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references/plot_types.md

Matplotlib Plot Types Guide

Comprehensive guide to different plot types in matplotlib with examples and use cases.

1. Line Plots

Use cases: Time series, continuous data, trends, function visualization

Basic Line Plot

fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(x, y, linewidth=2, label='Data')
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
ax.legend()

Multiple Lines

ax.plot(x, y1, label='Dataset 1', linewidth=2)
ax.plot(x, y2, label='Dataset 2', linewidth=2, linestyle='--')
ax.plot(x, y3, label='Dataset 3', linewidth=2, linestyle=':')
ax.legend()

Line with Markers

ax.plot(x, y, marker='o', markersize=8, linestyle='-',
        linewidth=2, markerfacecolor='red', markeredgecolor='black')

Step Plot

ax.step(x, y, where='mid', linewidth=2, label='Step function')
# where options: 'pre', 'post', 'mid'

Error Bars

ax.errorbar(x, y, yerr=error, fmt='o-', linewidth=2,
            capsize=5, capthick=2, label='With uncertainty')

2. Scatter Plots

Use cases: Correlations, relationships between variables, clusters, outliers

Basic Scatter

ax.scatter(x, y, s=50, alpha=0.6)

Sized and Colored Scatter

scatter = ax.scatter(x, y, s=sizes*100, c=colors,
                     cmap='viridis', alpha=0.6, edgecolors='black')
plt.colorbar(scatter, ax=ax, label='Color variable')

Categorical Scatter

for category in categories:
    mask = data['category'] == category
    ax.scatter(data[mask]['x'], data[mask]['y'],
               label=category, s=50, alpha=0.7)
ax.legend()

3. Bar Charts

Use cases: Categorical comparisons, discrete data, counts

Vertical Bar Chart

ax.bar(categories, values, color='steelblue',
       edgecolor='black', linewidth=1.5)
ax.set_ylabel('Values')

Horizontal Bar Chart

ax.barh(categories, values, color='coral',
        edgecolor='black', linewidth=1.5)
ax.set_xlabel('Values')

Grouped Bar Chart

x = np.arange(len(categories))
width = 0.35

ax.bar(x - width/2, values1, width, label='Group 1')
ax.bar(x + width/2, values2, width, label='Group 2')
ax.set_xticks(x)
ax.set_xticklabels(categories)
ax.legend()

Stacked Bar Chart

ax.bar(categories, values1, label='Part 1')
ax.bar(categories, values2, bottom=values1, label='Part 2')
ax.bar(categories, values3, bottom=values1+values2, label='Part 3')
ax.legend()

Bar Chart with Error Bars

ax.bar(categories, values, yerr=errors, capsize=5,
       color='steelblue', edgecolor='black')

Bar Chart with Patterns

bars1 = ax.bar(x - width/2, values1, width, label='Group 1',
               color='white', edgecolor='black', hatch='//')
bars2 = ax.bar(x + width/2, values2, width, label='Group 2',
               color='white', edgecolor='black', hatch='\\\\')

4. Histograms

Use cases: Distributions, frequency analysis

Basic Histogram

ax.hist(data, bins=30, edgecolor='black', alpha=0.7)
ax.set_xlabel('Value')
ax.set_ylabel('Frequency')

Multiple Overlapping Histograms

ax.hist(data1, bins=30, alpha=0.5, label='Dataset 1')
ax.hist(data2, bins=30, alpha=0.5, label='Dataset 2')
ax.legend()

Normalized Histogram (Density)

ax.hist(data, bins=30, density=True, alpha=0.7,
        edgecolor='black', label='Empirical')

# Overlay theoretical distribution
from scipy.stats import norm
x = np.linspace(data.min(), data.max(), 100)
ax.plot(x, norm.pdf(x, data.mean(), data.std()),
        'r-', linewidth=2, label='Normal fit')
ax.legend()

2D Histogram (Hexbin)

hexbin = ax.hexbin(x, y, gridsize=30, cmap='Blues')
plt.colorbar(hexbin, ax=ax, label='Counts')

2D Histogram (hist2d)

h = ax.hist2d(x, y, bins=30, cmap='Blues')
plt.colorbar(h[3], ax=ax, label='Counts')

