Transform nested dicts with pandas apply/lambda

Given a pandas DataFrame df with columns: user_id (int), ts (datetime64[ns]), events (list of dicts), attrs (dict). Example rows (conceptual):

user_id=1, ts=2025-08-08 09:12:00, events=[{"type":"click","ts":"2025-08-08T09:12:00"},{"type":"view","ts":"2025-08-08T09:12:05"}], attrs={"version":"1.2.0","flags":{"beta":true,"dark":false}}
user_id=1, ts=2025-08-09 15:00:00, events=[{"type":"purchase","ts":"2025-08-09T15:00:00","amount":20.0}], attrs={"version":"1.2.0","flags":{"beta":true,"dark":false}}
user_id=2, ts=2025-08-04 12:30:00, events=[{"type":"view","ts":"2025-08-04T12:30:00"}], attrs={"version":"1.1.0","flags":{"beta":false,"dark":true}}

Tasks (write Python/pandas):

1. Explode events into a long table with one row per event: columns [user_id, event_type, event_ts (datetime), amount (nullable), attrs_version, attrs_beta_flag, attrs_dark_flag]. Use Series.explode and apply/lambda to parse dicts; no for-loops over DataFrame rows.
2. Aggregate to a per-user features table with columns: click_count, view_count, purchase_count, last_event_ts, total_purchase_amount, version_most_recent, beta_flag_most_recent, dark_flag_most_recent. Use groupby with named aggregations; where multiple versions exist, take the attrs associated with the most recent event.
3. From the features table, compute a conversion_rate by user segment defined by (version_most_recent, beta_flag_most_recent, dark_flag_most_recent): purchases/users. Return a compact DataFrame with one row per segment and columns [version, beta_flag, dark_flag, users, purchasers, conversion_rate].
4. Implement a robust helper that safely extracts nested keys from attrs using a lambda and dictionary iteration (handle missing keys and None). Explain in comments why your approach avoids SettingWithCopy pitfalls and preserves vectorization.

Explode JSON event arrays into a flat event table

You are given the table user_sessions, which stores one row per user session. Each row carries a JSONB array of events (events) and a JSONB attributes object (attrs).

Write a single PostgreSQL SELECT that explodes the events array so the result has one row per individual event, joined with the session's denormalized attributes.

For each event, produce these output columns:

• user_id — the session's user id.
• event_type — the event's type field (text).
• event_ts — the event's ts field, cast to timestamp.
• amount — the event's amount field as numeric, or NULL when the event has no amount key.
• attrs_version — attrs.version (text).
• attrs_beta_flag — attrs.flags.beta cast to boolean.
• attrs_dark_flag — attrs.flags.dark cast to boolean.

Use set-based SQL (a LATERAL join over jsonb_array_elements) to unnest the array — do not use loops or procedural code.

Sort the output by user_id ascending, then by session_ts ascending, then by the event's position within its array (first event in the array first).

Table user_sessions

column	type	constraints
session_id	INT	PRIMARY KEY
user_id	INT	NOT NULL
session_ts	TIMESTAMP	NOT NULL
events	JSONB	NOT NULL
attrs	JSONB	NOT NULL

Sample data:

attrs	events	session_id	session_ts	user_id
{"version":"1.2.0","flags":{"beta":true,"dark":false}}	[{"type":"click","ts":"2025-08-08T09:12:00"},{"type":"view","ts":"2025-08-08T09:12:05"}]	1	2025-08-08 09:12:00	1
{"version":"1.2.0","flags":{"beta":true,"dark":false}}	[{"type":"purchase","ts":"2025-08-09T15:00:00","amount":20.0}]	2	2025-08-09 15:00:00	1
{"version":"1.1.0","flags":{"beta":false,"dark":true}}	[{"type":"view","ts":"2025-08-04T12:30:00"}]	3	2025-08-04 12:30:00	2

Expected output

One row per event, with user_id, event type/timestamp, optional numeric amount, and denormalized attrs (version + beta/dark flags), ordered by user_id, session_ts, then event position.

