Advanced Plotting

Advanced Plotting Techniques

Advanced plotting involves creating sophisticated visualizations that reveal deeper insights in your data. This includes customizing plots, combining multiple visualizations, and using advanced statistical plots.

Mastering advanced plotting techniques allows you to create publication-quality visualizations and communicate complex data relationships effectively.

Customizing Plot Appearance

Advanced plots require careful customization. Here's how to control every aspect:

customization.py
# Advanced Plot Customization
import matplotlib.pyplot as plt
import numpy as np

# Sample data
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

# Create figure with custom size
fig, ax = plt.subplots(figsize=(10, 6))

# Plot multiple lines with custom styling
ax.plot(x, y1, label='sin(x)', linewidth=2, color='#22d3ee', linestyle='-')
ax.plot(x, y2, label='cos(x)', linewidth=2, color='#a855f7', linestyle='--')

# Customize axes
ax.set_xlabel('X Axis', fontsize=12, fontweight='bold')
ax.set_ylabel('Y Axis', fontsize=12, fontweight='bold')
ax.set_title('Advanced Customized Plot', fontsize=14, fontweight='bold')

# Add legend, grid, and limits
ax.legend(loc='best', fontsize=10)
ax.grid(True, alpha=0.3, linestyle=':')
ax.set_xlim(0, 10)
ax.set_ylim(-1.5, 1.5)

plt.tight_layout()
plt.show()

print("Customized plot with multiple lines, legend, and styling")

Subplot Layouts

Creating complex layouts with multiple subplots:

subplot_layouts.py
# Advanced Subplot Layouts
import matplotlib.pyplot as plt
import numpy as np

# Create complex layout: 2 rows, 2 columns
fig = plt.figure(figsize=(12, 10))

# Method 1: Using subplot2grid for flexible layouts
ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=2)
ax2 = plt.subplot2grid((3, 3), (0, 2))
ax3 = plt.subplot2grid((3, 3), (1, 0), colspan=3)
ax4 = plt.subplot2grid((3, 3), (2, 0), colspan=3)

print("Advanced subplot layout created:")
print("  - Flexible grid positioning")
print("  - Different sized subplots")
print("  - Custom arrangement")

# Method 2: Using GridSpec for precise control
from matplotlib.gridspec import GridSpec
gs = GridSpec(3, 3, figure=fig)
ax5 = fig.add_subplot(gs[0, :])  # Full width top
ax6 = fig.add_subplot(gs[1, :2])  # Left side
ax7 = fig.add_subplot(gs[1, 2:])  # Right side

print("\nGridSpec provides even more control over layout")

Statistical Plots

Advanced statistical visualizations reveal data distributions and relationships:

statistical_plots.py
# Advanced Statistical Plots
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd

# Sample data with multiple groups
np.random.seed(42)
data = {
    'value': np.concatenate([
        np.random.normal(100, 15, 100),
        np.random.normal(120, 20, 100),
        np.random.normal(110, 18, 100)
    ]),
    'group': ['A'] * 100 + ['B'] * 100 + ['C'] * 100
}
df = pd.DataFrame(data)

print("Advanced Statistical Plot Types:")
print("  1. Violin Plot: Shows distribution shape")
print("     sns.violinplot(data=df, x='group', y='value')")
print("  2. Box Plot with Swarm: Shows individual points")
print("     sns.boxplot(...) + sns.swarmplot(...)")
print("  3. Pair Plot: Shows all pairwise relationships")
print("     sns.pairplot(df)")
print("  4. Joint Plot: Scatter + marginal distributions")
print("     sns.jointplot(data=df, x='x', y='y')")

print("\nData summary:")
print(df.groupby('group')['value'].describe())

3D Plots

Three-dimensional visualizations for complex data:

3d_plots.py
# 3D Plotting
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np

# Create 3D figure
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')

# Generate 3D data
x = np.linspace(-5, 5, 50)
y = np.linspace(-5, 5, 50)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

print("3D Plot Types:")
print("  1. Surface Plot: ax.plot_surface(X, Y, Z)")
print("  2. Scatter 3D: ax.scatter(x, y, z)")
print("  3. Wireframe: ax.plot_wireframe(X, Y, Z)")

print("\n3D plots help visualize complex relationships in high-dimensional data")

Exercise: Create Advanced Plots

Complete the exercise on the right side:

Task 1: Create a figure with 2x2 subplots
Task 2: Plot different chart types in each subplot (line, bar, scatter, histogram)
Task 3: Customize each plot with titles, labels, and colors
Task 4: Add a main title for the entire figure

Write your code to create these advanced multi-plot visualizations!

💡 Learning Tip

Practice is essential. Try modifying the code examples, experiment with different parameters, and see how changes affect the results. Hands-on experience is the best teacher!

🎉

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