Apps
Introduction
In addition to creating reports by running the computation on your own notebook, pipeline, or server, you may want to deploy Python scripts or notebooks to Datapane so they can be scheduled, be run with parameters, or run in response to API events.
Datapane provides an app platform, so you can generate reports in an automated fashion, in addition to creating them in your local environment.
Once you deploy your script or notebook as an app, it can be run in three ways:
Web Forms
Apps can be run with parameters entered through friendly web forms, which allows you to create interactive, self-service reporting tools for stakeholders.
On a schedule
Apps can generate and update reports on a schedule, allowing you to create "live" dashboards and automated reports.
Through an API
You can trigger report generation through our API, which allows you to generate reports in response to events from other tools, such as Slack and Microsoft Teams, or your own product.
Deploying an app
If you have a local Python script or notebook which creates a report using Datapane's Report.upload method (see Creating a Report), you can deploy it to Datapane using the CLI.
Let's use an example of a COVID report, which will be returned to the user when they run our app using the Datapane web interface.
Info
We recommend creating only one report per app. As many can be created as needed; however, only the last one in each app will be tracked in the web interface.
We'll place the folloding in a file named simple_script.py.
import pandas as pd
import altair as alt
import datapane as dp
dataset = pd.read_csv("https://covid.ourworldindata.org/data/owid-covid-data.csv")
df = (
dataset.groupby(["continent", "date"])["new_cases_smoothed_per_million"]
.mean()
.tail(1000)
.reset_index()
)
plot = (
alt.Chart(df)
.mark_area(opacity=0.4, stroke="black")
.encode(
x="date:T",
y=alt.Y("new_cases_smoothed_per_million:Q", stack=None),
color=alt.Color("continent:N", scale=alt.Scale(scheme="set1")),
tooltip="continent:N",
)
.interactive()
.properties(width="container")
)
report = dp.Report(dp.Plot(plot), dp.Table(df))
report.upload(name="covid_report")
To deploy it, use Datapane's CLI.
This makes your app available on your private instance, where you can share it with other users. If you send them your app, they are able to generate the report from the previous example dynamically by hitting the Run button.
Every time the app is run, it pulls new COVID data and generates a fresh report, which can be shared or embedded.
Configuration
In the previous example, we are deploying a single app and providing the name and file location through command-line arguments. This works well for simple scripts, but app often need other configuration, such as parameter definitions, other files or folders to deploy, and Python or OS requirements.
Datapane allows you to provide a configuration file called datapane.yaml. When you run deploy, Datapane looks for this file automatically. Before we continue, create a new folder and then run the datapane app init command inside it:
This will create a datapane.yaml file and a sample app.
We already have an app from our previous example, so we can delete the sample dp_script.py and copy in our own. Because we're replacing the default app, we should specify the filename of our app in datapane.yaml using the app field. Whilst we are there, we can also choose a name.
name: covid_script
app: simple_script.py # this could also be ipynb if it was a notebook
If we run datapane app deploy in this directory, Datapane will deploy our code with the configuration in datapane.yaml. Because we have given the script the same name as our previous one, this will overwrite covid_script. See the following link for the full reference on the configuration format.
In the next section, we will explore adding parameters to your app, to enable reports to be generated dynamically based on user inputs.