Segment Overview

Displays a heatmap matrix comparing computed metrics across segment values. Rows are metrics, columns are segment values. Click any cell to see the metric distribution for that segment, or shift-click two cells in the same row to compare distributions side by side.

Usage

es.segment_overview()

With options:

es.segment_overview(
    segment_col="plan",
    metrics_config=[
        {"metric": "length", "agg": "mean"},
        {"metric": "duration", "agg": "median"},
        {"metric": "event_count", "metric_args": {"event": "purchase"}, "agg": "mean"},
        {"metric": "has", "metric_args": {"events": ["add_to_cart", "purchase"]}, "agg": "mean"},
    ],
)

Parameters

Parameter	Type	Default	Description
`segment_col`	`str`	`""`	The segment column to split by. Must be declared in the schema's `segment_cols`. Required to trigger computation.
`metrics_config`	`list`	`[]`	List of metric configs. Can also be configured interactively via Configure Metrics. See Path Metrics.
`path_id_col`	`str \| None`	`None`	Override the path ID column.
`height`	`int`	`480`	Widget height in pixels.
`sidebar_open`	`bool`	`True`	Whether the settings sidebar starts open.

See Path Metrics for the full list of available metrics, their arguments, and aggregation options.

Headless mode

es.segment_overview_data() returns the metric heatmap as a DataFrame without rendering a widget. Rows are metrics, columns are segment values.

df = es.segment_overview_data(
    segment_col="platform",
    metrics_config=[
        {"metric": "length", "agg": "mean"},
        {"metric": "event_count", "metric_args": {"event": "purchase"}, "agg": "mean"},
    ],
)
print(df)
#                      mobile   desktop
# length_mean            8.3       5.1
# event_count_mean       0.4       0.6