Introduction to Matplotlib

Matplotlib Object Hierarchy:

Figure  ──── The entire canvas (window/image file)
  │
  ├── Axes  ── Individual plot area (one or many per Figure)
  │     ├── XAxis / YAxis  ── Tick marks and labels
  │     ├── Title
  │     ├── Lines / Bars / Patches
  │     └── Legend
  │
  └── Canvas (rendering backend)

Two APIs:
  pyplot (state-based, MATLAB-style):  plt.plot(), plt.title()
  OO (object-oriented, recommended):   fig, ax = plt.subplots(); ax.plot()

import matplotlib.pyplot as plt
import numpy as np

x = [1, 2, 3, 4, 5, 6]
y = [10000, 15000, 13000, 18000, 22000, 20000]

# API 1: pyplot (quick and simple)
plt.plot(x, y, color='#2196F3', linewidth=2, marker='o')
plt.title('Monthly Sales')
plt.xlabel('Month')
plt.ylabel('Revenue ($)')
plt.show()

# API 2: Object-Oriented (recommended — more control)
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(x, y, color='#2196F3', linewidth=2.5, marker='o',
        markersize=8, markerfacecolor='white', markeredgewidth=2)
ax.set_title('Monthly Sales Revenue', fontsize=14, fontweight='bold', pad=15)
ax.set_xlabel('Month', fontsize=12)
ax.set_ylabel('Revenue ($)', fontsize=12)
ax.set_xticks(x)
ax.set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'])
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'${x:,.0f}'))
ax.grid(True, alpha=0.3)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.tight_layout()
plt.savefig('sales_trend.png', dpi=150, bbox_inches='tight')
plt.show()

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots(figsize=(10, 6))

# --- PLOT TYPES ---
x = np.linspace(0, 10, 100)
ax.plot(x, np.sin(x), label='sin(x)')               # Line
ax.scatter([1,3,5,7], [0.8,0.1,-0.9,0.7], s=100, label='points')  # Scatter
ax.bar([1,3,5,7], [0.5,0.3,-0.5,0.4], width=0.4, label='bars')    # Bar

# --- ANNOTATIONS ---
ax.axhline(y=0, color='black', linewidth=0.8)  # Horizontal reference line
ax.axvline(x=np.pi, color='red', linestyle='--', label='π')

# Annotate a specific point
ax.annotate('Peak', xy=(np.pi/2, 1), xytext=(np.pi/2+1, 1.2),
            arrowprops=dict(arrowstyle='->', color='gray'),
            fontsize=11, color='gray')

# Add text directly
ax.text(8, 0.5, 'Decreasing\ntrend', fontsize=10, color='blue',
        ha='center', style='italic')

# --- FORMATTING ---
ax.set_title('Matplotlib Elements Demo', fontsize=14, fontweight='bold')
ax.set_xlabel('X Axis', fontsize=12)
ax.set_ylabel('Y Axis', fontsize=12)
ax.legend(loc='upper right', framealpha=0.8)
ax.set_xlim(0, 10)
ax.set_ylim(-1.5, 1.8)
ax.grid(True, linestyle='--', alpha=0.4)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.savefig('elements_demo.png', dpi=150)
plt.show()

import matplotlib.pyplot as plt
import numpy as np

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
          'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
revenue_2023 = [42000, 45000, 51000, 48000, 53000, 62000,
                58000, 61000, 67000, 72000, 89000, 95000]
revenue_2024 = [47000, 49000, 56000, 54000, 61000, 70000,
                65000, 69000, 75000, 82000, 98000, 110000]

fig, ax = plt.subplots(figsize=(12, 6))

# Plot both years
ax.plot(months, revenue_2023, 'o-', color='#90CAF9', linewidth=2,
        markersize=7, label='2023', alpha=0.8)
ax.plot(months, revenue_2024, 's-', color='#1565C0', linewidth=2.5,
        markersize=7, label='2024')

# Fill between years
ax.fill_between(months, revenue_2023, revenue_2024, alpha=0.1, color='#1565C0')

# Annotate peak
peak_idx = revenue_2024.index(max(revenue_2024))
ax.annotate(f'Peak: ${max(revenue_2024):,}',
            xy=(months[peak_idx], max(revenue_2024)),
            xytext=(months[peak_idx-2], max(revenue_2024) + 5000),
            arrowprops=dict(arrowstyle='->', color='darkblue'),
            fontsize=11, color='darkblue', fontweight='bold')

# Growth annotation
yoy_growth = (revenue_2024[-1] - revenue_2023[-1]) / revenue_2023[-1] * 100
ax.text(0.02, 0.95, f'YoY Growth: +{yoy_growth:.1f}%',
        transform=ax.transAxes, fontsize=12, color='green',
        fontweight='bold', va='top')

ax.set_title('Annual Revenue Comparison — 2023 vs 2024', fontsize=14, fontweight='bold')
ax.set_xlabel('Month', fontsize=12)
ax.set_ylabel('Revenue ($)', fontsize=12)
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'${x/1000:.0f}K'))
ax.legend(fontsize=11)
ax.grid(True, alpha=0.3)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.tight_layout()
plt.savefig('sales_comparison.png', dpi=150, bbox_inches='tight')
plt.show()