Animations: many PX functions support simple animation support via the animation_frame and animation_group arguments.Trendlines: px.scatter supports built-in trendlines with accessible model output.A Pandas backend: the 2D-cartesian plotting functions are available as a Pandas plotting backend so you can call them via df.plot().Marginal Plots: the 2D-cartesian plotting functions support marginal distribution plots with the marginal, marginal_x and marginal_y arguments.

Faceting: the 2D-cartesian plotting functions support row, column and wrapped facetting with facet_row, facet_col and facet_col_wrap arguments.Uniform Color Handling: PX functions automatically switch between continuous and categorical color based on the input type.Styling Control: PX functions read styling information from the default figure template, and support commonly-needed cosmetic controls like category_orders and color_discrete_map to precisely control categorical variables.Automatic Hover Labels: PX functions populate the hover-label using the labels mentioned above, and provide extra control with the hover_name and hover_data arguments.Automatic Figure Labelling: PX functions label axes, legends and colorbars based in the input DataFrame or xarray, and provide extra control with the labels argument.Traces are automatically linked to a correctly-configured subplot of the appropriate type. Traces' legendgroup and showlegend attributes are set such that only one legend item appears per unique combination of discrete color, symbol and/or line-dash. Automatic Trace and Layout configuration: PX functions will create one trace per animation frame for each unique combination of data values mapped to discrete color, symbol, line-dash, facet-row and/or facet-column.Flexible Input Formats: PX functions accept input in a variety of formats, from lists and dicts to long-form or wide-form DataFrames to numpy arrays and xarrays to GeoPandas GeoDataFrames.Sensible, Overridable Defaults: PX functions will infer sensible defaults wherever possible, and will always let you override them.Every PX function returns a aph_objects.Figure object, so you can edit it using all the same methods like update_layout and add_trace. A single entry point into plotly: just import plotly.express as px and get access to all the plotting functions, plus built-in demo datasets under px.data and built-in color scales and sequences under px.color.The Plotly Express API in general offers the following features: Ternary Charts: scatter_ternary, line_ternary.Polar Charts: scatter_polar, line_polar, bar_polar.Outline Maps: scatter_geo, line_geo, choropleth.Tile Maps: scatter_mapbox, line_mapbox, choropleth_mapbox, density_mapbox.

Multidimensional: scatter_matrix, parallel_coordinates, parallel_categories.

2D Distributions: density_heatmap, density_contour.

1D Distributions: histogram, box, violin, strip, ecdf.

Part-of-Whole: pie, sunburst, treemap, icicle, funnel_area.

Basics: scatter, line, area, bar, funnel, timeline.

Plotly Express currently includes the following functions: Here is a talk from the SciPy 2021 conference that gives a good introduction to Plotly Express and Dash: Scroll down for a gallery of Plotly Express plots, each made in a single function call. The API for these functions was carefully designed to be as consistent and easy to learn as possible, making it easy to switch from a scatter plot to a bar chart to a histogram to a sunburst chart throughout a data exploration session. Plotly Express provides more than 30 functions for creating different types of figures. Any figure created in a single function call with Plotly Express could be created using graph objects alone, but with between 5 and 100 times more code. Throughout the plotly documentation, you will find the Plotly Express way of building figures at the top of any applicable page, followed by a section on how to use graph objects to build similar figures. Every Plotly Express function uses graph objects internally and returns a aph_objects.Figure instance. Plotly Express is a built-in part of the plotly library, and is the recommended starting point for creating most common figures. The plotly.express module (usually imported as px) contains functions that can create entire figures at once, and is referred to as Plotly Express or PX.

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