Using Matplotlib's fill_between for Area Filling in Python

Area filling is a small plotting technique that often makes a figure much easier to read. With one extra layer of color, you can highlight trends, confidence ranges, threshold regions, or the distance between two curves. In Matplotlib, fill_between() is the standard tool for this job.

This article focuses on four practical uses:

1. Fill below a baseline
2. Fill up to a reference line
3. Fill between two curves
4. Highlight only the regions that satisfy a condition

Data Preparation

First, let’s create a sine wave as our base data:

import numpy as np
import matplotlib.pyplot as plt

n = 1024
x = np.linspace(-np.pi, np.pi, n, endpoint=True)
y = np.sin(2 * x)

plt.plot(x, y, color='blue', alpha=1.00)
plt.savefig("sine_wave.png", dpi=200)
plt.close()

The resulting sine function plot is:

Matplotlib Fill Below

To fill with color using fill_between, set y2=0 so the curve fills down to the y=0 baseline.

plt.plot(x, y, color='blue', alpha=1.00)
plt.fill_between(x, y, 0, color='blue', alpha=.1)
plt.savefig("sin_fill_below.png",dpi=200)
plt.close()

Key parameter explanations:

• x: X-axis values
• y: Main curve values
• 0: Fills down to y=0
• alpha: Controls transparency (0-1)

Matplotlib Fill Above

To fill upward, set y2=1 so the curve fills toward the y=1 reference line.

plt.plot(x, y, color='blue', alpha=1.00)
plt.fill_between(x, y, 1, color='green', alpha=.1)
plt.savefig("sin_fill_above.png",dpi=200)
plt.close()

Matplotlib Fill Between Two Lines

Similarly, the difference lies in changing fill_between to the corresponding values for the lines. Here, we use cosine as the second line and fill the area between sine and cosine with red. The code is as follows:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Data Generation
n = 1024
x = np.linspace(-np.pi, np.pi, n, endpoint=True)
y = np.sin(2 * x)
y2 = np.cos(2*x)
# Plotting
plt.plot(x, y, color='blue', alpha=1.00)
plt.plot(x, y2, color='green', alpha=1.00)
plt.fill_between(x, y, y2, color='red', alpha=0.5)
plt.savefig("sin_cos_fill.png",dpi=200)

The resulting graph is:

Conditional area filling

In real work, you often do not want to color the entire region. Instead, you may want to highlight only the intervals above zero, below a threshold, or inside a target range. That is where the where parameter becomes useful:

plt.plot(x, y, color='blue')
plt.fill_between(x, y, 0, where=(y > 0), color='red', alpha=0.3)
plt.fill_between(x, y, 0, where=(y <= 0), color='green', alpha=0.3)
plt.savefig("sin_where_fill.png", dpi=200)
plt.close()

This pattern is especially useful for threshold highlighting, positive-vs-negative regions, and confidence-interval emphasis.

Practical Application Scenarios

The fill_between function is particularly suitable for:

• Highlighting confidence intervals
• Visualizing data ranges
• Comparing time series data
• Creating stacked area charts

Common issues

1. The filled color hides the main line

Most common cause: opacity is too high or the draw order is wrong.

Fix:

1. Lower alpha
2. Draw the fill first and the main line afterward
3. Use a lighter fill color

2. Filling between two lines looks wrong

Most common cause: the two curves do not share the same x values, or there are missing values.

Fix:

1. Make sure both curves use the same x
2. Clean NaN values first
3. Then call fill_between(x, y1, y2)

3. The saved image is clipped

Fix:

• Keep bbox_inches='tight'
• Increase dpi
• Call tight_layout() before saving

Related reading

• Python: Creating Beautiful Lollipop Charts
• Drawing Raincloud Plots with Python

Summary

Mastering Matplotlib’s fill_between() function can significantly enhance Python data visualization effects. Whether for scientific plotting, business dashboards, or exploratory data analysis, these techniques can make your charts more informative and visually appealing.