Line plots are one of the most common types of plots in data visualization. They are used to display trends over a continuous variable, such as time, or to show relationships between variables. In this chapter, we’ll dive deeper into creating and customizing line plots in Matplotlib.

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1. What is a Line Plot?

A line plot connects individual data points with a straight line. It is ideal for showing:

• Trends over time (time series data)
• Comparisons between variables
• Patterns in continuous data

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2. Basic Line Plot

Let’s start with a simple example:

import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# Create line plot
plt.plot(x, y)

# Add labels and title
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.title("Basic Line Plot")

# Show plot
plt.show()

✅ This will generate a simple straight line connecting the points.

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3. Plotting Multiple Lines

You can plot multiple lines on the same axes to compare datasets.

x = [1, 2, 3, 4, 5]
y1 = [2, 4, 6, 8, 10]
y2 = [1, 3, 5, 7, 9]

plt.plot(x, y1, label="Line 1", color="blue", marker="o")
plt.plot(x, y2, label="Line 2", color="green", marker="s")

plt.title("Multiple Line Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.legend()  # Show legend
plt.show()

Here:

• label defines the legend entry
• marker adds markers for each point
• color changes the line color

Another example of Plotting Multiple lines:

# sales vs profit vs month line chart of 12 months 
months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
sales = [100, 120, 150, 180, 200, 220, 250, 280, 300, 350, 324, 340]
profit = [50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160]

plt.plot(months, sales, label="Sales", color="blue", marker="o")
plt.plot(months, profit, label="Profit", color="#1E9E44", marker="s")
plt.title("Sales Vs Profit per Month")
plt.xlabel("Months")
plt.ylabel("Sales/Profit")
plt.legend()
plt.show()

# Output is given below

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4. Line Styles and Markers

Matplotlib allows extensive customization:

plt.plot(x, y1, linestyle='-', color='red', marker='o', markersize=8)
plt.plot(x, y2, linestyle='--', color='blue', marker='s', markersize=8)
plt.title("Line Styles and Markers")
plt.show()

• linestyle options: '-' (solid), '--' (dashed), ':' (dotted), '-.' (dash-dot)
• marker options: 'o', 's', '^', 'D' and more
• markersize controls marker size

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5. Adding Gridlines

Gridlines make your plot easier to read:

plt.plot(x, y1, label="Line 1")
plt.plot(x, y2, label="Line 2")
plt.title("Line Plot with Grid")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.grid(True)  # Enable grid
plt.legend()
plt.show()

You can also customize grid style:

plt.grid(color='gray', linestyle='--', linewidth=0.5)

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6. Customizing Axis Limits

You can manually control the range of axes:

plt.plot(x, y1, label="Line 1")
plt.xlim(0, 6)
plt.ylim(0, 12)
plt.title("Custom Axis Limits")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.legend()
plt.show()

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7. Adding Annotations

Annotations help highlight important points:

plt.plot(x, y1, label="Line 1", marker='o')
plt.annotate("Peak", xy=(5, 10), xytext=(3, 9),
             arrowprops=dict(facecolor='black', shrink=0.05))
plt.title("Line Plot with Annotation")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.legend()
plt.show()

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8. Plotting Time Series Data

Line plots are perfect for time series data:

import matplotlib.pyplot as plt
import pandas as pd

dates = pd.date_range('2025-01-01', periods=5)
values = [100, 120, 90, 150, 130]

plt.plot(dates, values, marker='o')
plt.title("Time Series Line Plot")
plt.xlabel("Date")
plt.ylabel("Value")
plt.grid(True)
plt.show()

Matplotlib can handle datetime objects directly for the x-axis.

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✅ Summary

In this chapter, you learned how to:

• Create basic and multiple line plots
• Customize line styles, markers, and colors
• Add gridlines, legends, axis limits, and annotations
• Plot time series data

Line plots are versatile and form the foundation for more complex plots like trend analysis, moving averages, and forecasting.