Matplotlib Cheatsheet¶

Installation¶

Platform	Command
Ubuntu/Debian	`sudo apt install python3-matplotlib` or `pip3 install matplotlib`
RHEL/Fedora	`sudo dnf install python3-matplotlib` or `pip3 install matplotlib`
macOS	`brew install matplotlib` or `pip3 install matplotlib`
Windows	`pip install matplotlib`
Conda (All)	`conda install matplotlib`
Virtual Env	`python -m venv myenv && source myenv/bin/activate && pip install matplotlib`
With All Features	`pip install matplotlib[all]`
Verify Install	`python -c "import matplotlib; print(matplotlib.__version__)"`

Basic Commands¶

Command	Description
`import matplotlib.pyplot as plt`	Import pyplot module (standard convention)
`plt.plot(x, y)`	Create a basic line plot
`plt.scatter(x, y)`	Create a scatter plot
`plt.bar(x, height)`	Create a vertical bar chart
`plt.barh(y, width)`	Create a horizontal bar chart
`plt.hist(data, bins=30)`	Create a histogram with 30 bins
`plt.xlabel('Label')`	Set x-axis label
`plt.ylabel('Label')`	Set y-axis label
`plt.title('Title')`	Set plot title
`plt.legend()`	Display legend for labeled data
`plt.grid()`	Add grid lines to plot
`plt.xlim(min, max)`	Set x-axis limits
`plt.ylim(min, max)`	Set y-axis limits
`plt.show()`	Display the plot in a window
`plt.savefig('file.png')`	Save plot to file
`plt.close()`	Close current figure and free memory
`plt.figure(figsize=(10, 6))`	Create new figure with size in inches
`fig, ax = plt.subplots()`	Create figure and single axes (OO interface)
`fig, axes = plt.subplots(2, 2)`	Create 2x2 grid of subplots
`plt.subplot(2, 2, 1)`	Create subplot in 2x2 grid, position 1

Plot Customization¶

Command	Description
`plt.plot(x, y, 'r--')`	Red dashed line (color + linestyle shorthand)
`plt.plot(x, y, linewidth=2)`	Set line width
`plt.plot(x, y, color='#FF5733')`	Set color using hex code
`plt.plot(x, y, marker='o')`	Add circular markers at data points
`plt.plot(x, y, markersize=10)`	Set marker size
`plt.plot(x, y, alpha=0.5)`	Set transparency (0=transparent, 1=opaque)
`plt.plot(x, y, label='Data')`	Add label for legend
`plt.scatter(x, y, s=100, c=colors)`	Scatter with size and color arrays
`plt.scatter(x, y, cmap='viridis')`	Use colormap for scatter colors
`plt.xticks(rotation=45)`	Rotate x-axis tick labels 45 degrees
`plt.xticks([0, 1, 2], ['A', 'B', 'C'])`	Set custom tick positions and labels
`plt.grid(alpha=0.3, linestyle='--')`	Customize grid appearance
`plt.legend(loc='upper right')`	Position legend in specific location
`plt.tight_layout()`	Adjust spacing to prevent label overlap
`plt.axis('equal')`	Equal aspect ratio for x and y axes

Advanced Usage¶

Command	Description
`plt.errorbar(x, y, yerr=err)`	Plot with error bars
`plt.fill_between(x, y1, y2)`	Fill area between two curves
`plt.contour(X, Y, Z)`	Create contour plot from 2D data
`plt.contourf(X, Y, Z, levels=20)`	Filled contour plot with 20 levels
`plt.imshow(data, cmap='hot')`	Display 2D array as image/heatmap
`plt.colorbar()`	Add colorbar to current plot
`plt.boxplot(data)`	Create box and whisker plot
`plt.violinplot(data)`	Create violin plot (distribution shape)
`plt.pie(sizes, labels=labels, autopct='%1.1f%%')`	Pie chart with percentage labels
`ax.twinx()`	Create second y-axis sharing same x-axis
`plt.semilogx(x, y)`	Plot with logarithmic x-axis
`plt.semilogy(x, y)`	Plot with logarithmic y-axis
`plt.loglog(x, y)`	Plot with both axes logarithmic
`plt.step(x, y, where='mid')`	Step plot with steps at midpoints
`plt.stem(x, y)`	Create stem plot (lollipop chart)
`plt.hexbin(x, y, gridsize=30)`	Hexagonal binning plot
`plt.streamplot(X, Y, U, V)`	Streamlines for vector field
`plt.quiver(X, Y, U, V)`	Arrow plot for vector field
`ax.set_xscale('log')`	Set x-axis to logarithmic scale (OO)
`ax.annotate('text', xy=(x,y))`	Add annotation at specific point

