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Test functionality and performance#

This guide addresses how to use unit and performance testing on a Panel app with Pytest.

Testing is key to developing robust and performant applications. You can test Panel data apps using familiar Python testing tools.

Pytest is the most common Python testing framework. We will use it below to write unit and performance tests. Before we get started, you should

pip install panel pytest pytest-benchmark

Create the app#

Let’s create a simple data app for testing. The app sleeps 0.5 seconds (default) when loaded and when the button is clicked.

Create the file and add the code below (don’t worry about the contents of the app for now):
import time

import panel as pn
import param

class App(pn.viewable.Viewer):
    run = param.Event(doc="Runs for click_delay seconds when clicked")
    runs = param.Integer(doc="The number of runs")
    status = param.String(default="No runs yet")

    load_delay = param.Number(default=0.5)
    run_delay = param.Number(default=0.5)

    def __init__(self, **params):

        result = self._load()
        self._time = time.time()

        self._status_pane = pn.pane.Markdown(self.status, height=40, align="start", margin=(0,5,10,5))
        self._result_pane = pn.Column(result)

        button = pn.widgets.Button.from_param(, sizing_mode="fixed")
        self._view = pn.Column(
            pn.Row(button, self._status_pane),

    def __panel__(self):
        return self._view

    def _start_run(self):
        self.status = f"Running ..."
        self._time = time.time()

    def _stop_run(self):
        now = time.time()
        duration = round(now-self._time,3)
        self._time = now
        self.runs += 1
        self.status = f"Finished run {self.runs} in {duration}sec"

    @param.depends("run", watch=True)
    def _run_with_status_update(self):
        self._result_pane[:] = [self._run()]

    @param.depends("status", watch=True)
    def _update_status_pane(self):
        self._status_pane.object = self.status

    def _load(self):
        return "Loaded"

    def _run(self):
        return f"Result {self.runs+1}"

if pn.state.served:


Now serve the app via panel serve and open http://localhost:5006/app in your browser to see what it does.

Create the unit tests#

Let’s test:

  • The initial state of the App

  • That the app state changes appropriately when the Run button is clicked.

Create the file and add the code below.
import pytest

from app import App

def app():
    return App(sleep_delay=0.001, load_delay=0.001)

def test_constructor(app):
    """Tests default values of App"""
    # Then
    assert == False
    assert app.status == "No runs yet"
    assert app.runs == 0

def test_run(app):
    """Tests behaviour when Run button is clicked once"""
    # When
    # Then
    assert app.runs == 1
    assert app.status.startswith("Finished run 1 in")

def test_run_twice(app):
    """Tests behaviour when Run button is clicked twice"""
    # When
    # Then
    assert app.runs == 2
    assert app.status.startswith("Finished run 2 in")

Let’s run pytest

$ pytest
=================================== test session starts
collected 3 items ...                                                                       [100%]

=============================== 3 passed

Create a performance test#

The performance of your data app is key to providing a good user experience. You can test the performance of functions and methods using pytest-benchmark.

Let’s test that:

  • the duration of the run is as expected.

Create the file
import pytest
from app import App

def app():
    return App(run_delay=0.001, load_delay=0.001)

def test_run_performance(app: App, benchmark):
    """Test the duration when the Run button is clicked"""

    def run():

    assert benchmark.stats['min'] >= 0.3
    assert benchmark.stats['max'] < 0.4

Run pytest

$ pytest
============================================================================================================================= test session starts
collected 1 item .                                                                                                                                                                                                                                                 [100%]

------------------------------------------------- benchmark: 1 tests ------------------------------------------------
Name (time in ms)             Min       Max      Mean  StdDev    Median     IQR  Outliers     OPS  Rounds  Iterations
test_run_performance     307.6315  316.8270  312.2731  4.2335  314.1614  7.5190       3;0  3.2023       5           1

  Outliers: 1 Standard Deviation from Mean; 1.5 IQR (InterQuartile Range) from 1st Quartile and 3rd Quartile.
  OPS: Operations Per Second, computed as 1 / Mean
========================================================================================================================= 1 passed in 3.23s

Notice how we used the benchmark fixture of pytest-benchmark to test the performance of the run event.