STDEV Function

Computes the standard deviation across all column values of Integer or Decimal type. The standard deviation of a set of values attempts to measure the spread in values around the mean and is used to measure confidence in statistical results. A standard deviation of zero means that all values are the same, and a small standard deviation means that the values are closely bunched together. A high value for standard deviation indicates that the numbers are spread out widely. Standard deviation is always a positive value.

Standard deviation comes in two flavors:

  • Population standard deviation computes the variance from all possible values.
  • Sample standard deviation computes from a subset or sample of all values.
  • Since Cloud Dataprep by TRIFACTA® has access to all available values, the computation for population standard deviation is used across the entire dataset.

If a row contains a missing or null value, it is not factored into the calculation. If no numeric values are detected in the input column, the function returns 0.

The square of standard deviation is variance. See VAR Function.

Basic Usage

aggregate value:STDEV(myRating) group: postal_code

Output: Generates a two-column table containing the unique values from the postal_code column and the standard deviation of the group of values from the myRating column for the postal_code value.

Syntax

aggregate value:STDEV(function_col_ref) [group:group_col_ref]

ArgumentRequired?Data TypeDescription
function_col_refYstringName of column to which to apply the function

For more information on the group parameter, see Aggregate Transform.

For more information on syntax standards, see Language Documentation Syntax Notes.

function_col_ref

Name of the column the values of which you want to calculate the variance. Column must contain Integer or Decimal values.

  • Literal values are not supported as inputs.
  • Multiple columns and wildcards are not supported.

Usage Notes:

Required?Data TypeExample Value
YesString (column reference)myValues

Examples

This example illustrates how you can apply statistical functions to your dataset. Calculations include average (mean), max, min, standard deviation, and variance.

Source:

Students took a test and recorded the following scores. You want to perform some statistical analysis on them:

StudentScore
Anna84
Ben71
Caleb76
Danielle87
Evan85
Faith92
Gabe86
Hannah99
Ian73
Jane68

Transform:

You can use the following transforms to calculate the average (mean), minimum, and maximum scores:

derive value:AVERAGE(Score) as:'avgScore'

derive value:MIN(Score) as:'minScore'

derive value:MAX(Score) as:'maxScore'

To apply statistical functions to your data, you can use the VAR and STDEV functions, which can be used as the basis for other statistical calculations.

derive value:VAR(Score)

derive value:STDEV(Score)

For each score, you can now calculate the variation of each one from the average, using the following:

derive value:((Score - average_Score) / stdev_Score) as:'stDevs'

Now, you want to apply grades based on a formula:

Gradestandard deviations from avg (stDevs)
AstDevs > 1
BstDevs > 0.5
C-1 <= stDevs <= 0.5
DstDevs < -1
FstDevs < -2

You can build the following transform using the IF function to calculate grades.

derive value:IF((stDevs > 1),'A',IF((stDevs < -2),'F',IF((stDevs < -1),'D',IF((stDevs > 0.5),'B','C'))))

For more information, see IF Function.

To clean up the content, you might want to apply some formatting to the score columns. The following reformats the stdev_Score and stDevs columns to display two decimal places:

set col:stdev_Score value:NUMFORMAT(stdev_Score, '##.00')

set col:stDevs value:NUMFORMAT(stDevs, '##.00')

Results:

StudentScoreavgScoreminScoremaxScorevar_Scorestdev_ScorestDevsGrade
Anna8482.16899

87.69000000000142

9.360.20C
Ben7182.1689987.690000000001429.36-1.19D
Caleb7682.1689987.690000000001429.36-0.65C
Danielle8782.1689987.690000000001429.360.52B
Evan8582.1689987.690000000001429.360.31C
Faith9282.1689987.690000000001429.361.06A
Gabe8682.1689987.690000000001429.360.42C
Hannah9982.1689987.690000000001429.361.80A
Ian7382.1689987.690000000001429.36-0.97C
Jane6882.1689987.690000000001429.36-1.51D

Was this page helpful? Let us know how we did:

Send feedback about...

Google Cloud Dataprep Documentation