Matplotlib Markers

Markers are the symbols Matplotlib draws at each data point in a plot. Choosing the right marker style — and knowing how to resize, recolor, and fill it — can make the difference between a cluttered chart and one that communicates clearly. This page covers every marker style available in Matplotlib, how to customize their appearance, and when to use plot() versus scatter() for per-point control.

What is a Marker in Matplotlib?

A marker is a shape rendered at each (x, y) coordinate in a plot. You control which shape is used with the marker parameter (or as part of a format string). Markers are separate from the line connecting data points — you can show one without the other.

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 5, 3, 7, 4]

# Line with markers
plt.plot(x, y, marker='o')

# Markers only — no connecting line
plt.plot(x, y, marker='s', linestyle='None')

plt.show()

All Built-in Marker Styles

Matplotlib ships with more than 30 built-in marker codes. The table below lists the most commonly used ones.

To see every marker at once, you can iterate over matplotlib.markers.MarkerStyle.markers:

import matplotlib.pyplot as plt
import matplotlib.markers as mmarkers

print(list(mmarkers.MarkerStyle.markers.keys()))

Using Markers in a Line Plot

The plot() function accepts a marker argument. It applies the same marker to every data point.

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 15, 25, 30]

plt.plot(x, y, marker='o')
plt.xlabel('x')
plt.ylabel('y')
plt.title('Line plot with circle markers')
plt.show()

Using a Format String

Instead of separate keyword arguments, you can combine the line style, color, and marker into a single format string: '[color][marker][linestyle]'.

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 15, 25, 30]

# Red circles connected by a dashed line
plt.plot(x, y, 'ro--')
plt.title('Format string: red circles, dashed line')
plt.show()

Common format string components:

Customizing Marker Appearance

Matplotlib exposes four keyword arguments for fine-grained marker control:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 15, 25, 30]

plt.plot(
    x, y,
    marker='o',
    markersize=12,
    markerfacecolor='gold',
    markeredgecolor='navy',
    markeredgewidth=2,
)
plt.title('Customized circle markers')
plt.show()

Hollow Markers

Set markerfacecolor='none' (lowercase string) to draw only the border, creating a hollow marker:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [3, 1, 4, 1, 5]

plt.plot(x, y, marker='s', markersize=14, markerfacecolor='none', markeredgecolor='steelblue', markeredgewidth=2)
plt.title('Hollow square markers')
plt.show()

Fill Style

The fillstyle parameter controls which portion of the marker is filled. Valid values are 'full', 'left', 'right', 'bottom', 'top', and 'none'.

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 3, 5, 1]

plt.plot(x, y, marker='o', markersize=16, fillstyle='left', markerfacecolor='crimson', markeredgecolor='black')
plt.title('Half-filled circle markers (fillstyle="left")')
plt.show()

Per-Point Marker Control with scatter()

plot() applies a uniform marker to the entire line. When you need each point to have a different size or color — for example, to encode a third variable — use scatter() instead.

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 15, 25, 30]
sizes  = [40, 100, 200, 80, 160]   # area in points²
colors = [0.2, 0.5, 0.8, 0.3, 0.9]  # mapped through colormap

sc = plt.scatter(x, y, s=sizes, c=colors, cmap='plasma', edgecolors='black')
plt.colorbar(sc, label='Value')
plt.title('Per-point size and color with scatter()')
plt.show()

Key differences between plot() and scatter() for markers:

Multiple Data Series with Different Markers

Use separate plot() calls to assign a distinct marker to each series, then add a legend:

import matplotlib.pyplot as plt

months = [1, 2, 3, 4, 5, 6]
product_a = [120, 135, 110, 150, 140, 160]
product_b = [80,  95, 100,  90, 115, 130]
product_c = [60,  70,  65,  85,  90,  95]

plt.plot(months, product_a, marker='o', label='Product A')
plt.plot(months, product_b, marker='s', label='Product B')
plt.plot(months, product_c, marker='^', label='Product C')

plt.xlabel('Month')
plt.ylabel('Units sold')
plt.title('Monthly sales by product')
plt.legend()
plt.show()

Markers Without a Connecting Line

Passing linestyle='None' (or ls='None') removes the line and leaves only the markers — effectively a scatter plot using plot().

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5, 6, 7, 8]
y = [3, 1, 4, 1, 5, 9, 2, 6]

plt.plot(x, y, marker='D', linestyle='None', color='darkorange', markersize=10)
plt.title('Diamond markers, no line')
plt.show()

This pattern is useful when the order of data points matters (preserving the original x ordering) but you do not want a line implying a continuous trend.

Practical Tips

• Match the marker to the data density. Use small markers ('.' or ',') when plotting thousands of points; larger shapes ('o', 's') for a handful of measurements.
• Ensure contrast. On white backgrounds, dark markeredgecolor on a light markerfacecolor keeps each point visible even at small sizes.
• Avoid overloading a single plot. More than five distinct marker shapes in one chart makes the legend hard to read — consider subplots or color alone.
• Use scatter() for third-variable encoding. Size or color arrays mapped to a colormap communicate an additional dimension without adding more series to the legend.

Related Topics

• Matplotlib Line Plots — customize line style alongside your markers
• Matplotlib Scatter Plot — per-point size and color control
• Matplotlib Labels — add axis labels and titles to your charts
• Matplotlib Plotting Overview — a tour of all major plot types