hvPlot Development Skills

This document provides best practices for developing plots and charts with HoloViz hvPlot in notebooks and .py files.

Please develop as an Expert Python Developer developing advanced data-driven, analytics and testable data visualisations, dashboards and applications would do. Keep the code short, concise, documented, testable and professional.

Dependencies

Core dependencies provided with the hvplot Python package:

• hvplot: Core visualization framework
• holoviews: Declarative data visualization library with composable elements. Best for: complex multi-layered plots, advanced interactivity (linked brushing, selection), when you need fine control over plot composition, scientific visualizations. More powerful but steeper learning curve than hvPlot. hvPlot is built upon holoviews.
• colorcet: Perceptually uniform colormaps
• panel: Provides widgets and layouts enabling tool, dashboard and data app development.
• param: A declarative approach to creating classes with typed, validated, and documented parameters. Fundamental to the reactive programming model of hvPlot and the rest of the HoloViz ecosystem.
• pandas: Industry-standard DataFrame library for tabular data. Best for: data cleaning, transformation, time series analysis, datasets that fit in memory. The default choice for most data work.

Optional dependencies from the HoloViz Ecosystem:

• datashader: Renders large datasets (millions+ points) into images for visualization. Best for: big data visualization, geospatial datasets, scatter plots with millions of points, heatmaps of dense data. Requires hvPlot or HoloViews as frontend.
• geoviews: Geographic data visualization with map projections and tile sources. Best for: geographic/geospatial plots, map-based dashboards, when you need coordinate systems and projections. Built on HoloViews, works seamlessly with hvPlot.
• holoviz-mcp: Model Context Protocol server for HoloViz ecosystem. Provides access to detailed documentation, component search and agent skills.
• hvsampledata: Shared datasets for the HoloViz projects.

Installation for Development

pip install hvplot hvsampledata panel watchfiles

For development in .py files DO always include watchfiles for Panel hotreload.

Earthquake Sample Data

In the example below we will use the earthquakes sample data:

import hvsampledata

hvsampledata.earthquakes("pandas")

Tabular record of earthquake events from the USGS Earthquake Catalog that provides detailed
information including parameters such as time, location as latitude/longitude coordinates
and place name, depth, and magnitude. The dataset contains 596 events.

Note: The columns `depth_class` and `mag_class` were created by categorizing numerical values from
the `depth` and `mag` columns in the original dataset using custom-defined binning:

Depth Classification

| depth     | depth_class  |
|-----------|--------------|
| Below 70  | Shallow      |
| 70 - 300  | Intermediate |
| Above 300 | Deep         |

Magnitude Classification

| mag         | mag_class |
|-------------|-----------|
| 3.9 - <4.9  | Light     |
| 4.9 - <5.9  | Moderate  |
| 5.9 - <6.9  | Strong    |
| 6.9 - <7.9  | Major     |


Schema
------
| name        | type       | description                                                         |
|:------------|:-----------|:--------------------------------------------------------------------|
| time        | datetime   | UTC Time when the event occurred.                                   |
| lat         | float      | Decimal degrees latitude. Negative values for southern latitudes.   |
| lon         | float      | Decimal degrees longitude. Negative values for western longitudes   |
| depth       | float      | Depth of the event in kilometers.                                   |
| depth_class | category   | The depth category derived from the depth column.                   |
| mag         | float      | The magnitude for the event.                                        |
| mag_class   | category   | The magnitude category derived from the mag column.                 |
| place       | string     | Textual description of named geographic region near to the event.   |

Reference Data Exploration Example

Below is a simple reference example for data exploration.

import hvsampledata
# DO import panel if working in .py files
import panel as pn
# Do importing hvplot.pandas to add .hvplot namespace to Pandas DataFrames and Series
import hvplot.pandas  # noqa: F401

# DO always run pn.extension() to load panel javascript extensions
pn.extension()

# Do keep the extraction, transformation and plotting of data clearly separate
# Extract: earthquakes sample data
data = hvsampledata.earthquakes("pandas")

# Transform: Group by mag_class and count occurrences
mag_class_counts = data.groupby('mag_class').size().reset_index(name='counts')

# Plot: counts by mag_class
plot = mag_class_counts.hvplot.bar(x='mag_class', y='counts', title='Earthquake Counts by Magnitude Class')
# If working in notebook DO output to plot:
plot
# Else if working in .py file DO:
# DO provide a method to serve the app with `panel serve`
if pn.state.served:
    # DO remember to add .servable to the panel components you want to serve with the app
    pn.panel(plot, sizing_mode="stretch_both").servable()
# DON'T provide a `if __name__ == "__main__":` method to serve the app with `python`

If working in a .py file DO always serve the plot with hotreload for manual testing while developing:

panel serve path/to/file.py --dev --show

DONT serve with python path_to_this_file.py.

