# Interactive Visualizations

> This is the 3rd chapter of the Dash Fundamentals. The previous chapter covered basic callback usage. The next chapter describes how to share data between callbacks. Just getting started? Make sure to install the necessary dependencies.

The `dash_core_components` library includes a Graph component called `dcc_graph`.

`dcc_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 `dcc_graph` component is the same `figure` argument that is used by `plotly`. Check out the plotly.py documentation and gallery to learn more.

As we already saw, Dash components are described by a set of attributes. Any of these attributes can be updated by callback functions, but only a subset of these attributes are updated through user interaction, such as typing inside a `dcc_input` component or clicking an option in a `dcc_dropdown` component.

The `dcc_graph` component has four attributes that can change through user-interaction: `hoverData`, `clickData`, `selectedData`, `relayoutData`. These properties update when you hover over points, click on points, or select regions of points in a graph.

Here's an example that prints these attributes to the screen.

> This example has not been ported to Julia yet - showing the Python version instead.

> Visit the old docs site for Julia at: https://community.plotly.com/c/dash/julia/20

Show code

    from dash import Dash, dcc, html, Input, Output, callback

    import plotly.express as px

    import json
    import pandas as pd

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

    app = Dash(__name__, external_stylesheets=external_stylesheets)

    styles = {
        'pre': {
            'border': 'thin lightgrey solid',
            'overflowX': 'scroll'
        }
    }

    df = pd.DataFrame({
        "x": [1,2,1,2],
        "y": [1,2,3,4],
        "customdata": [1,2,3,4],
        "fruit": ["apple", "apple", "orange", "orange"]
    })

    fig = px.scatter(df, x="x", y="y", color="fruit", custom_data=["customdata"])

    fig.update_layout(clickmode='event+select')

    fig.update_traces(marker_size=20)

    app.layout = html.Div([
        dcc.Graph(
            id='basic-interactions',
            figure=fig
        ),

        html.Div(className='row', children=[
            html.Div([
                dcc.Markdown("""
                    **Hover Data**

                    Mouse over values in the graph.
                """),
                html.Pre(id='hover-data', style=styles['pre'])
            ], className='three columns'),

            html.Div([
                dcc.Markdown("""
                    **Click Data**

                    Click on points in the graph.
                """),
                html.Pre(id='click-data', style=styles['pre']),
            ], className='three columns'),

            html.Div([
                dcc.Markdown("""
                    **Selection Data**

                    Choose the lasso or rectangle tool in the graph's menu
                    bar and then select points in the graph.

                    Note that if `layout.clickmode = 'event+select'`, selection data also
                    accumulates (or un-accumulates) selected data if you hold down the shift
                    button while clicking.
                """),
                html.Pre(id='selected-data', style=styles['pre']),
            ], className='three columns'),

            html.Div([
                dcc.Markdown("""
                    **Zoom and Relayout Data**

                    Click and drag on the graph to zoom or click on the zoom
                    buttons in the graph's menu bar.
                """),
                html.Pre(id='relayout-data', style=styles['pre']),
            ], className='three columns')
        ])
    ])

    @callback(
        Output('hover-data', 'children'),
        Input('basic-interactions', 'hoverData'))
    def display_hover_data(hoverData):
        return json.dumps(hoverData, indent=2)

    @callback(
        Output('click-data', 'children'),
        Input('basic-interactions', 'clickData'))
    def display_click_data(clickData):
        return json.dumps(clickData, indent=2)

    @callback(
        Output('selected-data', 'children'),
        Input('basic-interactions', 'selectedData'))
    def display_selected_data(selectedData):
        return json.dumps(selectedData, indent=2)

    @callback(
        Output('relayout-data', 'children'),
        Input('basic-interactions', 'relayoutData'))
    def display_relayout_data(relayoutData):
        return json.dumps(relayoutData, indent=2)

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

Hover Data

Mouse over values in the graph.

    null

Click Data

Click on points in the graph.

    null

Selection Data

Choose the lasso or rectangle tool in the graph's menu bar and then select points in the graph.

Note that if `layout.clickmode = 'event+select'`, selection data also accumulates (or un-accumulates) selected data if you hold down the shift button while clicking.

    null

Zoom and Relayout Data

Click and drag on the graph to zoom or click on the zoom buttons in the graph's menu bar.

    {
      "autosize": true
    }

> For optimal user interaction and chart loading performance, Dash apps in production should consider the Job Queue, HPC, Datashader, and horizontal scaling capabilities of Dash Enterprise.

## Update Graphs on Hover

Let's update our world indicators example from the previous chapter by updating the time series when we hover over points in our scatter plot.

> This example has not been ported to Julia yet - showing the Python version instead.

