Seaborn Function Reference

This document provides a comprehensive reference for all major seaborn functions, organized by category.

Relational Plots

scatterplot()

Purpose: Create a scatter plot with points representing individual observations.

Key Parameters:

data - DataFrame, array, or dict of arrays
x, y - Variables for x and y axes
hue - Grouping variable for color encoding
size - Grouping variable for size encoding
style - Grouping variable for marker style
palette - Color palette name or list
hue_order - Order for categorical hue levels
hue_norm - Normalization for numeric hue (tuple or Normalize object)
sizes - Size range for size encoding (tuple or dict)
size_order - Order for categorical size levels
size_norm - Normalization for numeric size
markers - Marker style(s) (string, list, or dict)
style_order - Order for categorical style levels
legend - How to draw legend: "auto", "brief", "full", or False
ax - Matplotlib axes to plot on

Example:

python

sns.scatterplot(data=df, x='height', y='weight',
                hue='gender', size='age', style='smoker',
                palette='Set2', sizes=(20, 200))

lineplot()

Purpose: Draw a line plot with automatic aggregation and confidence intervals for repeated measures.

Key Parameters:

data - DataFrame, array, or dict of arrays
x, y - Variables for x and y axes
hue - Grouping variable for color encoding
size - Grouping variable for line width
style - Grouping variable for line style (dashes)
units - Grouping variable for sampling units (no aggregation within units)
estimator - Function for aggregating across observations (default: mean)
errorbar - Method for error bars: "sd", "se", "pi", ("ci", level), ("pi", level), or None
n_boot - Number of bootstrap iterations for CI computation
seed - Random seed for reproducible bootstrapping
sort - Sort data before plotting
err_style - "band" or "bars" for error representation
err_kws - Additional parameters for error representation
markers - Marker style(s) for emphasizing data points
dashes - Dash style(s) for lines
legend - How to draw legend
ax - Matplotlib axes to plot on

Example:

python

sns.lineplot(data=timeseries, x='time', y='signal',
             hue='condition', style='subject',
             errorbar=('ci', 95), markers=True)

relplot()

Purpose: Figure-level interface for drawing relational plots (scatter or line) onto a FacetGrid.

Key Parameters: All parameters from scatterplot() and lineplot(), plus:

kind - "scatter" or "line"
col - Categorical variable for column facets
row - Categorical variable for row facets
col_wrap - Wrap columns after this many columns
col_order - Order for column facet levels
row_order - Order for row facet levels
height - Height of each facet in inches
aspect - Aspect ratio (width = height * aspect)
facet_kws - Additional parameters for FacetGrid

Example:

python

sns.relplot(data=df, x='time', y='measurement',
            hue='treatment', style='batch',
            col='cell_line', row='timepoint',
            kind='line', height=3, aspect=1.5)

Distribution Plots

histplot()

Purpose: Plot univariate or bivariate histograms with flexible binning.

Key Parameters:

data - DataFrame, array, or dict
x, y - Variables (y optional for bivariate)
hue - Grouping variable
weights - Variable for weighting observations
stat - Aggregate statistic: "count", "frequency", "probability", "percent", "density"
bins - Number of bins, bin edges, or method ("auto", "fd", "doane", "scott", "stone", "rice", "sturges", "sqrt")
binwidth - Width of bins (overrides bins)
binrange - Range for binning (tuple)
discrete - Treat x as discrete (centers bars on values)
cumulative - Compute cumulative distribution
common_bins - Use same bins for all hue levels
common_norm - Normalize across hue levels
multiple - How to handle hue: "layer", "dodge", "stack", "fill"
element - Visual element: "bars", "step", "poly"
fill - Fill bars/elements
shrink - Scale bar width (for multiple="dodge")
kde - Overlay KDE estimate
kde_kws - Parameters for KDE
line_kws - Parameters for step/poly elements
thresh - Minimum count threshold for bins
pthresh - Minimum probability threshold
pmax - Maximum probability for color scaling
log_scale - Log scale for axis (bool or base)
legend - Whether to show legend
ax - Matplotlib axes

Example:

python

sns.histplot(data=df, x='measurement', hue='condition',
             stat='density', bins=30, kde=True,
             multiple='layer', alpha=0.5)

kdeplot()

Purpose: Plot univariate or bivariate kernel density estimates.

