Plotting with Seaborn and Matplotlib

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

Basic plotting with matplotlib

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.

Why Combine matplotlib and seaborn?

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.

Enhancing matplotlib with seaborn styles

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.