Morph allows you to create data apps using visualization libraries within the Python ecosystem. In this tutorial, we will introduce how to embed a rich dashboard created with PyGWalker into a data app.

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Prerequisites

Please install PyGWalker in advance by the following command.

pip install pygwalker

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Output

PyGWalker is a library that creates a dashboard for interactive data analysis from data on a DataFrame. You can easily introduce a BI app by placing it on the data app created by Morph and sharing it with your team.

PyGWalker

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Tutorial

In this tutorial, we will implement the app in the following steps:

  1. Create a Python function that returns HTML using PyGWalker.
  2. Pass the created Python function to the <Embed /> component in the MDX file to display the dashboard.

<Embed /> displays the HTML when a Python function that returns HTML is specified as the target of loadData.

Create sample data as a DataFrame, convert the data to a dashboard using PyGWalker, and return it as HTML.

import pandas as pd
import morph
from morph import MorphGlobalContext
from morph_lib.types import HtmlResponse
import pygwalker as pyg

@morph.func
def create_pygwalker_dashboard(context: MorphGlobalContext):
    data = pd.DataFrame({
        "city": [
            "Los Angeles", "San Francisco", "San Diego", "Sacramento",
            "Houston", "Dallas", "Austin", "San Antonio",
            "Miami", "Orlando", "Tampa", "Jacksonville",
            "New York", "Buffalo", "Rochester", "Syracuse",
            "Chicago", "Springfield", "Peoria", "Naperville"
        ],
        "state": [
            "California", "California", "California", "California",
            "Texas", "Texas", "Texas", "Texas",
            "Florida", "Florida", "Florida", "Florida",
            "New York", "New York", "New York", "New York",
            "Illinois", "Illinois", "Illinois", "Illinois"
        ],
        "population": [
            3980400, 883305, 1423851, 508529,
            2328000, 1343000, 964300, 1532200,
            470914, 285713, 399700, 903889,
            8399000, 256304, 206284, 142749,
            2716000, 116250, 112936, 147122
        ]
    })
    html_code = pyg.to_html(data, appearance="light")
    return HtmlResponse(html_code)