Plot curves in fivethirtyeight stylesheet in Matplotlib

The FiveThirtyEight stylesheet in Matplotlib provides a clean, professional look inspired by the popular data journalism website. This style features muted colors, subtle gridlines, and typography that makes charts publication-ready.

Setting Up the FiveThirtyEight Style

First, let's configure Matplotlib to use the FiveThirtyEight stylesheet ?

import matplotlib.pyplot as plt
import numpy as np

# Configure figure settings
plt.rcParams["figure.figsize"] = [7.50, 3.50]
plt.rcParams["figure.autolayout"] = True

# Apply the FiveThirtyEight style
plt.style.use('fivethirtyeight')

print("FiveThirtyEight style applied successfully!")

FiveThirtyEight style applied successfully!

Creating Multiple Curves

Now let's create three curves with different trends to showcase the stylesheet ?

import matplotlib.pyplot as plt
import numpy as np

# Configure figure settings
plt.rcParams["figure.figsize"] = [7.50, 3.50]
plt.rcParams["figure.autolayout"] = True

# Apply the FiveThirtyEight style
plt.style.use('fivethirtyeight')

# Create x data points
x = np.linspace(0, 10, 50)

# Create figure and subplot
fig, ax = plt.subplots()

# Plot three curves with different patterns
ax.plot(x, np.sin(x) + x + np.random.randn(50), label='Trend A')
ax.plot(x, np.sin(x) + 0.5 * x + np.random.randn(50), label='Trend B')
ax.plot(x, np.sin(x) + 2 * x + np.random.randn(50), label='Trend C')

# Set title and legend
ax.set_title("'fivethirtyeight' style sheet")
ax.legend()

plt.show()

Key Features of FiveThirtyEight Style

The FiveThirtyEight stylesheet includes several distinctive visual elements ?

• Muted color palette: Subtle, professional colors that work well together
• Grid styling: Light gray gridlines that don't overpower the data
• Typography: Clean, readable fonts optimized for data visualization
• Background: Light gray background that creates visual depth

Comparison with Default Style

Conclusion

The FiveThirtyEight stylesheet transforms your plots into professional, publication-ready visualizations. Use plt.style.use('fivethirtyeight') to apply this clean, modern aesthetic to any Matplotlib chart.