hvPlot.hexbin#

hvPlot.hexbin(x=None, y=None, C=None, colorbar=True, gridsize=50, logz=False, min_count=None, **kwds)[source]#

The hexbin plot uses hexagons to split the area into several parts and attribute a color to it.

hexbin offers a straightforward method for plotting dense data.

Reference: https://hvplot.holoviz.org/ref/api/manual/hvplot.hvPlot.hexbin.html

Plotting options: https://hvplot.holoviz.org/ref/plotting_options/index.html

Parameters:

xstring, optional: Field name to draw x coordinates from. If not specified, the index is used.
ystring: Field name to draw y-positions from
Cstring, optional: Field to draw hexbin color from. If not specified a simple count will be used.
colorbar: boolean, optional: Whether to display a colorbar. Default is True.
reduce_functionfunction, optional: Function to compute statistics for hexbins, for example np.mean. Default aggregation is a count of the values in the area.
gridsize: int or tuple, optional: Number of hexagonal bins along x- and y-axes. Defaults to uniform sampling along both axes when setting and integer but independent bin sampling can be specified a tuple of integers corresponding to the number of bins along each axis. Default is 50.
logzbool: Whether to apply log scaling to the z-axis. Default is False.
min_countnumber, optional: The display threshold before a bin is shown, by default bins with a count of less than 1 are hidden
**kwdsoptional: Additional keywords arguments are documented in Plotting Options. Run hvplot.help('hexbin') for the full method documentation.

Returns:

holoviews.element.HexTiles / Panel object

You can print the object to study its composition and run:

import holoviews as hv
hv.help(the_holoviews_object)

to learn more about its parameters and options.

References

Backend-specific styling options#

Bokeh

alpha, cmap, color, fill_alpha, fill_color, hover_alpha, hover_color, hover_fill_alpha, hover_fill_color, hover_line_alpha, hover_line_cap, hover_line_color, hover_line_dash, hover_line_dash_offset, hover_line_join, hover_line_width, line_alpha, line_cap, line_color, line_dash, line_dash_offset, line_join, line_width, muted, muted_alpha, muted_color, muted_fill_alpha, muted_fill_color, muted_line_alpha, muted_line_cap, muted_line_color, muted_line_dash, muted_line_dash_offset, muted_line_join, muted_line_width, nonselection_alpha, nonselection_color, nonselection_fill_alpha, nonselection_fill_color, nonselection_line_alpha, nonselection_line_cap, nonselection_line_color, nonselection_line_dash, nonselection_line_dash_offset, nonselection_line_join, nonselection_line_width, scale, selection_alpha, selection_color, selection_fill_alpha, selection_fill_color, selection_line_alpha, selection_line_cap, selection_line_color, selection_line_dash, selection_line_dash_offset, selection_line_join, selection_line_width, visible

Matplotlib

alpha, cmap, ec, edgecolor, edgecolors, linewidths, marginals

Examples#

Basic hexbin plot#

import hvplot.pandas  # noqa
import pandas as pd
import numpy as np

df = pd.DataFrame({
    "x": 2 + 2 * np.random.standard_normal(10000),
    "y": 2 + 2 * np.random.standard_normal(10000),
})

df.hvplot.hexbin(aspect=1)

Basic 2D density with earthquake locations#

This example shows how to use hexbin to visualize the spatial density of earthquake events based on latitude and longitude.

import hvplot.pandas # noqa

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

df.hvplot.hexbin(
    x="lon", y="lat", cmap="plasma",
    data_aspect=1, xlim=(120, 130), ylim=(0, 10)
)

Aggregate with a specific variable and function#

In this example the hexbin is colored by the aggregated mean depth, set with the keywords C and reduce_function.

import hvplot.pandas  # noqa
import numpy as np

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

df.hvplot.hexbin(
    x="lon", y="lat", cmap="plasma",
    data_aspect=1, xlim=(120, 130), ylim=(0, 10),
    C='depth', reduce_function=np.mean, clabel='Mean depth',
)

Display threshold and grid size#

Increasing min_count (default is 1) reduces the number of hexagonal cells plotted.

import hvplot.pandas  # noqa
import numpy as np

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

df.hvplot.hexbin(
    x="lon", y="lat", cmap="plasma", data_aspect=1,
    frame_width=300, xlim=(120, 130), ylim=(0, 10),
    min_count=1, title='min_count=1',
) +\
df.hvplot.hexbin(
    x="lon", y="lat", cmap="plasma", data_aspect=1,
    frame_width=300, xlim=(120, 130), ylim=(0, 10),
    min_count=3, title='min_count=3',
)

Increasing gridsize (default is 50) increases the number of hexagonal cells plotted. The number of cells in the x and y directions can be customized by setting gridsize with a tuple.

import hvplot.pandas  # noqa
import numpy as np

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

df.hvplot.hexbin(
    x="lon", y="lat", cmap="plasma", data_aspect=1, frame_width=300,
    gridsize=10, title='gridsize=10',
) +\
df.hvplot.hexbin(
    x="lon", y="lat", cmap="plasma", data_aspect=1, frame_width=300,
    gridsize=(20, 50), title='gridsize=(20, 50)',
)

Xarray example#

This example aggregates temperature readings across longitudes and latitudes using hexagonal bins. logz=True applies a logarithmic color scale, and gridsize controls hex resolution.

import hvplot.xarray  # noqa
import numpy as np

da = hvplot.sampledata.air_temperature("xarray").sel(time="2014-02-25 12:00")

da.hvplot.hexbin(
    x="lon", y="lat", cmap="inferno",
    bgcolor="#020210", data_aspect=1, frame_width=400,
    C="air", reduce_function=np.mean, clabel="Air temperature (K)",
    logz=True, gridsize=30,
)

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