What are the most common chart types in Matplotlib and when do you use each?

Choosing the right chart type communicates data clearly; choosing the wrong one obscures it. Here are the workhorses of exploratory data analysis:

import matplotlib.pyplot as plt
import numpy as np

fig, axes = plt.subplots(2, 3, figsize=(14, 8))

# 1. Line chart — trends over time or ordered x-axis
ax = axes[0, 0]
ax.plot([1, 2, 3, 4], [10, 15, 13, 18])
ax.set_title('Line: trends')

# 2. Bar chart — comparing discrete categories
ax = axes[0, 1]
ax.bar(['A', 'B', 'C'], [30, 45, 20])
ax.set_title('Bar: categories')

# 3. Scatter plot — relationship between two continuous variables
ax = axes[0, 2]
x = np.random.randn(100); y = x * 0.8 + np.random.randn(100) * 0.5
ax.scatter(x, y, alpha=0.5, c='steelblue')
ax.set_title('Scatter: correlation')

# 4. Histogram — distribution of one continuous variable
ax = axes[1, 0]
ax.hist(np.random.randn(1000), bins=30, color='salmon', edgecolor='white')
ax.set_title('Histogram: distribution')

# 5. Box plot — distribution summary with outliers
ax = axes[1, 1]
ax.boxplot([np.random.randn(100) for _ in range(3)], labels=['G1','G2','G3'])
ax.set_title('Box: spread & outliers')

# 6. Heatmap via imshow — 2-D matrix data (e.g., correlation matrix)
ax = axes[1, 2]
data = np.random.rand(4, 4)
im = ax.imshow(data, cmap='viridis')
plt.colorbar(im, ax=ax)
ax.set_title('Heatmap: 2-D matrix')

fig.tight_layout()
plt.show()

Rule of thumb: line for temporal/ordered data, bar for nominal comparisons, scatter for two-variable relationships, histogram for single-variable distributions, box for group comparisons with outlier context, heatmap for correlation matrices and confusion matrices.

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