> Visit the old docs site for Julia at: https://community.plotly.com/c/dash/julia/20

Show code

    from dash import Dash, html, dcc, Input, Output, callback
    import pandas as pd
    import plotly.express as px

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

    app = Dash(__name__, external_stylesheets=external_stylesheets)

    df = pd.read_csv('https://plotly.github.io/datasets/country_indicators.csv')

    app.layout = html.Div([
        html.Div([

            html.Div([
                dcc.Dropdown(
                    df['Indicator Name'].unique(),
                    'Fertility rate, total (births per woman)',
                    id='crossfilter-xaxis-column',
                ),
                dcc.RadioItems(
                    ['Linear', 'Log'],
                    'Linear',
                    id='crossfilter-xaxis-type',
                    labelStyle={'display': 'inline-block', 'marginTop': '5px'}
                )
            ],
            style={'width': '49%', 'display': 'inline-block'}),

            html.Div([
                dcc.Dropdown(
                    df['Indicator Name'].unique(),
                    'Life expectancy at birth, total (years)',
                    id='crossfilter-yaxis-column'
                ),
                dcc.RadioItems(
                    ['Linear', 'Log'],
                    'Linear',
                    id='crossfilter-yaxis-type',
                    labelStyle={'display': 'inline-block', 'marginTop': '5px'}
                )
            ], style={'width': '49%', 'float': 'right', 'display': 'inline-block'})
        ], style={
            'padding': '10px 5px'
        }),

        html.Div([
            dcc.Graph(
                id='crossfilter-indicator-scatter',
                hoverData={'points': [{'customdata': 'Japan'}]}
            )
        ], style={'width': '49%', 'display': 'inline-block', 'padding': '0 20'}),
        html.Div([
            dcc.Graph(id='x-time-series'),
            dcc.Graph(id='y-time-series'),
        ], style={'display': 'inline-block', 'width': '49%'}),

        html.Div(dcc.Slider(
            df['Year'].min(),
            df['Year'].max(),
            step=None,
            id='crossfilter-year--slider',
            value=df['Year'].max(),
            marks={str(year): str(year) for year in df['Year'].unique()}
        ), style={'width': '49%', 'padding': '0px 20px 20px 20px'})
    ])

    @callback(
        Output('crossfilter-indicator-scatter', 'figure'),
        Input('crossfilter-xaxis-column', 'value'),
        Input('crossfilter-yaxis-column', 'value'),
        Input('crossfilter-xaxis-type', 'value'),
        Input('crossfilter-yaxis-type', 'value'),
        Input('crossfilter-year--slider', 'value'))
    def update_graph(xaxis_column_name, yaxis_column_name,
                     xaxis_type, yaxis_type,
                     year_value):
        dff = df[df['Year'] == year_value]

        fig = px.scatter(x=dff[dff['Indicator Name'] == xaxis_column_name]['Value'],
                y=dff[dff['Indicator Name'] == yaxis_column_name]['Value'],
                hover_name=dff[dff['Indicator Name'] == yaxis_column_name]['Country Name']
                )

        fig.update_traces(customdata=dff[dff['Indicator Name'] == yaxis_column_name]['Country Name'])

        fig.update_xaxes(title=xaxis_column_name, type='linear' if xaxis_type == 'Linear' else 'log')

        fig.update_yaxes(title=yaxis_column_name, type='linear' if yaxis_type == 'Linear' else 'log')

        fig.update_layout(margin={'l': 40, 'b': 40, 't': 10, 'r': 0}, hovermode='closest')

        return fig

    def create_time_series(dff, axis_type, title):

        fig = px.scatter(dff, x='Year', y='Value')

        fig.update_traces(mode='lines+markers')

        fig.update_xaxes(showgrid=False)

        fig.update_yaxes(type='linear' if axis_type == 'Linear' else 'log')

        fig.add_annotation(x=0, y=0.85, xanchor='left', yanchor='bottom',
                           xref='paper', yref='paper', showarrow=False, align='left',
                           text=title)

        fig.update_layout(height=225, margin={'l': 20, 'b': 30, 'r': 10, 't': 10})

        return fig

    @callback(
        Output('x-time-series', 'figure'),
        Input('crossfilter-indicator-scatter', 'hoverData'),
        Input('crossfilter-xaxis-column', 'value'),
        Input('crossfilter-xaxis-type', 'value'))
    def update_x_timeseries(hoverData, xaxis_column_name, axis_type):
        country_name = hoverData['points'][0]['customdata']
        dff = df[df['Country Name'] == country_name]
        dff = dff[dff['Indicator Name'] == xaxis_column_name]
        title = '<b>{}</b><br>{}'.format(country_name, xaxis_column_name)
        return create_time_series(dff, axis_type, title)

    @callback(
        Output('y-time-series', 'figure'),
        Input('crossfilter-indicator-scatter', 'hoverData'),
        Input('crossfilter-yaxis-column', 'value'),
        Input('crossfilter-yaxis-type', 'value'))
    def update_y_timeseries(hoverData, yaxis_column_name, axis_type):
        dff = df[df['Country Name'] == hoverData['points'][0]['customdata']]
        dff = dff[dff['Indicator Name'] == yaxis_column_name]
        return create_time_series(dff, axis_type, yaxis_column_name)

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

Fertility rate, total (births per woman)
×
Linear Log
Life expectancy at birth, total (years)
×
Linear Log
1962 1967 1972 1977 1982 1987 1992 1997 2002 2007

Try moving the mouse over the points in the scatter plot on the left. Notice how the line graphs on the right update based on the point that you are hovering over.

