datapane 0.17.0

Datapane client library and CLI tool

Docs | Examples |

Note: Datapane is no longer actively maintained.

We've made the difficult decision to stop driving this project and therefore we will no longer actively respond to issues or pull requests. If you would like to take over maintaining this project independently, please let us know so we can add a link to your forked project here.

The final release, 0.17.0, has been optimized to focus on free local report saving, with unused features and analytics removed, and libraries updated to allow usage for as long as possible.

Thank You.

Overview

Build interactive reports in seconds using Python.

Datapane makes it simple to build interactive reports in seconds using Python.

Import Datapane's Python library into your script or notebook and build reports programmatically by wrapping components such as:

• Pandas DataFrames
• Plots from Python visualization libraries such as Bokeh, Altair, Plotly, and Folium
• Markdown and text
• Files, such as images, PDFs, JSON data, etc.
• Interactive forms which run backend Python functions

Datapane reports are interactive and can also contain pages, tabs, drop downs, and more. Once created, reports can be exported as HTML, shared as standalone files, or embedded into your own application, where your viewers can interact with your data and visualizations.

Gallery

Check out example reports:

• https://github.com/datapane/examples

Getting Started

Check out our Quickstart to build a report in 3m.

Installing Datapane

The best way to install Datapane is through pip or conda.

pip

$ pip3 install -U datapane

conda

$ conda install -c conda-forge "datapane>=0.17.0"

Datapane also works well in hosted Jupyter environments such as Colab or Binder, where you can install as follows:

!pip3 install --quiet datapane

Examples

📊 Share plots, data, and more as reports

Create reports from pandas DataFrames, plots from your favorite libraries, and text.

import altair as alt
from vega_datasets import data
import datapane as dp

df = data.iris()
fig = (
    alt.Chart(df)
    .mark_point()
    .encode(
        x="petalLength:Q",
        y="petalWidth:Q",
        color="species:N"
    )
)
view = dp.Blocks(
    dp.Plot(fig),
    dp.DataTable(df)
)
dp.save_report(view, path="my_app.html")

🎛 Layout using interactive blocks

Add dropdowns, selects, grid, pages, and 10+ other interactive blocks.

...

view = dp.Blocks(
    dp.Formula("x^2 + y^2 = z^2"),
    dp.Group(
        dp.BigNumber(
            heading="Number of percentage points",
            value="84%",
            change="2%",
            is_upward_change=True
        ),
        dp.BigNumber(
            heading="Simple Statistic", value=100
        ), columns=2
    ),
    dp.Select(
        dp.Plot(fig, label="Chart"),
        dp.DataTable(df, label="Data")
    ),
)
dp.save_report(view, path="layout_example.html")

Next Steps

• Quickstart - build a report in 3m
• View Examples
• Read the documentation