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4. Data Visualization with Matplotlib & Seaborn

Data visualization is one of the most powerful ways to explore data, communicate insights, and tell stories. Matplotlib is the foundational plotting library in Python (highly customizable but requires more code). Seaborn is built on top of Matplotlib — it provides beautiful, high-level statistical plots with minimal code.

Install (if not using Anaconda)

Bash

pip install matplotlib seaborn

Standard imports (always use these)

Python

import matplotlib.pyplot as plt import seaborn as sns import pandas as pd import numpy as np # Set beautiful default style (highly recommended) sns.set_style("whitegrid") # clean white background with grid plt.rcParams['figure.figsize'] = (10, 6) # default figure size

4.1 Matplotlib Basics – Line, Bar, Scatter & Histogram

Line Plot

Python

x = np.linspace(0, 10, 100) y1 = np.sin(x) y2 = np.cos(x) plt.plot(x, y1, label='sin(x)', color='blue', linewidth=2) plt.plot(x, y2, label='cos(x)', color='red', linestyle='--') plt.title("Sine and Cosine Waves") plt.xlabel("X-axis") plt.ylabel("Y-axis") plt.legend() plt.grid(True) plt.show()

Bar Plot

Python

categories = ['Python', 'R', 'SQL', 'Excel', 'Tableau'] usage = [85, 45, 70, 60, 55] plt.bar(categories, usage, color='skyblue') plt.title("Programming Language Popularity (2026)") plt.xlabel("Language") plt.ylabel("Usage (%)") plt.xticks(rotation=45) plt.show()

Scatter Plot

Python

np.random.seed(42) x = np.random.randn(100) y = 2 x + np.random.randn(100) 0.5 plt.scatter(x, y, color='purple', alpha=0.6, s=80, edgecolor='black') plt.title("Scatter Plot with Correlation") plt.xlabel("Feature X") plt.ylabel("Feature Y") plt.show()

Histogram

Python

data = np.random.normal(loc=50, scale=15, size=1000) # normal distribution plt.hist(data, bins=30, color='teal', edgecolor='black', alpha=0.7) plt.title("Distribution of Exam Scores") plt.xlabel("Score") plt.ylabel("Frequency") plt.axvline(data.mean(), color='red', linestyle='--', label=f'Mean = {data.mean():.1f}') plt.legend() plt.show()

4.2 Seaborn for Statistical Visualization

Seaborn makes statistical plots beautiful and easy.

Line Plot with confidence interval

Python

tips = sns.load_dataset("tips") # built-in dataset sns.lineplot(x="total_bill", y="tip", data=tips, hue="time", style="time") plt.title("Tip vs Total Bill by Time") plt.show()

Count Plot

Python

sns.countplot(x="day", data=tips, hue="sex", palette="Set2") plt.title("Number of Customers by Day and Gender") plt.show()

Pair Plot (exploratory)

Python

sns.pairplot(tips, hue="smoker", diag_kind="kde") plt.suptitle("Pair Plot of Tips Dataset", y=1.02) plt.show()

4.3 Advanced Plots – Heatmap, Pairplot, Boxplot

Heatmap (Correlation Matrix)

Python

# Correlation matrix corr = tips.corr(numeric_only=True) sns.heatmap(corr, annot=True, cmap="coolwarm", fmt=".2f", linewidths=0.5) plt.title("Correlation Heatmap of Tips Dataset") plt.show()

Boxplot (distribution & outliers)

Python

sns.boxplot(x="day", y="total_bill", hue="smoker", data=tips, palette="Set3") plt.title("Total Bill Distribution by Day & Smoking Status") plt.show()

Violin Plot (distribution + density)

Python

sns.violinplot(x="day", y="tip", hue="sex", data=tips, split=True, palette="muted") plt.title("Tip Distribution by Day and Gender") plt.show()

4.4 Creating Publication-Ready Visualizations

Tips to make plots look professional and publication-quality:

Best Practices Code Template

Python

plt.figure(figsize=(10, 6), dpi=120) # high resolution sns.set_context("paper", font_scale=1.3) # publication style # Your plot here sns.boxplot(x="day", y="total_bill", data=tips, palette="pastel") plt.title("Total Bill Distribution by Day", fontsize=16, fontweight='bold') plt.xlabel("Day of the Week", fontsize=14) plt.ylabel("Total Bill (USD)", fontsize=14) plt.xticks(fontsize=12) plt.yticks(fontsize=12) plt.grid(True, linestyle='--', alpha=0.7) # Save high-quality image plt.tight_layout() plt.savefig("publication_plot.png", dpi=300, bbox_inches='tight') plt.show()

Additional Tips for Publication/Report Quality:

• Use sns.set_style("whitegrid") or "ticks" for clean look

• Choose color palettes: "viridis", "magma", "coolwarm", "Set2", "pastel"

• Add annotations: plt.annotate(), sns.despine()

• Use fig, ax = plt.subplots() for multi-plot figures

• Export as PNG (300+ dpi) or SVG for journals

Mini Summary Project – Full EDA Visualization

Python

# Load built-in dataset df = sns.load_dataset("penguins") # 1. Pair plot sns.pairplot(df, hue="species", diag_kind="kde") plt.suptitle("Penguin Species Comparison", y=1.02) plt.show() # 2. Boxplot of bill length by species sns.boxplot(x="species", y="bill_length_mm", data=df, palette="Set2") plt.title("Bill Length Distribution by Penguin Species") plt.show() # 3. Heatmap of correlations corr = df.corr(numeric_only=True) sns.heatmap(corr, annot=True, cmap="coolwarm", fmt=".2f") plt.title("Correlation Matrix – Penguin Dataset") plt.show()

This completes the full Data Visualization with Matplotlib & Seaborn section — now you can create beautiful, insightful, and publication-ready visualizations!

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