3D Plotting¶

Command	Description
`from mpl_toolkits.mplot3d import Axes3D`	Import 3D plotting toolkit
`ax = fig.add_subplot(111, projection='3d')`	Create 3D axes
`ax.plot3D(x, y, z)`	3D line plot
`ax.scatter3D(x, y, z)`	3D scatter plot
`ax.plot_surface(X, Y, Z)`	3D surface plot
`ax.plot_wireframe(X, Y, Z)`	3D wireframe plot
`ax.contour3D(X, Y, Z)`	3D contour plot
`ax.set_xlabel('X')`	Set 3D x-axis label
`ax.view_init(elev=30, azim=45)`	Set 3D viewing angle

Object-Oriented Interface¶

Command	Description
`fig, ax = plt.subplots()`	Create figure and axes objects
`ax.plot(x, y)`	Plot using axes object
`ax.set_xlabel('Label')`	Set x-axis label (OO method)
`ax.set_ylabel('Label')`	Set y-axis label (OO method)
`ax.set_title('Title')`	Set title (OO method)
`ax.set_xlim(min, max)`	Set x-axis limits (OO method)
`ax.set_ylim(min, max)`	Set y-axis limits (OO method)
`ax.legend()`	Add legend (OO method)
`ax.grid(True)`	Enable grid (OO method)
`ax.set_aspect('equal')`	Set aspect ratio
`fig.suptitle('Main Title')`	Set overall figure title
`fig.tight_layout()`	Adjust subplot spacing
`ax.spines['top'].set_visible(False)`	Hide top spine/border
`ax.tick_params(labelsize=12)`	Customize tick parameters

Configuration¶

Runtime Configuration (rcParams)¶

import matplotlib.pyplot as plt

# Set default figure size
plt.rcParams['figure.figsize'] = (10, 6)

# Set default DPI
plt.rcParams['figure.dpi'] = 100

# Set default font size
plt.rcParams['font.size'] = 12

# Set default line width
plt.rcParams['lines.linewidth'] = 2

# Set default colormap
plt.rcParams['image.cmap'] = 'viridis'

# Use LaTeX for text rendering
plt.rcParams['text.usetex'] = True

# Set backend
plt.rcParams['backend'] = 'TkAgg'

Style Sheets¶

# Use built-in style
plt.style.use('seaborn-v0_8')
plt.style.use('ggplot')
plt.style.use('dark_background')

# List available styles
print(plt.style.available)

# Use multiple styles (later ones override earlier)
plt.style.use(['seaborn-v0_8', 'seaborn-v0_8-poster'])

# Temporary style context
with plt.style.context('dark_background'):
    plt.plot(x, y)

Configuration File (matplotlibrc)¶

Create ~/.config/matplotlib/matplotlibrc (Linux/macOS) or %USERPROFILE%\.matplotlib\matplotlibrc (Windows):

# Figure properties
figure.figsize: 10, 6
figure.dpi: 100

# Font properties
font.size: 12
font.family: sans-serif

# Line properties
lines.linewidth: 2
lines.markersize: 8

# Grid properties
grid.alpha: 0.3
grid.linestyle: --

# Legend properties
legend.frameon: False
legend.loc: best

# Axes properties
axes.grid: True
axes.titlesize: 14
axes.labelsize: 12

Common Use Cases¶

Use Case 1: Multiple Line Plots with Legend¶

import matplotlib.pyplot as plt
import numpy as np

# Generate data
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)
y3 = np.sin(x) * np.exp(-x/10)

# Create plot
plt.figure(figsize=(10, 6))
plt.plot(x, y1, 'b-', label='sin(x)', linewidth=2)
plt.plot(x, y2, 'r--', label='cos(x)', linewidth=2)
plt.plot(x, y3, 'g-.', label='damped sin(x)', linewidth=2)

