Customizing Matplotlib Visualizations with Markers

Introduction

Matplotlib is a popular Python library used to create visualizations, including charts, graphs, and plots. One of the key components of Matplotlib is markers, which are used to represent data points on a plot. Markers come in various shapes, sizes, and styles, and can be customized to fit a specific data set. In this lab, you will learn how to use Matplotlib markers to create custom visualizations that effectively communicate your data.

VM Tips

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Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Skills Graph

Unfilled Markers

Unfilled markers are single-colored. The following code demonstrates how to create unfilled markers:

unfilled_markers = [m for m, func in Line2D.markers.items()
                    if func != 'nothing' and m not in Line2D.filled_markers]

for ax, markers in zip(axs, split_list(unfilled_markers)):
    for y, marker in enumerate(markers):
        ax.text(-0.5, y, repr(marker), **text_style)
        ax.plot([y] * 3, marker=marker, **marker_style)
    format_axes(ax)

Filled Markers

Filled markers are the opposite of unfilled markers. The following code demonstrates how to create filled markers:

fig, axs = plt.subplots(ncols=2)
fig.suptitle('Filled markers', fontsize=14)
for ax, markers in zip(axs, split_list(Line2D.filled_markers)):
    for y, marker in enumerate(markers):
        ax.text(-0.5, y, repr(marker), **text_style)
        ax.plot([y] * 3, marker=marker, **marker_style)
    format_axes(ax)

Marker Fill Styles

The edge color and fill color of filled markers can be specified separately. Additionally, the fillstyle can be configured to be unfilled, fully filled, or half-filled in various directions. The half-filled styles use markerfacecoloralt as a secondary fill color. The following code demonstrates how to create marker fill styles:

fig, ax = plt.subplots()
fig.suptitle('Marker fillstyle', fontsize=14)
fig.subplots_adjust(left=0.4)

filled_marker_style = dict(marker='o', linestyle=':', markersize=15,
                           color='darkgrey',
                           markerfacecolor='tab:blue',
                           markerfacecoloralt='lightsteelblue',
                           markeredgecolor='brown')

for y, fill_style in enumerate(Line2D.fillStyles):
    ax.text(-0.5, y, repr(fill_style), **text_style)
    ax.plot([y] * 3, fillstyle=fill_style, **filled_marker_style)
format_axes(ax)

Markers Created from TeX Symbols

Use :ref:MathText <mathtext>, to use custom marker symbols, like e.g. "$\u266B$". For an overview over the STIX font symbols refer to the STIX font table <http://www.stixfonts.org/allGlyphs.html>_. Also see the :doc:/gallery/text_labels_and_annotations/stix_fonts_demo.

fig, ax = plt.subplots()
fig.suptitle('Mathtext markers', fontsize=14)
fig.subplots_adjust(left=0.4)

marker_style.update(markeredgecolor="none", markersize=15)
markers = ["$1$", r"$\frac{1}{2}$", "$f$", "$\u266B$", r"$\mathcal{A}$"]

for y, marker in enumerate(markers):
    ## Escape dollars so that the text is written "as is", not as mathtext.
    ax.text(-0.5, y, repr(marker).replace("$", r"\$"), **text_style)
    ax.plot([y] * 3, marker=marker, **marker_style)
format_axes(ax)

Markers Created from Paths

Any ~.path.Path can be used as a marker. The following example shows two simple paths star and circle, and a more elaborate path of a circle with a cut-out star.

import numpy as np

import matplotlib.path as mpath

star = mpath.Path.unit_regular_star(6)
circle = mpath.Path.unit_circle()
## concatenate the circle with an internal cutout of the star
cut_star = mpath.Path(
    vertices=np.concatenate([circle.vertices, star.vertices[::-1, ...]]),
    codes=np.concatenate([circle.codes, star.codes]))

fig, ax = plt.subplots()
fig.suptitle('Path markers', fontsize=14)
fig.subplots_adjust(left=0.4)

markers = {'star': star, 'circle': circle, 'cut_star': cut_star}

for y, (name, marker) in enumerate(markers.items()):
    ax.text(-0.5, y, name, **text_style)
    ax.plot([y] * 3, marker=marker, **marker_style)
format_axes(ax)

