IFMISSING Function
The IFMISSING
function writes out a specified value if the source value is a null or missing value. Otherwise, it writes the source value. Input can be a literal, a column reference, or a function.
The
ISMISSING
function simply tests if a value is missing. See ISMISSING Function.Missing values are different from null values. To test for null values, see IFNULL Function.
Astuce
Since this function captures both missing and null values, you may first wish to address the rows with null values using the IFNULL
or ISNULL
functions. Any remaining rows that are matched based on this function are exclusively missing 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
ifmissing(my_score,'0')
Output: Generates a new column called, final_score
, which contains the value 0
if the value in my_score
is a null or missing value.
Syntax and Arguments
ifmissing(column_string, computed_value)
Argument | Required? | Data Type | Description |
---|---|---|---|
source_value | Y | string | Name of column, string literal or function to be tested |
output_value | y | string | String literal value to write |
For more information on syntax standards, see Language Documentation Syntax Notes.
source_value
Name of the column, string literal, or function to be tested for missing values.
Missing literals or column values generate missing string results.
Multiple columns and wildcards are not supported.
Usage Notes:
Required? | Data Type | Example Value |
---|---|---|
Yes | String literal, column reference, or function | myColumn |
output_value
The output value to write if the tested value returns a null or missing value.
Usage Notes:
Required? | Data Type | Example Value |
---|---|---|
Yes | String or numeric literal | 'Missing input' |
Examples
Astuce
For additional examples, see Common Tasks.
Example - IF* functions for data type validation
This example illustrates how to use the IF* functions for data type validation.
Functions:
Item | Description |
---|---|
IFNULL Function | The |
IFMISSING Function | The |
IFMISMATCHED Function | The |
IFVALID Function | The |
MERGE Function | Merges two or more columns of String type to generate output of String type. Optionally, you can insert a delimiter between the merged values. |
Source:
The following simple table lists zip codes by customer identifier:
custId | custZip |
---|---|
C001 | 98123 |
C002 | 94105 |
C003 | 12415 |
C004 | 12451-2234 |
C005 | 12441-298 |
C006 | |
C007 | |
C008 | 1242 |
C009 | 1104 |
Transformation:
When the above is imported into the Transformer page, you notice the following:
The
custZip
column is typed as Integer.There are two missing and two mismatched values in the
custZip
column.
First, you test for valid values in the custZip
column. Using the IFVALID
function, you can validate against any data type:
Transformation Name | |
---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | IFVALID(custZip, 'Zipcode', 'ok') |
Parameter: New column name | 'status' |
Fix four-digit zips: In the status
column are instances of ok
for the top four rows. You notice that the bottom two rows contain four-digit codes.
Since the custZip
values were originally imported as Integer, any leading 0
values are deleted. In this case, you can add back the leading zero. Before the previous step, change the data type of zip
to String and insert the following:
Transformation Name | |
---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | IF(LEN(custZip)==4,'0','') |
Parameter: New column name | 'FourDigitZip' |
Transformation Name | |
---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | MERGE([FourDigitZip,custZip]) |
Parameter: New column name | 'custZip2' |
Transformation Name | |
---|---|
Parameter: Columns | zip |
Parameter: Formula | custZip2 |
Transformation Name | |
---|---|
Parameter: Columns | FourDigitZip,custZip2 |
Parameter: Action | Delete selected columns |
Now, when you click the last recipe step, you should see that two more rows in status
are listed as Ok
.
For the zip code with the three-digit extension, you can simply remove that extension to make it valid. Click the step above the last one. In the data grid, highlight the value. Click the Replace suggestion card. Select the option that uses the following for the matching pattern:
'-{digit}{3}{end}'
The above means that all three-digit extensions are deleted from the zip. You can do the same for any two- and one-digit extensions, although there are none in this sample.
Missing and null values: Now, you need to address how to handle missing and null values. The IFMISSING
tests for both missing and null values, while the IFNULL
tests just for null values. In this example, you want to delete null values, which could mean that the data for that row is malformed and to write a status of missing
for missing values.
Click above the last line in the recipe to insert the following:
Transformation Name | |
---|---|
Parameter: Columns | custZip |
Parameter: Formula | IFNULL(custZip, 'xxxxx') |
Transformation Name | |
---|---|
Parameter: Columns | custZip |
Parameter: Formula | IFMISSING(custZip, '00000') |
Now, when you click the last line of the recipe, only the null value is listed as having a status other than ok
. You can use the following to remove this row and all like it:
Transformation Name | |
---|---|
Parameter: Condition | Custom formula |
Parameter: Type of formula | Custom single |
Parameter: Condition | (status == 'xxxxx') |
Parameter: Action | Delete matching rows |
Results:
custId | custZip | status |
---|---|---|
C001 | 98123 | ok |
C002 | 94105 | ok |
C003 | 12415 | ok |
C004 | 12451-2234 | ok |
C005 | 12441-298 | ok |
C006 | 00000 | ok |
C008 | 1242 | ok |
C009 | 1104 | ok |
As an exercise, you might repeat the above steps starting with the IFMISMATCHED
function determining the value in the status
column:
Transformation Name | |
---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | IFMISMATCHED(custZip, 'Zipcode', 'mismatched') |
Parameter: New column name | 'status' |