Connect data science insights with end users

Turn a Python analysis into a self-service document or cloud app in 3 lines of code.

Report Example

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Datapane is the world's most popular way to share data science insights from Python.

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Featured Community Reports

Share and learn from reports built by Datapane's community of data storytellers.

Share Python documents in seconds

Build beautiful reports from blocks of DataFrames, plots, and files without leaving Python. Publish to Datapane to share and embed them online.

  • 1
    Analyse data in your own tools

    Write code and analyze data in your own editor or environment, whether its Jupyter, Colab, or Airflow.

  • 2
    Build reports in code

    Datapane's framework makes it easy to create rich reports from DataFrames, output files, and libraries like Altair and Plotly.

  • 3
    Publish and share

    Export as standalone HTML files, or publish to Datapane, where your reports can be shared and embedded.

"I like to do my analysis and visualization in Python, but I had no way to share results beyond screenshots. Datapane lets me create and share amazing interactive reports from Python in a few seconds."

Khuyen Tran, Data Scientist
import pandas as pd import altair as alt
import datapane as dp
df = pd.read_csv('https://covid.ourworldindata.org/data/vaccinations/vaccinations-by-manufacturer.csv', parse_dates=['date']) df = df.groupby(['vaccine', 'date'])['total_vaccinations'].sum().tail(1000).reset_index() plot = alt.Chart(df).mark_area(opacity=0.4, stroke='black').encode( x='date:T', y=alt.Y('total_vaccinations:Q'), color=alt.Color('vaccine:N', scale=alt.Scale(scheme='set1')), ).interactive().properties(width='container') total_df = df[df["date"] == df["date"].max()].sort_values("total_vaccinations", ascending=False).reset_index(drop=True) total_styled = total_df.style.bar(subset=["total_vaccinations"], color='#5fba7d', vmax=total_df["total_vaccinations"].sum())
dp.Report( "## Vaccination Report", dp.Plot(plot, caption="Vaccinations by manufacturer over time"), dp.DataTable(df, caption="Initial Dataset") ).publish(name='Covid Vaccinations Demo', description="Covid Vaccinations report, using data from ourworldindata", open=True)

Turn scripts and Jupyter Notebooks into self-service web apps

Deploy Jupyter Notebooks and Python scripts to Datapane Teams, where they can be run by your team or clients with parameters, scheduled to run automatically, and automate your other platforms with data science insights.

$ datapane app deploy
                [-] Uploaded forecast.ipynb to /apps/prophet-forecast/
                $ datapane schedule create prophet-forecast "* * * 1 *"
                [-] Scheduled prophet-forecast for "* * * 1 *"
              
Customer profile user interface

Datapane Teams

Everything you need to share data science insights

Datapane Teams provides a secure environment for sharing private reports and deploying and running data science apps.

On-premise Deployment

Install Datapane securely in your own cloud or bare-metal environment, so your data never leaves your system

App Deployment

Deploy Jupyter Notebooks or Python scripts as accessible, automated internal tools

End-user Forms

Allow stakeholders to provide custom parameters through rich web forms

Scheduling

Schedule app runs for dashboarding use-cases, or trigger through an API

Private sharing

Publish reports privately and securely share them with your team or external clients

Whitelabel Embedding

Embed reports, datasets, and visualizations into your own app or platforms

Solutions

Datapane Teams can be used for sharing data science with your clients or your team. Learn more about how teams use our APIs to deliver data science.

Contact us

Share data science with clients

Generate beautiful data science reports for your clients. Share them securely using Datapane Teams or embed them into your own product.

Make data science insights accessible

Instantly turn results into reports for business units. Turn notebooks and scripts into serverless cloud apps to make data science insights self-service.

Frequently asked questions

What is the difference between a Report and an App?
Reports are documents generated in your own environment (e.g. Jupyter, Airflow, Spark) and pushed to Datapane. Apps are Python scripts or notebooks which you deploy to Datapane so other people can run them.
What is Datapane Community?
Datapane Community is a free lightweight platform for hosting public reports, testing Datapane, and collaborating with the data science community. Sign up for free to get started.
Where is Datapane Teams hosted?
When you create a private Datapane server, it is hosted as a single-tenant environment hosted on Google Cloud and managed by us. Alternatively, we can provide you options to deploy on your own cloud environment.
Can I include my company's libraries in my Apps?
Yes. You can include local dependencies or Python libraries in your script's configuration, or provide a Docker container, which all scripts will execute in.
Can Apps talk to my data warehouse or internal APIs?
Yes. Once on Datapane, your Python script or notebook can connect to third-party systems, so can push or pull from a database or API.
How much does Datapane cost?
Our open-source reporting library and Community platform is free. Datapane Teams pricing is based on usage and can be found here.

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