Matplotlib - Data Visualization

import matplotlib.pyplot as plt # Basic line plot plt.plot([1, 2, 3], [1, 4, 9]) plt.title('y = x²') plt.xlabel('X axis') plt.ylabel('Y axis') plt.grid(True, alpha=0.3) plt.show() # Quick plot plt.plot([1,2,3]); plt.show()

# Professional styling plt.style.use('seaborn-v0_8-darkgrid') # Modern theme plt.rcParams['figure.figsize'] = (12, 8) plt.rcParams['font.size'] = 12 # Custom colors and styles colors = ['#EC4899', '#8B5CF6', '#06B6D4', '#10B981'] plt.plot(x, y1, color=colors[0], linewidth=3, marker='o', markersize=6) plt.plot(x, y2, color=colors[1], linewidth=2, linestyle='--') # Annotations plt.annotate('Peak Value', xy=(5, 10), xytext=(7, 12), arrowprops=dict(arrowstyle='->', color='white')) plt.tight_layout() plt.savefig('professional_plot.png', dpi=300, bbox_inches='tight')

# 2x2 subplot grid fig, axes = plt.subplots(2, 2, figsize=(15, 10)) axes[0,0].plot(x, np.sin(x)); axes[0,0].set_title('Sine') axes[0,1].scatter(x, np.cos(x)); axes[0,1].set_title('Cosine Scatter') axes[1,0].hist(np.random.randn(1000)); axes[1,0].set_title('Histogram') axes[1,1].bar(categories, values); axes[1,1].set_title('Categories') plt.tight_layout() plt.show() # Pandas integration df.plot(kind='line', subplots=True, layout=(2,2), figsize=(12,8)) plt.show()

# Heatmaps plt.imshow(matrix, cmap='viridis', aspect='auto') plt.colorbar() # Contour plots plt.contourf(X, Y, Z, levels=20, cmap='plasma') plt.contour(X, Y, Z, levels=10, colors='white', alpha=0.5) # 3D plots from mpl_toolkits.mplot3d import Axes3D ax = plt.axes(projection='3d') ax.plot_surface(X, Y, Z, cmap='viridis') # Animations from matplotlib.animation import FuncAnimation def animate(i): line.set_data(x[:i], y[:i]) anim = FuncAnimation(fig, animate, frames=len(x))

# Direct plotting from DataFrames df['sales'].plot(kind='line', title='Sales Trend') df.plot(x='date', y='sales', kind='scatter') df.groupby('city')['sales'].sum().plot(kind='bar') # Advanced: Seaborn-style import seaborn as sns sns.lineplot(data=df, x='date', y='sales', hue='city') # Export for web/reports fig = plt.gcf() fig.savefig('dashboard.png', dpi=300, bbox_inches='tight') fig.savefig('dashboard.pdf')

# Interactive backend %matplotlib widget # Jupyter plt.ion() # Interactive mode # Plotly conversion (interactive web) import plotly.express as px fig = px.line(df, x='date', y='sales', color='city') fig.show() # Dashboards import plotly.graph_objects as go from plotly.subplots import make_subplots # HTML export fig.write_html('interactive_dashboard.html')