visit <a href="http://127.0.0.1:8050/">http://127.0.0.1:8050/</a> in your web browser.

import dash
import dash_html_components as html
import pandas as pd

df = pd.read_csv('https://gist.githubusercontent.com/chriddyp/c78bf172206ce24f77d6363a2d754b59/raw/c353e8ef842413cae56ae3920b8fd78468aa4cb2/usa-agricultural-exports-2011.csv')


def generate_table(dataframe, max_rows=10):
    return html.Table([
        html.Thead(
            html.Tr([html.Th(col) for col in dataframe.columns])
        ),
        html.Tbody([
            html.Tr([
                html.Td(dataframe.iloc[i][col]) for col in dataframe.columns
            ]) for i in range(min(len(dataframe), max_rows))
        ])
    ])


external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

app.layout = html.Div(children=[
    html.H4(children='US Agriculture Exports (2011)'),
    generate_table(df)
])

if __name__ == '__main__':
    app.run_server(debug=True)

US Agriculture Exports (2011)

Unnamed: 0	state	total exports	beef	pork	poultry	dairy	fruits fresh	fruits proc	total fruits	veggies fresh	veggies proc	total veggies	corn	wheat	cotton
	Alabama	1390.63	34.4	10.6	481.0	4.06	8.0	17.1	25.11	5.5	8.9	14.33	34.9	70.0	317.61
1	Alaska	13.31	0.2	0.1		0.19				0.6	1.0	1.56
2	Arizona	1463.17	71.3	17.9		105.48	19.3	41.0	60.27	147.5	239.4	386.91	7.3	48.7	423.95
3	Arkansas	3586.02	53.2	29.4	562.9	3.53	2.2	4.7	6.88	4.4	7.1	11.45	69.5	114.5	665.44
4	California	16472.88	228.7	11.1	225.4	929.95	2791.8	5944.6	8736.4	803.2	1303.5	2106.79	34.6	249.3	1064.95
5	Colorado	1851.33	261.4	66.0	14.0	71.94	5.7	12.2	17.99	45.1	73.2	118.27	183.2	400.5
6	Connecticut	259.62	1.1	0.1	6.9	9.49	4.2	8.9	13.1	4.3	6.9	11.16
7	Delaware	282.19	0.4	0.6	114.7	2.3	0.5	1.0	1.53	7.6	12.4	20.03	26.9	22.9
8	Florida	3764.09	42.6	0.9	56.9	66.31	438.2	933.1	1371.36	171.9	279.0	450.86	3.5	1.8	78.24
9	Georgia	2860.84	31.0	18.9	630.4	38.38	74.6	158.9	233.51	59.0	95.8	154.77	57.8	65.4	1154.07

More about Visualization

The dash_core_components library includes a component called Graph.

Graph renders interactive data visualizations using the open source
plotly.js JavaScript graphing
library. Plotly.js supports over 35 chart types and renders charts in
both vector-quality SVG and high-performance WebGL.

The figure argument in the dash_core_components.Graph component is
the same figure argument that is used by plotly.py, Plotly’s
open source Python graphing library.
Check out the plotly.py documentation and gallery
to learn more.

Here's an example that creates a scatter plot from a Pandas dataframe. Create a file named `app.py` with the following code:

# Run this app with `python app.py` and
# visit <a href="http://127.0.0.1:8050/">http://127.0.0.1:8050/</a> in your web browser.

import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.express as px
import pandas as pd


external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

df = pd.read_csv('https://gist.githubusercontent.com/chriddyp/5d1ea79569ed194d432e56108a04d188/raw/a9f9e8076b837d541398e999dcbac2b2826a81f8/gdp-life-exp-2007.csv')

fig = px.scatter(df, x="gdp per capita", y="life expectancy",
                 size="population", color="continent", hover_name="country",
                 log_x=True, size_max=60)

app.layout = html.Div([
    dcc.Graph(
        id='life-exp-vs-gdp',
        figure=fig
    )
])

if __name__ == '__main__':
    app.run_server(debug=True)

These graphs are interactive and responsive.
Hover over points to see their values,
click on legend items to toggle traces,
click and drag to zoom,
hold down shift, and click and drag to pan.

