Matplotlib Line Charts

last modified February 25, 2025

Matplotlib is a powerful Python library for creating static, animated, and interactive visualizations. Line charts are one of the most common types of charts used to display data trends over time. This tutorial covers how to create various types of line charts using Matplotlib.

Line charts are ideal for visualizing continuous data, such as time series or trends. Matplotlib provides a flexible and easy-to-use interface for creating line charts with customizations.

Basic Line Chart

This example demonstrates how to create a basic line chart.

basic_line_chart.py

import matplotlib.pyplot as plt

Data

x = [1, 2, 3, 4, 5] y = [2, 3, 5, 7, 11]

Create a line chart

plt.plot(x, y)

Add labels and title

plt.xlabel(“X-axis”) plt.ylabel(“Y-axis”) plt.title(“Basic Line Chart”)

Display the chart

plt.show()

The plt.plot function is used to create a line chart. The plt.show function displays the chart.

Multiple Lines in a Single Chart

This example shows how to plot multiple lines in a single chart.

multiple_lines.py

import matplotlib.pyplot as plt

Data

x = [1, 2, 3, 4, 5] y1 = [2, 3, 5, 7, 11] y2 = [1, 4, 6, 8, 10]

Create multiple lines

plt.plot(x, y1, label=“Line 1”) plt.plot(x, y2, label=“Line 2”)

Add labels, title, and legend

plt.xlabel(“X-axis”) plt.ylabel(“Y-axis”) plt.title(“Multiple Lines in a Single Chart”) plt.legend()

Display the chart

plt.show()

The label parameter is used to differentiate between lines. The plt.legend function adds a legend to the chart.

Customizing Line Styles

This example demonstrates how to customize line styles, colors, and markers.

custom_line_styles.py

import matplotlib.pyplot as plt

Data

x = [1, 2, 3, 4, 5] y = [2, 3, 5, 7, 11]

Create a line chart with custom styles

plt.plot(x, y, linestyle="–", color=“green”, marker=“o”, label=“Custom Line”)

Add labels, title, and legend

plt.xlabel(“X-axis”) plt.ylabel(“Y-axis”) plt.title(“Custom Line Styles”) plt.legend()

Display the chart

plt.show()

The linestyle, color, and marker parameters are used to customize the line’s appearance.

Curved line chart

This example creates a smooth, curved line chart – specifically a sine wave – which is often used to represent continuous, periodic data like sound waves, electrical signals, or cyclical behavior in physics and engineering.

curved_line_chart.py

import numpy as np import matplotlib.pyplot as plt

t = np.arange(0.0, 3.0, 0.01) s = np.sin(2.5 * np.pi * t) plt.plot(t, s)

plt.xlabel(’time (s)’) plt.ylabel(‘voltage (mV)’)

plt.title(‘Sine Wave’) plt.grid(True)

plt.savefig(’linechart.png’)

This example creates a smooth, curved line chart representing a sine wave, often used to model periodic phenomena like sound waves or electrical signals. Using numpy, we generate an array t for time values from 0 to 3 seconds in 0.01-second increments, and s calculates the voltage as a sine wave with a frequency of 2.5 oscillations over 3 seconds. The plt.plot function draws the wave, while labels, a title, and a grid make the chart easy to read. Finally, the chart is saved as an image file called linechart.png for future use.

Stacked Line Chart

The example visualizes the monthly revenue of two product lines in a company over a year.

stacked_line_chart.py

import matplotlib.pyplot as plt

Months

months = [“Jan”, “Feb”, “Mar”, “Apr”, “May”, “Jun”, “Jul”, “Aug”, “Sep”, “Oct”, “Nov”, “Dec”]

Monthly revenue for two product lines (in $1000s)

product_a = [12, 14, 15, 18, 20, 22, 21, 23, 25, 27, 30, 32] product_b = [8, 9, 10, 12, 14, 15, 17, 18, 19, 20, 22, 24]

Total revenue (stacked on top of product A)

total_revenue = [a + b for a, b in zip(product_a, product_b)]

Plotting revenue for both products

plt.plot(months, product_a, marker=‘o’, label=“Product A Revenue”) plt.plot(months, total_revenue, marker=‘o’, label=“Total Revenue (A + B)”)

Labels and title

plt.xlabel(“Month”) plt.ylabel(“Revenue ($1000s)”) plt.title(“Monthly Revenue for Product Lines”) plt.legend()

Display the chart

plt.show()

The total_revenue stacks product_b on top of product_a.

Area Chart

This example demonstrates how to create an area chart.

area_chart.py

import matplotlib.pyplot as plt

Data

x = [1, 2, 3, 4, 5] y = [2, 3, 5, 7, 11]

Create an area chart

plt.fill_between(x, y, color=“skyblue”, alpha=0.4) plt.plot(x, y, color=“blue”, label=“Line”)

Add labels, title, and legend

plt.xlabel(“X-axis”) plt.ylabel(“Y-axis”) plt.title(“Area Chart”) plt.legend()

Display the chart

plt.show()

The plt.fill_between function is used to fill the area under the line. The alpha parameter controls the transparency of the fill.

Step Line Chart

Step charts are great for showing things like price changes over time, inventory levels, or subscription counts – anything that stays constant for a while and then jumps to a new value.

We track monthly subscription count for a service where users join in batches.

step_line_chart.py

import matplotlib.pyplot as plt

Months

months = [“Jan”, “Feb”, “Mar”, “Apr”, “May”, “Jun”, “Jul”, “Aug”, “Sep”, “Oct”, “Nov”, “Dec”]

Subscription count at the end of each month

subscriptions = [100, 150, 150, 200, 250, 300, 300, 350, 400, 400, 450, 500]

Create a step line chart

plt.step(months, subscriptions, where=“mid”, label=“Subscribers”, color=“teal”)

Add labels, title, and legend

plt.xlabel(“Month”) plt.ylabel(“Subscribers”) plt.title(“Monthly Subscription Growth”) plt.legend()

Add grid for clarity

plt.grid(True, linestyle="–", alpha=0.5)

Display the chart

plt.show()

The plt.step function creates a step line chart. The where parameter controls the step placement.

On the x-axis, we have the months of the year. On the y-axis, we have subscription count, which is an example of something that often changes in steps rather than continuously. The where=“mid” makes the step shifts more visually clear.

Best Practices for Line Charts

• Label Axes Clearly: Always label the X and Y axes to make the chart understandable.

• Use Legends: Add legends when plotting multiple lines to differentiate them.

• Choose Appropriate Colors: Use contrasting colors for multiple lines to improve readability.

• Limit Data Points: Avoid cluttering the chart with too many data points.

Source

Matplotlib Line Chart Documentation

In this article, we have explored various types of line charts using Matplotlib, including basic, multiple, stacked, area, and step line charts.

Author

My name is Jan Bodnar, and I am a passionate programmer with extensive programming experience. I have been writing programming articles since 2007. To date, I have authored over 1,400 articles and 8 e-books. I possess more than ten years of experience in teaching programming.

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