Seaborn Cheatsheet¶

Installation¶

Platform	Command
Ubuntu/Debian	`pip install seaborn` or `sudo pip3 install seaborn`
macOS	`pip3 install seaborn` or `conda install seaborn`
Windows	`pip install seaborn` or `conda install seaborn`
Conda (all platforms)	`conda install -c conda-forge seaborn`
Specific version	`pip install seaborn==0.12.2`
With dependencies	`pip install seaborn[all]`
Upgrade existing	`pip install --upgrade seaborn`
Verify installation	`python -c "import seaborn as sns; print(sns.__version__)"`

Basic Commands¶

Command	Description
`import seaborn as sns`	Standard import convention
`sns.set_theme()`	Apply default Seaborn styling
`sns.set_style("whitegrid")`	Set plot style (whitegrid, darkgrid, white, dark, ticks)
`sns.set_context("notebook")`	Set scaling context (paper, notebook, talk, poster)
`sns.histplot(data=df, x='col', kde=True)`	Create histogram with kernel density estimate
`sns.kdeplot(data=df, x='col')`	Plot kernel density estimate
`sns.boxplot(data=df, x='cat', y='val')`	Create box plot for categorical data
`sns.violinplot(data=df, x='cat', y='val')`	Create violin plot (box + KDE)
`sns.scatterplot(data=df, x='x', y='y', hue='cat')`	Create scatter plot with color grouping
`sns.lineplot(data=df, x='time', y='val')`	Create line plot with confidence intervals
`sns.barplot(data=df, x='cat', y='val')`	Create bar plot with error bars
`sns.countplot(data=df, x='cat')`	Count occurrences of categorical values
`sns.heatmap(df.corr(), annot=True)`	Create annotated correlation heatmap
`sns.pairplot(df, hue='species')`	Create scatter plot matrix for all variables
`sns.regplot(data=df, x='x', y='y')`	Scatter plot with linear regression line
`sns.stripplot(data=df, x='cat', y='val')`	Categorical scatter plot (all points visible)
`sns.swarmplot(data=df, x='cat', y='val')`	Non-overlapping categorical scatter plot
`sns.jointplot(data=df, x='x', y='y')`	Scatter plot with marginal distributions
`sns.color_palette()`	Get current color palette
`sns.set_palette("husl")`	Set color palette (husl, Set1, Set2, deep, etc.)

Advanced Usage¶

Command	Description
`sns.lmplot(data=df, x='x', y='y', hue='cat', col='group')`	Regression plot with faceting by columns
`sns.catplot(data=df, x='cat', y='val', kind='violin', col='group')`	Categorical plot with faceting
`sns.relplot(data=df, x='x', y='y', kind='scatter', col='cat', row='group')`	Relational plot with row/column faceting
`sns.clustermap(df.corr(), method='ward', metric='euclidean')`	Hierarchical clustered heatmap
`sns.kdeplot(data=df, x='x', y='y', fill=True, levels=10)`	2D bivariate KDE plot
`sns.ecdfplot(data=df, x='val', hue='cat', stat='proportion')`	Empirical cumulative distribution function
`sns.histplot(data=df, x='val', hue='cat', multiple='stack')`	Stacked histogram for multiple categories
`sns.violinplot(data=df, x='cat', y='val', hue='group', split=True)`	Split violin plot for comparison
`sns.regplot(data=df, x='x', y='y', order=3)`	Polynomial regression (order 3)
`sns.regplot(data=df, x='x', y='y', robust=True)`	Robust regression (outlier-resistant)
`sns.residplot(data=df, x='x', y='y')`	Plot residuals of regression
`sns.pointplot(data=df, x='cat', y='val', hue='group')`	Point plot with error bars
`sns.diverging_palette(250, 10, as_cmap=True)`	Create diverging color palette
`sns.cubehelix_palette(start=.5, rot=-.75)`	Create cubehelix color palette
`sns.despine(left=True, bottom=True)`	Remove plot spines for cleaner look
`sns.rugplot(data=df, x='val')`	Add rug plot (marginal tick marks)
`g = sns.FacetGrid(df, col='cat', row='group'); g.map(plt.scatter, 'x', 'y')`	Custom faceted plot grid
`sns.barplot(data=df, x='val', y='cat', orient='h', ci=95)`	Horizontal bar plot with 95% CI
`sns.heatmap(df, cmap='RdYlGn', center=0, vmin=-1, vmax=1)`	Heatmap with centered diverging colors
`sns.move_legend(ax, "upper left", bbox_to_anchor=(1, 1))`	Reposition legend outside plot

