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Data Science

Power Bi Dax Common Patterns

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Master Power Bi Dax Common Patterns with 158 free flashcards. Study using spaced repetition and focus mode for effective learning in Data Science.

158 cards · ~79 min · Advanced · Updated

Sample card

Difference between calculated column and measure?

Column: computed row-by-row at refresh; stored in model; uses row context.
Measure: computed at query time; aggregates; uses filter context.

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Q · 1 of 158

Difference between calculated column and measure?

Q · 2 of 158

Basic measure: total sales?

Q · 3 of 158

Distinct customer count?

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Difference between calculated column and measure?

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Column: computed row-by-row at refresh; stored in model; uses row context.
Measure: computed at query time; aggregates; uses filter context.

Basic measure: total sales?

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Total Sales := SUM(Sales[Amount])

Distinct customer count?

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Customers := DISTINCTCOUNT(Sales[CustomerId])

Sales for prior year (same period)?

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PY Sales := CALCULATE([Total Sales], SAMEPERIODLASTYEAR('Date'[Date]))

YoY % change?

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YoY % := DIVIDE([Total Sales] - [PY Sales], [PY Sales])

Year-to-date sales?

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YTD Sales := CALCULATE([Total Sales], DATESYTD('Date'[Date]))

Filter to specific category?

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Bike Sales := CALCULATE([Total Sales], Products[Category] = "Bikes")

Why use DIVIDE vs / ?

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DIVIDE handles divide-by-zero gracefully (returns BLANK or 3rd arg). Slash returns infinity error.

CALCULATE — main effect?

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Modifies filter context. Replaces or adds filters before evaluating its expression.

Remove all filters from a column?

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CALCULATE([Total Sales], ALL(Products[Category]))

Keep only existing filters from a single column?

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ALLEXCEPT(Products, Products[Category]) — clears all filters on Products except Category.

% of total in a visual?

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%Total := DIVIDE([Total Sales], CALCULATE([Total Sales], ALLSELECTED()))

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