Page tree



Contents:

The cloud-based version of Trifacta Wrangler is now available! Read all about it, and register for your free account.

This example shows how you can unpack data nested in an Object into separate columns using the following transforms:

Source:

You have the following information on used cars. The VIN column contains vehicle identifiers, and the Properties column contains key-value pairs describing characteristics of each vehicle. You want to unpack this data into separate columns.

VINProperties
XX3 JT4522year=2004,make=Subaru,model=Impreza,color=green,mileage=125422,cost=3199
HT4 UJ9122year=2006,make=VW,model=Passat,color=silver,mileage=102941,cost=4599
KC2 WZ9231year=2009,make=GMC,model=Yukon,color=black,mileage=68213,cost=12899
LL8 UH4921year=2011,make=BMW,model=328i,color=brown,mileage=57212,cost=16999

Transformation:

Add the following transformation, which identifies all of the key values in the column as beginning with alphabetical characters.

  • The valueafter string identifies where the corresponding value begins after the key.
  • The delimiter string indicates the end of each key-value pair.

Transformation Name Convert keys/values into Objects
Parameter: Column Properties
Parameter: Key `{alpha}+`
Parameter: Separator between key and value `=`
Parameter: Delimiter between pair ','

Now that the Object of values has been created, you can use the unnest transform to unpack this mapped data. In the following, each key is specified, which results in separate columns headed by the named key:

Transformation Name Unnest Objects into columns
Parameter: Column extractkv_Properties
Parameter: Paths to elements 'year','make','model','color','mileage','cost'

Results:

When you delete the unnecessary Properties columns, the dataset now looks like the following:

VINyearmakemodelcolormileagecost
XX3 JT45222004SubaruImprezagreen1254223199
HT4 UJ91222006VWPassatsilver1029414599
KC2 WZ92312009GMCYukonblack6821312899
LL8 UH49212011BMW328ibrown5721216999

Your Rating: Results: 1 Star2 Star3 Star4 Star5 Star 12 rates

This page has no comments.