Create a Dashboard with Panel

Use Panel to create a dashboard with Python
Try this example in seconds on Saturn Cloud

This Jupyter notebook uses Panel to create a dashboard with Python. While the dashboard can be viewed at the bottom of this notebook by running all the cells, it can also be continuously hosted on Saturn using a deployment, allowing people who don’t have access to the notebook to be able to see the dashboard. See the Saturn Cloud docs for instructions on how to deploy it.

Dashboard code

First, we import the packages, load the data, and do minor cleaning of it:

import numpy as np
import pandas as pd
import hvplot.pandas  # noqa
import panel as pn
import urllib.request

pickup_by_zone = pd.read_csv(
    "https://saturn-public-data.s3.us-east-2.amazonaws.com/examples/dashboard/pickup_grouped_by_zone.csv"
)
pickup_by_time = pd.read_csv(
    "https://saturn-public-data.s3.us-east-2.amazonaws.com/examples/dashboard/pickup_grouped_by_time.csv"
)
tip_timeseries = pd.read_csv(
    "https://saturn-public-data.s3.us-east-2.amazonaws.com/examples/dashboard/pickup_average_percent_tip_timeseries.csv"
)

tip_timeseries = tip_timeseries.set_index(tip_timeseries.pickup_datetime.astype(np.datetime64))

total_rides = pickup_by_zone.total_rides.sum()
total_fare = pickup_by_zone.total_fare.sum()

Next, we use define all the materials to go in the different parts of the dashboard:

def kpi_box(title, color, value, unit=""):
    if value > 1e9:
        value /= 1e9
        increment = "B"
    elif value > 1e6:
        value /= 1e6
        increment = "M"
    elif value > 1e3:
        value /= 1e3
        increment = "K"
    else:
        increment = ""

    return pn.pane.Markdown(
        f"""
        ### {title}
        # {unit}{value :.02f} {increment}
        """,
        style={
            "background-color": "#F6F6F6",
            "border": "2px solid black",
            "border-radius": "5px",
            "padding": "10px",
            "color": color,
        },
    )


fares = kpi_box("Total Fares", "#10874a", total_fare, "$")
rides = kpi_box("Total Rides", "#7a41ba", total_rides)
average = kpi_box("Average Fare", "coral", (total_fare / total_rides), "$")


def heatmap(C, data, **kwargs):
    return data.hvplot.heatmap(
        y="pickup_weekday",
        x="pickup_hour",
        C=C,
        hover_cols=["total_rides"] if C == "average_fare" else ["average_fare"],
        yticks=[
            (0, "Mon"),
            (1, "Tues"),
            (2, "Wed"),
            (3, "Thur"),
            (4, "Fri"),
            (5, "Sat"),
            (6, "Sun"),
        ],
        responsive=True,
        min_height=200,
        colorbar=False,
        **kwargs,
    ).opts(toolbar=None, padding=0)


tip_heatmap = heatmap(
    data=pickup_by_time,
    C="average_percent_tip",
    cmap="coolwarm",
    clim=(12, 18),
    title="Average Tip %",
)

date_range_slider = pn.widgets.DateRangeSlider(
    name="Show between",
    start=tip_timeseries.index[0],
    end=tip_timeseries.index[-1],
    value=(tip_timeseries.index.min(), tip_timeseries.index.max()),
)

discrete_slider = pn.widgets.DiscreteSlider(
    name="Rolling window",
    options=["1H", "2H", "4H", "6H", "12H", "1D", "2D", "7D", "14D", "1M"],
    value="1D",
)


def tip_plot(xlim, window):
    data = tip_timeseries.rolling(window).mean()
    return data.hvplot(
        y="percent_tip", xlim=xlim, ylim=(10, 18), responsive=True, min_height=200
    ).opts(toolbar="above")


tip_timeseries_plot = pn.pane.HoloViews(tip_plot(date_range_slider.value, discrete_slider.value))


def trim(target, event):
    target.object = tip_plot(event.new, discrete_slider.value)


def roll(target, event):
    target.object = tip_plot(date_range_slider.value, event.new)


discrete_slider.link(tip_timeseries_plot, callbacks={"value": roll})
date_range_slider.link(tip_timeseries_plot, callbacks={"value": trim})

with urllib.request.urlopen(
    "https://saturn-public-data.s3.us-east-2.amazonaws.com/examples/dashboard/pickup_map.html"
) as f:
    pickup_map = pn.pane.HTML(f.read().decode("utf-8"), min_height=500, min_width=500)

dashboard_intro = """
# NYC Taxi Data

This dashboard demonstrates one mechanism for displaying summary statistics of
the [NYC Taxi Dataset](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page).
This particular page uses data from 2017 to 2020.

