Build a sophisticated dashboard using Plotly

Morph allows you to customize and create dynamic dashboard applications with input forms. In this tutorial, you will create an application that filters and displays Plotly charts based on values received from input forms.

Prerequisites

Please install pageckages in advance by the following command.

pip

pip install plotly

Output

Tutorial

To create a dynamic application that switches the display content based on user input, you need to create an input form in the MDX file and pass its values to a Python function. In the Python function, use context.vars to receive the values entered in the input form and filter the data for the chart to be returned.

In this tutorial, we also introduce how to obtain the options for the input form using SQL in addition to the above.

• 1. Python
• 2. SQL
• 3. MDX(pages)

Here, we create the following two functions:

• generate_population_data: Generates the data used in the tutorial. In a production environment, this function can be used to fetch data. Additionally, this function can be replaced with an SQL file to fetch data from a database.
• filter_plotly_chart: Filters the data generated by generate_population_data and returns a Plotly chart.

Plotly and Matplotlib are automatically converted to HTML by Morph’s library when returned as a Figure, making them usable with <Embed />.

import numpy as np
import pandas as pd
import plotly.express as px

import morph
from morph import MorphGlobalContext

@morph.func
def generate_population_data(context: MorphGlobalContext):
    # Initialize
    dates = pd.date_range(start="2022-01-01", end="2024-12-31", freq="M").strftime("%Y-%m-%d")
    states = ["California", "Texas", "Florida", "New York", "Illinois"]  # List of states

    # Generate dummy data
    data = pd.DataFrame({
        "date": np.tile(dates, len(states)),  # Repeat dates for all states
        "state": np.repeat(states, len(dates)),  # Repeat each state for all dates
        "population": np.random.randint(100000, 10000000, size=len(dates) * len(states))  # Generate random population data
    })
    return data


@morph.func
@morph.load_data("generate_population_data")
def filter_plotly_chart(context: MorphGlobalContext):
    data = context.data["generate_population_data"]
    start_date = context.vars["start_date"]
    end_date = context.vars["end_date"]
    state = context.vars["state"]

    # apply filter
    df = data[data["date"].between(start_date, end_date)]
    if state != "all":
        df = df[df["state"] == state]
    fig = px.bar(df, x="date", y="population", color="state", title="Population Over Time by State")
    return fig

Here, we create the following two functions:

• generate_population_data: Generates the data used in the tutorial. In a production environment, this function can be used to fetch data. Additionally, this function can be replaced with an SQL file to fetch data from a database.
• filter_plotly_chart: Filters the data generated by generate_population_data and returns a Plotly chart.

Plotly and Matplotlib are automatically converted to HTML by Morph’s library when returned as a Figure, making them usable with <Embed />.

import numpy as np
import pandas as pd
import plotly.express as px

import morph
from morph import MorphGlobalContext

@morph.func
def generate_population_data(context: MorphGlobalContext):
    # Initialize
    dates = pd.date_range(start="2022-01-01", end="2024-12-31", freq="M").strftime("%Y-%m-%d")
    states = ["California", "Texas", "Florida", "New York", "Illinois"]  # List of states

    # Generate dummy data
    data = pd.DataFrame({
        "date": np.tile(dates, len(states)),  # Repeat dates for all states
        "state": np.repeat(states, len(dates)),  # Repeat each state for all dates
        "population": np.random.randint(100000, 10000000, size=len(dates) * len(states))  # Generate random population data
    })
    return data


@morph.func
@morph.load_data("generate_population_data")
def filter_plotly_chart(context: MorphGlobalContext):
    data = context.data["generate_population_data"]
    start_date = context.vars["start_date"]
    end_date = context.vars["end_date"]
    state = context.vars["state"]

    # apply filter
    df = data[data["date"].between(start_date, end_date)]
    if state != "all":
        df = df[df["state"] == state]
    fig = px.bar(df, x="date", y="population", color="state", title="Population Over Time by State")
    return fig

This SQL uses the data generated by generate_population_data to obtain the options used in filter_plotly_chart. In other SQL, Python, and MDX load_data, you can access the results using the get_state_list specified by name.

{{
    config(
        name = "get_state_list"
    )
}}

select
    distinct state
from
    {{load_data("generate_population_data")}}

Create a dashboard using Python functions and SQL.

The defineState() function allows you to declare states that can be used within the page.

• dateStart, dateEnd: The selected date range.
• state: The value of the selected state among the options.

Using <DataTable /> and <Embed />, the original data and chart are displayed. By using variables, the values selected in the input forms are passed to the Python function.

export const title = "Filter plotly chart";

import { defineState } from "@morph-data/components";

export const { dateStart, dateEnd, state } = defineState({
  dateStart: "2024-01-01",
  dateEnd: "2024-09-30",
  state: "all",
});

# Filter plotly chart

This example shows how to filter a plotly chart.

<div className="p-4 border border-gray-300 rounded-lg shadow-md">
  <Grid cols="2">
    <div>
      <DataTable loadData="generate_population_data" />
    </div>
    <div>
      <div className="flex items-center gap-3">
        Date Range
        <div className="flex-1">
          <DateRangePicker state={[dateStart, dateEnd]} />
        </div>
      </div>
      <div className="flex items-center gap-3">
        State
        <div className="flex-1">
          <Select state={state}>
            <SelectItem value="all">Not Selected</SelectItem>
            <SelectItems
              loadData="get_state_list"
              valueKey="state"
              labelKey="state"
            />
          </Select>
        </div>
      </div>
      <Embed
        loadData="filter_plotly_chart"
        variables={{
          start_date: dateStart.value,
          end_date: dateEnd.value,
          state: state.value,
        }}
        height={400}
      />
    </div>
  </Grid>
</div>