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Plotting with Seaborn and Matplotlib

Last Updated : 23 Jul, 2025

Matplotlib and Seaborn are two of the most powerful Python libraries for data visualization. While Matplotlib provides a low-level, flexible approach to plotting, Seaborn simplifies the process by offering built-in themes and functions for common plots.

Before diving into plotting, ensure you have both libraries installed:

pip install matplotlib seaborn

After installation, Import them in your script:

import matplotlib.pyplot as plt

import seaborn as sns

Matplotlib allows you to create simple plots using plt.plot(). Here’s an example of plotting lines and dots:

import matplotlib.pyplot as plt

plt.plot([0, 1], [10, 11], label='Line 1')
plt.plot([0, 1], [11, 10], label='Line 2')
plt.scatter([0, 1], [10.5, 10.5], color='blue', marker='o', label='Dots')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Simple Line and Dot Plot')
plt.legend()
plt.show()

Explanation:

• plt.plot([0, 1], [10, 11], label='Line 1') plots a line moving upward from (0,10) to (1,11).
• plt.plot([0, 1], [11, 10], label='Line 2') plots a line moving downward from (0,11) to (1,10).
• label='Line 1' / 'Line 2' assigns names for the legend.

Seaborn makes plotting easier, but it is built on top of Matplotlib, so we can use both together for better results:

• Customization: Matplotlib lets us fully control the plot (axes, labels, grid, colors, etc.).
• Better Looks: Seaborn has built-in themes and styles that make plots look nicer.
• Statistical Plots: Seaborn includes special plots like violin plots and KDE plots.
• More Flexibility: Matplotlib allows extra customization and combining multiple plots.

Seaborn simplifies data visualization with built-in themes and high-level functions.

Example 1. Applying seaborn style to matplotlib plots

import matplotlib.pyplot as plt
import seaborn as sns

# Apply Seaborn theme
sns.set_theme(style="darkgrid")

# Creating a simple Matplotlib plot
x = [1, 2, 3, 4, 5]
y = [10, 12, 15, 18, 22]

plt.plot(x, y, marker='o', linestyle='-', color='blue', label="Trend")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.title("Matplotlib Plot with Seaborn Theme")
plt.legend()
plt.show()

Output:

Explanation:

• sns.set_theme(style="darkgrid") applies a Seaborn theme for a cleaner look.
• The plot consists of a simple line with markers, enhanced with labels and a legend.
  

Example 2. Customizing a seaborn plot with matplotlib

import matplotlib.pyplot as plt
import seaborn as sns
import panda as pd

data = pd.DataFrame({
    'Year': [2018, 2019, 2020, 2021, 2022],
    'Sales': [100, 150, 200, 250, 300]
})

plt.figure(figsize=(8, 5))
sns.lineplot(x='Year', y='Sales', data=data, marker='o')

# Customizing using Matplotlib
plt.title("Yearly Sales Growth", fontsize=14, fontweight='bold')
plt.xlabel("Year", fontsize=12)
plt.ylabel("Total Sales", fontsize=12)
plt.xticks(rotation=45)
plt.grid(True, linestyle='--')

plt.show()

Output:

Explanation:

• Seaborn’s sns.lineplot() creates a line plot from a DataFrame.
• Matplotlib functions customize the title, axis labels and grid styling.

Example 3. Overlaying seaborn and matplotlib plots

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

x = np.linspace(0, 10, 20)
y = np.sin(x)

plt.figure(figsize=(8, 5))

# Seaborn Line Plot
sns.lineplot(x=x, y=y, color='blue', label='Sine Wave')

# Matplotlib Scatter Plot
plt.scatter(x, y, color='red', marker='o', label="Data Points")

plt.title("Seaborn Line Plot with Matplotlib Scatter Overlay")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.legend()
plt.show()

Output:

Explanation:

• sns.lineplot() creates a smooth sine wave.
• plt.scatter() overlays red data points for better visualization.

Example 4. Enhancing Seaborn Histogram with Matplotlib Annotations

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

data = np.random.randn(1000)

plt.figure(figsize=(8, 5))
sns.histplot(data, kde=True, bins=30, color='purple')

# Adding Mean Line using Matplotlib
mean_value = np.mean(data)
plt.axvline(mean_value, color='red', linestyle='dashed', linewidth=2)
plt.text(mean_value + 0.1, 50, f'Mean: {mean_value:.2f}', color='red')

plt.title("Distribution with Seaborn and Matplotlib Customization")
plt.xlabel("Value")
plt.ylabel("Frequency")
plt.show()

Output:

Explanation:

• sns.histplot() creates a histogram with a KDE curve.
• plt.axvline() draws a dashed red line at the mean value.
• plt.text() annotates the mean value on the plot.

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