PyCafe - Vizro - Advanced Data Visualizations with Plotly and Vizro

app.py
requirements.txt
app.py
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# Vizro is an open-source toolkit for creating modular data visualization applications.
# check out https://github.com/mckinsey/vizro for more info about Vizro
# and checkout https://vizro.readthedocs.io/en/stable/ for documentation.

import pandas as pd
import plotly.graph_objects as go
import vizro.models as vm
import vizro.plotly.express as px
from vizro import Vizro
from vizro.figures import kpi_card, kpi_card_reference
from vizro.models._components.form._text_area import TextArea
from vizro.models._components.form._user_input import UserInput
from vizro.models.types import capture
from vizro.tables import dash_ag_grid

iris = px.data.iris()
tips = px.data.tips()
stocks = px.data.stocks(datetimes=True)
gapminder_2007 = px.data.gapminder().query("year == 2007")
df_kpi = pd.DataFrame({"Actual": [100, 200, 700], "Reference": [100, 300, 500], "Category": ["A", "B", "C"]})

waterfall_data = pd.DataFrame(
    {
        "x": [
            "Sales",
            "Consulting",
            "Net revenue",
            "Purchases",
            "Other expenses",
            "Profit before tax",
        ],
        "y": [60, 80, 0, -40, -20, 0],
        "measure": [
            "relative",
            "relative",
            "total",
            "relative",
            "relative",
            "total",
        ],
    }
)

@capture("graph")
def waterfall(data_frame: pd.DataFrame, x: str, y: str, measure: list[str]):
    """Custom waterfall chart."""
    fig = go.Figure(
        data=go.Waterfall(
            x=data_frame[x],
            y=data_frame[y],
            measure=data_frame[measure],
            showlegend=False,
        ),
    )
    fig.update_layout(showlegend=False)
    return fig


@capture("graph")
def sankey(data_frame: pd.DataFrame, source: str, target: str, value: str):
    """Custom Sankey diagram."""
    fig = go.Figure(
        data=[
            go.Sankey(
                node={"label": ["A1", "A2", "B1", "B2", "C1", "C2"]},
                link={
                    "source": [0, 1, 0, 2, 3, 3],  # indices correspond to labels, eg A1, A2, A1, B1, ...
                    "target": [2, 3, 3, 4, 4, 5],
                    "value": [8, 4, 2, 8, 4, 2],
                },
            )
        ]
    )
    return fig


graphs = vm.Page(
    title="Graphs",
    layout=vm.Grid(grid=[[0, 1, 2], [3, 4, 5], [6, 7, 8]]),
    components=[
        vm.Graph(
            figure=px.scatter(
                gapminder_2007,
                x="gdpPercap",
                y="lifeExp",
                size="pop",
                size_max=60,
                color="continent",
            )
        ),
        vm.Graph(figure=px.box(tips, y="total_bill", x="day", color="day")),
        vm.Graph(figure=px.histogram(tips, x="total_bill")),
        vm.Graph(
            figure=px.histogram(
                tips,
                y="day",
                x="total_bill",
                color="sex",
                barmode="group",
                orientation="h",
            )
        ),
        vm.Graph(figure=px.density_heatmap(tips, x="day", y="size", z="tip", histfunc="avg", text_auto="$.2f")),
        vm.Graph(figure=px.histogram(tips, x="sex", y="total_bill", color="day")),
        vm.Graph(figure=waterfall(waterfall_data, x="x", y="y", measure="measure")),
        vm.Graph(figure=px.pie(tips, values="tip", names="day", hole=0.85)),
        vm.Graph(
            figure=px.bar(
                gapminder_2007.query("country.isin(['United States', 'Pakistan', 'India', 'China', 'Indonesia'])"),
                x="pop",
                y="country",
                orientation="h",
            )
        ),
    ],
)

sankey_page = vm.Page(
    title="Sankey",
    components=[
        vm.Graph(
            title="Energy flow",
            figure=sankey(pd.DataFrame(), source="source", target="target", value="value"),
            footer="Sample energy flow from sources to sectors.",
        ),
    ],
)

# DASHBOARD -------------------------------------------------------------------
dashboard = vm.Dashboard(
    pages=[
        graphs,
    ],
)

Vizro().build(dashboard).run()
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Advanced Data Visualizations with Plotly and Vizro