Key Parameters:

data - DataFrame, array, or dict
x, y - Variables (y optional for bivariate)
hue - Grouping variable
weights - Variable for weighting observations
palette - Color palette
hue_order - Order for hue levels
hue_norm - Normalization for numeric hue
multiple - How to handle hue: "layer", "stack", "fill"
common_norm - Normalize across hue levels
common_grid - Use same grid for all hue levels
cumulative - Compute cumulative distribution
bw_method - Method for bandwidth: "scott", "silverman", or scalar
bw_adjust - Bandwidth multiplier (higher = smoother)
log_scale - Log scale for axis
levels - Number or values for contour levels (bivariate)
thresh - Minimum density threshold for contours
gridsize - Grid resolution
cut - Extension beyond data extremes (in bandwidth units)
clip - Data range for curve (tuple)
fill - Fill area under curve/contours
legend - Whether to show legend
ax - Matplotlib axes

Example:

python

# Univariate
sns.kdeplot(data=df, x='measurement', hue='condition',
            fill=True, common_norm=False, bw_adjust=1.5)

# Bivariate
sns.kdeplot(data=df, x='var1', y='var2',
            fill=True, levels=10, thresh=0.05)

ecdfplot()

Purpose: Plot empirical cumulative distribution functions.

Key Parameters:

data - DataFrame, array, or dict
x, y - Variables (specify one)
hue - Grouping variable
weights - Variable for weighting observations
stat - "proportion" or "count"
complementary - Plot complementary CDF (1 - ECDF)
palette - Color palette
hue_order - Order for hue levels
hue_norm - Normalization for numeric hue
log_scale - Log scale for axis
legend - Whether to show legend
ax - Matplotlib axes

Example:

python

sns.ecdfplot(data=df, x='response_time', hue='treatment',
             stat='proportion', complementary=False)

rugplot()

Purpose: Plot tick marks showing individual observations along an axis.

Key Parameters:

data - DataFrame, array, or dict
x, y - Variable (specify one)
hue - Grouping variable
height - Height of ticks (proportion of axis)
expand_margins - Add margin space for rug
palette - Color palette
hue_order - Order for hue levels
hue_norm - Normalization for numeric hue
legend - Whether to show legend
ax - Matplotlib axes

Example:

python

sns.rugplot(data=df, x='value', hue='category', height=0.05)

displot()

Purpose: Figure-level interface for distribution plots onto a FacetGrid.

Key Parameters: All parameters from histplot(), kdeplot(), and ecdfplot(), plus:

kind - "hist", "kde", "ecdf"
rug - Add rug plot on marginal axes
rug_kws - Parameters for rug plot
col - Categorical variable for column facets
row - Categorical variable for row facets
col_wrap - Wrap columns
col_order - Order for column facets
row_order - Order for row facets
height - Height of each facet
aspect - Aspect ratio
facet_kws - Additional parameters for FacetGrid

Example:

python

sns.displot(data=df, x='measurement', hue='treatment',
            col='timepoint', kind='kde', fill=True,
            height=3, aspect=1.5, rug=True)

jointplot()

Purpose: Draw a bivariate plot with marginal univariate plots.

Key Parameters:

data - DataFrame
x, y - Variables for x and y axes
hue - Grouping variable
kind - "scatter", "kde", "hist", "hex", "reg", "resid"
height - Size of the figure (square)
ratio - Ratio of joint to marginal axes
space - Space between joint and marginal axes
dropna - Drop missing values
xlim, ylim - Axis limits (tuples)
marginal_ticks - Show ticks on marginal axes
joint_kws - Parameters for joint plot
marginal_kws - Parameters for marginal plots
hue_order - Order for hue levels
palette - Color palette

Example:

python

sns.jointplot(data=df, x='var1', y='var2', hue='group',
              kind='scatter', height=6, ratio=4,
              joint_kws={'alpha': 0.5})

pairplot()

Purpose: Plot pairwise relationships in a dataset.

Key Parameters:

data - DataFrame
hue - Grouping variable for color encoding
hue_order - Order for hue levels
palette - Color palette
vars - Variables to plot (default: all numeric)
x_vars, y_vars - Variables for x and y axes (non-square grid)
kind - "scatter", "kde", "hist", "reg"
diag_kind - "auto", "hist", "kde", None
markers - Marker style(s)
height - Height of each facet
aspect - Aspect ratio
corner - Plot only lower triangle
dropna - Drop missing values
plot_kws - Parameters for non-diagonal plots
diag_kws - Parameters for diagonal plots
grid_kws - Parameters for PairGrid

Example:

python

sns.pairplot(data=df, hue='species', palette='Set2',
             vars=['sepal_length', 'sepal_width', 'petal_length'],
             corner=True, height=2.5)

Categorical Plots

stripplot()

Purpose: Draw a categorical scatterplot with jittered points.

