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PivotToColumns[tab,varcol->valcol]

pivots the tabular object tab to another table with new column keys taken from the column varcol and values taken from the column valcol.

PivotToColumns[tab,varcol{valcol1,valcol2,…}]

creates columns with values from all valcoli for each value from varcol.

PivotToColumns[tab,{prule1,prule2,…}]

pivots tab using several pivot rules prulei.

Details
Details and Options Details and Options
Examples  
Basic Examples  
Scope  
Applications  
Penguins  
Sales Data  
Life Expectancy  
Lake Mead  
Properties & Relations  
See Also
Related Guides
History
Cite this Page

PivotToColumns

PivotToColumns[tab,varcol->valcol]

pivots the tabular object tab to another table with new column keys taken from the column varcol and values taken from the column valcol.

PivotToColumns[tab,varcol{valcol1,valcol2,…}]

creates columns with values from all valcoli for each value from varcol.

PivotToColumns[tab,{prule1,prule2,…}]

pivots tab using several pivot rules prulei.

Details

  • PivotToColumns is also known as spread and pivot wider.
  • PivotToColumns is typically used as a structural data cleaning operation, where an observation is scattered across multiple rows. PivotToColumns pivots the role of variable and value by spreading the values from one column into several columns.
  • The column keys of the result have the form ExtendedKey[valcol,vari], where vari are the values of the column varcol.
  • PivotFromColumns is effectively the inverse of PivotToColumns.

Examples

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Basic Examples  (1)

Pivot the weekdays in the "day" column into new columns of temperature values:

Wolfram Language code: table = ToTabular[...]
Wolfram Language code: PivotToColumns[table, "day" -> "temp"]

Scope  (2)

Pivot a table on two sets of columns:

Wolfram Language code: table = ToTabular[...]
Wolfram Language code: PivotToColumns[table, {"hlm" -> "temp", "horizon" -> "time"}]

Pivot a table on two sets of values:

Wolfram Language code: table = ToTabular[...]
Wolfram Language code: PivotToColumns[table, "day" -> {"temp", "time"}]

Keep the data for each day adjacent:

Wolfram Language code: PivotToColumns[table, {"day" -> "temp", "day" -> "time"}]

Applications  (4)

Penguins  (1)

Take penguin data collected at Palmer Archipelago:

Wolfram Language code: data = ResourceData["Sample Tabular Data: Palmer Penguins"]

Use PivotToColumns to split "body_mass" column into two columns based on "sex":

Wolfram Language code: PivotToColumns[data, "sex" -> "body_mass"]

Compare using PairedHistogram:

Wolfram Language code: PairedHistogram[% -> "male", % -> "female", ChartLabels -> {"male", "female"}, PlotLabel -> "body mass"]

Sales Data  (1)

Get retail data:

Wolfram Language code: data = Tabular[{...}]

Use PivotToColumns to separate sales for each city:

Wolfram Language code: PivotToColumns[data, "City" -> "Sales"]

Create a TemporalData with "Date" used as the "TimeColumn":

Wolfram Language code: td = FromTabular[%, "TemporalData", <|"TimeColumn" -> "Date"|>]

The column headers are stored in "MetaInformation":

Wolfram Language code: td["MetaInformation"]

Visualize the sales time series:

Wolfram Language code: DateListPlot[td, PlotLegends -> Last /@ td["PathNames"]]

Life Expectancy  (1)

Download a Tabular object from the Wolfram Data Repository:

Wolfram Language code: expect = ResourceData["Sample Tabular Data: Health Expenditure"]

Pivot by country so that each row is a year, to compare the data between countries:

Wolfram Language code: PivotToColumns[expect, "Country" -> {"TotalHealthExpenditure", "LifeExpectancy"}]

Have the spending and life expectancy columns appear together:

Wolfram Language code: PivotToColumns[expect, {"Country" -> "TotalHealthExpenditure", "Country" -> "LifeExpectancy"}]

Lake Mead  (1)

Time series of Lake Mead water levels:

Wolfram Language code: ts = TemporalData[TimeSeries, {{{Missing[], Quantity[708.7, "Feet"], Quantity[701.7, "Feet"], Quantity[752.4, "Feet"], Quantity[806.6, "Feet"], Quantity[909.1, "Feet"], Quantity[928.4, "Feet"], Quantity[925.9, "Feet"], Quantity[920.8, "Feet"], ... fication[DateObject[{1935, 1, 31}, "Day"], {2024, 6, 30, 0, 0, 0}, "EndOfMonth", "DayRange"]}, 1, {"Continuous", 1}, {"Discrete", 1}, 1, {ResamplingMethod -> {"Interpolation", InterpolationOrder -> 1}, ValueDimensions -> 1}}, True, 14.2];

Create a Tabular object:

Wolfram Language code: tab1 = ToTabular[ts, "TimeSeries"]
Wolfram Language code: tab2 = DeleteColumns[TransformColumns[tab1, {"Year" -> Function[DateValue[#Date, "Year"]], "Month" -> Function[DateValue[#Date, "MonthName"]]}], "Date"]

Use PivotToColumns to convert into a more compact form for visualization:

Wolfram Language code: tab3 = PivotToColumns[tab2, "Month" -> "Value"]

Properties & Relations  (1)

Use PivotToColumns on a Tabular object:

Wolfram Language code: table = ToTabular[...]
Wolfram Language code: PivotToColumns[table, "day" -> "temp"]

Simplify the column keys by removing the common part in ExtendedKey:

Wolfram Language code: RenameColumns[%, ExtendedKey["temp", a_] :> a]

Use PivotFromColumns to recover the original Tabular object:

Wolfram Language code: PivotFromColumns[%, {"M", "Tu", "W", "Th", "F"} -> {"day", "temp"}]
Wolfram Language code: % === table
Wolfram Research (2025), PivotToColumns, Wolfram Language function, https://reference.wolfram.com/language/ref/PivotToColumns.html.

Text

Wolfram Research (2025), PivotToColumns, Wolfram Language function, https://reference.wolfram.com/language/ref/PivotToColumns.html.

CMS

Wolfram Language. 2025. "PivotToColumns." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/PivotToColumns.html.

APA

Wolfram Language. (2025). PivotToColumns. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/PivotToColumns.html

BibTeX

@misc{reference.wolfram_2026_pivottocolumns, author="Wolfram Research", title="{PivotToColumns}", year="2025", howpublished="\url{https://reference.wolfram.com/language/ref/PivotToColumns.html}", note=[Accessed: 13-June-2026]}

BibLaTeX

@online{reference.wolfram_2026_pivottocolumns, organization={Wolfram Research}, title={PivotToColumns}, year={2025}, url={https://reference.wolfram.com/language/ref/PivotToColumns.html}, note=[Accessed: 13-June-2026]}