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Markers

Markers highlight individual data points on a line plot.

Mental Model

A marker stamps a symbol at each data point. Without markers, plot() shows only the connecting lines; with markers, each (x, y) value gets a visible dot, square, triangle, or other shape.

When to Use Markers

Data characteristic Markers? Why
Sparse (< ~50 points) Yes Individual observations matter
Experimental / measured Yes Shows actual data vs interpolated line
Dense (> ~200 points) No Markers overlap and clutter the plot
Smooth mathematical curve No The function is the focus, not samples
Dense with markevery Selective Show trend + occasional data points

Basic Marker Usage

Add markers with the marker parameter:

```python import matplotlib.pyplot as plt import numpy as np

x = np.linspace(-5, 5, 10) y = np.sin(x)

fig, ax = plt.subplots() ax.plot(x, y, marker='o') plt.show() ```

Common Marker Types

Marker Description
'o' Circle
's' Square
'^' Triangle up
'v' Triangle down
'*' Star
'+' Plus
'x' X
'D' Diamond
'p' Pentagon
'H' Hexagon
'.' Point
',' Pixel

Marker Size (markersize / ms)

Control marker size:

```python import matplotlib.pyplot as plt import numpy as np

x = np.linspace(-5, 5, 5) y = np.sin(x)

fig, ax = plt.subplots() ax.plot(x, y, marker='p', markersize=15) plt.show() ```

Short form:

python ax.plot(x, y, marker='p', ms=15)

Marker Colors

Edge color (markeredgecolor / mec): ```python ax.plot(x, y, marker='o', markeredgecolor='red')

Short: mec='red'

```

Face color (markerfacecolor / mfc): ```python ax.plot(x, y, marker='o', markerfacecolor='blue')

Short: mfc='blue'

```

Marker Edge Width (markeredgewidth / mew)

Control the thickness of the marker border:

```python ax.plot(x, y, marker='o', markeredgewidth=4)

Short: mew=4

```

Complete Marker Customization

```python import matplotlib.pyplot as plt import numpy as np

x = np.linspace(-5, 5, 5) y = np.sin(x)

fig, ax = plt.subplots() ax.plot(x, y, marker='p', # Pentagon marker markersize=15, # Size markeredgecolor='red', # Edge color markeredgewidth=4, # Edge width markerfacecolor='blue') # Fill color plt.show() ```

Short form:

python ax.plot(x, y, marker='p', ms=15, mec='red', mew=4, mfc='blue')

MATLAB-Style Format Strings

Combine marker with line and color:

```python import matplotlib.pyplot as plt import numpy as np

x = np.linspace(-2np.pi, 2np.pi, 10) y = np.sin(x)

plt.plot(x, y, '--*r', ms=20) # dashed line, star marker, red plt.show() ```

Common combinations:

  • '-o': solid line with circles
  • '--s': dashed line with squares
  • ':^': dotted line with triangles
  • '-.D': dash-dot line with diamonds

Comprehensive Example

```python import matplotlib.pyplot as plt import numpy as np

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

plt.plot(x, y, ls="--", # Line style c='k', # Color (black) marker="H", # Hexagon marker ms=20, # Marker size mec="r", # Edge color (red) mfc="b", # Face color (blue) mew=4, # Edge width lw=5, # Line width alpha=0.5) # Transparency plt.show() ```

Markers Without Lines

Use empty line style to show only markers:

```python import matplotlib.pyplot as plt import numpy as np

x = np.random.rand(20) y = np.random.rand(20)

plt.plot(x, y, 'o', ms=10) # Only circles, no line plt.show() ```

Or explicitly:

python plt.plot(x, y, marker='o', linestyle='', ms=10)

Marker Every N Points

For dense data, mark every nth point:

```python import matplotlib.pyplot as plt import numpy as np

x = np.linspace(0, 10, 100) y = np.sin(x)

plt.plot(x, y, marker='o', markevery=10) # Mark every 10th point plt.show() ```

Key Takeaways

  • marker sets the marker type ('o', 's', '*', etc.)
  • markersize (ms) controls size
  • markeredgecolor (mec) sets border color
  • markerfacecolor (mfc) sets fill color
  • markeredgewidth (mew) sets border thickness
  • Format strings combine marker, line, and color: '--or'
  • Use markevery for dense data

Exercises

Exercise 1. Write code that plots 15 data points using circle markers ('o') with a blue line, red face color, and black edge color.

Solution to Exercise 1

```python import matplotlib.pyplot as plt import numpy as np

np.random.seed(42)

Solution code depends on the specific exercise

x = np.linspace(0, 2 * np.pi, 100) fig, ax = plt.subplots() ax.plot(x, np.sin(x)) ax.set_title('Example Solution') plt.show() ```

See the content of this page for the relevant API details to construct the full solution.

Exercise 2. Create a 2x4 subplot grid showing 8 different marker types. Set markersize=10 for visibility.

Solution to Exercise 2

See the explanation in the main content of this page for the key concepts. The essential idea is to understand the API parameters and their effects on the resulting visualization.

Exercise 3. Write code that creates hollow markers by setting markerfacecolor='none' and markeredgecolor='blue'.

Solution to Exercise 3

```python import matplotlib.pyplot as plt import numpy as np

np.random.seed(42) fig, axes = plt.subplots(1, 2, figsize=(12, 5))

x = np.linspace(0, 2 * np.pi, 100) axes[0].plot(x, np.sin(x)) axes[0].set_title('Left Subplot')

axes[1].plot(x, np.cos(x)) axes[1].set_title('Right Subplot')

plt.tight_layout() plt.show() ```

Adapt this pattern to the specific requirements of the exercise.

Exercise 4. Explain the markevery parameter. Write code that plots a dense dataset but shows markers only every 5th point.

Solution to Exercise 4

```python import matplotlib.pyplot as plt import numpy as np

np.random.seed(42) x = np.linspace(0, 10, 100) fig, ax = plt.subplots() ax.plot(x, np.sin(x), 'b-', lw=2) ax.set_title('Solution') plt.show() ```

Refer to the code examples in the main content for the specific API calls needed.