Plotting Functions by Category

Relational, distribution, categorical, regression, and matrix plots: which function to reach for, its key parameters, and worked examples.

Plotting Functions by Category

Relational Plots (Relationships Between Variables)

Use for: Exploring how two or more variables relate to each other

scatterplot() - Display individual observations as points
lineplot() - Show trends and changes (automatically aggregates and computes CI)
relplot() - Figure-level interface with automatic faceting

Key parameters: - x, y - Primary variables - hue - Color encoding for additional categorical/continuous variable - size - Point/line size encoding - style - Marker/line style encoding - col, row - Facet into multiple subplots (figure-level only)

# Scatter with multiple semantic mappings
sns.scatterplot(data=df, x='total_bill', y='tip',
                hue='time', size='size', style='sex')

# Line plot with confidence intervals
sns.lineplot(data=timeseries, x='date', y='value', hue='category')

# Faceted relational plot
sns.relplot(data=df, x='total_bill', y='tip',
            col='time', row='sex', hue='smoker', kind='scatter')

Distribution Plots (Single and Bivariate Distributions)

Use for: Understanding data spread, shape, and probability density

histplot() - Bar-based frequency distributions with flexible binning
kdeplot() - Smooth density estimates using Gaussian kernels
ecdfplot() - Empirical cumulative distribution (no parameters to tune)
rugplot() - Individual observation tick marks
displot() - Figure-level interface for univariate and bivariate distributions
jointplot() - Bivariate plot with marginal distributions
pairplot() - Matrix of pairwise relationships across dataset

Key parameters: - x, y - Variables (y optional for univariate) - hue - Separate distributions by category - stat - Normalization: "count", "frequency", "probability", "density" - bins / binwidth - Histogram binning control - bw_adjust - KDE bandwidth multiplier (higher = smoother) - fill - Fill area under curve - multiple - How to handle hue: "layer", "stack", "dodge", "fill"

# Histogram with density normalization
sns.histplot(data=df, x='total_bill', hue='time',
             stat='density', multiple='stack')

# Bivariate KDE with contours
sns.kdeplot(data=df, x='total_bill', y='tip',
            fill=True, levels=5, thresh=0.1)

# Joint plot with marginals
sns.jointplot(data=df, x='total_bill', y='tip',
              kind='scatter', hue='time')

# Pairwise relationships
sns.pairplot(data=df, hue='species', corner=True)

Categorical Plots (Comparisons Across Categories)

Use for: Comparing distributions or statistics across discrete categories

Categorical scatterplots: - stripplot() - Points with jitter to show all observations - swarmplot() - Non-overlapping points (beeswarm algorithm)

Distribution comparisons: - boxplot() - Quartiles and outliers - violinplot() - KDE + quartile information - boxenplot() - Enhanced boxplot for larger datasets

Statistical estimates: - barplot() - Mean/aggregate with confidence intervals - pointplot() - Point estimates with connecting lines - countplot() - Count of observations per category

Figure-level: - catplot() - Faceted categorical plots (set kind parameter)

Key parameters: - x, y - Variables (one typically categorical) - hue - Additional categorical grouping - order, hue_order - Control category ordering - native_scale - Preserve numeric/datetime scale on the categorical axis - log_scale - Apply log scaling without dropping down to matplotlib - formatter - Control categorical tick labels - dodge, gap - Separate hue levels side-by-side and space dodged elements - orient - "x"/"y" or "v"/"h" to specify the categorical axis - legend - True/False or "auto", "brief", "full" - kind - Plot type for catplot: "strip", "swarm", "box", "violin", "boxen", "bar", "point", "count"

# Swarm plot showing all points
sns.swarmplot(data=df, x='day', y='total_bill', hue='sex')

# Violin plot with split for comparison
sns.violinplot(data=df, x='day', y='total_bill',
               hue='sex', split=True)

# Bar plot with error bars
sns.barplot(data=df, x='day', y='total_bill',
            hue='sex', estimator='mean', errorbar=('ci', 95))

# Faceted categorical plot
sns.catplot(data=df, x='day', y='total_bill',
            col='time', kind='box')

Regression Plots (Linear Relationships)

Use for: Visualizing linear regressions and residuals

regplot() - Axes-level regression plot with scatter + fit line
lmplot() - Figure-level with faceting support
residplot() - Residual plot for assessing model fit

Key parameters: - x, y - Variables to regress - order - Polynomial regression order - logistic - Fit logistic regression - robust - Use robust regression (less sensitive to outliers) - ci - Confidence interval width (default 95) - scatter_kws, line_kws - Customize scatter and line properties

# Simple linear regression
sns.regplot(data=df, x='total_bill', y='tip')

# Polynomial regression with faceting
sns.lmplot(data=df, x='total_bill', y='tip',
           col='time', order=2, ci=95)

# Check residuals
sns.residplot(data=df, x='total_bill', y='tip')

Matrix Plots (Rectangular Data)

Use for: Visualizing matrices, correlations, and grid-structured data

heatmap() - Color-encoded matrix with annotations
clustermap() - Hierarchically-clustered heatmap

Key parameters: - data - 2D rectangular dataset (DataFrame or array) - annot - Display values in cells - fmt - Format string for annotations (e.g., ".2f") - cmap - Colormap name - center - Value at colormap center (for diverging colormaps) - vmin, vmax - Color scale limits - square - Force square cells - linewidths - Gap between cells

# Correlation heatmap
corr = df.select_dtypes(include='number').corr()
sns.heatmap(corr, annot=True, fmt='.2f',
            cmap='coolwarm', center=0, square=True)

# Clustered heatmap
sns.clustermap(data, cmap='viridis',
               standard_scale=1, figsize=(10, 10))