Matplotlib Plot Types Guide

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