Using hvPlot effectively¶

This skill provides correct patterns and common pitfalls for hvPlot to effectively visualize data interactively.

Before plotting, consider: what story does the data tell? What comparison matters most? Then choose the plot type, encoding, and labels that make that story obvious. For publication-quality figures needing fine-grained control, use HoloViews directly with the Matplotlib backend instead of Bokeh.

Dependencies¶

Activate the .hvplot accessor with the appropriate backend import: import hvplot.pandas, hvplot.polars, hvplot.xarray, hvplot.duckdb, or hvplot.dask. Backends like Polars and DuckDB must be installed separately. Optional: datashader for resampling, geoviews or geopandas for geographic data. Prefer the Bokeh backend (default) for interactivity, Matplotlib for static/print output, Plotly as a last resort.

For HoloViews elements (VLine, HLine, Text, Overlay, etc.), import holoviews directly: import holoviews as hv. Do not access via hvplot.pandas.hv — it doesn't exist.

Plot Labels¶

Notes: - Label axes and colorbars with descriptive names and units. - Titles should tell the story (what, where, when); don't repeat what the axes already show. - Compute dynamic labels (date ranges, region names) as variables before the plot call to keep it readable. - Use coastline or tiles for geographic context on geospatial plots.

import hvplot.pandas  # noqa

earthquakes = hvplot.sampledata.earthquakes("pandas")
time_range_label = f"({earthquakes['time'].min():%b %Y} to {earthquakes['time'].max():%b %Y})"
title = f"Indonesian Archipelago Earthquakes {time_range_label}"
earthquakes.hvplot.points(
    x="lon",
    y="lat",
    color="mag",
    xlabel="Longitude (°E)",
    ylabel="Latitude (°N)",
    clabel="Magnitude",
    title=title,
    coastline=True,
)

Hover Tooltips¶

Notes: - Pair hover_cols with hover_tooltips: columns not used as x/y/color are not sent to the client by default, producing ??? in tooltips. - Only include the columns you need to avoid sending all data to the client. - hover_tooltips replaces the deprecated hover_formatters — models may hallucinate the old name. - Bokeh format syntax: {0.1f} floats, {0,0} thousands, {%F %H:%M} datetimes. Literal text like km can follow the format.

...
earthquakes.hvplot.points(
    ...,
    hover_tooltips=[
        ("Place", "@place"),
        ("Time", "@time{%F %H:%M}"),
        ("Magnitude", "@mag{0.1f}"),
        ("Depth", "@depth{0.0f} km"),
    ],
    hover_cols=["place", "time", "mag", "depth"],
    title=title,
    coastline=True,
)

Managing Dimensions¶

Notes: - color= and size= are vectorized; they scale to large data. Use cmap=/clabel= with color, scale= with size. - by= overlays color-coded layers with a legend; useful for stacked/side-by-side bars. - groupby= adds a dropdown widget to filter to one category at a time. - by= and groupby= loop over categories, creating a separate element per value. Slow with many categories; prefer color= for high-cardinality columns. - Options not directly exposed by hvPlot (e.g. color_levels for a discrete colorbar) can be set via .opts("Points", ...). Use cticks= and clim= in hvPlot to control colorbar tick count and range.

...
earthquakes.hvplot.points(
    ...,
    color="mag",
    cmap="viridis_r",
    clabel="Magnitude",
    cticks=5,
    clim=(4, 7),
    size="depth",
    scale=0.5,
).opts("Points", color_levels=6)

Big Data¶

Notes: - If data exceeds 100k points, consider rasterize=True over datashade=True. Rasterize aggregates server-side but colormaps in the browser, preserving hover tooltips, colorbars, and cnorm/cmap control. Datashade sends an opaque RGB image — use it only for categorical color mixing (aggregator='count_cat'). - resample_when=N disables resampling when the viewport contains fewer than N points (e.g. after zooming in), effectively creating a dynamic overlay that toggles opacity of either the rasterized or original layer. However, because it is an overlay, it can be finicky and may cause confusion, e.g. the hover tooltips will only work when the original layer is active.

...
earthquakes.hvplot.points(
    ...,
    coastline=True,
    rasterize=True,
    resample_when=5000,
)

Styling¶

Notes: - Sort values so the largest is at top (or bottom) for easy comparison. - Use a single neutral color by default; reserve color encoding for when it maps to data. Use c="column", cmap={val: "#hex", ...} for categorical coloring (a list of hex values via color= does not work for bar/barh). For stacked bars with by=, pass the same dict via cmap=. - Simplify when labels carry the information: xaxis=False, yaxis=False, show_frame=False. - Overlay hvplot.labels to show values directly on bars, eliminating the need for axis ticks entirely. Pass responsive=True and hover=False on both plot and labels calls. Inside bars: y="pos" where pos = value * 0.98, text_align="right", text_color="white". Outside bars: pos = value + offset, text_align="left", text_color="black". For stacked bars + labels, the labels DataFrame must include stacked columns via hover_cols. - backend_opts accesses Bokeh model properties (e.g. "outline_line_alpha": 0). .opts() accesses HoloViews plot options (e.g. show_frame=False). - Prefer hvPlot kwargs over .opts() — they take precedence. Pass responsive=True in the hvPlot call, not via .opts(), which can conflict with default dimensions. - Use NumeralTickFormatter(format='0a') for large-number axes via xformatter=/yformatter=. - Use fontscale= for readability. - This example is heavily stylized to illustrate what's possible; use discretion.

