hvPlot.points#

hvPlot.points(x=None, y=None, **kwds)[source]#

A points plot visualizes positions in a 2D space. This is useful for example for geographic plots.

Although the hvplot.hvPlot.scatter() plot is superficially similar to the points plot (they can generate plots that look identical), the two plot types are semantically quite different. The fundamental difference is that scatter is used to visualize data where the y variable is dependent, unlike points.

This difference means that points plots can most naturally overlay with other plots that express independent variables in two-dimensional space, such as raster types like hvplot.hvPlot.image(). Conversely, scatter expresses a dependent relationship between x and y and thus most naturally overlay with chart types such as hvplot.hvPlot.line().

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

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

Parameters:

xstring, optional: The coordinate variable along the x-axis. Default is the first numeric field.
ystring, optional: The coordinate variable along the y-axis. Default is the second numeric field.
cstring, optional: The dimension to color the points by
sint, optional, also available as ‘size’: The size of the marker
markerstring, optional: The marker shape specified above can be any supported by matplotlib, e.g. s, d, o etc. See https://matplotlib.org/stable/api/markers_api.html.
scale: number, optional: Scaling factor to apply to point scaling. Default is 1.
logzbool: Whether to apply log scaling to the z-axis. Default is False.
**kwdsoptional: Additional keywords arguments are documented in Plotting Options. Run hvplot.help('points') for the full method documentation.

Returns:

holoviews.element.Points / 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

HoloViews: https://holoviews.org/reference/elements/bokeh/Points.html

Backend-specific styling options#

Bokeh

alpha, angle, cmap, color, fill_alpha, fill_color, hit_dilation, 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, marker, 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, palette, radius, radius_dimension, 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, size, visible

Matplotlib

alpha, c, cmap, color, ec, ecolor, edgecolor, edgecolors, facecolors, linewidth, lw, marker, norm, s, visible, vmax, vmin

Examples#

Basic points plot#

This example shows how to create a simple points plot.

import hvplot.pandas  # noqa
import pandas as pd

df = pd.DataFrame({"x": [0, 1, 2, 3], "y": [0, 1, 4, 9]})

df.hvplot.points(x="x", y="y")

Let’s use a more realistic dataset.

import hvplot.pandas  # noqa

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

df.hvplot.points(x='lon', y='lat', title='Earthquakes location')

Grouping by categories#

To distinguish categories visually, you can use the by parameter. This automatically colors points based on the specified column.

import hvplot.pandas  # noqa

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

df.hvplot.points(
    x='lon', y='lat', marker='s',
    by=['depth_class', 'mag_class'], title='Points plot colored by depth and magnitude classes',
)

Grouping by categories#

To distinguish categories visually, you can use the by parameter. This automatically colors points based on the specified column(s). The generated plot is a HoloViews NdOverlay.

import hvplot.pandas  # noqa

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

df.hvplot.points(
    x='lon', y='lat', by=['depth_class', 'mag_class'],
    title='Points plot grouped by depth and magnitude classes with "by"',
)

Note

If your goal is to simply color the plot by a given categorical variable, then you can use the color option instead of by. The former will vectorize the color styling (i.e., each marker has its own color) while the latter will generate an overlay of points plots. As a consequence, using color is much more efficient in this case.

import hvplot.pandas  # noqa

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

df.hvplot.points(
    x='lon', y='lat', color='depth_class',
    title='Points plot colored by depth class with "color"',
)

Control marker style#

The marker keyword can be used to control the markers style, see the scatter reference page for more information.

Control color and size#

You can also vary marker size with the s option and color with c (or color) using numeric columns.

import hvplot.pandas  # noqa

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

df.hvplot.points(
    x="lon", y="lat", c="mag", s="depth", cmap="inferno_r",
    clabel="Magnitude values", title="Earthquake depth (color by magnitude)",
)

Scatter plot with scaling and logarithmic color mapping#

This example shows how to fine-tune scatter plots by scaling point sizes and applying a logarithmic color scale. Note we set the scale option to uniformally increase the marker size by a factor of 3.

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

df = pd.DataFrame({
    'x': np.random.rand(100) * 10,
    'y': np.random.rand(100) * 10,
    'size': np.random.rand(100) * 100 + 10,
    'intensity': np.random.lognormal(mean=2, sigma=1, size=100)
})

df.hvplot.points(
    x='x', y='y', s='size', scale=3,
    c='intensity', cmap='Blues', logz=True,
    title='Points plot with size scaling and log color'
)

Geographic plot#

points plots are 2D plots and well suited for displaying maps. In this example we set geo=True to enable plotting with GeoViews and tiles=True to overlay the points on web map tiles.

import hvplot.pandas  # noqa

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

df.hvplot.points(
    x="lon", y="lat", c="mag", s="depth", cmap="inferno_r",
    clabel="Magnitude values", title="Earthquake depth (color by magnitude)",
    geo=True, tiles=True,
)

Geographic plot with GeoPandas#

import geopandas as gpd
import hvplot.pandas  # noqa

data = {
    'City': ['London', 'Paris', 'Berlin', 'Madrid', 'Rome', 'Vienna', 'Warsaw', 'Amsterdam'],
    'Country': ['United Kingdom', 'France', 'Germany', 'Spain', 'Italy', 'Austria', 'Poland', 'Netherlands'],
    'Latitude': [51.5074, 48.8566, 52.5200, 40.4168, 41.9028, 48.2082, 52.2297, 52.3676],
    'Longitude': [-0.1278, 2.3522, 13.4050, -3.7038, 12.4964, 16.3738, 21.0122, 4.9041]
}
cities = gpd.GeoDataFrame(
    data,
    geometry=gpd.points_from_xy(data['Longitude'], data['Latitude']),
    crs="EPSG:4326",
)

cities.hvplot.points(geo=True, tiles=True)

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