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Seaborn Cheatsheet¶

• Installation

Platform	Command
Ubuntu/Debian	INLINE_CODE_10 or INLINE_CODE_11
macOS	INLINE_CODE_12 or INLINE_CODE_13
Windows	INLINE_CODE_14 or INLINE_CODE_15
Conda (all platforms)	INLINE_CODE_16
Specific version	INLINE_CODE_17
With dependencies	INLINE_CODE_18
Upgrade existing	INLINE_CODE_19
Verify installation	INLINE_CODE_20
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oder Grundlegende Befehle

Command	Description
INLINE_CODE_21	Standard import convention
INLINE_CODE_22	Apply default Seaborn styling
INLINE_CODE_23	Set plot style (whitegrid, darkgrid, white, dark, ticks)
INLINE_CODE_24	Set scaling context (paper, notebook, talk, poster)
INLINE_CODE_25	Create histogram with kernel density estimate
INLINE_CODE_26	Plot kernel density estimate
INLINE_CODE_27	Create box plot for categorical data
INLINE_CODE_28	Create violin plot (box + KDE)
INLINE_CODE_29	Create scatter plot with color grouping
INLINE_CODE_30	Create line plot with confidence intervals
INLINE_CODE_31	Create bar plot with error bars
INLINE_CODE_32	Count occurrences of categorical values
INLINE_CODE_33	Create annotated correlation heatmap
INLINE_CODE_34	Create scatter plot matrix for all variables
INLINE_CODE_35	Scatter plot with linear regression line
INLINE_CODE_36	Categorical scatter plot (all points visible)
INLINE_CODE_37	Non-overlapping categorical scatter plot
INLINE_CODE_38	Scatter plot with marginal distributions
INLINE_CODE_39	Get current color palette
INLINE_CODE_40	Set color palette (husl, Set1, Set2, deep, etc.)
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/ Fortgeschrittene Nutzung

Command	Description
INLINE_CODE_41	Regression plot with faceting by columns
INLINE_CODE_42	Categorical plot with faceting
INLINE_CODE_43	Relational plot with row/column faceting
INLINE_CODE_44	Hierarchical clustered heatmap
INLINE_CODE_45	2D bivariate KDE plot
INLINE_CODE_46	Empirical cumulative distribution function
INLINE_CODE_47	Stacked histogram for multiple categories
INLINE_CODE_48	Split violin plot for comparison
INLINE_CODE_49	Polynomial regression (order 3)
INLINE_CODE_50	Robust regression (outlier-resistant)
INLINE_CODE_51	Plot residuals of regression
INLINE_CODE_52	Point plot with error bars
INLINE_CODE_53	Create diverging color palette
INLINE_CODE_54	Create cubehelix color palette
INLINE_CODE_55	Remove plot spines for cleaner look
INLINE_CODE_56	Add rug plot (marginal tick marks)
INLINE_CODE_57	Custom faceted plot grid
INLINE_CODE_58	Horizontal bar plot with 95% CI
INLINE_CODE_59	Heatmap with centered diverging colors
INLINE_CODE_60	Reposition legend outside plot
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Konfiguration

Theme and Style Configuration¶

# Comprehensive theme setup
sns.set_theme(
    style="darkgrid",           # whitegrid, darkgrid, white, dark, ticks
    palette="deep",             # Color palette
    font="sans-serif",          # Font family
    font_scale=1.2,             # Scale all fonts
    color_codes=True,           # Use color codes
    rc={                        # Matplotlib rcParams
        "figure.figsize": (12, 8),
        "axes.labelsize": 14,
        "axes.titlesize": 16,
        "xtick.labelsize": 12,
        "ytick.labelsize": 12,
        "legend.fontsize": 12
    }
)

Context-Based Scaling¶

# Scale plots for different contexts
sns.set_context(
    "talk",                     # paper, notebook, talk, poster
    font_scale=1.5,
    rc={"lines.linewidth": 2.5}
)

Benutzerdefinierte Farbpaletten¶

# Define custom color palette
custom_colors = ["#FF6B6B", "#4ECDC4", "#45B7D1", "#FFA07A", "#98D8C8"]
sns.set_palette(sns.color_palette(custom_colors))