## Why Use Dashboards?

Dashboards provide a simple alternative to notebooks that can be more easily digested
by less technical audiences. A mixture of visualizations, text, and tables lets the reader
explore the data in a guided manner without having to write code.
"""

about_saturn = """
## Deploying in Saturn Cloud

This example uses [Panel](https://panel.holoviz.org) to create a deployable interactive dashboard.
In Saturn it is equally easy to create a dashboard using any of the other popular dashboarding
libraries such as: voila, dash, and bokeh. Learn more about how to deploy models and dashboards
in our [documentation](https://saturncloud.io/docs/concepts/projects/deployments).
"""

with urllib.request.urlopen(
    "https://saturn-public-data.s3.us-east-2.amazonaws.com/examples/dashboard/logo.svg"
) as f:
    with open("logo.svg", "wb") as g:
        g.write(f.read())
logo = pn.pane.SVG("logo.svg", style={"float": "right"})

Finally, we create the actual dashboard and load the newly created components into different parts of it. The last step is to serve up the dashboard as well:

dashboard = pn.GridSpec(
    name="dashboard", sizing_mode="stretch_both", min_width=800, min_height=600, max_height=850
)

dashboard[0:5, :3] = pn.Column(dashboard_intro, tip_heatmap, about_saturn)
dashboard[0, 3] = fares
dashboard[0, 4] = rides
dashboard[0, 5] = average
dashboard[0, 6] = logo
dashboard[1:5, 3:6] = pickup_map
dashboard[5:8, 0:2] = pn.Column(
    date_range_slider,
    discrete_slider,
    "*Use widgets to control rolling window average on the timeseries plot or and to restrict to between certain dates*",
)
dashboard[5:7, 2:6] = tip_timeseries_plot

dashboard.servable(title="Saturn Taxi")

Now you’ve created a dashboard! To deploy it, follow the steps in the Saturn Cloud docs.

import numpy as np
import pandas as pd
import hvplot.pandas  # noqa
import panel as pn
import urllib.request

pickup_by_zone = pd.read_csv(
    "https://saturn-public-data.s3.us-east-2.amazonaws.com/examples/dashboard/pickup_grouped_by_zone.csv"
)
pickup_by_time = pd.read_csv(
    "https://saturn-public-data.s3.us-east-2.amazonaws.com/examples/dashboard/pickup_grouped_by_time.csv"
)
tip_timeseries = pd.read_csv(
    "https://saturn-public-data.s3.us-east-2.amazonaws.com/examples/dashboard/pickup_average_percent_tip_timeseries.csv"
)

tip_timeseries = tip_timeseries.set_index(tip_timeseries.pickup_datetime.astype(np.datetime64))

total_rides = pickup_by_zone.total_rides.sum()
total_fare = pickup_by_zone.total_fare.sum()

def kpi_box(title, color, value, unit=""):
    if value > 1e9:
        value /= 1e9
        increment = "B"
    elif value > 1e6:
        value /= 1e6
        increment = "M"
    elif value > 1e3:
        value /= 1e3
        increment = "K"
    else:
        increment = ""

    return pn.pane.Markdown(
        f"""
        ### {title}
        # {unit}{value :.02f} {increment}
        """,
        style={
            "background-color": "#F6F6F6",
            "border": "2px solid black",
            "border-radius": "5px",
            "padding": "10px",
            "color": color,
        },
    )


fares = kpi_box("Total Fares", "#10874a", total_fare, "$")
rides = kpi_box("Total Rides", "#7a41ba", total_rides)
average = kpi_box("Average Fare", "coral", (total_fare / total_rides), "$")


