Make interactive data workflows

This guide addresses how to bind interactive data pipelines to a component using hvplot.interactive. This is done by combining Panels widgets with hvplot.

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hvplot.interactive is a tool to get better control over your data pipelines. This is done by replacing the constant parameters in your pipeline with widgets (e.g., a number slider) that will automatically get displayed next to your pipeline output and trigger an output update on changes. With this approach, all your pipeline parameters are available in one place, and you get complete interactive control over the pipeline. For more information, check out the hvPlot documentation.

Let’s start by fetching some data:

import pandas as pd

df = pd.read_csv('https://datasets.holoviz.org/penguins/v1/penguins.csv')
df.head()

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	male	2007
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	female	2007
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	female	2007
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN	2007
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	female	2007

We now want to create select widgets for the column species and a slider for year. We can do this with Panel’s widgets:

import panel as pn

pn.extension('tabulator')

species_widget = pn.widgets.Select(name="species", options=["Adelie", "Gentoo", "Chinstrap"])
year_widget = pn.widgets.IntSlider(name="year", start=2007, end=2009)

Let’s then use these to filter the data. We can do this by using hvplot.interactive and passing the species_widget as the species parameter and the year_widget as the year parameter. In our case, we want the year always to be greater than or equal to the widget’s value.

import hvplot.pandas  # Enable interactive

idf = df.interactive()
idf = idf[(idf["species"] == species_widget) & (idf["year"] >= year_widget)]

idf.head()

Because we are already using hvplot, we can use the other powerful API of plotting the data with hvplot:

idf.hvplot(kind="scatter", x="bill_length_mm", y="bill_depth_mm", by="sex")

However we can also use bind the interactive pipeline we have built to a Panel component, e.g. a Tabulator widget:

pn.Row(
    idf.widgets(),
    pn.widgets.Tabulator(idf, page_size=10, pagination='remote'),
)

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