Matplotlib is a fundamental plotting library in Python and offers a wide range of plotting functionalities. It provides a low-level interface for creating basic plots from data.

**Example of a basic Matplotlib plot**:

```
import matplotlib.pyplot as plt
# Sample data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
# Plotting
plt.plot(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Simple Matplotlib Plot')
plt.show()
```

**Seaborn**:

Seaborn is built on top of Matplotlib and provides a high-level interface for creating statistical visualizations. It simplifies the process of creating complex plots and offers attractive default styles.

Example of a Seaborn plot:

```
import seaborn as sns
import matplotlib.pyplot as plt
# Sample data
tips = sns.load_dataset('tips')
# Creating a violin plot using Seaborn
sns.violinplot(x='day', y='total_bill', data=tips)
plt.xlabel('Day of the Week')
plt.ylabel('Total Bill ($)')
plt.title('Seaborn Violin Plot of Total Bill by Day')
plt.show()
```

Differences:

**Code Complexity**: Seaborn often requires less code to create complex visualizations compared to Matplotlib, especially for statistical plots.

**Default Styles**: Seaborn comes with aesthetically pleasing default styles and color palettes, making plots visually appealing without much customization.

**Statistical Plotting**: Seaborn provides specialized functions for statistical plotting, such as violin plots, box plots, and regression plots, making it easier to visualize statistical relationships in data.

**Integration with Pandas**: Seaborn integrates seamlessly with pandas DataFrames, allowing for easy plotting of data directly from DataFrames without extensive data manipulation.

Customization: Matplotlib offers more fine-grained control over plot customization, allowing users to tweak every aspect of a plot. Seaborn sacrifices some of this control for simplicity and ease of use.

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