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references/plot_types.md
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references/plot_types.md
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# Matplotlib Plot Types Guide
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Comprehensive guide to different plot types in matplotlib with examples and use cases.
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## 1. Line Plots
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**Use cases:** Time series, continuous data, trends, function visualization
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### Basic Line Plot
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```python
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fig, ax = plt.subplots(figsize=(10, 6))
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ax.plot(x, y, linewidth=2, label='Data')
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ax.set_xlabel('X axis')
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ax.set_ylabel('Y axis')
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ax.legend()
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```
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### Multiple Lines
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```python
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ax.plot(x, y1, label='Dataset 1', linewidth=2)
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ax.plot(x, y2, label='Dataset 2', linewidth=2, linestyle='--')
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ax.plot(x, y3, label='Dataset 3', linewidth=2, linestyle=':')
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ax.legend()
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```
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### Line with Markers
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```python
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ax.plot(x, y, marker='o', markersize=8, linestyle='-',
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linewidth=2, markerfacecolor='red', markeredgecolor='black')
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```
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### Step Plot
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```python
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ax.step(x, y, where='mid', linewidth=2, label='Step function')
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# where options: 'pre', 'post', 'mid'
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```
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### Error Bars
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```python
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ax.errorbar(x, y, yerr=error, fmt='o-', linewidth=2,
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capsize=5, capthick=2, label='With uncertainty')
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```
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## 2. Scatter Plots
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**Use cases:** Correlations, relationships between variables, clusters, outliers
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### Basic Scatter
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```python
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ax.scatter(x, y, s=50, alpha=0.6)
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```
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### Sized and Colored Scatter
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```python
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scatter = ax.scatter(x, y, s=sizes*100, c=colors,
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cmap='viridis', alpha=0.6, edgecolors='black')
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plt.colorbar(scatter, ax=ax, label='Color variable')
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```
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### Categorical Scatter
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```python
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for category in categories:
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mask = data['category'] == category
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ax.scatter(data[mask]['x'], data[mask]['y'],
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label=category, s=50, alpha=0.7)
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ax.legend()
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```
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## 3. Bar Charts
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**Use cases:** Categorical comparisons, discrete data, counts
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### Vertical Bar Chart
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```python
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ax.bar(categories, values, color='steelblue',
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edgecolor='black', linewidth=1.5)
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ax.set_ylabel('Values')
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```
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### Horizontal Bar Chart
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```python
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ax.barh(categories, values, color='coral',
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edgecolor='black', linewidth=1.5)
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ax.set_xlabel('Values')
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```
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### Grouped Bar Chart
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```python
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x = np.arange(len(categories))
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width = 0.35
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ax.bar(x - width/2, values1, width, label='Group 1')
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ax.bar(x + width/2, values2, width, label='Group 2')
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ax.set_xticks(x)
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ax.set_xticklabels(categories)
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ax.legend()
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```
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### Stacked Bar Chart
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```python
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ax.bar(categories, values1, label='Part 1')
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ax.bar(categories, values2, bottom=values1, label='Part 2')
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ax.bar(categories, values3, bottom=values1+values2, label='Part 3')
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ax.legend()
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```
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### Bar Chart with Error Bars
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```python
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ax.bar(categories, values, yerr=errors, capsize=5,
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color='steelblue', edgecolor='black')
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```
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### Bar Chart with Patterns
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```python
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bars1 = ax.bar(x - width/2, values1, width, label='Group 1',
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color='white', edgecolor='black', hatch='//')
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bars2 = ax.bar(x + width/2, values2, width, label='Group 2',
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color='white', edgecolor='black', hatch='\\\\')
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```
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## 4. Histograms
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**Use cases:** Distributions, frequency analysis
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### Basic Histogram
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```python
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ax.hist(data, bins=30, edgecolor='black', alpha=0.7)
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ax.set_xlabel('Value')
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ax.set_ylabel('Frequency')
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```
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### Multiple Overlapping Histograms
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```python
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ax.hist(data1, bins=30, alpha=0.5, label='Dataset 1')
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ax.hist(data2, bins=30, alpha=0.5, label='Dataset 2')
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ax.legend()
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```
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### Normalized Histogram (Density)
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```python
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ax.hist(data, bins=30, density=True, alpha=0.7,
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edgecolor='black', label='Empirical')
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# Overlay theoretical distribution
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from scipy.stats import norm
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x = np.linspace(data.min(), data.max(), 100)
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ax.plot(x, norm.pdf(x, data.mean(), data.std()),
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'r-', linewidth=2, label='Normal fit')
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ax.legend()
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```
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### 2D Histogram (Hexbin)
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```python
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hexbin = ax.hexbin(x, y, gridsize=30, cmap='Blues')
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plt.colorbar(hexbin, ax=ax, label='Counts')
