#aggregation

Pandas Medium Theory

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groupby with agg vs transform vs apply

`agg` reduces groups, `transform` returns group-shaped output, and `apply` handles custom logic but is usually slower.

• agg changes shape
• transform keeps original row count
• apply is flexible but costly

#aggregation #groupby #pandas

groupby with agg vs transform vs apply

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Pandas Medium Theory

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pivot vs pivot_table in pandas

`pivot` reshapes unique values only, while `pivot_table` can aggregate duplicates.

• pivot needs unique index-column pairs
• pivot_table accepts aggfunc
• pivot_table is safer for real data

#aggregation #pandas #reshaping

pivot vs pivot_table in pandas

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SQL Easy Theory

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HAVING vs WHERE in SQL

WHERE filters rows before grouping, while HAVING filters groups after aggregation.

• WHERE happens earlier
• HAVING works on grouped results
• Common with aggregate conditions

#aggregation #queries #sql

HAVING vs WHERE in SQL

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SQL Easy Theory

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What does GROUP BY do?

GROUP BY collects rows by one or more columns so aggregate functions can run per group.

• Works with count sum avg
• Every selected non-aggregate column must be grouped
• HAVING filters groups

#aggregation #sql

What does GROUP BY do?

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