--- title: 'Matplotlib' date: 06-06-2025 background: bg-[#1a073e] tags: - 'data visualization' - 'matplotlib' - 'plotting' categories: - 'Python' intro: | [Matplotlib](https://matplotlib.org/) is a comprehensive library for creating static, animated, and interactive plots in Python. This cheat sheet provides a quick reference from basic to advanced usage, covering essential features for data science, machine learning, and scientific computing. plugins: - copyCode --- --- ## Getting Started {.cols-2} ### Importing ```python import matplotlib.pyplot as plt # Core plotting library import numpy as np # For numerical operations ``` ### Basic Plot ```python x = np.linspace(0, 10, 100) # 100 points between 0 and 10 y = np.sin(x) # Sine function values plt.plot(x, y) # Create a line plot plt.show() # Display the plot ``` --- ## Plot Types {.cols-2} ### Line Plot ```python plt.plot(x, y) # Line plot of y vs x plt.title("Sine Wave") # Set title plt.xlabel("x-axis") # Label x-axis plt.ylabel("y-axis") # Label y-axis plt.grid(True) # Show gridlines plt.show() ``` ### Scatter Plot ```python plt.scatter(x, y) # Scatter plot plt.title("Scatter Plot") plt.show() ``` ### Bar Plot ```python categories = ['A', 'B', 'C'] values = [10, 20, 15] plt.bar(categories, values) # Vertical bar chart plt.title("Bar Chart") plt.show() ``` ### Horizontal Bar Plot ```python plt.barh(categories, values) # Horizontal bar chart plt.title("Horizontal Bar Chart") plt.show() ``` ### Histogram ```python data = np.random.randn(1000) # Random normal distribution plt.hist(data, bins=30) # Histogram with 30 bins plt.title("Histogram") plt.show() ``` ### Pie Chart ```python sizes = [25, 35, 20, 20] labels = ['A', 'B', 'C', 'D'] plt.pie(sizes, labels=labels, autopct='%1.1f%%') # Pie chart with % labels plt.title("Pie Chart") plt.show() ``` --- ## Customization {.row-span-2} | Feature | Code Example | Description | | --------------- | ------------------------------------------------------------------------------------ | ------------------------------------------- | | Title | `plt.title("Title")` | Set the title of the plot | | X/Y Labels | `plt.xlabel("X")`, `plt.ylabel("Y")` | Label the axes | | Grid | `plt.grid(True)` | Show grid | | Legend | `plt.legend(["line1"])` | Add legend | | Line Style | `plt.plot(x, y, linestyle='--')` | Dashed line | | Color | `plt.plot(x, y, color='green')` | Set line color | | Marker | `plt.plot(x, y, marker='o')` | Show markers on points | | Axis Limits | `plt.xlim(0, 10)`, `plt.ylim(-1, 1)` | Set axis range | | Tick Labels | `plt.xticks([...])`, `plt.yticks([...])` | Customize tick positions | | Text Annotation | `plt.text(5, 0, "Midpoint")` | Add text at a specific coordinate | | Arrow | `plt.annotate("Peak", xy=(7, 1), xytext=(6, 1.5), arrowprops=dict(arrowstyle="->"))` | Add annotation arrow | | Style Sheets | `plt.style.use('ggplot')` | Use predefined styles like `seaborn`, `bmh` | --- ## Subplots & Layouts ### Multiple Subplots ```python fig, axs = plt.subplots(2, 2) # Create 2x2 grid of subplots axs[0, 0].plot(x, y) # Top-left subplot axs[0, 1].scatter(x, y) # Top-right subplot axs[1, 0].bar(categories, values) # Bottom-left subplot axs[1, 1].hist(data) # Bottom-right subplot plt.tight_layout() # Adjust spacing to prevent overlap plt.show() ``` ### Figure Size ```python plt.figure(figsize=(10, 5)) # Set figure size (width, height in inches) ``` --- ## Advanced Visualizations {.cols-2} ### Heatmap ```python data = np.random.rand(10, 10) # Random 10x10 matrix plt.imshow(data, cmap='hot', interpolation='nearest') # Display as image plt.colorbar() # Show color scale plt.title("Heatmap") plt.show() ``` ### Contour Plot ```python X, Y = np.meshgrid(x, x) Z = np.sin(X) * np.cos(Y) plt.contour(X, Y, Z) # Contour lines plt.title("Contour Plot") plt.show() ``` ### 3D Plot ```python from mpl_toolkits.mplot3d import Axes3D fig = plt.figure() ax = fig.add_subplot(111, projection='3d') # 3D subplot ax.plot3D(x, y, np.cos(x)) # 3D line plt.title("3D Plot") plt.show() ``` --- ## Working with Images ```python import matplotlib.image as mpimg img = mpimg.imread('image.jpg') # Load image plt.imshow(img) # Display image plt.axis('off') # Hide axes plt.title("Image Display") plt.show() ``` --- ## For Machine Learning ### Plotting Loss vs Epoch ```python epochs = range(1, 11) loss = [0.9, 0.7, 0.5, 0.4, 0.3, 0.25, 0.2, 0.18, 0.15, 0.13] plt.plot(epochs, loss) plt.title("Training Loss") plt.xlabel("Epoch") plt.ylabel("Loss") plt.show() ``` ### Confusion Matrix (Heatmap) ```python from sklearn.metrics import confusion_matrix import seaborn as sns y_true = [0, 1, 2, 2, 0] y_pred = [0, 0, 2, 2, 1] cm = confusion_matrix(y_true, y_pred) # Create confusion matrix sns.heatmap(cm, annot=True, fmt='d', cmap='Blues') # Heatmap visualization plt.title("Confusion Matrix") plt.show() ``` ### ROC Curve ```python from sklearn.metrics import roc_curve, auc fpr, tpr, _ = roc_curve([0, 0, 1, 1], [0.1, 0.4, 0.35, 0.8]) # Compute ROC roc_auc = auc(fpr, tpr) # Area under curve plt.plot(fpr, tpr, label=f'AUC = {roc_auc:.2f}') plt.plot([0, 1], [0, 1], 'k--') # Diagonal line plt.xlabel("False Positive Rate") plt.ylabel("True Positive Rate") plt.title("ROC Curve") plt.legend() plt.show() ``` --- ## Saving Plots ```python plt.savefig("figure.png", dpi=300, bbox_inches='tight') # Save plot to file ``` --- ## Show & Clear ```python plt.show() # Show plot window plt.clf() # Clear current figure (useful when plotting in loops) plt.close() # Close figure window (useful in scripts or GUI apps) ``` --- ## More Useful Functions | Function | Use Case | | ------------------------------ | ------------------------------- | | `plt.fill_between(x, y1, y2)` | Fill area between curves | | `plt.axhline(y=value)` | Draw horizontal line at y | | `plt.axvline(x=value)` | Draw vertical line at x | | `plt.errorbar(x, y, yerr=...)` | Plot with error bars | | `plt.twinx()` | Create secondary y-axis | | `plt.subplots_adjust(...)` | Manually adjust subplot spacing | | `plt.gca()` | Get current axes | | `plt.gcf()` | Get current figure | ---