BP101T • Unit 3Topic 1 of 4⏱ 5 Min Read

1. Matplotlib & Seaborn Architecture for Scientific Plotting

💡 Core Concept & Clinical Context:

Matplotlib is the foundational 2D plotting library in Python. It uses a hierarchy of objects: the `Figure` (the overall canvas window) containing one or more `Axes` (individual coordinate systems with x-axis, y-axis, titles, and plot elements). Seaborn builds on top of Matplotlib to provide high-level statistical themes and beautiful color palettes tailored for multi-variable bio-data.

Pharmaceutical Code & Calculation Examples:

1Basic Scientific Plotting Architecture

Setting up a publication-quality figure canvas with axes labels, grid lines, and custom legends.

example_1.py

import matplotlib.pyplot as plt

# Create Figure and Axes
fig, ax = plt.subplots(figsize=(8, 5), dpi=150)

time = [0, 1, 2, 4, 6, 8, 12]
conc = [0.0, 8.4, 12.6, 9.1, 5.2, 2.8, 0.7]

ax.plot(time, conc, marker='o', color='#10b981', linewidth=2, label='500mg Tablet')
ax.set_title('In Vivo Pharmacokinetic Absorption Profile', fontsize=14, fontweight='bold')
ax.set_xlabel('Time Post-Administration (hours)', fontsize=11)
ax.set_ylabel('Plasma Drug Concentration (ug/mL)', fontsize=11)
ax.grid(True, linestyle='--', alpha=0.6)
ax.legend(frameon=True)
plt.tight_layout()

📋Key Takeaways & Exam Highlights

`plt.subplots(nrows, ncols)` creates figure window and subplot axes arrays.
`ax.plot()` creates continuous line charts; `ax.scatter()` plots discrete point data.
`plt.savefig('pk_profile.png', dpi=300)` exports high-resolution figures for journal publications.

🇮🇳 हिन्दी व्याख्या (1. Matplotlib & Seaborn Architecture for Scientific Plotting)

Matplotlib और Seaborn से वैज्ञानिक ग्राफ बनाए जाते हैं। Figure पूरा कैनवास होता है और Axes वह ग्राफ है जिस पर डेटा बिंदु और रेखाएं खींची जाती हैं।