5. Box and Violin Plots

Use cases: Statistical distributions, outlier detection, comparing distributions

Box Plot

ax.boxplot([data1, data2, data3],
           labels=['Group A', 'Group B', 'Group C'],
           showmeans=True, meanline=True)
ax.set_ylabel('Values')

Horizontal Box Plot

ax.boxplot([data1, data2, data3], vert=False,
           labels=['Group A', 'Group B', 'Group C'])
ax.set_xlabel('Values')

Violin Plot

parts = ax.violinplot([data1, data2, data3],
                      positions=[1, 2, 3],
                      showmeans=True, showmedians=True)
ax.set_xticks([1, 2, 3])
ax.set_xticklabels(['Group A', 'Group B', 'Group C'])

6. Heatmaps

Use cases: Matrix data, correlations, intensity maps

Basic Heatmap

im = ax.imshow(matrix, cmap='coolwarm', aspect='auto')
plt.colorbar(im, ax=ax, label='Values')
ax.set_xlabel('X')
ax.set_ylabel('Y')

Heatmap with Annotations

im = ax.imshow(matrix, cmap='coolwarm')
plt.colorbar(im, ax=ax)

# Add text annotations
for i in range(matrix.shape[0]):
    for j in range(matrix.shape[1]):
        text = ax.text(j, i, f'{matrix[i, j]:.2f}',
                       ha='center', va='center', color='black')

Correlation Matrix

corr = data.corr()
im = ax.imshow(corr, cmap='RdBu_r', vmin=-1, vmax=1)
plt.colorbar(im, ax=ax, label='Correlation')

# Set tick labels
ax.set_xticks(range(len(corr)))
ax.set_yticks(range(len(corr)))
ax.set_xticklabels(corr.columns, rotation=45, ha='right')
ax.set_yticklabels(corr.columns)

7. Contour Plots

Use cases: 3D data on 2D plane, topography, function visualization

Contour Lines

contour = ax.contour(X, Y, Z, levels=10, cmap='viridis')
ax.clabel(contour, inline=True, fontsize=8)
plt.colorbar(contour, ax=ax)

Filled Contours

contourf = ax.contourf(X, Y, Z, levels=20, cmap='viridis')
plt.colorbar(contourf, ax=ax)

Combined Contours

contourf = ax.contourf(X, Y, Z, levels=20, cmap='viridis', alpha=0.8)
contour = ax.contour(X, Y, Z, levels=10, colors='black',
                     linewidths=0.5, alpha=0.4)
ax.clabel(contour, inline=True, fontsize=8)
plt.colorbar(contourf, ax=ax)

8. Pie Charts

Use cases: Proportions, percentages (use sparingly)

Basic Pie Chart

ax.pie(sizes, labels=labels, autopct='%1.1f%%',
       startangle=90, colors=colors)
ax.axis('equal')  # Equal aspect ratio ensures circular pie

Exploded Pie Chart

explode = (0.1, 0, 0, 0)  # Explode first slice
ax.pie(sizes, explode=explode, labels=labels,
       autopct='%1.1f%%', shadow=True, startangle=90)
ax.axis('equal')

Donut Chart

ax.pie(sizes, labels=labels, autopct='%1.1f%%',
       wedgeprops=dict(width=0.5), startangle=90)
ax.axis('equal')

9. Polar Plots

Use cases: Cyclic data, directional data, radar charts

Basic Polar Plot

theta = np.linspace(0, 2*np.pi, 100)
r = np.abs(np.sin(2*theta))

ax = plt.subplot(111, projection='polar')
ax.plot(theta, r, linewidth=2)

Radar Chart

categories = ['A', 'B', 'C', 'D', 'E']
values = [4, 3, 5, 2, 4]

# Add first value to the end to close the polygon
angles = np.linspace(0, 2*np.pi, len(categories), endpoint=False)
values_closed = np.concatenate((values, [values[0]]))
angles_closed = np.concatenate((angles, [angles[0]]))

ax = plt.subplot(111, projection='polar')
ax.plot(angles_closed, values_closed, 'o-', linewidth=2)
ax.fill(angles_closed, values_closed, alpha=0.25)
ax.set_xticks(angles)
ax.set_xticklabels(categories)