import hvplot.pandas  # noqa

earthquakes = hvplot.sampledata.earthquakes("pandas")

counts = earthquakes.value_counts("depth_class").sort_values()
counts = counts.reset_index().assign(**{"position": counts.values + 3})

barh = counts.hvplot.barh(
    x="depth_class",
    y="count",
    title="Most Earthquakes Occur at Shallow Depths",
    color="#4e79a7",
    hover_tooltips=[("Depth Class", "@depth_class"), ("Count", "@count{0,0}")],
    hover_cols=["depth_class", "count"],
    xaxis=False,
    yaxis="bare",
    legend=False,
    fontscale=1.35,
    padding=(0, 0.25),
    backend_opts={"plot.toolbar.autohide": True, "plot.outline_line_alpha": 0},
).opts(show_frame=False)

labels = counts.hvplot.labels(
    x="depth_class",
    y="position",
    text="{count} {depth_class}",
    hover_cols=["count"],
    text_align="left",
    text_color="black",
)
barh * labels

Interaction¶

Notes: - Hide the toolbar for polished output: backend_opts={"plot.toolbar.autohide": True} (shows on hover) or .opts(toolbar=None) (removes entirely). Hide on secondary panels in a layout (e.g. the bottom chart) and keep on the main chart. - For timeseries, use hover_mode='vline' to snap the crosshair to the nearest x-value — much easier to read than the default point hover. - Configure wheel zoom axis: tools=['xwheel_zoom'] for timeseries (zoom x only), tools=['ywheel_zoom'] for vertical, or default 'wheel_zoom' for both. Replace the active tool with active_scroll=.

# Timeseries with vline crosshair, x-only zoom, toolbar hidden
apple.hvplot.line(
    y='close',
    hover_mode='vline',
    tools=['xwheel_zoom'],
    active_tools=['xwheel_zoom'],
    responsive=True, height=300,
).opts(toolbar=None)

Timeseries¶

Notes: - subcoordinate_y=True gives each series its own y sub-axis — useful for multi-stock views with different scales. - Access datetime index components directly: 'index.month', 'index.hour', 'index.year' work inside hvPlot for aggregation. - Use xformatter=DatetimeTickFormatter(months='%b %Y') for custom date formatting. - For large timeseries, use downsample=True (LTTB algorithm) to reduce points sent to the browser while preserving visual shape. Updates dynamically on zoom.

import hvplot.pandas  # noqa
import numpy as np
from bokeh.models.formatters import DatetimeTickFormatter

apple = hvplot.sampledata.apple_stocks('pandas').set_index('date')
stocks = hvplot.sampledata.stocks('pandas').set_index('date')

# Each series gets its own y sub-axis
stocks.hvplot.line(y=['Apple', 'Amazon', 'Google', 'Meta'], subcoordinate_y=True)

# Custom date formatting + LTTB downsampling
apple.hvplot.line(
    y='close',
    xformatter=DatetimeTickFormatter(months='%b %Y'),
    downsample=True,
)

# Aggregate by datetime component
apple.hvplot.heatmap(x='index.hour', y='index.month', C='close', cmap='reds', reduce_function=np.mean)

Subplots and Layouts¶

Notes: - col=/row= creates a cleaner faceted grid than subplots=True for categorical splits. Use shared_axes=False for independent ranges.

stocks = hvplot.sampledata.stocks("pandas")

# Linked subplots, 2 per row
stocks.hvplot(
    x="date", y=["Apple", "Amazon", "Google"],
    subplots=True, shared_axes=False,
    width=300, height=200,
).cols(2)

penguins = hvplot.sampledata.penguins("pandas")

# Faceted grid: one panel per species × island
penguins.hvplot.scatter(
    x="bill_length_mm", y="bill_depth_mm",
    col="species", row="island", alpha=0.5,
)

Statistical Functions¶

These are top-level functions, not .hvplot accessor methods.

import hvplot
import hvplot.pandas  # noqa

penguins = hvplot.sampledata.penguins('pandas')[[
    'bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g', 'species'
]].dropna()
apple = hvplot.sampledata.apple_stocks('pandas').set_index('date')

hvplot.scatter_matrix(penguins, c='species')          # pairwise scatter with linked brushing
hvplot.parallel_coordinates(penguins, 'species')       # multivariate structure
hvplot.andrews_curves(penguins, 'species')             # Fourier-series class separation
hvplot.lag_plot(apple[['close']], lag=5, alpha=0.3)    # autocorrelation detection