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.

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('')
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


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)]


Similarly we can use other pandas features in the same way.

head_widget = pn.widgets.IntSlider(name="Head", start=1, end=10)\


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")

The default is to include both the widgets and the interactive panel (graph or table) when we display the interactive dataframe. If we wish to display them separately we can access the widgets and the panel as .widgets and .panel() respectively.

    "Selected penguins",

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

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