Advanced Marker Modifications with Transform

Markers can be modified by passing a transform to the MarkerStyle constructor. Following example shows how a supplied rotation is applied to several marker shapes.

common_style = {k: v for k, v in filled_marker_style.items() if k != 'marker'}
angles = [0, 10, 20, 30, 45, 60, 90]

fig, ax = plt.subplots()
fig.suptitle('Rotated markers', fontsize=14)

ax.text(-0.5, 0, 'Filled marker', **text_style)
for x, theta in enumerate(angles):
    t = Affine2D().rotate_deg(theta)
    ax.plot(x, 0, marker=MarkerStyle('o', 'left', t), **common_style)

ax.text(-0.5, 1, 'Un-filled marker', **text_style)
for x, theta in enumerate(angles):
    t = Affine2D().rotate_deg(theta)
    ax.plot(x, 1, marker=MarkerStyle('1', 'left', t), **common_style)

ax.text(-0.5, 2, 'Equation marker', **text_style)
for x, theta in enumerate(angles):
    t = Affine2D().rotate_deg(theta)
    eq = r'$\frac{1}{x}$'
    ax.plot(x, 2, marker=MarkerStyle(eq, 'left', t), **common_style)

for x, theta in enumerate(angles):
    ax.text(x, 2.5, f"{theta}°", horizontalalignment="center")
format_axes(ax)

fig.tight_layout()

Setting Marker Cap Style and Join Style

Markers have default cap and join styles, but these can be customized when creating a MarkerStyle.

from matplotlib.markers import CapStyle, JoinStyle

marker_inner = dict(markersize=35,
                    markerfacecolor='tab:blue',
                    markerfacecoloralt='lightsteelblue',
                    markeredgecolor='brown',
                    markeredgewidth=8,
                    )

marker_outer = dict(markersize=35,
                    markerfacecolor='tab:blue',
                    markerfacecoloralt='lightsteelblue',
                    markeredgecolor='white',
                    markeredgewidth=1,
                    )

fig, ax = plt.subplots()
fig.suptitle('Marker CapStyle', fontsize=14)
fig.subplots_adjust(left=0.1)

for y, cap_style in enumerate(CapStyle):
    ax.text(-0.5, y, cap_style.name, **text_style)
    for x, theta in enumerate(angles):
        t = Affine2D().rotate_deg(theta)
        m = MarkerStyle('1', transform=t, capstyle=cap_style)
        ax.plot(x, y, marker=m, **marker_inner)
        ax.plot(x, y, marker=m, **marker_outer)
        ax.text(x, len(CapStyle) - .5, f'{theta}°', ha='center')
format_axes(ax)

Modifying the Join Style

The join style of markers can also be modified in a similar manner.

fig, ax = plt.subplots()
fig.suptitle('Marker JoinStyle', fontsize=14)
fig.subplots_adjust(left=0.05)

for y, join_style in enumerate(JoinStyle):
    ax.text(-0.5, y, join_style.name, **text_style)
    for x, theta in enumerate(angles):
        t = Affine2D().rotate_deg(theta)
        m = MarkerStyle('*', transform=t, joinstyle=join_style)
        ax.plot(x, y, marker=m, **marker_inner)
        ax.text(x, len(JoinStyle) - .5, f'{theta}°', ha='center')
format_axes(ax)

plt.show()

Summary

In this lab, you learned how to use Matplotlib markers to create custom visualizations. Specifically, you learned how to create unfilled and filled markers, marker fill styles, markers created from TeX symbols, markers created from paths, advanced marker modifications with transform, and how to set marker cap style and join style. By using these techniques, you can create effective visualizations that communicate your data with clarity and precision.