DataFrame-based plotting

Axes-level with ax=

Paper context set

Ticks style applied

Colorblind palette

Semantic mappings used

despine called

Matplotlib ax.set() usage

High DPI save

PDF format saved

tight_layout called

Output files present

Long-form data preference

melt() for reshaping

Figure-level function for faceting

height and aspect sizing

lineplot errorbar customized

hue semantic mapping

violinplot split comparison

histplot stat parameter

pairplot corner=True

FacetGrid set_axis_labels

Output files present

Diverging colormap for correlation

center=0 for heatmap

annot=True with fmt

square=True for correlation heatmap

Upper triangle mask

clustermap normalization

clustermap method/metric

clustermap row_colors or col_colors

Sequential colormap for clustermap

set_theme applied

High DPI save

Output files present

Uses so.Plot

Method chaining

Multiple .add() layers

Stat in .add()

Move in .add() (Dodge)

.facet() usage

.scale() usage

.label() usage

.save() not plt.savefig()

PNG and PDF outputs

Color encoding via objects API

regplot used

polynomial order parameter

lmplot for faceted regression

residplot used

ci parameter specified

scatter_kws or line_kws

lmplot height+aspect

ax= for axes-level layout

DataFrame-based plotting

lmplot set_axis_labels

Output files present

PairGrid used

map_diag, map_upper, map_lower

PairGrid add_legend

bw_adjust parameter used

ecdfplot used

ecdfplot hue grouping

Talk context applied

Different context from default

DataFrame-based plotting

Output files present

High-quality save