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```
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### 2D Histogram (hist2d)
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```python
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h = ax.hist2d(x, y, bins=30, cmap='Blues')
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plt.colorbar(h[3], ax=ax, label='Counts')
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```
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## 5. Box and Violin Plots
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**Use cases:** Statistical distributions, outlier detection, comparing distributions
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### Box Plot
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```python
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ax.boxplot([data1, data2, data3],
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labels=['Group A', 'Group B', 'Group C'],
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showmeans=True, meanline=True)
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ax.set_ylabel('Values')
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```
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### Horizontal Box Plot
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```python
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ax.boxplot([data1, data2, data3], vert=False,
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labels=['Group A', 'Group B', 'Group C'])
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ax.set_xlabel('Values')
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```
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### Violin Plot
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```python
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parts = ax.violinplot([data1, data2, data3],
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positions=[1, 2, 3],
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showmeans=True, showmedians=True)
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ax.set_xticks([1, 2, 3])
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ax.set_xticklabels(['Group A', 'Group B', 'Group C'])
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```
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## 6. Heatmaps
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**Use cases:** Matrix data, correlations, intensity maps
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### Basic Heatmap
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```python
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im = ax.imshow(matrix, cmap='coolwarm', aspect='auto')
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plt.colorbar(im, ax=ax, label='Values')
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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```
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### Heatmap with Annotations
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```python
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im = ax.imshow(matrix, cmap='coolwarm')
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plt.colorbar(im, ax=ax)
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# Add text annotations
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for i in range(matrix.shape[0]):
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for j in range(matrix.shape[1]):
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text = ax.text(j, i, f'{matrix[i, j]:.2f}',
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ha='center', va='center', color='black')
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```
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### Correlation Matrix
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```python
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corr = data.corr()
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im = ax.imshow(corr, cmap='RdBu_r', vmin=-1, vmax=1)
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plt.colorbar(im, ax=ax, label='Correlation')
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# Set tick labels
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ax.set_xticks(range(len(corr)))
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ax.set_yticks(range(len(corr)))
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ax.set_xticklabels(corr.columns, rotation=45, ha='right')
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ax.set_yticklabels(corr.columns)
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```
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## 7. Contour Plots
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**Use cases:** 3D data on 2D plane, topography, function visualization
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### Contour Lines
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```python
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contour = ax.contour(X, Y, Z, levels=10, cmap='viridis')
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ax.clabel(contour, inline=True, fontsize=8)
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plt.colorbar(contour, ax=ax)
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```
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### Filled Contours
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```python
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contourf = ax.contourf(X, Y, Z, levels=20, cmap='viridis')
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plt.colorbar(contourf, ax=ax)
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```
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### Combined Contours
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```python
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contourf = ax.contourf(X, Y, Z, levels=20, cmap='viridis', alpha=0.8)
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contour = ax.contour(X, Y, Z, levels=10, colors='black',
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linewidths=0.5, alpha=0.4)
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ax.clabel(contour, inline=True, fontsize=8)
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plt.colorbar(contourf, ax=ax)
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```
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## 8. Pie Charts
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**Use cases:** Proportions, percentages (use sparingly)
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### Basic Pie Chart
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```python
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ax.pie(sizes, labels=labels, autopct='%1.1f%%',
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startangle=90, colors=colors)
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ax.axis('equal') # Equal aspect ratio ensures circular pie
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```
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### Exploded Pie Chart
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```python
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explode = (0.1, 0, 0, 0) # Explode first slice
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ax.pie(sizes, explode=explode, labels=labels,
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autopct='%1.1f%%', shadow=True, startangle=90)
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ax.axis('equal')
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```
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### Donut Chart
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```python
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ax.pie(sizes, labels=labels, autopct='%1.1f%%',
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wedgeprops=dict(width=0.5), startangle=90)
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ax.axis('equal')
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```
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## 9. Polar Plots
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**Use cases:** Cyclic data, directional data, radar charts
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### Basic Polar Plot
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```python
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theta = np.linspace(0, 2*np.pi, 100)
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r = np.abs(np.sin(2*theta))
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ax = plt.subplot(111, projection='polar')
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ax.plot(theta, r, linewidth=2)
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```
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### Radar Chart
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```python
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categories = ['A', 'B', 'C', 'D', 'E']
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values = [4, 3, 5, 2, 4]
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# Add first value to the end to close the polygon
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angles = np.linspace(0, 2*np.pi, len(categories), endpoint=False)
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values_closed = np.concatenate((values, [values[0]]))
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angles_closed = np.concatenate((angles, [angles[0]]))
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ax = plt.subplot(111, projection='polar')
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ax.plot(angles_closed, values_closed, 'o-', linewidth=2)
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ax.fill(angles_closed, values_closed, alpha=0.25)
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ax.set_xticks(angles)
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ax.set_xticklabels(categories)
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```
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## 10. Stream and Quiver Plots
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**Use cases:** Vector fields, flow visualization
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### Quiver Plot (Vector Field)
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```python
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ax.quiver(X, Y, U, V, alpha=0.8)
