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MODE Function

Computes the mode (most frequent value) from all row values in a column, according to their grouping. Input column can be of Integer, Decimal, or Datetime type.

  • If a row contains a missing or null value, it is not factored into the calculation. If the entire column contains no values, the function returns a null value.

  • If there is a tie in which the most occurrences of a value is shared between values, then the lowest value of the evaluated set is returned.

  • When used in apivottransform, the function is computed for each instance of the value specified in thegroupparameter. See Pivot Transform.

For a version of this function computed over a rolling window of rows, see ROLLINGMODE Function.

Datetime inputs to this function return Unixtime values.

Wrangle vs. SQL: This function is part of Wrangle, a proprietary data transformation language. Wrangle is not SQL. For more information, see Wrangle Language.

Basic Usage

mode(count_visits)

Output: Returns the mode of the values in the count_visits column.

Syntax and Arguments

mode(function_col_ref) [group:group_col_ref] [limit:limit_count]

Argument

Required?

Data Type

Description

function_col_ref

Y

string

Name of column to which to apply the function

For more information on the group and limit parameters, see Pivot 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 function. Column must contain Integer, Decimal, or Datetime values.

Nota

If the input is in Datetime type, the output is in unixtime format. You can wrap these outputs in the DATEFORMAT function to generate the results in the appropriate Datetime format. See DATEFORMAT Function.

  • Literal values are not supported as inputs.

  • Multiple columns and wildcards are not supported.

Usage Notes:

Required?

Data Type

Example Value

Yes

String (column reference)

myValues

Examples

Sugerencia

For additional examples, see Common Tasks.

Example - Statistics on Test Scores

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

Functions:

Item

Description

AVERAGE Function

Computes the average (mean) from all row values in a column or group. Input column can be of Integer or Decimal.

MIN Function

Computes the minimum value found in all row values in a column. Input column can be of Integer, Decimal or Datetime.

MAX Function

Computes the maximum value found in all row values in a column. Inputs can be Integer, Decimal, or Datetime.

VAR Function

Computes the variance among all values in a column. Input column can be of Integer or Decimal. If no numeric values are detected in the input column, the function returns 0.

STDEV Function

Computes the standard deviation across all column values of Integer or Decimal type.

NUMFORMAT Function

Formats a numeric set of values according to the specified number formatting. Source values can be a literal numeric value, a function returning a numeric value, or reference to a column containing an Integer or Decimal values.

MODE Function

Computes the mode (most frequent value) from all row values in a column, according to their grouping. Input column can be of Integer, Decimal, or Datetime type.

Source:

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

Student

Score

Anna

84

Ben

71

Caleb

76

Danielle

87

Evan

85

Faith

92

Gabe

85

Hannah

99

Ian

73

Jane

68

Transformation:

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

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

AVERAGE(Score)

Parameter: New column name

'avgScore'

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

MIN(Score)

Parameter: New column name

'minScore'

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

MAX(Score)

Parameter: New column name

'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.

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

VAR(Score)

Parameter: New column name

var_Score

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

STDEV(Score)

Parameter: New column name

stdev_Score

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

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

((Score - avg_Score) / stdev_Score)

Parameter: New column name

'stDevs'

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

Grade

standard deviations from avg (stDevs)

A

stDevs > 1

B

stDevs > 0.5

C

-1 <= stDevs <= 0.5

D

stDevs < -1

F

stDevs < -2

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

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

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

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:

Transformation Name

Edit column with formula

Parameter: Columns

stdev_Score

Parameter: Formula

NUMFORMAT(stdev_Score, '##.00')

Transformation Name

Edit column with formula

Parameter: Columns

stDevs

Parameter: Formula

NUMFORMAT(stDevs, '##.00')

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

MODE(Score)

Parameter: New column name

'modeScore'

Results:

Student

Score

modeScore

avgScore

minScore

maxScore

var_Score

stdev_Score

stDevs

Grade

Anna

84

85

82

68

99

87.00000000000001

9.33

0.21

C

Ben

71

85

82

68

99

87.00000000000001

9.33

-1.18

D

Caleb

76

85

82

68

99

87.00000000000001

9.33

-0.64

C

Danielle

87

85

82

68

99

87.00000000000001

9.33

0.54

B

Evan

85

85

82

68

99

87.00000000000001

9.33

0.32

C

Faith

92

85

82

68

99

87.00000000000001

9.33

1.07

A

Gabe

85

85

82

68

99

87.00000000000001

9.33

0.32

C

Hannah

99

85

82

68

99

87.00000000000001

9.33

1.82

A

Ian

73

85

82

68

99

87.00000000000001

9.33

-0.96

C

Jane

68

85

82

68

99

87.00000000000001

9.33

-1.50

D