pandas / Intermediate: grouping, joins and temporal quality

pandas β€” Intermediate: grouping, joins and temporal quality

Control output grain, match coverage, reshape identity and calendar interpretation.

6 lessons Β· No account required

What you will learn

  • Choose aggregation denominators, merge cardinality and reshape rules.
  • Validate time, text, categories and duplicate occurrence identity.

  1. GroupBy aggregation and transform

    Choose the output grain, missing-key policy and denominator before summarizing groups or broadcasting group results.

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  2. Merge keys, cardinality and indicators

    State the expected relationship between tables and verify matched, unmatched and multiplied populations.

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  3. Concat, reshape, pivot and melt

    Preserve observation identity while combining axes or changing between wide and long representations.

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  4. Datetimes, timezones and resampling

    Parse an explicit temporal contract and distinguish an instant from a local calendar label before grouping time.

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  5. Strings, categories and text contracts

    Normalize only under a declared identity rule and distinguish text representation from category meaning and order.

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  6. Duplicates, quality rules and reconciliation

    Distinguish repeated observations, repeated identities and duplicate labels before choosing a correction.

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