Using Animations

Animations bring data visualizations to life by enabling dynamic transitions that reveal changes in data over time or across other dimensions. By animating charts, users can intuitively illustrate trends, progressions, or cycles that might not be apparent in static plots.

Plotly Express makes animations straightforward by utilizing the animation_frame parameter, which defines the variable responsible for animating the chart. For example, in a time-series dataset, setting animation_frame to a date or time column allows the chart to update frame by frame, presenting a smooth progression of data over time. In addition to animation_frame, users can enhance animations with other parameters like animation_group, which groups data points to maintain their identity across frames, creating a cohesive flow. These features are especially valuable for visualizing changes in metrics such as sales growth, population shifts, or climate trends, as it captures how patterns evolve dynamically.The speed and appearance of the animation can also be customized to match the context, such as adjusting the frame duration or easing transitions for smoother playback. These animations are interactive, allowing users to pause, play, or scrub through the frames using built-in playback controls. This interactivity makes animations highly engaging and useful for presentations, dashboards, or exploratory data analysis, where they can effectively communicate complex stories hidden within multi-dimensional datasets.

Here is an example of animations using animations in Plotly Express with the Gapminder data.

import plotly.express as px

df = px.data.gapminder()

fig = px.scatter(df, x="gdpPercap", y="lifeExp", 
animation_frame="year", animation_group="country",
           size="pop", color="continent", 
           hover_name="country", facet_col="continent",
           log_x=True, size_max=45, range_x=[100,100000], range_y=[25,90])

fig.show()