# Customize
plt.xlabel('X axis', fontsize=12)
plt.ylabel('Y axis', fontsize=12)
plt.title('Trigonometric Functions', fontsize=14)
plt.legend(loc='upper right')
plt.grid(True, alpha=0.3)

# Save and display
plt.savefig('trig_functions.png', dpi=300, bbox_inches='tight')
plt.show()

Use Case 2: Subplots with Different Plot Types¶

import matplotlib.pyplot as plt
import numpy as np

# Generate data
x = np.linspace(0, 10, 50)
y = 2 * x + 1 + np.random.randn(50) * 2
categories = ['A', 'B', 'C', 'D']
values = [23, 45, 56, 78]

# Create 2x2 subplot grid
fig, axes = plt.subplots(2, 2, figsize=(12, 10))

# Scatter plot
axes[0, 0].scatter(x, y, alpha=0.6)
axes[0, 0].set_title('Scatter Plot')
axes[0, 0].set_xlabel('X')
axes[0, 0].set_ylabel('Y')

# Line plot
axes[0, 1].plot(x, np.sin(x), 'r-', linewidth=2)
axes[0, 1].set_title('Line Plot')
axes[0, 1].grid(True)

# Bar chart
axes[1, 0].bar(categories, values, color='skyblue', edgecolor='black')
axes[1, 0].set_title('Bar Chart')
axes[1, 0].set_ylabel('Values')

# Histogram
axes[1, 1].hist(y, bins=15, color='green', alpha=0.7, edgecolor='black')
axes[1, 1].set_title('Histogram')
axes[1, 1].set_xlabel('Value')

# Adjust layout and save
plt.tight_layout()
plt.savefig('subplots_example.png', dpi=300)
plt.show()

Use Case 3: Heatmap with Colorbar¶

import matplotlib.pyplot as plt
import numpy as np

# Generate 2D data
data = np.random.randn(10, 10)

# Create heatmap
fig, ax = plt.subplots(figsize=(8, 6))
im = ax.imshow(data, cmap='RdYlBu_r', aspect='auto')

# Add colorbar
cbar = plt.colorbar(im, ax=ax)
cbar.set_label('Values', rotation=270, labelpad=20)

# Customize ticks
ax.set_xticks(np.arange(10))
ax.set_yticks(np.arange(10))
ax.set_xticklabels([f'Col {i}' for i in range(10)])
ax.set_yticklabels([f'Row {i}' for i in range(10)])

# Rotate x-axis labels
plt.setp(ax.get_xticklabels(), rotation=45, ha='right')

# Add title
ax.set_title('Heatmap Example')

plt.tight_layout()
plt.savefig('heatmap.png', dpi=300, bbox_inches='tight')
plt.show()

Use Case 4: Time Series with Dual Y-Axes¶

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# Generate time series data
dates = pd.date_range('2023-01-01', periods=100)
temperature = 20 + 10 * np.sin(np.linspace(0, 4*np.pi, 100)) + np.random.randn(100)
humidity = 60 + 20 * np.cos(np.linspace(0, 4*np.pi, 100)) + np.random.randn(100) * 5

# Create figure with primary axis
fig, ax1 = plt.subplots(figsize=(12, 6))

# Plot temperature on primary y-axis
color = 'tab:red'
ax1.set_xlabel('Date')
ax1.set_ylabel('Temperature (°C)', color=color)
ax1.plot(dates, temperature, color=color, linewidth=2, label='Temperature')
ax1.tick_params(axis='y', labelcolor=color)