General Instructions

• Always import hvplot for your data backend:

import hvplot.pandas # will add .hvplot namespace to Pandas dataframes
import hvplot.polars # will add .hvplot namespace to Polars dataframes
...

• Prefer Bokeh > Plotly > Matplotlib plotting backend for interactivity
• DO use bar charts over pie Charts. Pie charts are not supported.
• DO use NumeralTickFormatter and 'a' formatter for axis formatting:

from bokeh.models.formatters import NumeralTickFormatter

df.hvplot(
    ...,
    yformatter=NumeralTickFormatter(format='0.00a'),  # Format as 1.00M, 2.50M, etc.
)

• For detailed styling and publication quality charts use HoloViz instead of hvPlot.

Developing

When developing a hvplot please serve it for development using Panel:

import pandas as pd
import hvplot.pandas  # noqa
import panel as pn

import numpy as np

np.random.seed(42)
dates = pd.date_range("2022-08-01", periods=30, freq="B")
open_prices = np.cumsum(np.random.normal(100, 2, size=len(dates)))
high_prices = open_prices + np.random.uniform(1, 5, size=len(dates))
low_prices = open_prices - np.random.uniform(1, 5, size=len(dates))
close_prices = open_prices + np.random.uniform(-3, 3, size=len(dates))

data = pd.DataFrame({
    "open": open_prices.round(2),
    "high": high_prices.round(2),
    "low": low_prices.round(2),
    "close": close_prices.round(2),
}, index=dates)


# Create a scatter plot of date vs close price
scatter_plot = data.hvplot.scatter(x="index", y="close", grid=True, title="Close Price Scatter Plot", xlabel="Date", ylabel="Close Price")


# Create a Panel app
app = pn.Column("# Close Price Scatter Plot", scatter_plot)

if pn.state.served:
    app.servable()

panel serve plot.py --dev

Recommended Plot Types

line - Line plots for time series and continuous data scatter - Scatter plots for exploring relationships between variables bar - Bar charts for categorical comparisons hist - Histograms for distribution analysis area - Area plots for stacked or filled visualizations

Workflows

Lookup additional information

• If the HoloViz MCP server tools are available, DO use them: search (documentation), hvplot_list (available plot types), hvplot_get (docstrings and signatures), skill_get (best-practice skills).
• If MCP tools are not available but the holoviz-mcp CLI is installed (also available as hv), use the equivalent CLI commands: holoviz-mcp search, holoviz-mcp hvplot list, holoviz-mcp hvplot get.
• If neither is available, DO search the web. For example searching the hvplot website for streaming related information via https://hvplot.holoviz.org/en/docs/latest/search.html?q=streaming url.

Test the app with pytest

DO add tests to the tests folder and run them with pytest tests/path/to/test_file.py.

• DO separate data extraction and transformation from plotting code.
• DO fix any test errors and rerun the tests
• DO run the tests and fix errors before displaying or serving the plots

Test the app manually with panel serve

DO always start and keep running a development server panel serve path_to_file.py --dev --show with hot reload while developing!

• Due to --show flag, a browser tab will automatically open showing your app.
• Due to --dev flag, the panel server and app will automatically reload if you change the code.
• The app will be served at http://localhost:5006/.
• DO make sure the correct virtual environment is activated before serving the app.
• DO fix any errors that show up in the terminal. Consider adding new tests to ensure they don't happen again.
• DON'T stop or restart the server after changing the code. The app will automatically reload.
• If you see 'Cannot start Bokeh server, port 5006 is already in use' in the terminal, DO serve the app on another port with --port {port-number} flag.
• DO remind the user to test the plot on multiple screen sizes (desktop, tablet, mobile)
• DON'T use legacy --autoreload flag
• DON't run python path_to_file.py to test or serve the app.
• DO use pn.Column, pn.Tabs, pn.Accordion to layout multiple plots
• If you close the server to run other commands DO remember to restart it.