Build a Report#

In this section, we will collaboratively create a Wind Turbine Report. Together, we will:

Layout and style the report nicely.
Export the report to a static .html file using the .save method.
Distribute the report via email.

Code

import altair as alt
import pandas as pd
import panel as pn

pn.extension("vega", sizing_mode="stretch_width")

# Extract Data

TEXT ="""# Wind Turbine

A wind turbine is a device that converts the kinetic energy of wind into \
[electrical energy](https://en.wikipedia.org/wiki/Electrical_energy).

The most visible part of a wind turbine is its *rotor*, which typically consists of two or three long *blades* attached to a central hub. These blades are meticulously designed to efficiently capture the energy of the wind as it passes through them. Through careful aerodynamic engineering, the shape, length, and angle of the blades are optimized to maximize the amount of kinetic energy they can extract from the wind.

As the wind blows, it causes the rotor blades to rotate. This rotational motion is transferred to a generator housed within the turbine's nacelle, a large enclosure situated atop a tall tower. The generator converts the mechanical energy of the rotating blades into electrical energy through the principles of electromagnetic induction. This electricity is then transmitted via cables down the tower and into the electrical grid for distribution to homes, businesses, and industries.

The height of the tower plays a crucial role in the efficiency of a wind turbine. By elevating the rotor assembly high above the ground, turbines can access stronger and more consistent wind speeds, which translates to higher energy production. Taller towers also help minimize the impact of surface friction and turbulence near the ground, allowing the rotor blades to operate more smoothly and efficiently.

Read more [here](https://en.wikipedia.org/wiki/Wind_turbine).
"""

@pn.cache
def get_data():
    return pd.read_csv("https://assets.holoviz.org/panel/tutorials/turbines.csv.gz")

df = get_data()

# Transform Data

count = len(df)
total_capacity = df.t_cap.sum()
avg_capacity = df.t_cap.mean() / 10**3
avg_rotor_diameter = df.t_rd.mean()
top_manufacturers = (
    df.groupby("t_manu").p_cap.sum().sort_values().iloc[-10:].index.to_list()
)
example_df = df.dropna().sample(5).iloc[:5,:13].reset_index(drop=True)

# Plot Data

df = df[df.t_manu.isin(top_manufacturers)]
fig = (
    alt.Chart(
        df.sample(5000),
        title="Capacity by Manufacturer",
    )
    .mark_circle(size=8)
    .encode(
        y="t_manu:N",
        x="p_cap:Q",
        yOffset="jitter:Q",
        color=alt.Color("t_manu:N").legend(None),
        tooltip=["t_manu", "p_cap"],
    )
    .transform_calculate(jitter="sqrt(-2*log(random()))*cos(2*PI*random())")
    .properties(
        height=400,
        width="container",
    )
)

## Build components: display, layout, and style the objects

BRAND_COLOR = "teal"
BRAND_TEXT_ON_COLOR = "white"

CARD_STYLE = {
  "box-shadow": "rgba(50, 50, 93, 0.25) 0px 6px 12px -2px, rgba(0, 0, 0, 0.3) 0px 3px 7px -3px",
  "padding": "10px",
  "border-radius": "5px"
}

header = pn.Row(
    pn.pane.Markdown(
        "# Wind Turbine Report", styles={"color": BRAND_TEXT_ON_COLOR}, margin=(5, 20)
    ),
    styles={"background": BRAND_COLOR},
)
indicators = pn.FlexBox(
    pn.indicators.Number(
        value=total_capacity / 1e6,
        name="Total Capacity (GW)",
        format="{value:,.0f}",
        styles=CARD_STYLE,
    ),
    pn.indicators.Number(
        value=avg_capacity,
        name="Avg. Capacity (MW)",
        format="{value:,.1f}",
        styles=CARD_STYLE,
    ),
    pn.indicators.Number(
        value=avg_rotor_diameter,
        name="Avg. Rotor Diameter (m)",
        format="{value:,.0f}",
        styles=CARD_STYLE,
    ),
    pn.indicators.Number(
        value=avg_rotor_diameter,
        name="Avg. Rotor Diameter (m)",
        format="{value:,.0f}",
        styles=CARD_STYLE,
    ),
    pn.indicators.Number(
        value=count,
        name="Count",
        format="{value:,.0f}",
        styles=CARD_STYLE,
    ),
    margin=(20, 5),
)

plot = pn.pane.Vega(
    fig,
    styles=CARD_STYLE,
    margin=10,
)

table = pn.pane.DataFrame(example_df, styles=CARD_STYLE)