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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ax.set_aspect('equal')
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```
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### Stream Plot
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```python
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ax.streamplot(X, Y, U, V, density=1.5, color='k', linewidth=1)
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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ax.set_aspect('equal')
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```
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## 11. Fill Between
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**Use cases:** Uncertainty bounds, confidence intervals, areas under curves
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### Fill Between Two Curves
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```python
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ax.plot(x, y, 'k-', linewidth=2, label='Mean')
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ax.fill_between(x, y - std, y + std, alpha=0.3,
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label='±1 std dev')
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ax.legend()
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```
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### Fill Between with Condition
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```python
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ax.plot(x, y1, label='Line 1')
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ax.plot(x, y2, label='Line 2')
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ax.fill_between(x, y1, y2, where=(y2 >= y1),
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alpha=0.3, label='y2 > y1', interpolate=True)
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ax.legend()
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```
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## 12. 3D Plots
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**Use cases:** Three-dimensional data visualization
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### 3D Scatter
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```python
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from mpl_toolkits.mplot3d import Axes3D
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fig = plt.figure(figsize=(10, 8))
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ax = fig.add_subplot(111, projection='3d')
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scatter = ax.scatter(x, y, z, c=colors, cmap='viridis',
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marker='o', s=50)
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plt.colorbar(scatter, ax=ax)
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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ax.set_zlabel('Z')
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```
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### 3D Surface Plot
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```python
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fig = plt.figure(figsize=(10, 8))
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ax = fig.add_subplot(111, projection='3d')
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surf = ax.plot_surface(X, Y, Z, cmap='viridis',
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edgecolor='none', alpha=0.9)
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plt.colorbar(surf, ax=ax)
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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ax.set_zlabel('Z')
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```
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### 3D Wireframe
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```python
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fig = plt.figure(figsize=(10, 8))
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ax = fig.add_subplot(111, projection='3d')
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ax.plot_wireframe(X, Y, Z, color='black', linewidth=0.5)
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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ax.set_zlabel('Z')
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```
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### 3D Contour
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```python
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fig = plt.figure(figsize=(10, 8))
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ax = fig.add_subplot(111, projection='3d')
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ax.contour(X, Y, Z, levels=15, cmap='viridis')
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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ax.set_zlabel('Z')
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```
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## 13. Specialized Plots
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### Stem Plot
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```python
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ax.stem(x, y, linefmt='C0-', markerfmt='C0o', basefmt='k-')
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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```
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### Filled Polygon
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```python
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vertices = [(0, 0), (1, 0), (1, 1), (0, 1)]
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from matplotlib.patches import Polygon
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polygon = Polygon(vertices, closed=True, edgecolor='black',
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facecolor='lightblue', alpha=0.5)
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ax.add_patch(polygon)
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ax.set_xlim(-0.5, 1.5)
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ax.set_ylim(-0.5, 1.5)
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```
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### Staircase Plot
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```python
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ax.stairs(values, edges, fill=True, alpha=0.5)
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```
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### Broken Barh (Gantt-style)
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```python
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ax.broken_barh([(10, 50), (100, 20), (130, 10)], (10, 9),
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facecolors='tab:blue')
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ax.broken_barh([(10, 20), (50, 50), (120, 30)], (20, 9),
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facecolors='tab:orange')
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ax.set_ylim(5, 35)
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ax.set_xlim(0, 200)
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ax.set_xlabel('Time')
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ax.set_yticks([15, 25])
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ax.set_yticklabels(['Task 1', 'Task 2'])
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```
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## 14. Time Series Plots
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### Basic Time Series
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```python
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import pandas as pd
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import matplotlib.dates as mdates
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ax.plot(dates, values, linewidth=2)
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
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ax.xaxis.set_major_locator(mdates.DayLocator(interval=7))
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plt.xticks(rotation=45)
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ax.set_xlabel('Date')
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ax.set_ylabel('Value')
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```
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### Time Series with Shaded Regions
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```python
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ax.plot(dates, values, linewidth=2)
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# Shade weekends or specific periods
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ax.axvspan(start_date, end_date, alpha=0.2, color='gray')
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```
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## Plot Selection Guide
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| Data Type | Recommended Plot | Alternative Options |
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|-----------|-----------------|---------------------|
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| Single continuous variable | Histogram, KDE | Box plot, Violin plot |
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| Two continuous variables | Scatter plot | Hexbin, 2D histogram |
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| Time series | Line plot | Area plot, Step plot |
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| Categorical vs continuous | Bar chart, Box plot | Violin plot, Strip plot |
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| Two categorical variables | Heatmap | Grouped bar chart |
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| Three continuous variables | 3D scatter, Contour | Color-coded scatter |
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| Proportions | Bar chart | Pie chart (use sparingly) |
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| Distributions comparison | Box plot, Violin plot | Overlaid histograms |
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| Correlation matrix | Heatmap | Clustered heatmap |
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| Vector field | Quiver plot, Stream plot | - |
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| Function visualization | Line plot, Contour | 3D surface |
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