Quickstart¶

These instructions will help you create a report in <1 minute. We will build a simple HTML report using Python based on the Iris dataset.

Installation¶

First, install Datapane using pip or conda. Check out our installation page for installation options.

Note

You can also download this Quickstart as a Jupyter Notebook.

pip3 install -U datapane

Setting things up¶

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

We've imported datapane, the popular visualization library altair, and vega_datasets which contains some sample datasets.

Let's load the Iris dataset and get a list of the features.

df = data.iris()
columns = list(df.columns)
print(columns)

['sepalLength', 'sepalWidth', 'petalLength', 'petalWidth', 'species']

Blocks¶

Your report is comprised of Blocks, which are Python objects that wrap around common objects such as datasets, plots, and other blocks. There are display blocks, such as Plot or DataTable, and layout blocks, such as Select and Group.

Having loaded our DataFrame above and with knowledge of our column names, we first create a simple scatterplot using the Altair plotting library.

We then build a simple set of blocks which presents two tabs: one with our plot, and one with our DataFrame.

fig = (
    alt.Chart(df)
    .mark_point()
    .encode(x=alt.X("sepalLength", scale=alt.Scale(zero=False)), 
            y=alt.X("sepalWidth", scale=alt.Scale(zero=False)),
            color="species")
)

view = dp.Select(dp.Plot(fig, label="Plot"), dp.DataTable(df, label="Data"))
view

Reports¶

Once we have a view, we can save it as an HTML report to share.

Let's save it as a report and open it in a new window.

dp.save_report(view, "quickstart_report.html", open=True)

App saved to ./quickstart_report.html

That's it! As Datapane has created a standalone HTML file, you can now share this Slack or email without deploying any code or configuring a backend server.