Key Parameters:

data - DataFrame, array, or dict
x, y - Variables (one categorical, one continuous)
hue - Grouping variable
order - Order for categorical levels
hue_order - Order for hue levels
jitter - Amount of jitter: True, float, or False
dodge - Separate hue levels side-by-side
orient - "v" or "h" (usually inferred)
color - Single color for all elements
palette - Color palette
size - Marker size
edgecolor - Marker edge color
linewidth - Marker edge width
native_scale - Use numeric scale for categorical axis
formatter - Formatter for categorical axis
legend - Whether to show legend
ax - Matplotlib axes

Example:

python

sns.stripplot(data=df, x='day', y='total_bill',
              hue='sex', dodge=True, jitter=0.2)

swarmplot()

Purpose: Draw a categorical scatterplot with non-overlapping points.

Key Parameters: Same as stripplot(), except:

No jitter parameter
size - Marker size (important for avoiding overlap)
warn_thresh - Threshold for warning about too many points (default: 0.05)

Note: Computationally intensive for large datasets. Use stripplot for >1000 points.

Example:

python

sns.swarmplot(data=df, x='day', y='total_bill',
              hue='time', dodge=True, size=5)

boxplot()

Purpose: Draw a box plot showing quartiles and outliers.

Key Parameters:

data - DataFrame, array, or dict
x, y - Variables (one categorical, one continuous)
hue - Grouping variable
order - Order for categorical levels
hue_order - Order for hue levels
orient - "v" or "h"
color - Single color for boxes
palette - Color palette
saturation - Color saturation intensity
width - Width of boxes
dodge - Separate hue levels side-by-side
fliersize - Size of outlier markers
linewidth - Box line width
whis - IQR multiplier for whiskers (default: 1.5)
notch - Draw notched boxes
showcaps - Show whisker caps
showmeans - Show mean value
meanprops - Properties for mean marker
boxprops - Properties for boxes
whiskerprops - Properties for whiskers
capprops - Properties for caps
flierprops - Properties for outliers
medianprops - Properties for median line
native_scale - Use numeric scale
formatter - Formatter for categorical axis
legend - Whether to show legend
ax - Matplotlib axes

Example:

python

sns.boxplot(data=df, x='day', y='total_bill',
            hue='smoker', palette='Set3',
            showmeans=True, notch=True)

violinplot()

Purpose: Draw a violin plot combining boxplot and KDE.

Key Parameters: Same as boxplot(), plus:

bw_method - KDE bandwidth method
bw_adjust - KDE bandwidth multiplier
cut - KDE extension beyond extremes
density_norm - "area", "count", "width"
inner - "box", "quartile", "point", "stick", None
split - Split violins for hue comparison
scale - Scaling method: "area", "count", "width"
scale_hue - Scale across hue levels
gridsize - KDE grid resolution

Example:

python

sns.violinplot(data=df, x='day', y='total_bill',
               hue='sex', split=True, inner='quartile',
               palette='muted')

boxenplot()

Purpose: Draw enhanced box plot for larger datasets showing more quantiles.

Key Parameters: Same as boxplot(), plus:

k_depth - "tukey", "proportion", "trustworthy", "full", or int
outlier_prop - Proportion of data as outliers
trust_alpha - Alpha for trustworthy depth
showfliers - Show outlier points

Example:

python

sns.boxenplot(data=df, x='day', y='total_bill',
              hue='time', palette='Set2')

barplot()

Purpose: Draw a bar plot with error bars showing statistical estimates.

Key Parameters:

data - DataFrame, array, or dict
x, y - Variables (one categorical, one continuous)
hue - Grouping variable
order - Order for categorical levels
hue_order - Order for hue levels
estimator - Aggregation function (default: mean)
errorbar - Error representation: "sd", "se", "pi", ("ci", level), ("pi", level), or None
n_boot - Bootstrap iterations
seed - Random seed
units - Identifier for sampling units
weights - Observation weights
orient - "v" or "h"
color - Single bar color
palette - Color palette
saturation - Color saturation
width - Bar width
dodge - Separate hue levels side-by-side
errcolor - Error bar color
errwidth - Error bar line width
capsize - Error bar cap width
native_scale - Use numeric scale
formatter - Formatter for categorical axis
legend - Whether to show legend
ax - Matplotlib axes

Example:

python

sns.barplot(data=df, x='day', y='total_bill',
            hue='sex', estimator='median',
            errorbar=('ci', 95), capsize=0.1)

countplot()

Purpose: Show counts of observations in each categorical bin.