# Or use built-in palettes
sns.set_palette("Set2", n_colors=8)

Temporärer Stil Kontext¶

# Apply style temporarily
with sns.axes_style("white"):
    sns.boxplot(data=df, x='category', y='value')
    plt.show()
# Original style restored after block

Variable Funktionsparameter¶

# Common parameters for figure-level functions
g = sns.relplot(
    data=df,
    x='x', y='y',
    hue='category',
    col='group',
    col_wrap=3,              # Wrap columns after 3
    height=4,                # Height of each facet
    aspect=1.5,              # Aspect ratio (width = height * aspect)
    palette='viridis',
    legend='full'            # 'auto', 'brief', 'full', False
)

Häufige Anwendungsfälle

Use Case 1: Exploratory Datenanalyse¶

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

# Load data
df = pd.read_csv('data.csv')

# Quick overview with pairplot
sns.pairplot(df, hue='target_variable', diag_kind='kde')
plt.savefig('pairplot.png', dpi=300, bbox_inches='tight')

# Correlation heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(df.corr(), annot=True, fmt='.2f', cmap='coolwarm', center=0)
plt.title('Feature Correlation Matrix')
plt.tight_layout()
plt.savefig('correlation.png', dpi=300)

Use Case 2: Distribution Vergleich¶

# Compare distributions across categories
fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Histogram with KDE
sns.histplot(data=df, x='value', hue='category', kde=True, ax=axes[0, 0])
axes[0, 0].set_title('Distribution with KDE')

# Violin plot
sns.violinplot(data=df, x='category', y='value', ax=axes[0, 1])
axes[0, 1].set_title('Violin Plot')

# Box plot with swarm overlay
sns.boxplot(data=df, x='category', y='value', ax=axes[1, 0])
sns.swarmplot(data=df, x='category', y='value', color='black', alpha=0.3, ax=axes[1, 0])
axes[1, 0].set_title('Box + Swarm Plot')

# ECDF
sns.ecdfplot(data=df, x='value', hue='category', ax=axes[1, 1])
axes[1, 1].set_title('Cumulative Distribution')

plt.tight_layout()
plt.show()

Use Case 3: Time Series Visualisierung¶

# Time series with confidence intervals
df['date'] = pd.to_datetime(df['date'])

# Line plot with shaded confidence interval
plt.figure(figsize=(14, 6))
sns.lineplot(
    data=df,
    x='date',
    y='value',
    hue='category',
    style='category',
    markers=True,
    dashes=False,
    ci=95,
    err_style='band'
)
plt.xticks(rotation=45)
plt.title('Time Series with 95% Confidence Intervals')
plt.tight_layout()
plt.show()

Use Case 4: Multi-Variable Facettenanalyse¶

# Create faceted plot for complex relationships
g = sns.FacetGrid(
    df,
    col='region',
    row='product_type',
    hue='customer_segment',
    height=4,
    aspect=1.2,
    margin_titles=True
)

g.map_dataframe(sns.scatterplot, x='price', y='sales', alpha=0.6)
g.add_legend(title='Customer Segment')
g.set_axis_labels('Price ($)', 'Sales Volume')
g.set_titles(col_template="{col_name}", row_template="{row_name}")
g.tight_layout()
plt.savefig('faceted_analysis.png', dpi=300, bbox_inches='tight')

Use Case 5: Statistische Regressionsanalyse¶

# Multiple regression visualizations
fig, axes = plt.subplots(1, 3, figsize=(18, 5))

# Linear regression
sns.regplot(data=df, x='feature1', y='target', ax=axes[0])
axes[0].set_title('Linear Regression')

# Polynomial regression
sns.regplot(data=df, x='feature2', y='target', order=3, ax=axes[1])
axes[1].set_title('Polynomial Regression (Order 3)')