Configuration¶

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}
)

Custom Color Palettes¶

# 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)

Temporary Style Context¶

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

Figure-Level Function Parameters¶

# 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
)

Common Use Cases¶

Use Case 1: Exploratory Data Analysis¶

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 Comparison¶

# 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 Visualization¶

# 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 Faceted Analysis¶

# 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: Statistical Regression Analysis¶

# 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()

Best Practices¶

Use figure-level functions for faceting: Functions like relplot(), catplot(), and lmplot() automatically handle figure and axes creation, making faceted plots easier to manage
Choose appropriate plot types: Use violin plots for distribution comparison, box plots for outlier detection, swarm plots for small datasets, and strip plots for medium-sized datasets
Leverage the data parameter: Always use the data parameter with pandas DataFrames for cleaner code and better integration with data manipulation workflows
Set themes early: Call sns.set_theme() or sns.set_style() at the beginning of your script to ensure consistent styling across all plots
Use semantic mappings: Take advantage of hue, size, and style parameters to encode additional dimensions in your visualizations without creating separate plots
Save high-resolution figures: Use plt.savefig('file.png', dpi=300, bbox_inches='tight') for publication-quality images with proper margins
Combine Seaborn with Matplotlib: Use Seaborn for quick statistical plots and Matplotlib for fine-tuned customization (titles, labels, annotations, etc.)
Handle large datasets efficiently: For datasets with >10,000 points, consider using rasterized=True in scatter plots or aggregating data before plotting
Use color palettes strategically: Choose sequential palettes for ordered data, diverging palettes for data with a meaningful center, and qualitative palettes for categorical data
Document your color choices: When creating visualizations for colorblind audiences, use colorblind-friendly palettes like "colorblind" or test with sns.color_palette("colorblind")

Troubleshooting¶

Issue	Solution
Plot not displaying	Add `plt.show()` at the end or use `%matplotlib inline` in Jupyter notebooks
Legend overlapping plot	Use `sns.move_legend(ax, "upper left", bbox_to_anchor=(1, 1))` or `plt.legend(bbox_to_anchor=(1.05, 1))`
Categorical axis labels overlapping	Rotate labels with `plt.xticks(rotation=45, ha='right')` or use horizontal plots with `orient='h'`
Heatmap annotations too small	Increase figure size `plt.figure(figsize=(12, 10))` or adjust font size `annot_kws={"size": 10}`
Color palette not applying	Set palette before creating plot with `sns.set_palette()` or use `palette` parameter directly in plot function
FacetGrid plots too small	Increase `height` parameter (e.g., `height=5`) and adjust `aspect` ratio (e.g., `aspect=1.5`)
Missing confidence intervals	Ensure sufficient data points; CI requires multiple observations per x-value. Use `ci=None` to disable
Seaborn overriding Matplotlib settings	Reset with `sns.reset_orig()` or `sns.reset_defaults()` to restore Matplotlib defaults
Memory issues with large datasets	Use `rasterized=True` for scatter plots or downsample data before plotting
KDE plot looks wrong	Adjust `bw_adjust` parameter (e.g., `bw_adjust=0.5` for narrower bandwidth) or use `cut=0` to limit range
Regression line not fitting data	Try polynomial regression with `order=2` or `order=3`, or use `robust=True` for outlier resistance
Heatmap colors not centered	Use `center=0` parameter and diverging colormap like `cmap='RdBu_r'` for data with meaningful zero point
Plot style not persisting	Use `sns.set_theme()` instead of `sns.set()` (deprecated) or set `rc` parameters explicitly
Facet titles cut off	Add `plt.tight_layout()` or use `g.tight_layout()` for FacetGrid objects
Import errors after installation	Restart Python kernel/IDE or verify installation with `pip show seaborn` and check dependencies