hvPlot.heatmap#

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

heatmap visualises tabular data indexed by two key dimensions as a grid of colored values. This allows spotting correlations in multivariate data and provides a high-level overview of how the two variables are plotted. The data can either be shaped as a 2-D array (in which case no aggregate will be computed) or as a set of two axis variables and one aggregation variable (on which an aggregation is computed).

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

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

Parameters:

xstring, optional: Field name to draw x-positions from. In the data as an array case, setting x to None assumes drawing x labels from the column names, which can be explicitly declared by setting x to 'columns'. Can refer to continuous and categorical data.
ystring, optional: Field name to draw y-positions from. In the data as an array case, setting y to None assumes drawing y labels from the index, which can be explicitly declared by setting y to 'index' or to the index name. Can refer to continuous and categorical data.
Cstring, optional: Field to draw heatmap color from. If not specified a simple count will be used.
colorbar: boolean, optional: Whether to display a colorbar. Default is True.
logzbool: Whether to apply log scaling to the z-axis. Default is False.
reduce_functionfunction, optional: Function to compute statistics for heatmap, for example np.mean. If omitted, no aggregation is applied and duplicate values are dropped.
**kwdsoptional: Additional keywords arguments are documented in Plotting Options. Run hvplot.help('heatmap') for the full method documentation.

Returns:

holoviews.element.HeatMap / 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, annular_alpha, annular_color, annular_fill_alpha, annular_fill_color, annular_hover_alpha, annular_hover_color, annular_hover_fill_alpha, annular_hover_fill_color, annular_hover_line_alpha, annular_hover_line_cap, annular_hover_line_color, annular_hover_line_dash, annular_hover_line_dash_offset, annular_hover_line_join, annular_hover_line_width, annular_line_alpha, annular_line_cap, annular_line_color, annular_line_dash, annular_line_dash_offset, annular_line_join, annular_line_width, annular_muted, annular_muted_alpha, annular_muted_color, annular_muted_fill_alpha, annular_muted_fill_color, annular_muted_line_alpha, annular_muted_line_cap, annular_muted_line_color, annular_muted_line_dash, annular_muted_line_dash_offset, annular_muted_line_join, annular_muted_line_width, annular_nonselection_alpha, annular_nonselection_color, annular_nonselection_fill_alpha, annular_nonselection_fill_color, annular_nonselection_line_alpha, annular_nonselection_line_cap, annular_nonselection_line_color, annular_nonselection_line_dash, annular_nonselection_line_dash_offset, annular_nonselection_line_join, annular_nonselection_line_width, annular_selection_alpha, annular_selection_color, annular_selection_fill_alpha, annular_selection_fill_color, annular_selection_line_alpha, annular_selection_line_cap, annular_selection_line_color, annular_selection_line_dash, annular_selection_line_dash_offset, annular_selection_line_join, annular_selection_line_width, annular_visible, cmap, color, dilate, 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, 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, ticks_text_align, ticks_text_alpha, ticks_text_baseline, ticks_text_color, ticks_text_font, ticks_text_font_size, ticks_text_font_style, visible, xmarks_alpha, xmarks_color, xmarks_hover_alpha, xmarks_hover_color, xmarks_hover_line_alpha, xmarks_hover_line_cap, xmarks_hover_line_color, xmarks_hover_line_dash, xmarks_hover_line_dash_offset, xmarks_hover_line_join, xmarks_hover_line_width, xmarks_line_alpha, xmarks_line_cap, xmarks_line_color, xmarks_line_dash, xmarks_line_dash_offset, xmarks_line_join, xmarks_line_width, xmarks_muted, xmarks_muted_alpha, xmarks_muted_color, xmarks_muted_line_alpha, xmarks_muted_line_cap, xmarks_muted_line_color, xmarks_muted_line_dash, xmarks_muted_line_dash_offset, xmarks_muted_line_join, xmarks_muted_line_width, xmarks_nonselection_alpha, xmarks_nonselection_color, xmarks_nonselection_line_alpha, xmarks_nonselection_line_cap, xmarks_nonselection_line_color, xmarks_nonselection_line_dash, xmarks_nonselection_line_dash_offset, xmarks_nonselection_line_join, xmarks_nonselection_line_width, xmarks_selection_alpha, xmarks_selection_color, xmarks_selection_line_alpha, xmarks_selection_line_cap, xmarks_selection_line_color, xmarks_selection_line_dash, xmarks_selection_line_dash_offset, xmarks_selection_line_join, xmarks_selection_line_width, xmarks_visible, ymarks_alpha, ymarks_color, ymarks_hover_alpha, ymarks_hover_color, ymarks_hover_line_alpha, ymarks_hover_line_cap, ymarks_hover_line_color, ymarks_hover_line_dash, ymarks_hover_line_dash_offset, ymarks_hover_line_join, ymarks_hover_line_width, ymarks_line_alpha, ymarks_line_cap, ymarks_line_color, ymarks_line_dash, ymarks_line_dash_offset, ymarks_line_join, ymarks_line_width, ymarks_muted, ymarks_muted_alpha, ymarks_muted_color, ymarks_muted_line_alpha, ymarks_muted_line_cap, ymarks_muted_line_color, ymarks_muted_line_dash, ymarks_muted_line_dash_offset, ymarks_muted_line_join, ymarks_muted_line_width, ymarks_nonselection_alpha, ymarks_nonselection_color, ymarks_nonselection_line_alpha, ymarks_nonselection_line_cap, ymarks_nonselection_line_color, ymarks_nonselection_line_dash, ymarks_nonselection_line_dash_offset, ymarks_nonselection_line_join, ymarks_nonselection_line_width, ymarks_selection_alpha, ymarks_selection_color, ymarks_selection_line_alpha, ymarks_selection_line_cap, ymarks_selection_line_color, ymarks_selection_line_dash, ymarks_selection_line_dash_offset, ymarks_selection_line_join, ymarks_selection_line_width, ymarks_visible

