1. Basic Plot Creation
Single plot workflow:
```python
import matplotlib.pyplot as plt
import numpy as np
# Create figure and axes (OO interface - RECOMMENDED)
fig, ax = plt.subplots(figsize=(10, 6))
# Generate and plot data
x = np.linspace(0, 2*np.pi, 100)
ax.plot(x, np.sin(x), label='sin(x)')
ax.plot(x, np.cos(x), label='cos(x)')
# Customize
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Trigonometric Functions')
ax.legend()
ax.grid(True, alpha=0.3)
# Save and/or display
plt.savefig('plot.png', dpi=300, bbox_inches='tight')
plt.show()
```
2. Multiple Subplots
Creating subplot layouts:
```python
# Method 1: Regular grid
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
axes[0, 0].plot(x, y1)
axes[0, 1].scatter(x, y2)
axes[1, 0].bar(categories, values)
axes[1, 1].hist(data, bins=30)
# Method 2: Mosaic layout (more flexible)
fig, axes = plt.subplot_mosaic([['left', 'right_top'],
['left', 'right_bottom']],
figsize=(10, 8))
axes['left'].plot(x, y)
axes['right_top'].scatter(x, y)
axes['right_bottom'].hist(data)
# Method 3: GridSpec (maximum control)
from matplotlib.gridspec import GridSpec
fig = plt.figure(figsize=(12, 8))
gs = GridSpec(3, 3, figure=fig)
ax1 = fig.add_subplot(gs[0, :]) # Top row, all columns
ax2 = fig.add_subplot(gs[1:, 0]) # Bottom two rows, first column
ax3 = fig.add_subplot(gs[1:, 1:]) # Bottom two rows, last two columns
```
3. Plot Types and Use Cases
Line plots - Time series, continuous data, trends
```python
ax.plot(x, y, linewidth=2, linestyle='--', marker='o', color='blue')
```
Scatter plots - Relationships between variables, correlations
```python
ax.scatter(x, y, s=sizes, c=colors, alpha=0.6, cmap='viridis')
```
Bar charts - Categorical comparisons
```python
ax.bar(categories, values, color='steelblue', edgecolor='black')
# For horizontal bars:
ax.barh(categories, values)
```
Histograms - Distributions
```python
ax.hist(data, bins=30, edgecolor='black', alpha=0.7)
```
Heatmaps - Matrix data, correlations
```python
im = ax.imshow(matrix, cmap='coolwarm', aspect='auto')
plt.colorbar(im, ax=ax)
```
Contour plots - 3D data on 2D plane
```python
contour = ax.contour(X, Y, Z, levels=10)
ax.clabel(contour, inline=True, fontsize=8)
```
Box plots - Statistical distributions
```python
ax.boxplot([data1, data2, data3], labels=['A', 'B', 'C'])
```
Violin plots - Distribution densities
```python
ax.violinplot([data1, data2, data3], positions=[1, 2, 3])
```
For comprehensive plot type examples and variations, refer to references/plot_types.md.
4. Styling and Customization
Color specification methods:
- Named colors:
'red', 'blue', 'steelblue' - Hex codes:
'#FF5733' - RGB tuples:
(0.1, 0.2, 0.3) - Colormaps:
cmap='viridis', cmap='plasma', cmap='coolwarm'
Using style sheets:
```python
plt.style.use('seaborn-v0_8-darkgrid') # Apply predefined style
# Available styles: 'ggplot', 'bmh', 'fivethirtyeight', etc.
print(plt.style.available) # List all available styles
```
Customizing with rcParams:
```python
plt.rcParams['font.size'] = 12
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['axes.titlesize'] = 16
plt.rcParams['xtick.labelsize'] = 10
plt.rcParams['ytick.labelsize'] = 10
plt.rcParams['legend.fontsize'] = 12
plt.rcParams['figure.titlesize'] = 18
```
Text and annotations:
```python
ax.text(x, y, 'annotation', fontsize=12, ha='center')
ax.annotate('important point', xy=(x, y), xytext=(x+1, y+1),
arrowprops=dict(arrowstyle='->', color='red'))
```
For detailed styling options and colormap guidelines, see references/styling_guide.md.
5. Saving Figures
Export to various formats:
```python
# High-resolution PNG for presentations/papers
plt.savefig('figure.png', dpi=300, bbox_inches='tight', facecolor='white')
# Vector format for publications (scalable)
plt.savefig('figure.pdf', bbox_inches='tight')
plt.savefig('figure.svg', bbox_inches='tight')
# Transparent background
plt.savefig('figure.png', dpi=300, bbox_inches='tight', transparent=True)
```
Important parameters:
dpi: Resolution (300 for publications, 150 for web, 72 for screen)bbox_inches='tight': Removes excess whitespacefacecolor='white': Ensures white background (useful for transparent themes)transparent=True: Transparent background
6. Working with 3D Plots
```python
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
# Surface plot
ax.plot_surface(X, Y, Z, cmap='viridis')
# 3D scatter
ax.scatter(x, y, z, c=colors, marker='o')
# 3D line plot
ax.plot(x, y, z, linewidth=2)
# Labels
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
```
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