Plot

AeroViz.plot needs the plot extra (pip install "AeroViz[plot]"). The matplotlib functions return (fig, ax) and accept ax= to draw into an existing axis; timeseries_interactive returns a Plotly figure.

Page	Functions
Basic charts	scatter, linear_regression, multiple_linear_regression, box, bar, violin, pie, donuts, radar
Time series	timeseries, timeseries_stacked, timeseries_template, timeseries_interactive
Size distribution	plot_dist, heatmap, heatmap_tms, three_dimension, curve_fitting
Meteorology	meteorology.wind_rose, meteorology.CBPF, meteorology.hysplit
Optical	optical.Q_plot, optical.RI_couple, optical.RRI_2D, optical.scattering_phase, optical.response_surface
Templates	diurnal_pattern, corr_matrix, cross_corr_matrix, contour, koschmieder, ammonium_rich, metal_heatmaps

Worked usage — which input shape each function wants, the two box modes, multi-panel figures — is in the guide: Visualization; journal-ready styling is in Publication figures. A picture of what each family produces is on the home page gallery.

Quick reference

from AeroViz import plot

plot.timeseries(data, y='BC')                       # one column, or a list; y2= for a second axis
plot.timeseries(data, y=['BC', 'PM2.5', 'PM10'])
plot.scatter(data, x='BC', y='PM2.5', c='PM10', s='PM1')   # c colours points, s sizes them
plot.box(data, x='WS', y='PM2.5', x_bins=np.arange(0, 11, 2))   # binned; omit x_bins for categorical x
plot.diurnal_pattern(data, y='BC')                  # mean ± spread by hour of day

Common parameters: df / data (time-indexed DataFrame for the time-series style functions), ax (existing axis, optional), title; timeseries adds y2 (secondary axis) and rolling (window smoothing); scatter adds c (colour column) and s (size column).