amount	attrs_beta_flag	attrs_dark_flag	attrs_version	event_ts	event_type	user_id
NULL	true	false	1.2.0	2025-08-08T09:12:00	click	1
NULL	true	false	1.2.0	2025-08-08T09:12:05	view	1
20.0	true	false	1.2.0	2025-08-09T15:00:00	purchase	1
NULL	false	true	1.1.0	2025-08-04T12:30:00	view	2

Hints

1. Use jsonb_array_elements(events) in a CROSS JOIN LATERAL to emit one row per element of the array.
2. ->> returns NULL when a key is absent — so (ev->>'amount')::numeric is automatically NULL for events with no amount.
3. Add WITH ORDINALITY to the unnest so you can ORDER BY the original event position and get a stable, deterministic result.

Aggregate per-user event features from exploded events

You are given a single table, user_sessions, where each row is one browsing session. Each session stores its events as a JSON array in the events column (each event is an object with a type of 'click', 'view', or 'purchase', a ts timestamp string, and — for purchases — an amount), plus session-level metadata in the attrs column (an object with a version string and a nested flags object holding boolean beta and dark flags).

Write one PostgreSQL query that first explodes the events array into one row per individual event, then aggregates to produce a per-user feature table with exactly one row per user_id. The result must contain these columns:

• user_id
• click_count — number of 'click' events for that user
• view_count — number of 'view' events for that user
• purchase_count — number of 'purchase' events for that user
• last_event_ts — timestamp of the user's most recent event
• total_purchase_amount — sum of amount over the user's purchase events (treat a missing/NULL amount as 0)
• version_most_recent — the version from the attrs of the session that contains the user's most recent event
• beta_flag_most_recent — the flags.beta boolean from that same most-recent session's attrs
• dark_flag_most_recent — the flags.dark boolean from that same most-recent session's attrs

When a user has events across multiple sessions/versions, the version_most_recent and the two flag columns must come from the session attributes attached to the single most recent event (by event timestamp). Use JSON unnesting, GROUP BY, and window functions as needed. Order the output by user_id ascending.

Table user_sessions

column	type	constraints
session_id	INT	PRIMARY KEY
user_id	INT	NOT NULL
session_ts	TIMESTAMP	NOT NULL
events	JSONB	NOT NULL
attrs	JSONB	NOT NULL

Sample data:

attrs	events	session_id	session_ts	user_id
{"version":"1.2.0","flags":{"beta":true,"dark":false}}	[{"type":"click","ts":"2025-08-08T09:12:00"},{"type":"view","ts":"2025-08-08T09:12:05"}]	1	2025-08-08 09:12:00	1
{"version":"1.2.0","flags":{"beta":true,"dark":false}}	[{"type":"purchase","ts":"2025-08-09T15:00:00","amount":20.0}]	2	2025-08-09 15:00:00	1
{"version":"1.1.0","flags":{"beta":false,"dark":true}}	[{"type":"view","ts":"2025-08-04T12:30:00"}]	3	2025-08-04 12:30:00	2

Expected output

One row per user (ordered by user_id) with per-type event counts, the most recent event timestamp, total purchase amount (missing amounts as 0), and the version/beta/dark flags taken from the most recent event's session attrs.

beta_flag_most_recent	click_count	dark_flag_most_recent	last_event_ts	purchase_count	total_purchase_amount	user_id	version_most_recent	view_count
true	1	false	2025-08-09T15:00:00	1	20.0	1	1.2.0	1
false	0	true	2025-08-04T12:30:00	0	0	2	1.1.0	1

Hints

1. Use CROSS JOIN LATERAL jsonb_array_elements(events) to turn each session's JSON event array into one row per event, carrying the session's attrs along.
2. ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_ts DESC) marks each user's most recent event; combine the aggregate with FILTER (WHERE rn = 1) to read that event's session attributes.
3. PostgreSQL has no MAX() over booleans — use bool_or(flag) FILTER (WHERE rn = 1) for the beta/dark flag columns.

