plotly.express: high-level interface for data visualization

The plotly.express module is plotly’s high-level API for rapid figure generation.

>>> import plotly.express as px

scatter([data_frame, x, y, color, symbol, …])

In a scatter plot, each row of data_frame is represented by a symbol

scatter_3d([data_frame, x, y, z, color, …])

In a 3D scatter plot, each row of data_frame is represented by a

scatter_polar([data_frame, r, theta, color, …])

In a polar scatter plot, each row of data_frame is represented by a

scatter_ternary([data_frame, a, b, c, …])

In a ternary scatter plot, each row of data_frame is represented by a

scatter_mapbox([data_frame, lat, lon, …])

In a Mapbox scatter plot, each row of data_frame is represented by a

scatter_geo([data_frame, lat, lon, …])

In a geographic scatter plot, each row of data_frame is represented

line([data_frame, x, y, line_group, color, …])

In a 2D line plot, each row of data_frame is represented as vertex of

line_3d([data_frame, x, y, z, color, …])

In a 3D line plot, each row of data_frame is represented as vertex of

line_polar([data_frame, r, theta, color, …])

In a polar line plot, each row of data_frame is represented as vertex

line_ternary([data_frame, a, b, c, color, …])

In a ternary line plot, each row of data_frame is represented as

line_mapbox([data_frame, lat, lon, color, …])

In a Mapbox line plot, each row of data_frame is represented as

line_geo([data_frame, lat, lon, locations, …])

In a geographic line plot, each row of data_frame is represented as

area([data_frame, x, y, line_group, color, …])

In a stacked area plot, each row of data_frame is represented as

bar([data_frame, x, y, color, facet_row, …])

In a bar plot, each row of data_frame is represented as a rectangular

bar_polar([data_frame, r, theta, color, …])

In a polar bar plot, each row of data_frame is represented as a wedge

violin([data_frame, x, y, color, facet_row, …])

In a violin plot, rows of data_frame are grouped together into a

box([data_frame, x, y, color, facet_row, …])

In a box plot, rows of data_frame are grouped together into a

strip([data_frame, x, y, color, facet_row, …])

In a strip plot each row of data_frame is represented as a jittered

histogram([data_frame, x, y, color, …])

In a histogram, rows of data_frame are grouped together into a

pie([data_frame, names, values, color, …])

In a pie plot, each row of data_frame is represented as a sector of a

treemap([data_frame, names, values, …])

A treemap plot represents hierarchial data as nested rectangular

sunburst([data_frame, names, values, …])

A sunburst plot represents hierarchial data as sectors laid out over

funnel([data_frame, x, y, color, facet_row, …])

In a funnel plot, each row of data_frame is represented as a

funnel_area([data_frame, names, values, …])

In a funnel area plot, each row of data_frame is represented as a

scatter_matrix([data_frame, dimensions, …])

In a scatter plot matrix (or SPLOM), each row of data_frame is

parallel_coordinates([data_frame, …])

In a parallel coordinates plot, each row of data_frame is represented

parallel_categories([data_frame, …])

In a parallel categories (or parallel sets) plot, each row of

choropleth([data_frame, lat, lon, …])

In a choropleth map, each row of data_frame is represented by a

choropleth_mapbox([data_frame, geojson, …])

In a Mapbox choropleth map, each row of data_frame is represented by a

density_contour([data_frame, x, y, z, …])

In a density contour plot, rows of data_frame are grouped together

density_heatmap([data_frame, x, y, z, …])

In a density heatmap, rows of data_frame are grouped together into

density_mapbox([data_frame, lat, lon, z, …])

In a Mapbox density map, each row of data_frame contributes to the intensity of

imshow(img[, zmin, zmax, origin, …])

Display an image, i.e.