{
    "action": "final",
    "content": "**Short answer:** There are no exact duplicates, but there are 21 repeated keys, and a larger set of near-duplicates once page URLs are normalized.\n\n**Exact duplicates: none.**\n- All 62,496 rows are distinct across the business columns (step-0), so no row is repeated verbatim.\n\n**Repeated keys: 21.**\n- The expected grain is WEBSITE + YEAR + PAGE URL, which has 62,475 distinct values. That leaves 21 keys that each appear twice (step-1: 21 keys, each with n = 2).\n- The two rows for each key have very different page-view values. For example:\n  - `http://brgov.com/` in 2016 has one row with 2 views and another with 323,169.\n  - `http://www.brla.gov/` in 2019 has rows with 1 and 224,668 views.\n  - `http://www.brla.gov/headstart` in 2024 has rows with 106 and 59,811 views.\n- Each pair comes from a single source file, so this is not a file loaded twice.\n- The pattern suggests one page's traffic was split across separate rows, for example by a trailing slash, a query string or a very long path. The long budget.brla.gov URL is one such case.\n- Because the measures differ so much, treat these as overlapping or fragmented records rather than true copies. Summing page views, unique views and entrances across a pair is reasonable. Bounce and exit rates should be combined with a weighted average (for example weighted by entrances or page views), not a simple average.\n\n**Near-duplicates after normalization: more widespread.**\n- Normalizing the page URL key reduces the distinct count from 62,475 to 61,742. That means about 733 further rows collapse into another row's key.\n- Normalization rules like these can merge distinct pages, so review a sample before deduplicating.\n\n**Recommendation:** Keep all rows, since none are verbatim copies. Where per-page analysis is needed, aggregate to a normalized URL key. Sum the counts and recompute the rates with weights."
}