Reactive Plots#
Download this notebook from GitHub (right-click to download).
The iris dataset is a standard example used to illustrate machine-learning and visualization techniques. Here, we show how to use Panel to create a dashboard for visualizing the dataset. The Panel dashboard uses hvPlot to create plots and Param objects to create options for selecting the X and Y axis for the plot. First, let’s import the packages we are using:
import hvplot.pandas
import param
import panel as pn
from bokeh.sampledata.iris import flowers
pn.extension(sizing_mode="stretch_width")
The flowers dataset we imported from Bokeh has five columns: sepal_length, sepal_width, petal_length, petal width, and species.
flowers.head(2)
We will start by using the dataframe with these five features and then create a Selector object to develop menu options for different input features. Later we will define the core plotting function in a plot method and define the layout in the panel method of the IrisDashboard class.
The plot method watches the X_variable and Y_variable using the param.depends decorator, setting the watch option of this decorator to True. The plot method plots the features selected for X_variable and Y_variable and colors them using the species column.
inputs = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
class IrisDashboard(param.Parameterized):
X_variable = param.Selector(inputs, default=inputs[0])
Y_variable = param.Selector(inputs, default=inputs[1])
@param.depends('X_variable', 'Y_variable')
def plot(self):
return flowers.hvplot.scatter(x=self.X_variable, y=self.Y_variable, by='species').opts(height=600)
def panel(self):
return pn.Row(
pn.Param(self, width=300, name = "Plot Settings", sizing_mode="fixed"),
self.plot
)
dashboard = IrisDashboard(name='Iris_Dashboard')
And now you can explore how each of the input columns relate to each other, either here in the notebook or when served as a separate dashboard using panel serve --show Iris_dataset.ipynb:
component = dashboard.panel()
component
App#
Lets wrap it into nice template that can be served via panel serve reactive_plots.ipynb
pn.template.FastListTemplate(site="Panel", title="Machine Learning Data Visualization",
main=[
"The [iris dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set) is a standard example used to illustrate **machine-learning and visualization techniques**.\n\nHere, we show how to use [Panel](http://panel.pyviz.org) to create a dashboard for visualizing the dataset. The Panel dashboard uses [hvPlot](http://hvplot.pyviz.org) to create plots and [Param](https://param.pyviz.org) objects to create options for selecting the `X` and `Y` axis for the plot.",
component
]).servable();
Download this notebook from GitHub (right-click to download).