Python for AI & ML - Day 15: Data Visualization with Matplotlib

Goal: Master Matplotlib, Python’s foundational plotting library, to create insightful visualizations for data exploration and presentation in AI/ML workflows. Learn to generate line plots, bar charts, histograms, scatter plots, and customize them for clarity.

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1. What is Matplotlib?

Matplotlib is a comprehensive Python library for creating static, animated, and interactive visualizations. Its pyplot module provides a MATLAB-like interface for quick plotting, while its object-oriented API offers fine-grained control.

Key Components:

• Figure: The top-level container for all plot elements.

• Axes: The area where data is plotted (a single plot or subplot).

• Axis: The x/y-axis with ticks, labels, and limits.

• Artist: Everything visible on the figure (lines, text, legends).

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2. Basic Plots

Line Plot

import matplotlib.pyplot as plt  
import numpy as np  

# Generate data  
x = np.linspace(0, 10, 100)  
y = np.sin(x)  

# Create plot  
plt.figure(figsize=(8, 4))  
plt.plot(x, y, label="sin(x)", color="blue", linestyle="--", linewidth=2)  
plt.title("Sine Wave")  
plt.xlabel("x")  
plt.ylabel("sin(x)")  
plt.grid(True)  
plt.legend()  
plt.show()  

Bar Chart

categories = ["A", "B", "C"]  
values = [25, 40, 30]  

plt.bar(categories, values, color=["red", "green", "blue"])  
plt.title("Category Performance")  
plt.xlabel("Category")  
plt.ylabel("Value")  
plt.show()  

Histogram

data = np.random.randn(1000)  # Normal distribution  

plt.hist(data, bins=30, edgecolor="black", alpha=0.7)  
plt.title("Data Distribution")  
plt.xlabel("Value")  
plt.ylabel("Frequency")  
plt.show()  

Scatter Plot

x = np.random.rand(50)  
y = x + np.random.randn(50) * 0.1  

plt.scatter(x, y, color="purple", alpha=0.5, marker="o")  
plt.title("X vs Y Correlation")  
plt.xlabel("X")  
plt.ylabel("Y")  
plt.show()  

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3. Customizing Plots

Styling

• Colors: Use named colors ("red"), hex codes ("#FF5733"), or RGB tuples.

• Markers: o, s, ^, D, etc.

• Line Styles: -, --, -., :.

plt.plot(x, y, color="#FF5733", linestyle="-.", marker="o", markersize=5)  

Labels & Legends

plt.title("Customized Plot", fontsize=14, fontweight="bold")  
plt.xlabel("X-Axis", fontsize=12)  
plt.ylabel("Y-Axis", fontsize=12)  
plt.legend(loc="upper right", fontsize=10)  

Subplots

fig, axes = plt.subplots(2, 2, figsize=(10, 6))  # 2x2 grid  
axes[0, 0].plot(x, y)  
axes[0, 1].scatter(x, y)  
axes[1, 0].bar(categories, values)  
axes[1, 1].hist(data, bins=30)  
plt.tight_layout()  # Avoid overlapping  
plt.show()  

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4. Advanced Features

Annotations

Highlight specific points:

plt.plot(x, y)  
plt.annotate("Peak", xy=(np.pi/2, 1), xytext=(3, 0.8),  
             arrowprops=dict(facecolor="black", arrowstyle="->"))  

Styling with plt.style

Use predefined styles:

plt.style.use("ggplot")  # Options: seaborn, dark_background, etc.  

Saving Figures

plt.savefig("plot.png", dpi=300, bbox_inches="tight")  

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5. Integrating with Pandas

Plot directly from DataFrames:

import pandas as pd  

# Load data  
df = pd.read_csv("titanic.csv")  

# Grouped bar chart  
survival_rate = df.groupby("Pclass")["Survived"].mean()  
survival_rate.plot(kind="bar", color="skyblue")  
plt.title("Survival Rate by Class")  
plt.xlabel("Class")  
plt.ylabel("Survival Rate")  
plt.xticks(rotation=0)  
plt.show()  

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6. Real-World Use Cases in AI/ML

1. Feature Analysis:

   • Plot distributions of input features (e.g., df["Age"].hist()).

2. Model Evaluation:

   • Visualize training vs validation loss curves.

   • Plot confusion matrices or ROC curves.

3. Clustering:

   • Scatter plots for 2D/3D cluster visualization.

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7. Best Practices

1. Avoid Overplotting: Use transparency (alpha) or jittering.

2. Choose the Right Chart:

   • Line plots: Trends over time.

   • Bar charts: Categorical comparisons.

   • Scatter plots: Relationships between variables.

3. Label Clearly: Always include titles, axis labels, and legends.

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8. Practice Exercise

1. Load the Titanic dataset (pd.read_csv("titanic.csv")).

2. Plot a histogram of passenger ages.

3. Create a bar chart showing survival rates by gender.

4. Generate a scatter plot of Age vs Fare, colored by survival.

Solution:

# 1. Load data  
titanic = pd.read_csv("titanic.csv")  

# 2. Age histogram  
plt.hist(titanic["Age"].dropna(), bins=20, edgecolor="black")  
plt.title("Age Distribution")  
plt.xlabel("Age")  
plt.ylabel("Count")  
plt.show()  

# 3. Survival by gender  
survival_gender = titanic.groupby("Sex")["Survived"].mean()  
survival_gender.plot(kind="bar", color=["pink", "blue"])  
plt.title("Survival Rate by Gender")  
plt.ylabel("Survival Rate")  
plt.xticks(rotation=0)  
plt.show()  

# 4. Age vs Fare scatter plot  
plt.scatter(titanic["Age"], titanic["Fare"], c=titanic["Survived"], alpha=0.5)  
plt.colorbar(label="Survived")  
plt.title("Age vs Fare (Colored by Survival)")  
plt.xlabel("Age")  
plt.ylabel("Fare")  
plt.show()  

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Key Takeaways

• Matplotlib provides granular control over every plot element.

• Use Pandas integration to streamline visualization from DataFrames.

• Prioritize clarity and simplicity to communicate insights effectively.