## Put the components together

main = pn.Column(
    "# Summary",
    indicators,
    TEXT,
    "## Manufacturer Capacities",
    plot,
    "## Turbine Examples",
    table,
)

main_container = pn.Row(
    main,
    max_width=1024,
    styles={"margin-right": "auto", "margin-left": "auto", "margin-top": "10px", "margin-bottom": "20px"},
)
report = pn.Column(header, main_container)

## Export and save it

report.save("report.html")

report.servable() # Added such that the report can be served for development

Install the dependencies#

Please ensure that Altair and Panel are installed.

pip

pip install altair panel

conda

conda install -y -c conda-forge altair panel

Explanation#

Let’s break down and explain the code:

import altair as alt
import pandas as pd
import panel as pn

Here, we import the necessary libraries: Altair for data visualization, Pandas for data manipulation, and Panel for creating interactive web apps.

pn.extension("vega", sizing_mode="stretch_width")

Additionally we load the "vega" renderer, which enables rendering Altair plots, and set the sizing mode to "stretch_width" to make sure the content fills the available horizontal space.

TEXT ="""# Wind Turbine

A wind turbine is a device that converts the kinetic energy of wind into \
[electrical energy](https://en.wikipedia.org/wiki/Electrical_energy).

The most visible part of a wind turbine is its *rotor*, which typically consists of two or three long *blades* attached to a central hub. These blades are meticulously designed to efficiently capture the energy of the wind as it passes through them. Through careful aerodynamic engineering, the shape, length, and angle of the blades are optimized to maximize the amount of kinetic energy they can extract from the wind.

As the wind blows, it causes the rotor blades to rotate. This rotational motion is transferred to a generator housed within the turbine's nacelle, a large enclosure situated atop a tall tower. The generator converts the mechanical energy of the rotating blades into electrical energy through the principles of electromagnetic induction. This electricity is then transmitted via cables down the tower and into the electrical grid for distribution to homes, businesses, and industries.

The height of the tower plays a crucial role in the efficiency of a wind turbine. By elevating the rotor assembly high above the ground, turbines can access stronger and more consistent wind speeds, which translates to higher energy production. Taller towers also help minimize the impact of surface friction and turbulence near the ground, allowing the rotor blades to operate more smoothly and efficiently.

Read more [here](https://en.wikipedia.org/wiki/Wind_turbine).
"""

@pn.cache
def get_data():
    return pd.read_csv("https://assets.holoviz.org/panel/tutorials/turbines.csv.gz")

df = get_data()

We define a multiline string TEXT containing some markdown text describing wind turbines. Then, we define a function get_data() decorated with @pn.cache to cache the data retrieval operation. This function reads a CSV file containing turbine data from a URL and returns a Pandas DataFrame, which we store in the variable df.

count = len(df)
total_capacity = df.t_cap.sum()
avg_capacity = df.t_cap.mean() / 10**3
avg_rotor_diameter = df.t_rd.mean()
top_manufacturers = (
    df.groupby("t_manu").p_cap.sum().sort_values().iloc[-10:].index.to_list()
)
example_df = df.dropna().sample(5).iloc[:5,:13].reset_index(drop=True)

We calculate various summary statistics from the dataset, such as the total count of turbines, the total capacity, the average capacity (converted to MW), the average rotor diameter, and a list of top manufacturers by total capacity. Additionally, we create an example DataFrame (example_df) containing a random sample of turbine data to display in a table later.

df = df[df.t_manu.isin(top_manufacturers)]
fig = (
    alt.Chart(
        df.sample(5000),
        title="Capacity by Manufacturer",
    )
    .mark_circle(size=8)
    .encode(
        y="t_manu:N",
        x="p_cap:Q",
        yOffset="jitter:Q",
        color=alt.Color("t_manu:N").legend(None),
        tooltip=["t_manu", "p_cap"],
    )
    .transform_calculate(jitter="sqrt(-2*log(random()))*cos(2*PI*random())")
    .properties(
        height=400,
        width="container",
    )
)

We filter the dataset to include only the turbines from the top manufacturers. Then, we create an Altair scatter plot (fig) to visualize the capacity of turbines by manufacturer. We encode the manufacturer on the y-axis, the capacity on the x-axis, and use jitter to prevent overplotting. The tooltip shows the manufacturer and capacity when hovering over data points.

