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'Table Contents' settings Reference

Overview​

Table Content Settings

The Table Contents section controls what data is displayed in your table and how it's aggregated.

Table Type​

Setting: Table type (Percentage or Mean)
Default: Percentage

Percentage Tables​

Used to analyze distributions, proportions, and response rates.

Available Displays:

  • Values: Raw count or sum
  • Vertical Percentage (%): Percentage within each column
  • Horizontal Percentage (%): Percentage within each row
  • Indice: Index value (typically 100 = average)

Example: In a survey of 100 respondents:

            Very Satisfied    Satisfied    Neutral
North       45 (60%)         30 (40%)     25 (33%)
South       35 (47%)         45 (60%)     45 (60%)
Total       80 (53%)         75 (50%)     70 (47%)

Mean Tables​

Used to analyze averages and their statistical properties.

Available Displays:

  • Mean Values: Average of the metric
  • Count: Number of observations used in calculation
  • Standard Deviation: Measure of variability
  • Indice: Index based on mean values

Example: Average customer satisfaction score:

alt text

Display Options​

Values​

Label: Show values
Type: Toggle
Default: Off

When enabled, displays the raw count or sum in each cell.

Vertical Percentage​

Label: Vertical percentage
Type: Toggle
Default: On

Shows each cell's value as a percentage of its column total. Useful for comparing across categories within groups.

Horizontal Percentage​

Label: Horizontal percentage
Type: Toggle
Default: Off

Shows each cell's value as a percentage of its row total. Useful for comparing category distribution within segments.

Indice​

Label: Show indice
Type: Toggle
Default: Off

Displays index values (typically with 100 as base). Useful for comparing relative performance against an average.

Calculation: (Cell Value / Average) × 100

Masking​

WHY USE MASKING WHEN POWER BI CAN FILTER DATA?

When you filter data in Power BI, the underlying data is removed from the visual, which can affect calculations like totals and percentages. Masking allows you to hide specific data points while still including them in calculations, ensuring accurate results.

Mask Pattern​

Label: Mask Pattern
Type: Text area
Default: Empty
Available in: Pro, Premium editions

Allows you to mask (hide) data from specific rows or columns using regular expressions.

Mask Pattern

User will input a pattern to match the rows/columns to be hidden. As an example, if you want to show ONLY Brand A and Brand B, you need to hide everything which is NOT Brand A or B.

Label_Brand != BrandA AND Label_Brand != BrandB

or better with the IN operator

NOT Label_Brand IN (BrandA, BrandB)

Therefore, if also you do not want the "No answer" to gender question while still using it in calculations:

(NOT Label_Brand IN (BrandA, BrandB)) OR Customer_Gender = "Not Stated"

etc ...

(NOT Label_Brand IN (BrandA, BrandB)) OR (Customer_Gender IN ("Not Stated", Other))
Beware of the inverted logic of masking.

The pattern defines what to HIDE, this can be somewaht counter-intuitive at first.

Valid operators are:

  • IN - for multiple values
  • AND - for logical conjunction
  • OR - for logical disjunction
  • NOT - for negation
  • = - Equals
  • != - Not equals
  • < - Less than
  • > - Greater than
  • LIKE - Pattern matching with % wildcard
<= and >= does not exist

if you want to filter for years greater than or equal to 2020, you need to use Year > 2019

The LIKE operator works with the % symbol, for example:

Customer_Gender LIKE %ale

Will hide M'ale' AND Fem'ale', and therefore show only "Not Stated" and "Other"

To show only Male AND Female, you need to invert the logic: NOT Customer_Gender LIKE %ale

Will show ONLY Male and Female

Use Cases:

  • Hide sensitive data in shared reports
  • Remove small brands/categories from view
  • Focus on top-performing segments
  • Filter out "Other" or "Not answered" categories while still taking them into account for calculations

Series Configuration​

For detailed series configuration options (Value Series, Base Series, Mean Series, etc.), see:

Best Practices​

  1. Choose Table Type First: Percentage vs Mean fundamentally changes how data is displayed
  2. Limit Display Fields: Use 2-3 display options maximum for clarity
  3. Use Masking Sparingly: Masked data is still calculated but hidden
  4. Precision for Business: Use 0-1 decimal places for most business reporting
  5. Totals Context: Show totals to help readers understand the base size

Troubleshooting​

Q: Values appear as "0%" or very small numbers
A: Check that your base series is correctly mapped in percentage series settings.

Q: Indice values don't look right
A: Ensure you've configured both values and base series correctly.

Q: Mask pattern isn't working
A: Check your regular expression syntax. Use simple patterns like Total before complex regex.

For more help, see the Quick Start Guide.