# Residual plot
sns.residplot(data=df, x='feature1', y='target', ax=axes[2])
axes[2].set_title('Residual Plot')
axes[2].axhline(0, ls='--', color='red', alpha=0.5)

plt.tight_layout()
plt.show()

oder Best Practices

** Verwenden Sie Figuren-Level-Funktionen für Facetten**: Funktionen wie relplot(), __INLINE_CODE_62_ und lmplot() automatisch mit der Erstellung von Figuren und Achsen umgehen, so dass gesichtete Handlungen einfacher zu verwalten
**Wählen Sie entsprechende Grundstückstypen*: Verwenden Sie Geigendiagramme zum Verteilungsvergleich, Felddiagramme zur Ausreißerdetektion, Swarm-Plots für kleine Datensätze und Streifen-Plots für mittlere Datensätze
Verwalten Sie den data Parameter: Verwenden Sie immer den data Parameter mit pandas DataFrames für saubereren Code und bessere Integration mit Daten Manipulation Workflows
**Die Themen früh einrichten*: Rufen Sie sns.set_theme() oder sns.set_style()_ am Anfang Ihres Skripts an, um ein einheitliches Styling über alle Plots sicherzustellen
** Verwenden Sie semantische Mappings*: Nutzen Sie die hue, __INLINE_CODE_69_ und style Parameter, um zusätzliche Dimensionen in Ihrer Visualisierung zu kodieren, ohne separate Plots zu erstellen
**Save hochauflösende Zahlen*: Verwenden plt.savefig('file.png', dpi=300, bbox_inches='tight') für Veröffentlichungsqualitätsbilder mit richtigen Margen
** Kombinieren Sie Seaborn mit Matplotlib**: Verwenden Sie Seaborn für schnelle statistische Plots und Matplotlib für fein abgestimmte Anpassung (Titel, Labels, Anmerkungen, etc.)
**Große Datensätze effizient handhaben*: Bei Datensätzen mit > 10.000 Punkten ist die Verwendung rasterized=True in Streufeldern oder die Aggregation von Daten vor der Erstellung zu berücksichtigen.
** Verwenden Sie Farbpaletten strategisch*: Wählen Sie sequentielle Paletten für bestellte Daten, divergierende Paletten für Daten mit einem aussagekräftigen Zentrum und qualitativen Paletten für kategorische Daten
**Dokumentieren Sie Ihre Farbwahl*: Bei der Erstellung von Visualisierungen für colorblind-Publikum verwenden Sie colorblind-freundliche Paletten wie __INLINE_CODE_73_ oder testen Sie mit sns.color_palette("colorblind")_

Fehlerbehebung

Issue	Solution
Plot not displaying	Add INLINE_CODE_75 at the end or use INLINE_CODE_76 in Jupyter notebooks
Legend overlapping plot	Use INLINE_CODE_77 or INLINE_CODE_78
Categorical axis labels overlapping	Rotate labels with INLINE_CODE_79 or use horizontal plots with INLINE_CODE_80
Heatmap annotations too small	Increase figure size INLINE_CODE_81 or adjust font size INLINE_CODE_82
Color palette not applying	Set palette before creating plot with INLINE_CODE_83 or use INLINE_CODE_84 parameter directly in plot function
FacetGrid plots too small	Increase INLINE_CODE_85 parameter (e.g., INLINE_CODE_86) and adjust INLINE_CODE_87 ratio (e.g., INLINE_CODE_88)
Missing confidence intervals	Ensure sufficient data points; CI requires multiple observations per x-value. Use INLINE_CODE_89 to disable
Seaborn overriding Matplotlib settings	Reset with INLINE_CODE_90 or INLINE_CODE_91 to restore Matplotlib defaults
Memory issues with large datasets	Use INLINE_CODE_92 for scatter plots or downsample data before plotting
KDE plot looks wrong	Adjust INLINE_CODE_93 parameter (e.g., INLINE_CODE_94 for narrower bandwidth) or use INLINE_CODE_95 to limit range
Regression line not fitting data	Try polynomial regression with INLINE_CODE_96 or INLINE_CODE_97, or use INLINE_CODE_98 for outlier resistance
Heatmap colors not centered	Use INLINE_CODE_99 parameter and diverging colormap like INLINE_CODE_100 for data with meaningful zero point
Plot style not persisting	Use INLINE_CODE_101 instead of INLINE_CODE_102 (deprecated) or set INLINE_CODE_103 parameters explicitly
Facet titles cut off	Add INLINE_CODE_104 or use INLINE_CODE_105 for FacetGrid objects
Importfehler nach der Installation	Starten Sie Python kernel/IDE aus oder überprüfen Sie die Installation mit `pip show seaborn` und überprüfen Sie Abhängigkeiten