# Create secondary y-axis
ax2 = ax1.twinx()
color = 'tab:blue'
ax2.set_ylabel('Humidity (%)', color=color)
ax2.plot(dates, humidity, color=color, linewidth=2, linestyle='--', label='Humidity')
ax2.tick_params(axis='y', labelcolor=color)

# Add title and grid
plt.title('Temperature and Humidity Over Time')
ax1.grid(True, alpha=0.3)

# Format x-axis dates
fig.autofmt_xdate()

plt.tight_layout()
plt.savefig('dual_axis_timeseries.png', dpi=300)
plt.show()

Use Case 5: Statistical Visualization with Error Bars¶

import matplotlib.pyplot as plt
import numpy as np

# Generate data with error
x = np.arange(0, 10, 1)
y = 2 * x + 5
error = np.random.uniform(0.5, 2.0, len(x))

# Create error bar plot
fig, ax = plt.subplots(figsize=(10, 6))
ax.errorbar(x, y, yerr=error, fmt='o-', capsize=5, capthick=2, 
            ecolor='red', linewidth=2, markersize=8, 
            label='Measured Data')

# Add fitted line
z = np.polyfit(x, y, 1)
p = np.poly1d(z)
ax.plot(x, p(x), 'g--', linewidth=2, label='Fitted Line')

# Customize
ax.set_xlabel('X Variable', fontsize=12)
ax.set_ylabel('Y Variable', fontsize=12)
ax.set_title('Measurements with Error Bars', fontsize=14)
ax.legend()
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('error_bars.png', dpi=300)
plt.show()

Best Practices¶

Use Object-Oriented Interface: For complex plots, use fig, ax = plt.subplots() instead of pyplot state machine for better control and clarity
Close Figures: Call plt.close() after saving plots in scripts to free memory, especially when generating many plots
Set Figure Size Early: Define figsize when creating figures to ensure proper layout before adding elements
Use Vectorized Formats: Save publication figures as PDF or SVG (plt.savefig('plot.pdf')) for scalability and quality
Leverage Style Sheets: Use plt.style.use() for consistent styling across multiple plots instead of setting parameters individually
Label Everything: Always include axis labels, titles, and legends to make plots self-explanatory
Use Tight Layout: Call plt.tight_layout() or bbox_inches='tight' when saving to prevent label cutoff
Choose Appropriate Colormaps: Use perceptually uniform colormaps like 'viridis', 'plasma', or 'cividis' for scientific data
Optimize DPI: Use dpi=300 for print quality, dpi=100 for screen display to balance quality and file size
Batch Processing: When creating multiple plots, wrap in functions and use context managers to ensure proper resource cleanup

Troubleshooting¶

Issue	Solution
Plot doesn't display	Call `plt.show()` at the end, or check if running in non-interactive environment. Use `plt.ion()` for interactive mode
"Matplotlib is currently using agg" backend error	Install GUI backend: `pip install pyqt5` or set backend: `plt.switch_backend('TkAgg')`
Labels cut off when saving	Use `plt.tight_layout()` before saving or `plt.savefig('file.png', bbox_inches='tight')`
Memory leak in loop	Call `plt.close()` or `plt.close('all')` after each plot to free memory
Import error on macOS	Reinstall with framework build: `pip uninstall matplotlib && pip install matplotlib` or use conda
Blurry plots	Increase DPI: `plt.savefig('plot.png', dpi=300)` or set `plt.rcParams['figure.dpi'] = 300`
Unicode/LaTeX errors	Disable LaTeX: `plt.rcParams['text.usetex'] = False` or install LaTeX distribution
Slow rendering	Reduce data points, use `rasterized=True` for large scatter plots, or switch backend
Legend overlaps data	Use `plt.legend(loc='best')` for auto-positioning or `bbox_to_anchor` for manual placement
Subplot spacing issues	Use `plt.tight_layout()` or manually adjust with `plt.subplots_adjust(hspace=0.3, wspace=0.3)`
Color not changing	Check if using correct parameter name: `color` or `c`, ensure color string is valid
3D plot not working	Import toolkit: `from mpl_toolkits.mplot3d import Axes3D` and use `projection='3d'`