hvPlot.scatter

hvPlot.scatter(x=None, y=None, **kwds)

The scatter plot visualizes your points as markers in 2D space. You can visualize one more dimension by using colors.

The scatter plot is a good first way to plot data with non continuous axes.

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

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

Parameters:

xstring, optional

Field name(s) to draw x-positions from. If not specified, the index is used. Can refer to continuous and categorical data.

ystring or list, optional

Field name(s) to draw y-positions from. If not specified, all numerical fields are used.

markerstring, optional

The marker shape depends on the activated plotting backend:

• Bokeh: Bokeh marker styles and a subset of Matplotlib styles, e.g. 'circle' (default), 'dot', 'cross', 'x', 'square' for Bokeh markers and '+', 'x', 's' for Matplotlib- compatible markers. See https://docs.bokeh.org/en/latest/docs/examples/basic/scatters/markertypes.html for the list of Bokeh markers.

• Matplotlib: Any supported marker, e.g. 's' (square), 'x' (cross), '+', etc. See https://matplotlib.org/stable/api/markers_api.html for the list of Matplotlib markers.

cstring, optional

A color or a field name to draw the color of the marker from. Alias of color.

sint, optional, also available as ‘size’

The size of the marker.

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('scatter') for the full method documentation.

Returns:

holoviews.element.Scatter / 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

• Bokeh: https://docs.bokeh.org/en/latest/docs/examples/basic/scatters/color_scatter.html

• HoloViews: https://holoviews.org/reference/elements/matplotlib/Scatter.html

• Pandas: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.scatter.html

• Plotly: https://plotly.com/python/line-and-scatter/

• Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html

• Seaborn: https://seaborn.pydata.org/generated/seaborn.scatterplot.html

• Wiki: https://en.wikipedia.org/wiki/Scatter_plot

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

Scatter plots are useful for exploring relationships, distributions, and potential correlations between numeric variables.

Basic scatter plot

This example shows how to create a simple scatter plot.

import hvplot.pandas  # noqa
import pandas as pd

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

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

Let’s use a more realistic dataset.

import hvplot.pandas  # noqa

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

df.hvplot.scatter(
    x='bill_length_mm', y='flipper_length_mm',
    title='Bill Length vs Flipper Length'
)

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.penguins("pandas")

df.hvplot.scatter(
    x='bill_length_mm', y='flipper_length_mm',
    by=['sex', 'species'], title='Scatter plot grouped by sex and species 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 scatter plots. As a consequence, using color is much more efficient in this case.

import hvplot.pandas  # noqa

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

df.hvplot.scatter(
    x='bill_length_mm', y='flipper_length_mm',
    color='species', title='Scatter plot colored by species with "color"',
)

Control marker style

The marker style can be controlled with the styling option marker. For Bokeh plots, the option accepts Bokeh-based markers (see the plot below) and a subset of Matplotlib-compatible markers like '+' (note these markers cannot be vectorized). Matplotlib plots accept Matplotlib markers.

import bokeh as bk
import holoviews as hv
import hvplot.pandas  # noqa
import itertools
import pandas as pd

bokeh_orig_markers = list(bk.core.enums.MarkerType)
hv_bk_mpl_compat_markers = list(hv.plotting.bokeh.styles.markers)
print('Bokeh original markers:')
print(*map(repr, bokeh_orig_markers), sep=', ', end='\n\n')
print('Matplotlib-compatible markers for Bokeh:')
print(*map(repr, hv_bk_mpl_compat_markers), sep=', ')

df = pd.DataFrame(list(itertools.product(range(6), range(6))), columns=['x', 'y'])
df['marker_col'] = bokeh_orig_markers + [''] * (len(df) - len(bokeh_orig_markers))

df.hvplot.scatter(
    x='x', y='y', marker='marker_col', s=150, title='Bokeh-specific markers'
) *\
df.assign(y=df.y+0.2).hvplot.labels(
    x='x', y='y', text='marker_col', text_color='black',
    text_baseline='bottom', text_font_size='9pt', padding=0.2
)

Bokeh original markers:
'asterisk', 'circle', 'circle_cross', 'circle_dot', 'circle_x', 'circle_y', 'cross', 'dash', 'diamond', 'diamond_cross', 'diamond_dot', 'dot', 'hex', 'hex_dot', 'inverted_triangle', 'plus', 'square', 'square_cross', 'square_dot', 'square_pin', 'square_x', 'star', 'star_dot', 'triangle', 'triangle_dot', 'triangle_pin', 'x', 'y'

Matplotlib-compatible markers for Bokeh:
'+', 'x', 's', 'd', '^', '>', 'v', '<', '1', '2', '3', '4', 'o', '*'

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

Xarray example

import hvplot.xarray  # noqa

ds = hvplot.sampledata.air_temperature("xarray").sel(lon=285.,lat=40.)

ds.hvplot.scatter(y="air")

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