Dashboards

Just like the plotting functions, generating dashboards with dx is very experimental and prone to change.

Making a Dashboard

If you create charts using dx functions, you may want to combine them into a single view or dashboard. This can be done with dashboard().

Similar to the chart functions, dashboard() mainly requires a pandas DataFrame, as well as a list of views in a matrix-like orientation. (Each item in the list is treated as a row, and each row can be a list of views to specify column positioning.)

Simple

Here's a quick example where we make a dashboard using two rows -- the top row will have scatter and bar charts, and the bottom will be our default grid view:

dx.dashboard(
    df,
    views=[
        ["scatter", "pie"],
        ["grid"],
    ],
)

With Chart Views

Using chart functions, you can specify return_view=True and pass the resulting DEXView object into a dashboard.

custom_bar_chart = dx.bar(
    df,
    x='keyword_column',
    y='integer_column',
    column_sort_order='desc',
    column_sort_type='string',
    show_bar_labels=True,
    return_view=True,
)

simple_tilemap = dx.tilemap(
    df,
    lat='index',
    lon='integer_column',
    return_view=True,
)

dx.dashboard(
    df,
    views=[
        ["parallel_coordinates", custom_bar_chart],
        [simple_tilemap, 'pie', 'grid']
    ]
)

Customized

If you want to provide some additional arguments, instead of passing a string to indicate the chart type, you can pass a dictionary with {"chart_mode": CHART TYPE, **extra_kwargs}.

custom_chart = {
    "chart_mode": "hexbin",
    "decoration": {
        "title": "look at this sweet hexbin"
    },
}

dx.dashboard(
    df, 
    views=[
        [custom_chart, "force_directed_network"], 
        ["ridgeline"]
    ]
)