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## Generic Crossfilter Recipe

Here's an example of crossfiltering across a six-column data set. Each scatter plot's selection filters the underlying dataset.

> This example has not been ported to Julia yet - showing the Python version instead.

> Visit the old docs site for Julia at: https://community.plotly.com/c/dash/julia/20

Show code

    from dash import Dash, dcc, html, Input, Output, callback
    import numpy as np
    import pandas as pd
    import plotly.express as px

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

    app = Dash(__name__, external_stylesheets=external_stylesheets)

    # make a sample data frame with 6 columns
    np.random.seed(0)  # no-display
    df = pd.DataFrame({"Col " + str(i + 1): np.random.rand(30) for i in range(6)})

    app.layout = html.Div(
        [
            html.Div(
                dcc.Graph(id="g1", config={"displayModeBar": False}),
                className="four columns",
            ),
            html.Div(
                dcc.Graph(id="g2", config={"displayModeBar": False}),
                className="four columns",
            ),
            html.Div(
                dcc.Graph(id="g3", config={"displayModeBar": False}),
                className="four columns",
            ),
        ],
        className="row",
    )

    def get_figure(df, x_col, y_col, selectedpoints, selectedpoints_local):

        if selectedpoints_local and selectedpoints_local["range"]:
            ranges = selectedpoints_local["range"]
            selection_bounds = {
                "x0": ranges["x"][0],
                "x1": ranges["x"][1],
                "y0": ranges["y"][0],
                "y1": ranges["y"][1],
            }
        else:
            selection_bounds = {
                "x0": np.min(df[x_col]),
                "x1": np.max(df[x_col]),
                "y0": np.min(df[y_col]),
                "y1": np.max(df[y_col]),
            }

        # set which points are selected with the `selectedpoints` property
        # and style those points with the `selected` and `unselected`
        # attribute. see
        # https://medium.com/@plotlygraphs/notes-from-the-latest-plotly-js-release-b035a5b43e21
        # for an explanation
        fig = px.scatter(df, x=df[x_col], y=df[y_col], text=df.index)

        fig.update_traces(
            selectedpoints=selectedpoints,
            customdata=df.index,
            mode="markers+text",
            marker={"color": "rgba(0, 116, 217, 0.7)", "size": 20},
            unselected={
                "marker": {"opacity": 0.3},
                "textfont": {"color": "rgba(0, 0, 0, 0)"},
            },
        )

        fig.update_layout(
            margin={"l": 20, "r": 0, "b": 15, "t": 5},
            dragmode="select",
            hovermode=False,
            newselection_mode="gradual",
        )

        fig.add_shape(
            dict(
                {"type": "rect", "line": {"width": 1, "dash": "dot", "color": "darkgrey"}},
                **selection_bounds
            )
        )
        return fig

    # this callback defines 3 figures
    # as a function of the intersection of their 3 selections
    @callback(
        Output("g1", "figure"),
        Output("g2", "figure"),
        Output("g3", "figure"),
        Input("g1", "selectedData"),
        Input("g2", "selectedData"),
        Input("g3", "selectedData"),
    )
    def callback(selection1, selection2, selection3):
        selectedpoints = df.index
        for selected_data in [selection1, selection2, selection3]:
            if selected_data and selected_data["points"]:
                selectedpoints = np.intersect1d(
                    selectedpoints, [p["customdata"] for p in selected_data["points"]]
                )

        return [
            get_figure(df, "Col 1", "Col 2", selectedpoints, selection1),
            get_figure(df, "Col 3", "Col 4", selectedpoints, selection2),
            get_figure(df, "Col 5", "Col 6", selectedpoints, selection3),
        ]

    if __name__ == "__main__":
        app.run(debug=True)

On every selection, the three graph callbacks are fired with the latest selected regions of each plot. A dataframe is filtered based on the selected points and the graphs are replotted with the selected points highlighted and the selected region drawn as a dashed rectangle.

> As an aside, if you find yourself filtering and visualizing highly-dimensional datasets, you should consider checking out the parallel coordinates chart type.

## Current Limitations

There are a few limitations in graph interactions right now.

- It is not currently possible to customize the style of the hover interactions or the select box. This issue is being worked on in https://github.com/plotly/plotly.js/issues/1847.

There's a lot that you can do with these interactive plotting features. If you need help exploring your use case, open up a thread in the Dash Community Forum.

The next chapter of the Dash Fundamentals explains how to share data between callbacks. Dash Fundamentals Part 4. Sharing Data Between Callbacks