Filter Paths

Keep only paths that satisfy a metric condition.

The condition is a tree of comparison nodes connected by and / or / not branch nodes. Per-path metrics are computed once and the condition is evaluated in SQL.

Raises EmptyEventstreamError when no paths match.

Usage

# Keep paths that contain at least one purchase
stream.filter_paths({"op": ">", "metric": "event_count", "value": 0, "metric_args": {"event": "purchase"}})

# Keep paths that contain a promo_view or a discount_applied event
stream.filter_paths({"op": "=", "metric": "has_any_event", "value": True,
                      "metric_args": {"events": ["promo_view", "discount_applied"]}})

# Keep paths longer than 3 events that match a funnel pattern
# (a top-level list means AND)
stream.filter_paths([
    {"op": ">", "metric": "length", "value": 3},
    {"op": "=", "metric": "matches_pattern", "value": True,
     "metric_args": {"pattern": "registration->.*->purchase"}},
])

Parameters

Parameter	Type	Description
`condition`	`dict or list`	Condition tree. Leaf nodes have the keys: Branch nodes have `op` set to `and`, `or`, or `not` and an `args` list of child nodes. A plain list of nodes is shorthand for AND: `[cond1, cond2]` ≡ `{"op": "and", "args": [cond1, cond2]}`. `has_event`/`event_count` take a single `event` (string) — for a multi-event AND/OR condition use `has_all_events`/`has_any_event` with an `events` list instead. `has_event_bulk`/`event_count_bulk` (which expand into one column per event, for `segment_overview`/`add_clusters`) cannot be used here since a condition needs exactly one value per path.
`path_col`	`str, optional`	Path ID column override; defaults to `schema.path_col`.
`event_col`	`str, optional`	Event column override; defaults to `schema.event_col`.

Leaf node keys

op — comparison operator: >, >=, <, <=, = (or ==), !=.
metric — metric name (see the Path Metrics documentation page for the full list).
value — threshold value.
metric_args (optional) — dict of extra arguments for the metric.