BRAND_COLOR = "teal"
BRAND_TEXT_ON_COLOR = "white"

CARD_STYLE = {
  "box-shadow": "rgba(50, 50, 93, 0.25) 0px 6px 12px -2px, rgba(0, 0, 0, 0.3) 0px 3px 7px -3px",
  "padding": "10px",
  "border-radius": "5px"
}

header = pn.Row(
    pn.pane.Markdown(
        "# Wind Turbine Report", styles={"color": BRAND_TEXT_ON_COLOR}, margin=(5, 20)
    ),
    styles={"background": BRAND_COLOR},
)

Here, we define some styling constants and create a header for the report, which consists of a Markdown title styled with the brand color and a teal background.

indicators = pn.FlexBox(
    pn.indicators.Number(
        value=total_capacity / 1e6,
        name="Total Capacity (GW)",
        format="{value:,.0f}",
        styles=CARD_STYLE,
    ),
    pn.indicators.Number(
        value=avg_capacity,
        name="Avg. Capacity (MW)",
        format="{value:,.1f}",
        styles=CARD_STYLE,
    ),
    pn.indicators.Number(
        value=avg_rotor_diameter,
        name="Avg. Rotor Diameter (m)",
        format="{value:,.0f}",
        styles=CARD_STYLE,
    ),
    pn.indicators.Number(
        value=avg_rotor_diameter,
        name="Avg. Rotor Diameter (m)",
        format="{value:,.0f}",
        styles=CARD_STYLE,
    ),
    pn.indicators.Number(
        value=count,
        name="Count",
        format="{value:,.0f}",
        styles=CARD_STYLE,
    ),
    margin=(20, 5),
)

plot = pn.pane.Vega(
    fig,
    styles=CARD_STYLE,
    margin=10,
)
table = pn.pane.DataFrame(example_df, styles=CARD_STYLE)

We create various components for the report, including a set of indicators (total capacity, average capacity, etc.), a Vega plot (plot) displaying the capacity by manufacturer, and a DataFrame (table) showing example turbine data.

main = pn.Column(
    "# Summary",
    indicators,
    TEXT,
    "## Manufacturer Capacities",
    plot,
    "## Turbine Examples",
    table,
)

We assemble the main content of the report (main) as a Column layout, including the summary section, the indicators, the Markdown text, the plot, and the table.

main_container = pn.Row(
    main,
    max_width=1024,
    styles={"margin-right": "auto", "margin-left": "auto", "margin-top": "10px", "margin-bottom": "20px"},
)
report = pn.Column(header, main_container)
report

We create a container (main_container) to hold the main content with a maximum width and some margin styling. Then, we compose the entire report (report) by combining the header and the main container.

report.save("report.html")

Finally, we save the report as an HTML file named “report.html”. This HTML file will contain the rendered content of the report, including the header, indicators, text, plot, and table.

Note

With .save you can produce static reports that display content and have very limited interactivity. It is possible to add more interactivity. Check out the Embed State or Convert Panel Applications guides for more detail.

Run the Code#

Run the code with python app.py.

Please verify that the file report.html has been created.

Please open the report.html file in your browser. It should look like:

Distribute the report#

Attach the report.html file generated in the previous section to an email and send it to yourself. When you receive the email, then open the report.html attachment.

🥳 Congrats! Together, we have successfully created and distributed an interactive report based on DataFrames and one of our favorite plotting libraries. This is an amazing achievement!

Recap#

In this section we have built a Wind Turbine Report with a nice user experience, exported it to a .html file using the .save method and distributed it via email.

Build a Report#

Install the dependencies#

Explanation#

Run the Code#

Distribute the report#

Recap#

Resources#

How-to#