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Overview

Data In Pane Reports are data-centric documents which you can generate using Python. You can think of them as replacements for sending a PDF or PowerPoint, and they can be exported as standalone HTML files and sent over Slack or Email. Reports are often created programmatically from inside other platforms, such as a data pipelines ("LightGBM training results v230"), CI, or generated on a cadence ("Monday's sales forecast").

Tip

Generating a Report is somewhat similar to generating static-site generator for HTML, like Hugo or Jekyll

Data In Pane Reports provide the fastest way to share with others, for instance:

  • Export as a standalone HTML file (with all it's assets inline) which you can share on Slack or Email
  • Host on GitHub Pages or S3 for a public presentation
  • Embed within your own internal applications

Example

The example below builds upon the example in the previous section, comprised of a heading, figure, interactive figure, and interactive data table.

Data In Pane makes it simple to convert this into a Report, as per the examples below (see the API reference for further details).

import altair as alt
import datainpane as dip
from vega_datasets import data

df = data.iris()

fig = (
    alt.Chart(df)
    .mark_point()
    .encode(x="petalLength", y="petalWidth", color="species")
)

view = dip.Blocks("# My report", dip.Plot(fig), dip.DataTable(df))

Saving Reports

Reports can be saved / exported as standalone files which can be shared without a server. All data and plots are encapsulated in a single HTML file, which provides a secure and low-barrier sharing method. Views can be exported as a standalone report using dip.save_report:

# save as a static HTML-based report
dip.save_report(view, path="my_report.html")

You can view the saved report, download it, open it locally, or send via email and slack.

Note

Although standalone and serverless, Internet access is required to retrieve Data In Pane front-end libraries needed to render the report