Interactive applications

The pandas-profiling report, through several of its interfaces, can be integrated in interactive data applications such as those developed with Streamlit or Panel.

Streamlit

Streamlit is an open-source Python library made to build web-apps for machine learning and data science.

import pandas as pd
import pandas_profiling
import streamlit as st
from streamlit_pandas_profiling import st_profile_report

df = pd.read_csv(
    "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
)
pr = df.profile_report()

st.title("Pandas Profiling in Streamlit")
st.write(df)
st_profile_report(pr)

You can install the pandas-profiling component for Streamlit with pip:

pip install streamlit-pandas-profiling

Dash

Dash is a Python framework for building machine learning & data science web apps, built on top of Plotly.js, React and Flask. It is commonly used for interactive data exploration, precisely where pandas-profiling also focuses. Inline access to the insights provided by pandas-profiling can help guide the exploratory work allowed by Dash. To integrate a Profiling Report inside a Dash app, two options exist:

Load HTML version of report as an asset

Assuming the HTML version of the report is in report.html, move it to a folder called assets. The snippet below shows a simple Dash app, app.py, embedding this report:

import dash
from dash import html

app = dash.Dash(__name__)

app.layout = html.Div(
    children=[
        html.Iframe(
            src="assets/census_report.html",  # must be under assets/ to be properly served
            style={"height": "1080px", "width": "100%"},
        )
    ]
)

if __name__ == "__main__":
    app.run_server(debug=True)

When running python app.py, a Dash app with the report embedded will be available on http://127.0.0.1:8050.

Directly embed the raw HTML

A more unorthodox option requiring no explicit file handling involves using the dash-dangerously-set-inner-html library to directly embed the HTML raw text (thus requiring no HTML export). Install the library through pip:

pip install dash-dangerously-set-inner-html

And configure the Dash app as in the following snippet:

import pandas as pd
from pandas_profiling import ProfileReport
import dash
from dash import html
import dash_dangerously_set_inner_html

# Creating the Report
df = pd.read_csv(
    "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
)
profile = ProfileReport(df, title="Titanic Dataset")
text_raw = profile.to_html()

# Creating the Dash app

app = dash.Dash(__name__)

app.layout = html.Div(
    [dash_dangerously_set_inner_html.DangerouslySetInnerHTML(text_raw)]
)

app.layout = html.Div(
    [dash_dangerously_set_inner_html.DangerouslySetInnerHTML(text_raw)]
)

if __name__ == "__main__":
    app.run_server(debug=True)

When running python app.py, a Dash app with the report embedded will be available on ``<http://127.0.0.1:8050>`_. While this option is somewhat more direct, the embedded report will not be fully interactive, with some buttons unclickable.

Panel

For more information on how to use pandas-profiling in Panel, see this GitHub issue and this integration example.