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Matplotlib Cheatsheet¶

• Installation

Platform	Command
Ubuntu/Debian	INLINE_CODE_8 or INLINE_CODE_9
RHEL/Fedora	INLINE_CODE_10 or INLINE_CODE_11
macOS	INLINE_CODE_12 or INLINE_CODE_13
Windows	INLINE_CODE_14
Conda (All)	INLINE_CODE_15
Virtual Env	INLINE_CODE_16
With All Features	INLINE_CODE_17
Verify Install	INLINE_CODE_18
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oder Grundlegende Befehle

Command	Description
INLINE_CODE_19	Import pyplot module (standard convention)
INLINE_CODE_20	Create a basic line plot
INLINE_CODE_21	Create a scatter plot
INLINE_CODE_22	Create a vertical bar chart
INLINE_CODE_23	Create a horizontal bar chart
INLINE_CODE_24	Create a histogram with 30 bins
INLINE_CODE_25	Set x-axis label
INLINE_CODE_26	Set y-axis label
INLINE_CODE_27	Set plot title
INLINE_CODE_28	Display legend for labeled data
INLINE_CODE_29	Add grid lines to plot
INLINE_CODE_30	Set x-axis limits
INLINE_CODE_31	Set y-axis limits
INLINE_CODE_32	Display the plot in a window
INLINE_CODE_33	Save plot to file
INLINE_CODE_34	Close current figure and free memory
INLINE_CODE_35	Create new figure with size in inches
INLINE_CODE_36	Create figure and single axes (OO interface)
INLINE_CODE_37	Create 2x2 grid of subplots
INLINE_CODE_38	Create subplot in 2x2 grid, position 1
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Individuelle Anpassung

Command	Description
INLINE_CODE_39	Red dashed line (color + linestyle shorthand)
INLINE_CODE_40	Set line width
INLINE_CODE_41	Set color using hex code
INLINE_CODE_42	Add circular markers at data points
INLINE_CODE_43	Set marker size
INLINE_CODE_44	Set transparency (0=transparent, 1=opaque)
INLINE_CODE_45	Add label for legend
INLINE_CODE_46	Scatter with size and color arrays
INLINE_CODE_47	Use colormap for scatter colors
INLINE_CODE_48	Rotate x-axis tick labels 45 degrees
INLINE_CODE_49	Set custom tick positions and labels
INLINE_CODE_50	Customize grid appearance
INLINE_CODE_51	Position legend in specific location
INLINE_CODE_52	Adjust spacing to prevent label overlap
INLINE_CODE_53	Equal aspect ratio for x and y axes
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/ Fortgeschrittene Nutzung

Command	Description
INLINE_CODE_54	Plot with error bars
INLINE_CODE_55	Fill area between two curves
INLINE_CODE_56	Create contour plot from 2D data
INLINE_CODE_57	Filled contour plot with 20 levels
INLINE_CODE_58	Display 2D array as image/heatmap
INLINE_CODE_59	Add colorbar to current plot
INLINE_CODE_60	Create box and whisker plot
INLINE_CODE_61	Create violin plot (distribution shape)
INLINE_CODE_62	Pie chart with percentage labels
INLINE_CODE_63	Create second y-axis sharing same x-axis
INLINE_CODE_64	Plot with logarithmic x-axis
INLINE_CODE_65	Plot with logarithmic y-axis
INLINE_CODE_66	Plot with both axes logarithmic
INLINE_CODE_67	Step plot with steps at midpoints
INLINE_CODE_68	Create stem plot (lollipop chart)
INLINE_CODE_69	Hexagonal binning plot
INLINE_CODE_70	Streamlines for vector field
INLINE_CODE_71	Arrow plot for vector field
INLINE_CODE_72	Set x-axis to logarithmic scale (OO)
INLINE_CODE_73	Add annotation at specific point
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3D Plotting

Command	Description
INLINE_CODE_74	Import 3D plotting toolkit
INLINE_CODE_75	Create 3D axes
INLINE_CODE_76	3D line plot
INLINE_CODE_77	3D scatter plot
INLINE_CODE_78	3D surface plot
INLINE_CODE_79	3D wireframe plot
INLINE_CODE_80	3D contour plot
INLINE_CODE_81	Set 3D x-axis label
INLINE_CODE_82	Set 3D viewing angle
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Objektorientierte Schnittstelle