def heatmap(C, data, **kwargs):
    return data.hvplot.heatmap(
        y="pickup_weekday",
        x="pickup_hour",
        C=C,
        hover_cols=["total_rides"] if C == "average_fare" else ["average_fare"],
        yticks=[
            (0, "Mon"),
            (1, "Tues"),
            (2, "Wed"),
            (3, "Thur"),
            (4, "Fri"),
            (5, "Sat"),
            (6, "Sun"),
        ],
        responsive=True,
        min_height=200,
        colorbar=False,
        **kwargs,
    ).opts(toolbar=None, padding=0)


tip_heatmap = heatmap(
    data=pickup_by_time,
    C="average_percent_tip",
    cmap="coolwarm",
    clim=(12, 18),
    title="Average Tip %",
)

date_range_slider = pn.widgets.DateRangeSlider(
    name="Show between",
    start=tip_timeseries.index[0],
    end=tip_timeseries.index[-1],
    value=(tip_timeseries.index.min(), tip_timeseries.index.max()),
)

discrete_slider = pn.widgets.DiscreteSlider(
    name="Rolling window",
    options=["1H", "2H", "4H", "6H", "12H", "1D", "2D", "7D", "14D", "1M"],
    value="1D",
)


def tip_plot(xlim, window):
    data = tip_timeseries.rolling(window).mean()
    return data.hvplot(
        y="percent_tip", xlim=xlim, ylim=(10, 18), responsive=True, min_height=200
    ).opts(toolbar="above")


tip_timeseries_plot = pn.pane.HoloViews(tip_plot(date_range_slider.value, discrete_slider.value))


def trim(target, event):
    target.object = tip_plot(event.new, discrete_slider.value)


def roll(target, event):
    target.object = tip_plot(date_range_slider.value, event.new)


discrete_slider.link(tip_timeseries_plot, callbacks={"value": roll})
date_range_slider.link(tip_timeseries_plot, callbacks={"value": trim})

with urllib.request.urlopen(
    "https://saturn-public-data.s3.us-east-2.amazonaws.com/examples/dashboard/pickup_map.html"
) as f:
    pickup_map = pn.pane.HTML(f.read().decode("utf-8"), min_height=500, min_width=500)

dashboard_intro = """
# NYC Taxi Data

This dashboard demonstrates one mechanism for displaying summary statistics of
the [NYC Taxi Dataset](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page).
This particular page uses data from 2017 to 2020.

## Why Use Dashboards?

Dashboards provide a simple alternative to notebooks that can be more easily digested
by less technical audiences. A mixture of visualizations, text, and tables lets the reader
explore the data in a guided manner without having to write code.
"""

about_saturn = """
## Deploying in Saturn Cloud

This example uses [Panel](https://panel.holoviz.org) to create a deployable interactive dashboard.
In Saturn it is equally easy to create a dashboard using any of the other popular dashboarding
libraries such as: voila, dash, and bokeh. Learn more about how to deploy models and dashboards
in our [documentation](https://saturncloud.io/docs/concepts/projects/deployments).
"""

with urllib.request.urlopen(
    "https://saturn-public-data.s3.us-east-2.amazonaws.com/examples/dashboard/logo.svg"
) as f:
    with open("logo.svg", "wb") as g:
        g.write(f.read())
logo = pn.pane.SVG("logo.svg", style={"float": "right"})

dashboard = pn.GridSpec(
    name="dashboard", sizing_mode="stretch_both", min_width=800, min_height=600, max_height=850
)

dashboard[0:5, :3] = pn.Column(dashboard_intro, tip_heatmap, about_saturn)
dashboard[0, 3] = fares
dashboard[0, 4] = rides
dashboard[0, 5] = average
dashboard[0, 6] = logo
dashboard[1:5, 3:6] = pickup_map
dashboard[5:8, 0:2] = pn.Column(
    date_range_slider,
    discrete_slider,
    "*Use widgets to control rolling window average on the timeseries plot or and to restrict to between certain dates*",
)
dashboard[5:7, 2:6] = tip_timeseries_plot

dashboard.servable(title="Saturn Taxi")

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