Matplotlib

alpha, annular_edgecolors, annular_linewidth, clims, cmap, ec, edgecolor, edgecolors, hatch, linestyles, linewidths, norm, shading, visible, xmarks_edgecolor, xmarks_linewidth, ymarks_edgecolor, ymarks_linewidth

Examples#

Heatmaps are ideal for visualizing values across two dimensions as a color-coded matrix. They are commonly used for temporal data, correlation grids, and density summaries.

Basic heatmap plot with aggregation#

The heatmap plot can aggregate the data when provided in long form.

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

df = pd.DataFrame({
    "cat1": ["A", "A", "A", "B", "B", "B"],
    "cat2": ["X", "X", "Y", "Y", "Y", "Y"],
    "values": [1, 2, 3, 4, 5, 6],
})

df.hvplot.heatmap(x="cat1", y="cat2", C="values", reduce_function=np.mean)

Basic heatmap plot without aggregation#

No aggregation is required when the data is provided in wide format. The color/tooltip of a cell is derived from its corresponding value in the dataset.

import hvplot.pandas  # noqa
import pandas as pd

df = pd.DataFrame(
    {"2020": [10, 35], "2021": [40, 30], "2022": [20, 25]},
    index=["A", "B"],
)

df.hvplot.heatmap()

Heatmap of earthquake counts by depth and magnitude#

Let’s use a more realistic dataset. This plot visualizes the count of earthquakes categorized by both depth and magnitude class. It gives a clear overview of how often each type of earthquake occurs.

import hvplot.pandas # noqa
import numpy as np

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

df.hvplot.heatmap(
    x='mag_class',
    y='depth_class',
    C='mag',
    reduce_function=np.size,
    fields={'mag': 'count'},  # for the tooltip to display count instead of mag 
)

Use of `reduce_function`#

reduce_function specifies the function used to aggregate the values of C for each combination of x and y after grouping. Numpy functions are appropriate, like in the example below with np.nanmedian.

import hvplot.pandas  # noqa

df = hvplot.sampledata.penguins('pandas')

df.hvplot.heatmap(
    x='species', y='island', C='body_mass_g',
    reduce_function=np.nanmedian, clabel='Median body mass (g)',
)

Use of `logz`#

This version applies a logarithmic color scale to better distinguish lower-frequency combinations, which may be overshadowed by dominant bins in a linear scale.

import hvplot.pandas # noqa
import numpy as np

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

df.hvplot.heatmap(
    x='mag_class', y='depth_class', C='mag', logz=True,
    reduce_function=np.size, fields={'mag': 'count'}
)

More information when no aggregation#

x and y can be set differently in this case.

import hvplot.pandas  # noqa
import pandas as pd

df = pd.DataFrame(
    {"2020": [10, 35], "2021": [40, 30], "2022": [20, 25]},
    index=["A", "B"],
)
df.index.name = "Group"

plot_opts = dict(xaxis="top", width=400, height=200, flip_yaxis=True)
(
    df.hvplot.heatmap(**plot_opts, title="From array, without setting x and y") +
    df.hvplot.heatmap(x="columns", y="index", title="From array, setting columns/index", **plot_opts) +
    df.hvplot.heatmap(x="columns", y="Group", title="From array, setting columns/index name", **plot_opts)
).cols(1)

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