10. Stream and Quiver Plots

Use cases: Vector fields, flow visualization

Quiver Plot (Vector Field)

ax.quiver(X, Y, U, V, alpha=0.8)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_aspect('equal')

Stream Plot

ax.streamplot(X, Y, U, V, density=1.5, color='k', linewidth=1)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_aspect('equal')

11. Fill Between

Use cases: Uncertainty bounds, confidence intervals, areas under curves

Fill Between Two Curves

ax.plot(x, y, 'k-', linewidth=2, label='Mean')
ax.fill_between(x, y - std, y + std, alpha=0.3,
                label='±1 std dev')
ax.legend()

Fill Between with Condition

ax.plot(x, y1, label='Line 1')
ax.plot(x, y2, label='Line 2')
ax.fill_between(x, y1, y2, where=(y2 >= y1),
                alpha=0.3, label='y2 > y1', interpolate=True)
ax.legend()

12. 3D Plots

Use cases: Three-dimensional data visualization

3D Scatter

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
scatter = ax.scatter(x, y, z, c=colors, cmap='viridis',
                     marker='o', s=50)
plt.colorbar(scatter, ax=ax)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

3D Surface Plot

fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
surf = ax.plot_surface(X, Y, Z, cmap='viridis',
                       edgecolor='none', alpha=0.9)
plt.colorbar(surf, ax=ax)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

3D Wireframe

fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
ax.plot_wireframe(X, Y, Z, color='black', linewidth=0.5)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

3D Contour

fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
ax.contour(X, Y, Z, levels=15, cmap='viridis')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

13. Specialized Plots

Stem Plot

ax.stem(x, y, linefmt='C0-', markerfmt='C0o', basefmt='k-')
ax.set_xlabel('X')
ax.set_ylabel('Y')

Filled Polygon

vertices = [(0, 0), (1, 0), (1, 1), (0, 1)]
from matplotlib.patches import Polygon
polygon = Polygon(vertices, closed=True, edgecolor='black',
                  facecolor='lightblue', alpha=0.5)
ax.add_patch(polygon)
ax.set_xlim(-0.5, 1.5)
ax.set_ylim(-0.5, 1.5)

Staircase Plot

ax.stairs(values, edges, fill=True, alpha=0.5)

Broken Barh (Gantt-style)

ax.broken_barh([(10, 50), (100, 20), (130, 10)], (10, 9),
               facecolors='tab:blue')
ax.broken_barh([(10, 20), (50, 50), (120, 30)], (20, 9),
               facecolors='tab:orange')
ax.set_ylim(5, 35)
ax.set_xlim(0, 200)
ax.set_xlabel('Time')
ax.set_yticks([15, 25])
ax.set_yticklabels(['Task 1', 'Task 2'])

14. Time Series Plots

Basic Time Series

import pandas as pd
import matplotlib.dates as mdates

ax.plot(dates, values, linewidth=2)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax.xaxis.set_major_locator(mdates.DayLocator(interval=7))
plt.xticks(rotation=45)
ax.set_xlabel('Date')
ax.set_ylabel('Value')

Time Series with Shaded Regions

ax.plot(dates, values, linewidth=2)
# Shade weekends or specific periods
ax.axvspan(start_date, end_date, alpha=0.2, color='gray')

Plot Selection Guide

Data Type Recommended Plot Alternative Options
Single continuous variable Histogram, KDE Box plot, Violin plot
Two continuous variables Scatter plot Hexbin, 2D histogram
Time series Line plot Area plot, Step plot
Categorical vs continuous Bar chart, Box plot Violin plot, Strip plot
Two categorical variables Heatmap Grouped bar chart
Three continuous variables 3D scatter, Contour Color-coded scatter
Proportions Bar chart Pie chart (use sparingly)
Distributions comparison Box plot, Violin plot Overlaid histograms
Correlation matrix Heatmap Clustered heatmap
Vector field Quiver plot, Stream plot -
Function visualization Line plot, Contour 3D surface
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