Build an app#
At this point you should have set up your environment and installed Panel so you should be ready to get going.
On this page, we’re going to be building a basic interactive application based on Numpy, Pandas and hvplot. If you want to implement this app yourself as you follow along, we recommend starting with a Jupyter notebook. You can also launch the Notebook with JupyterLite on the right.
Fetch the data#
First let’s load the UCI ML dataset that measured the environment in a meeting room:
Important
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import panel as pn
import hvplot.pandas
import pandas as pd
import numpy as np
pn.extension(design='material')
csv_file = ("https://raw.githubusercontent.com/holoviz/panel/main/examples/assets/occupancy.csv")
data = pd.read_csv(csv_file, parse_dates=["date"], index_col="date")
data.tail()
Temperature | Humidity | Light | CO2 | HumidityRatio | Occupancy | |
---|---|---|---|---|---|---|
date | ||||||
2015-02-10 09:29:00 | 21.05 | 36.0975 | 433.0 | 787.250000 | 0.005579 | 1 |
2015-02-10 09:29:59 | 21.05 | 35.9950 | 433.0 | 789.500000 | 0.005563 | 1 |
2015-02-10 09:30:59 | 21.10 | 36.0950 | 433.0 | 798.500000 | 0.005596 | 1 |
2015-02-10 09:32:00 | 21.10 | 36.2600 | 433.0 | 820.333333 | 0.005621 | 1 |
2015-02-10 09:33:00 | 21.10 | 36.2000 | 447.0 | 821.000000 | 0.005612 | 1 |
Visualize a subset of the data#
Before we utilize Panel, let’s write a function that smooths one of our time series and finds the outliers. We will then plot the result using hvplot.
def transform_data(variable, window, sigma):
''' Calculates the rolling average and the outliers '''
avg = data[variable].rolling(window=window).mean()
residual = data[variable] - avg
std = residual.rolling(window=window).std()
outliers = np.abs(residual) > std * sigma
return avg, avg[outliers]
def create_plot(variable="Temperature", window=30, sigma=10):
''' Plots the rolling average and the outliers '''
avg, highlight = transform_data(variable, window, sigma)
return avg.hvplot(height=300, width=400, legend=False) * highlight.hvplot.scatter(
color="orange", padding=0.1, legend=False
)
We can now call our create_plot
function with specific parameters to get a plot with a single set of parameters.
create_plot(variable='Temperature', window=20, sigma=10)
It works! But now we want explore how values for window
and sigma
affect the plot. We could reevaluate the above cell a lot of times, but that would be a slow and painful process. Instead, let’s use Panel to quickly add some interactive controls and quickly determine how different parameter values impact the output.
Explore parameter space#
Let’s create some Panel slider widgets for the range of parameter values that we want to explore.
variable_widget = pn.widgets.Select(name="variable", value="Temperature", options=list(data.columns))
window_widget = pn.widgets.IntSlider(name="window", value=30, start=1, end=60)
sigma_widget = pn.widgets.IntSlider(name="sigma", value=10, start=0, end=20)
Now that we have a function and some widgets, let’s link them together so that updates to the widgets rerun the function. One easy way to create this link in Panel is with pn.bind
:
bound_plot = pn.bind(create_plot, variable=variable_widget, window=window_widget, sigma=sigma_widget)
Once you have bound the widgets to the function’s arguments you can lay out the resulting bound_plot
component along with the widget
components using a Panel layout such as Column
:
first_app = pn.Column(variable_widget, window_widget, sigma_widget, bound_plot)
first_app
As long as you have a live Python process running, dragging these widgets will trigger a call to the create_plot
callback function, evaluating it for whatever combination of parameter values you select and displaying the results.
Next Steps#
Now that we have given you a taste of how easy it is to build a little application in Panel, it’s time to introduce you to some of the core concepts behind Panel.