PARSESTRING Function
Evaluates an input against the String datatype. If the input matches, the function outputs a String value. Input can be a literal, a column of values, or a function returning values. Values can be of any data type.
After you have converted your values to strings, if a sufficient percentage of inputs from a column are successfully converted to the other data type, the column may be retyped.
Astuce
If the column is not automatically retyped as a result of this function, you can manually set the type to String in a subsequent recipe step.
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
parsestring(strInput)
Output: Returns the String data type value for strInput values.
Syntax and Arguments
parsestring(str_input)
Argument | Required? | Data Type | Description |
|---|---|---|---|
str_input | Y | any | Literal, name of a column, or a function returning values to match |
For more information on syntax standards, see Language Documentation Syntax Notes.
str_input
Literal, column name, or function returning values that are to be evaluated for conversion to String values.
Missing values for this function in the source data result in null values in the output.
Multiple columns and wildcards are not supported.
Usage Notes:
Required? | Data Type | Example Values |
|---|---|---|
Yes | any | 5 "Porsche" 3.4 |
Examples
Astuce
For additional examples, see Common Tasks.
Example - type parsing functions
Source:
The following table contains values for city, state, and zip code for locations in the United States:
City | State | Zip |
|---|---|---|
San Francisco | CA | 94105 |
Seattle | WA | 98109 |
Portland | OR | 97202 |
San Diego | CA | 92109 |
Brooklyn | NY | 11203 |
Portland | ME | 4101 |
Boston | MA | 2170 |
In the above table, you can see that some of the values are listed as four-digit zip codes, which are invalid. These values are likely to be interpreted as Integer values, which means that any leading zeroes are dropped. You can use the steps below to fix it.
Transformation:
Since you are working with integer values, you can use the following transformation to test the length of the values as if they were strings using the PARSESTRING function. If the values are only four characters long, then the value is merged with a leading 0:
Transformation Name | |
|---|---|
Parameter: Columns | Zip |
Parameter: Formula | if(len(parsestring($col)) == 4, merge(['0',parsestring($col)]), $col) |
The $col reference points to the column that has been selected to be edited. In this case, that column is Zip. For more information, see Source Metadata References.
Depending on the number of rows in your dataset, the Cloud Portal may not re-infer the data as Zip type. You can use the following transformation to change the data type for the column to Zip:
Transformation Name | |
|---|---|
Parameter: Columns | Zip |
Parameter: New type | Zipcode |
Results:
City | State | Zip |
|---|---|---|
San Francisco | CA | 94105 |
Seattle | WA | 98109 |
Portland | OR | 97202 |
San Diego | CA | 92109 |
Brooklyn | NY | 11203 |
Portland | ME | 04101 |
Boston | MA | 02170 |