Compute conversion rate by user segment from feature table

Suppose you have a precomputed user_features table that contains one row per user with aggregated metrics. Using this table, compute a conversion_rate by user segment defined by the tuple (version_most_recent, beta_flag_most_recent, dark_flag_most_recent). For each segment, return: version, beta_flag, dark_flag, users (number of users in the segment), purchasers (number of users in the segment with purchase_count > 0), and conversion_rate defined as purchasers divided by users as a decimal. Return one row per segment.

Table user_features

column	type	constraints
user_id	INT	PRIMARY KEY
click_count	INT	NOT NULL
view_count	INT	NOT NULL
purchase_count	INT	NOT NULL
last_event_ts	TIMESTAMP	NOT NULL
total_purchase_amount	DECIMAL(10,2)	NOT NULL
version_most_recent	VARCHAR(20)	NOT NULL
beta_flag_most_recent	BOOLEAN	NOT NULL
dark_flag_most_recent	BOOLEAN	NOT NULL

Sample data:

beta_flag_most_recent	click_count	dark_flag_most_recent	last_event_ts	purchase_count	total_purchase_amount	user_id	version_most_recent	view_count
true	1	false	2025-08-09 15:00:00	1	20	1	1.2.0	1
false	0	true	2025-08-04 12:30:00	0	0	2	1.1.0	1

Expected output

One row per (version_most_recent, beta_flag_most_recent, dark_flag_most_recent) segment, with user counts, purchaser counts, and conversion rate as purchasers/users.

beta_flag	conversion_rate	dark_flag	purchasers	users	version
false	0	true	0	1	1.1.0
true	1	false	1	1	1.2.0

Hints

1. Group by the three segment-defining columns (version_most_recent, beta_flag_most_recent, dark_flag_most_recent).
2. Use COUNT() FILTER (WHERE purchase_count > 0) to count purchasers and divide by COUNT() as a decimal to get conversion_rate.

Safely extract nested JSON attributes with robust SQL

Using the user_sessions table, write a SELECT that extracts attrs_version, attrs_beta_flag, and attrs_dark_flag from the attrs JSONB column into separate columns. Your query should be robust to missing keys or NULL values in attrs: if attrs.version is missing, return 'unknown'; if attrs.flags.beta or attrs.flags.dark are missing or NULL, default to false. Use JSON operators and COALESCE so the query does not fail when keys are absent, and add brief SQL comments explaining how this approach safely handles missing keys in a set-based way.

Table user_sessions

column	type	constraints
session_id	INT	PRIMARY KEY
user_id	INT	NOT NULL
session_ts	TIMESTAMP	NOT NULL
events	JSONB	NOT NULL
attrs	JSONB	NOT NULL

Sample data:

attrs	events	session_id	session_ts	user_id
{"version":"1.2.0","flags":{"beta":true,"dark":false}}	[{"type":"click","ts":"2025-08-08T09:12:00"},{"type":"view","ts":"2025-08-08T09:12:05"}]	1	2025-08-08 09:12:00	1
{"version":"1.2.0","flags":{"beta":true,"dark":false}}	[{"type":"purchase","ts":"2025-08-09T15:00:00","amount":20.0}]	2	2025-08-09 15:00:00	1
{"version":"1.1.0","flags":{"beta":false,"dark":true}}	[{"type":"view","ts":"2025-08-04T12:30:00"}]	3	2025-08-04 12:30:00	2

Expected output

One row per session with user_id and three derived columns extracted from attrs, applying defaults for missing keys (in this data all keys are present, so defaults are not used).

attrs_beta_flag	attrs_dark_flag	attrs_version	session_id	user_id
true	false	1.2.0	1	1
true	false	1.2.0	2	1
false	true	1.1.0	3	2

Hints

1. Use the -> and ->> operators to navigate attrs and attrs->'flags', then wrap the extractions in COALESCE to provide default values.
2. Remember that casting NULL to a boolean is still NULL, so you can safely cast first and then COALESCE to false.