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('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()








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


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

idf.head(head_widget)








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.


pn.Column(
    idf.widgets(),
    pn.Spacer(height=30),
    "Selected penguins",
    idf.head().panel(),
)








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'),
)