Transition Graph

Displays an interactive directed graph where nodes are events and edges represent transitions between them. Edge weights can show transition probabilities, counts, or time-based metrics. Supports diff mode to compare two user segments side by side.

Usage

es.transition_graph()

With options:

es.transition_graph(
    edge_weight="proba_out",
    diff=["plan", "pro", "free"],
    cloud_file_name="my_graph",
)

Parameters

Parameter	Type	Default	Description
`edge_weight`	`str`	`"proba_out"`	Values shown on edges. See available values below.
`diff`	`list \| None`	`None`	Enables diff mode. A 3-element list `[segment_col, value1, value2]`. Use `"<OUTER>"` as `value2` to compare against all other users.
`path_id_col`	`str \| None`	`None`	Override the path ID column. Defaults to the schema primary path column.
`cloud_file_name`	`str \| None`	`None`	Saves and restores widget configuration to the cloud. See Cloud save section in the Widgets overview.
`height`	`int`	`500`	Widget height in pixels.
`sidebar_open`	`bool`	`True`	Whether the settings sidebar starts open.

Edge weights

Value	Description
`proba_out`	Probability of transitioning from the source node to this target — share of outgoing transitions. Default.
`proba_in`	Probability of arriving at the target from this source — share of incoming transitions.
`count`	Number of times this transition occurred.
`unique_paths`	Number of unique paths that include this transition.
`transition_rate`	Share of paths that contain this transition out of all transitions in the eventstream.
`per_path`	Average number of times this transition occurs per path.
`time_median`	Median time in seconds between the source and target events.
`time_q95`	95th percentile time in seconds between the source and target events.

Headless mode

es.transition_graph_data() returns the transition matrix as a DataFrame without rendering a widget.

Mode	Returns
Standard	`DataFrame` — events × events matrix of the selected edge weight
Diff (`diff=`)	`tuple[DataFrame, DataFrame, DataFrame]` — `(diff, group1, group2)`

tm = es.transition_graph_data(edge_weight="proba_out")

# diff mode
diff, g1, g2 = es.transition_graph_data(
    edge_weight="count",
    diff=["platform", "mobile", "desktop"],
)