Key Parameters: Same as barplot(), but:

Only specify one of x or y (the categorical variable)
No estimator or errorbar (shows counts)
stat - "count" or "percent"

Example:

python

sns.countplot(data=df, x='day', hue='time',
              palette='pastel', dodge=True)

pointplot()

Purpose: Show point estimates and confidence intervals with connecting lines.

Key Parameters: Same as barplot(), plus:

markers - Marker style(s)
linestyles - Line style(s)
scale - Scale for markers
join - Connect points with lines
capsize - Error bar cap width

Example:

python

sns.pointplot(data=df, x='time', y='total_bill',
              hue='sex', markers=['o', 's'],
              linestyles=['-', '--'], capsize=0.1)

catplot()

Purpose: Figure-level interface for categorical plots onto a FacetGrid.

Key Parameters: All parameters from categorical plots, plus:

kind - "strip", "swarm", "box", "violin", "boxen", "bar", "point", "count"
col - Categorical variable for column facets
row - Categorical variable for row facets
col_wrap - Wrap columns
col_order - Order for column facets
row_order - Order for row facets
height - Height of each facet
aspect - Aspect ratio
sharex, sharey - Share axes across facets
legend - Whether to show legend
legend_out - Place legend outside figure
facet_kws - Additional FacetGrid parameters

Example:

python

sns.catplot(data=df, x='day', y='total_bill',
            hue='smoker', col='time',
            kind='violin', split=True,
            height=4, aspect=0.8)

Regression Plots

regplot()

Purpose: Plot data and a linear regression fit.

Key Parameters:

data - DataFrame
x, y - Variables or data vectors
x_estimator - Apply estimator to x bins
x_bins - Bin x for estimator
x_ci - CI for binned estimates
scatter - Show scatter points
fit_reg - Plot regression line
ci - CI for regression estimate (int or None)
n_boot - Bootstrap iterations for CI
units - Identifier for sampling units
seed - Random seed
order - Polynomial regression order
logistic - Fit logistic regression
lowess - Fit lowess smoother
robust - Fit robust regression
logx - Log-transform x
x_partial, y_partial - Partial regression (regress out variables)
truncate - Limit regression line to data range
dropna - Drop missing values
x_jitter, y_jitter - Add jitter to data
label - Label for legend
color - Color for all elements
marker - Marker style
scatter_kws - Parameters for scatter
line_kws - Parameters for regression line
ax - Matplotlib axes

Example:

python

sns.regplot(data=df, x='total_bill', y='tip',
            order=2, robust=True, ci=95,
            scatter_kws={'alpha': 0.5})

lmplot()

Purpose: Figure-level interface for regression plots onto a FacetGrid.

Key Parameters: All parameters from regplot(), plus:

hue - Grouping variable
col - Column facets
row - Row facets
palette - Color palette
col_wrap - Wrap columns
height - Facet height
aspect - Aspect ratio
markers - Marker style(s)
sharex, sharey - Share axes
hue_order - Order for hue levels
col_order - Order for column facets
row_order - Order for row facets
legend - Whether to show legend
legend_out - Place legend outside
facet_kws - FacetGrid parameters

Example:

python

sns.lmplot(data=df, x='total_bill', y='tip',
           hue='smoker', col='time', row='sex',
           height=3, aspect=1.2, ci=None)

residplot()

Purpose: Plot residuals of a regression.

Key Parameters: Same as regplot(), but:

Always plots residuals (y - predicted) vs x
Adds horizontal line at y=0
lowess - Fit lowess smoother to residuals

Example:

python

sns.residplot(data=df, x='x', y='y', lowess=True,
              scatter_kws={'alpha': 0.5})

Matrix Plots

heatmap()

Purpose: Plot rectangular data as a color-encoded matrix.

Key Parameters:

data - 2D array-like data
vmin, vmax - Anchor values for colormap
cmap - Colormap name or object
center - Value at colormap center
robust - Use robust quantiles for colormap range
annot - Annotate cells: True, False, or array
fmt - Format string for annotations (e.g., ".2f")
annot_kws - Parameters for annotations
linewidths - Width of cell borders
linecolor - Color of cell borders
cbar - Draw colorbar
cbar_kws - Colorbar parameters
cbar_ax - Axes for colorbar
square - Force square cells
xticklabels, yticklabels - Tick labels (True, False, int, or list)
mask - Boolean array to mask cells
ax - Matplotlib axes

Example:

python

# Correlation matrix
corr = df.corr()
mask = np.triu(np.ones_like(corr, dtype=bool))
sns.heatmap(corr, mask=mask, annot=True, fmt='.2f',
            cmap='coolwarm', center=0, square=True,
            linewidths=1, cbar_kws={'shrink': 0.8})

clustermap()

Purpose: Plot a hierarchically-clustered heatmap.

