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pandas Group-By and Aggregation Pitfalls With Verification Steps

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Avoid wrong totals when summarising.

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[VERIFY] is an editorial flag, not a blank to fill. Wherever the prompt shows [VERIFY], check that item against a reliable source before you use it.

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Help me summarise data correctly with groupby. Columns and a sample (invented rows): [COLUMNS_AND_SAMPLE]. The summary I want (by customer, by month, top N, share of total, running totals): [SUMMARY_NEEDED].

Explain and demonstrate, with code for each need: average of averages versus weighted average, missing values in grouping keys being dropped by default [VERIFY option names], categorical groupers producing empty groups, merging group results back, ranking within groups, and percentage of total within group. Worked traps with invented data: groups of 10 orders averaging 50 and 90 orders averaging 30 have an overall average of (10 x 50 + 90 x 30) / 100 = 32, not (50 + 30) / 2 = 40; a groupby on 1,000 rows that counts 940 because 60 rows have a missing key, silently excluded unless handled; and a share-of-total that is wrong because the denominator is taken after filtering. Verification for each: the sum of the group totals plus the excluded rows equals the raw total; print rows before and after each group operation and the sum of the main value column as an audit trail.

Describe a test harness in words: a ten-row DataFrame with known groups and values, a hand-written expected table, and an equality check that tolerates floating-point rounding, with one case with a missing group key, one single-row group, and one duplicate row. Reconcile summaries to the raw total always. Treat version-dependent syntax as unconfirmed [VERIFY] and test on a tiny dataset first.

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