[Python] Customizing Line Plots in Matplotlib: Styles and Markers

Overview

This is a guide for creating line plots using the plot method in Matplotlib. Beyond just drawing simple lines, we will explain how to control line styles (solid, dashed), colors, thickness, transparency, and data point markers to make different data series easier to distinguish and visualize.

Specifications

• Input: X-axis data (lists, etc.), Y-axis data (lists, etc.), and decoration parameters (format strings, colors, widths, etc.).
• Output: A graph displayed in a window or saved as an image file.
• Requirements: The matplotlib library must be installed.

Minimum Usage

import matplotlib.pyplot as plt

x_values = [1, 2, 3, 4]
y_values = [10, 20, 15, 30]

fig, ax = plt.subplots()
# The simplest way to plot
ax.plot(x_values, y_values)
plt.show()

Full Code Example

The following code demonstrates four different data series, each using a unique style (solid, dashed, dash-dot, dotted) and markers.

import matplotlib.pyplot as plt

def main():
    # 1. Prepare datasets (e.g., four sensor values over time)
    time_points = [0, 1, 2, 3, 4, 5]
    
    sensor_a = [10, 12, 15, 14, 18, 20]
    sensor_b = [20, 18, 16, 15, 12, 10]
    sensor_c = [5, 8, 5, 8, 5, 8]
    sensor_d = [15, 15, 20, 20, 25, 25]

    # 2. Create the drawing area
    fig, ax = plt.subplots(figsize=(8, 6))

    # 3. Plot the graphs with specific styles
    
    # Pattern A: Solid line, Red, Thick, Star marker, Opaque
    ax.plot(
        time_points, sensor_a,
        fmt="-",            # Line style (solid)
        c="#FF5733",        # Color (Hex)
        linewidth=3,        # Line width
        marker="*",         # Marker shape
        markersize=10,      # Marker size
        alpha=1.0,          # Transparency
        label="Sensor A"
    )

    # Pattern B: Dashed line, Green, Standard width, Circle marker, Semi-transparent
    ax.plot(
        time_points, sensor_b,
        fmt="--",           # Line style (dashed)
        c="green",          # Color (Name)
        linewidth=2,
        marker="o",
        alpha=0.7,
        label="Sensor B"
    )

    # Pattern C: Dash-dot line, Blue, Thick, Diamond marker
    ax.plot(
        time_points, sensor_c,
        fmt="-.",           # Line style (dash-dot)
        c="blue",
        linewidth=2.5,
        marker="D",         # Diamond
        alpha=0.6,
        label="Sensor C"
    )

    # Pattern D: Dotted line, Purple, Thin, X marker
    ax.plot(
        time_points, sensor_d,
        fmt=":",            # Line style (dotted)
        c="purple",
        linewidth=1.5,
        marker="x",
        alpha=0.8,
        label="Sensor D"
    )

    # 4. Configure Title, Labels, Legend, and Grid
    ax.set_title("Sensor Readings Over Time")
    ax.set_xlabel("Time (h)")
    ax.set_ylabel("Value")
    ax.legend()     # Displays content from 'label' arguments
    ax.grid(True)   # Displays grid lines

    # 5. Display
    plt.show()

if __name__ == "__main__":
    main()

Customization Points

The following table lists the main parameters for controlling the appearance of graphs using the ax.plot() method.

• fmt vs linestyle: You can specify line types either through linestyle='--' or as a short-hand in fmt (e.g., '--').
• Emphasizing Markers: To make markers stand out, you can adjust markersize or markeredgecolor (marker border color).

Important Notes

• Parameter Priority: If you specify a color in fmt (e.g., ‘r’ in ‘r–‘) and also use the color argument, the color argument usually takes priority. It is best to use one method to avoid confusion.
• Data Consistency: The length of the X and Y lists must match exactly. Otherwise, a ValueError will occur.
• Avoiding Over-Styling: Making lines too thick or markers too large can make data overlap and become hard to read. Use alpha to add transparency and make overlapping points visible.

Variations (Optional)

Step Plot

If the value changes are discrete (such as inventory levels), a step plot is more suitable than a regular line graph. Use the drawstyle argument.

import matplotlib.pyplot as plt

def create_step_plot():
    days = [1, 2, 3, 4, 5]
    stock = [50, 50, 30, 30, 20]

    fig, ax = plt.subplots()
    
    # Use drawstyle='steps-post' for a stair-like drawing
    ax.plot(days, stock, drawstyle='steps-post', marker='o', label='Stock Level')
    
    ax.set_title("Inventory Level (Step Plot)")
    ax.legend()
    plt.show()

if __name__ == "__main__":
    create_step_plot()

Summary

The Matplotlib plot method is highly versatile. By combining fmt, marker, and linestyle, you can create expressions tailored to your data’s characteristics. When overlaying multiple lines, changing not just the color but also the line style and markers helps ensure the graph is readable even in black and white or for users with color vision deficiencies.