Key Parameters: All parameters from heatmap(), plus:

pivot_kws - Parameters for pivoting (if needed)
method - Linkage method: "single", "complete", "average", "weighted", "centroid", "median", "ward"
metric - Distance metric for clustering
standard_scale - Standardize data: 0 (rows), 1 (columns), or None
z_score - Z-score normalize data: 0 (rows), 1 (columns), or None
row_cluster, col_cluster - Cluster rows/columns
row_linkage, col_linkage - Precomputed linkage matrices
row_colors, col_colors - Additional color annotations
dendrogram_ratio - Ratio of dendrogram to heatmap
colors_ratio - Ratio of color annotations to heatmap
cbar_pos - Colorbar position (tuple: x, y, width, height)
tree_kws - Parameters for dendrogram
figsize - Figure size

Example:

python

sns.clustermap(data, method='average', metric='euclidean',
               z_score=0, cmap='viridis',
               row_colors=row_colors, col_colors=col_colors,
               figsize=(12, 12), dendrogram_ratio=0.1)

Multi-Plot Grids

FacetGrid

Purpose: Multi-plot grid for plotting conditional relationships.

Initialization:

python

g = sns.FacetGrid(data, row=None, col=None, hue=None,
                  col_wrap=None, sharex=True, sharey=True,
                  height=3, aspect=1, palette=None,
                  row_order=None, col_order=None, hue_order=None,
                  hue_kws=None, dropna=False, legend_out=True,
                  despine=True, margin_titles=False,
                  xlim=None, ylim=None, subplot_kws=None,
                  gridspec_kws=None)

Methods:

map(func, *args, **kwargs) - Apply function to each facet
map_dataframe(func, *args, **kwargs) - Apply function with full DataFrame
set_axis_labels(x_var, y_var) - Set axis labels
set_titles(template, **kwargs) - Set subplot titles
set(kwargs) - Set attributes on all axes
add_legend(legend_data, title, label_order, **kwargs) - Add legend
savefig(*args, **kwargs) - Save figure

Example:

python

g = sns.FacetGrid(df, col='time', row='sex', hue='smoker',
                  height=3, aspect=1.5, margin_titles=True)
g.map(sns.scatterplot, 'total_bill', 'tip', alpha=0.7)
g.add_legend()
g.set_axis_labels('Total Bill ($)', 'Tip ($)')
g.set_titles('{col_name} | {row_name}')

PairGrid

Purpose: Grid for plotting pairwise relationships in a dataset.

Initialization:

python

g = sns.PairGrid(data, hue=None, vars=None,
                 x_vars=None, y_vars=None,
                 hue_order=None, palette=None,
                 hue_kws=None, corner=False,
                 diag_sharey=True, height=2.5,
                 aspect=1, layout_pad=0.5,
                 despine=True, dropna=False)

Methods:

map(func, **kwargs) - Apply function to all subplots
map_diag(func, **kwargs) - Apply to diagonal
map_offdiag(func, **kwargs) - Apply to off-diagonal
map_upper(func, **kwargs) - Apply to upper triangle
map_lower(func, **kwargs) - Apply to lower triangle
add_legend(legend_data, **kwargs) - Add legend
savefig(*args, **kwargs) - Save figure

Example:

python

g = sns.PairGrid(df, hue='species', vars=['a', 'b', 'c', 'd'],
                 corner=True, height=2.5)
g.map_upper(sns.scatterplot, alpha=0.5)
g.map_lower(sns.kdeplot)
g.map_diag(sns.histplot, kde=True)
g.add_legend()

JointGrid

Purpose: Grid for bivariate plot with marginal univariate plots.

Initialization:

python

g = sns.JointGrid(data=None, x=None, y=None, hue=None,
                  height=6, ratio=5, space=0.2,
                  dropna=False, xlim=None, ylim=None,
                  marginal_ticks=False, hue_order=None,
                  palette=None)

Methods:

plot(joint_func, marginal_func, **kwargs) - Plot both joint and marginals
plot_joint(func, **kwargs) - Plot joint distribution
plot_marginals(func, **kwargs) - Plot marginal distributions
refline(x, y, **kwargs) - Add reference line
set_axis_labels(xlabel, ylabel, **kwargs) - Set axis labels
savefig(*args, **kwargs) - Save figure

Example:

python

g = sns.JointGrid(data=df, x='x', y='y', hue='group',
                  height=6, ratio=5, space=0.2)
g.plot_joint(sns.scatterplot, alpha=0.5)
g.plot_marginals(sns.histplot, kde=True)
g.set_axis_labels('Variable X', 'Variable Y')