Markdown

While Dash exposes HTML through the dash_html_components library,
it can be tedious to write your copy in HTML.
For writing blocks of text, you can use the Markdown component in the
dash_core_components library. Create a file named app.py with the following code:

# Run this app with `python app.py` and
# visit <a href="http://127.0.0.1:8050/">http://127.0.0.1:8050/</a> in your web browser.

import dash
import dash_core_components as dcc
import dash_html_components as html

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

markdown_text = '''
### Dash and Markdown

Dash apps can be written in Markdown.
Dash uses the [CommonMark](http://commonmark.org/)
specification of Markdown.
Check out their [60 Second Markdown Tutorial](http://commonmark.org/help/)
if this is your first introduction to Markdown!
'''

app.layout = html.Div([
    dcc.Markdown(children=markdown_text)
])

if __name__ == '__main__':
    app.run_server(debug=True)

Dash and Markdown

Dash apps can be written in Markdown.
Dash uses the CommonMark
specification of Markdown.
Check out their 60 Second Markdown Tutorial
if this is your first introduction to Markdown!

Core Components

The dash_core_components includes a set of higher-level components like
dropdowns, graphs, markdown blocks, and more.

Like all Dash components, they are described entirely declaratively.
Every option that is configurable is available as a keyword argument
of the component.

We'll see many of these components throughout the tutorial. You can view all of the available components in the Dash Core Components Gallery

Here are a few of the available components. Create a file named `app.py` with the following code:

# -*- coding: utf-8 -*-

# Run this app with `python app.py` and
# visit <a href="http://127.0.0.1:8050/">http://127.0.0.1:8050/</a> in your web browser.

import dash
import dash_core_components as dcc
import dash_html_components as html

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

app.layout = html.Div([
    html.Label('Dropdown'),
    dcc.Dropdown(
        options=[
            {'label': 'New York City', 'value': 'NYC'},
            {'label': u'Montréal', 'value': 'MTL'},
            {'label': 'San Francisco', 'value': 'SF'}
        ],
        value='MTL'
    ),

    html.Label('Multi-Select Dropdown'),
    dcc.Dropdown(
        options=[
            {'label': 'New York City', 'value': 'NYC'},
            {'label': u'Montréal', 'value': 'MTL'},
            {'label': 'San Francisco', 'value': 'SF'}
        ],
        value=['MTL', 'SF'],
        multi=True
    ),

    html.Label('Radio Items'),
    dcc.RadioItems(
        options=[
            {'label': 'New York City', 'value': 'NYC'},
            {'label': u'Montréal', 'value': 'MTL'},
            {'label': 'San Francisco', 'value': 'SF'}
        ],
        value='MTL'
    ),

    html.Label('Checkboxes'),
    dcc.Checklist(
        options=[
            {'label': 'New York City', 'value': 'NYC'},
            {'label': u'Montréal', 'value': 'MTL'},
            {'label': 'San Francisco', 'value': 'SF'}
        ],
        value=['MTL', 'SF']
    ),

    html.Label('Text Input'),
    dcc.Input(value='MTL', type='text'),

    html.Label('Slider'),
    dcc.Slider(
        min=0,
        max=9,
        marks={i: 'Label {}'.format(i) if i == 1 else str(i) for i in range(1, 6)},
        value=5,
    ),
], style={'columnCount': 2})

if __name__ == '__main__':
    app.run_server(debug=True)

Dropdown Multi-Select Dropdown Radio Items

Checkboxes

Text Input

Slider

Calling `help`

Dash components are declarative: every configurable aspect of these
components is set during instantiation as a keyword argument.
Call help in your Python console on any of the components to
learn more about a component and its available arguments.

```python

help(dcc.Dropdown)
class Dropdown(dash.development.base_component.Component)
| A Dropdown component.
| Dropdown is an interactive dropdown element for selecting one or more
| items.
| The values and labels of the dropdown items are specified in the options
| property and the selected item(s) are specified with the value property.
|
| Use a dropdown when you have many options (more than 5) or when you are
| constrained for space. Otherwise, you can use RadioItems or a Checklist,
| which have the benefit of showing the users all of the items at once.
|
| Keyword arguments:
| - id (string; optional)
| - className (string; optional)
| - disabled (boolean; optional): If true, the option is disabled
| - multi (boolean; optional): If true, the user can select multiple values
| - options (list; optional)
| - placeholder (string; optional): The grey, default text shown when no option is selected
| - value (string | list; optional): The value of the input. If multi is false (the default)
| then value is just a string that corresponds to the values
| provided in the options property. If multi is true, then
| multiple values can be selected at once, and value is an
| array of items with values corresponding to those in the
| options prop.```

Summary

The layout of a Dash app describes what the app looks like.
The layout is a hierarchical tree of components.
The dash_html_components library provides classes for all of the HTML
tags and the keyword arguments describe the HTML attributes like style,
className, and id.
The dash_core_components library generates higher-level
components like controls and graphs.

For reference, see:

The next part of the Dash tutorial covers how to make these apps interactive.

Dash Tutorial Part 3: Basic Callbacks