Command	Description
INLINE_CODE_83	Create figure and axes objects
INLINE_CODE_84	Plot using axes object
INLINE_CODE_85	Set x-axis label (OO method)
INLINE_CODE_86	Set y-axis label (OO method)
INLINE_CODE_87	Set title (OO method)
INLINE_CODE_88	Set x-axis limits (OO method)
INLINE_CODE_89	Set y-axis limits (OO method)
INLINE_CODE_90	Add legend (OO method)
INLINE_CODE_91	Enable grid (OO method)
INLINE_CODE_92	Set aspect ratio
INLINE_CODE_93	Set overall figure title
INLINE_CODE_94	Adjust subplot spacing
INLINE_CODE_95	Hide top spine/border
INLINE_CODE_96	Customize tick parameters
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Konfiguration

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)

Konfigurationsdatei (matplotlibrc)¶

Erstellen ~/.config/matplotlib/matplotlibrc (Linux/macOS) oder %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

Häufige Anwendungsfälle

Use Case 1: Multiple Line Plots mit Legende¶

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 mit verschiedenen Plottypen¶

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 mit 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 mit 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: Statistische Visualisierung mit Fehlern¶

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()

oder Best Practices

**Use Object-Oriented Interface*: Für komplexe Handlungen verwenden Sie __INLINE_CODE_99_ anstelle von Pyplot-Zustandsmaschine für bessere Kontrolle und Klarheit
**Close Figures*: Call __INLINE_CODE_100_ nach dem Speichern von Plots in Skripten zum freien Speicher, vor allem bei der Generierung vieler Plots
Set Abbildungsgröße Früh: Define figsize bei der Erstellung von Zahlen, um ein korrektes Layout vor dem Hinzufügen von Elementen sicherzustellen
**Verwendete Vectorized Formate*: Publikationszahlen als PDF oder SVG (plt.savefig('plot.pdf')) für Skalierbarkeit und Qualität speichern
**Leverage Style Sheets*: Verwenden Sie plt.style.use() zur konsequenten Styling über mehrere Parzellen statt Parameter individuell einzustellen
**Label Everything*: Immer Achsetiketten, Titel und Legenden enthalten, um Grundstücke selbsterklärend zu machen
**Use Tight Layout*: Call plt.tight_layout() oder bbox_inches='tight' beim Speichern von Etikettenabschaltungen
**Wählen Sie entsprechende Farbkarten*: Verwendung perzeptuell einheitlicher Farbkarten wie 'viridis', 'plasma' oder 'cividis' für wissenschaftliche Daten
Optimize DPI: Verwenden Sie dpi=300 für Druckqualität dpi=100 für Bildschirmanzeige zur Balance von Qualität und Dateigröße
**Batch Processing*: Wenn Sie mehrere Parzellen erstellen, Funktionen einpacken und Kontextmanager verwenden, um eine ordnungsgemäße Ressourcenreinigung zu gewährleisten

Fehlerbehebung

Issue	Solution
Plot doesn't display	Call INLINE_CODE_108 at the end, or check if running in non-interactive environment. Use INLINE_CODE_109 for interactive mode
"Matplotlib is currently using agg" backend error	Install GUI backend: INLINE_CODE_110 or set backend: INLINE_CODE_111
Labels cut off when saving	Use INLINE_CODE_112 before saving or INLINE_CODE_113
Memory leak in loop	Call INLINE_CODE_114 or INLINE_CODE_115 after each plot to free memory
Import error on macOS	Reinstall with framework build: INLINE_CODE_116 or use conda
Blurry plots	Increase DPI: INLINE_CODE_117 or set INLINE_CODE_118
Unicode/LaTeX errors	Disable LaTeX: INLINE_CODE_119 or install LaTeX distribution
Slow rendering	Reduce data points, use INLINE_CODE_120 for large scatter plots, or switch backend
Legend overlaps data	Use INLINE_CODE_121 for auto-positioning or INLINE_CODE_122 for manual placement
Subplot spacing issues	Use INLINE_CODE_123 or manually adjust with INLINE_CODE_124
Color not changing	Check if using correct parameter name: INLINE_CODE_125 or INLINE_CODE_126, ensure color string is valid
3D-Plot funktioniert nicht	Import Toolkit: `from mpl_toolkits.mplot3d import Axes3D` und verwenden `projection='3d'`