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This example shows how to use parsing functions for evaluating input values against the function-specific data type.

Functions:

Source:

The following table contains data on a series of races.

raceId	disqualified	date	racerId	time_sc
1	FALSE	2/1/20	1	24.22
2	f	2/8/20	1	25
3	no	2/8/20	1	24.11
4	n	1-Feb-20	2	26.1
5	TRUE	8-Feb-20	2.2	-25.22
6	t	2/8/2020 10:16:00 AM	2	25.44
7	yes	2/1/20	3	24
8	y	2/8/20	33	29.22
9	0	2/8/20	3	24.78
10	1	1-Feb-20	4	26.2.1
11	FALSE	8-Feb-20		28.22 sec
12	FALSE	2/8/2020 10:16:00 AM	4	27.11

As you can see, this dataset has variation in values (FALSE, f, no, n) and problems with the data.

Transformation:

When the data is first imported, it may be properly typed for each column. To use the parsing functions, these columns should be converted to String data type:

Now, you can parse individual columns.

disqualified column:

racerId column:

time_sc column:

date column:

For the date column, the PARSEDATE function supports a default set of Datetime formats. Since some of the listed formats are different from these defaults, you must specify all of the formats. These formats are specified as an array of string values as the second argument of the function:

Tip: For the PARSEDATE function, it's useful to use the Preview to verify that all of the dates in the column are represented in the array of output formats. You can see the available output formats through the data type menu at the top of a column in the Transformer Page.

After all of the date values have been standardized to the output format of the PARSEDATE function, you may choose to remove the time element of the values:

Results:

After executing the above steps, the data appears as follows. Notes on each column's output are below the table.

raceId	disqualified	date	racerId	time_sc
1	false	2020-02-01	1	24.22
2	false	2020-02-08	1	25
3	false	2020-02-08	1	24.11
4	false	2020-02-01	2	26.1
5	true	2020-02-08	null	-25.22
6	true	2020-02-08	2	25.44
7	true	2020-02-01	3	24
8	true	2020-02-08	33	29.22
9	false	2020-02-08	3	24.78
10	true	2020-02-01	4	null
11	false	2020-02-08	null	null
12	false	2020-02-08	4	27.11

disqualified column:

The PARSEBOOL function normalizes all valid Boolean values to either false or true.

racerId column:

The PARSEINT function writes invalid values as null values.
The function writes empty values as null values.
The value 33 remains, since it is a valid Integer. This value should be fixed manually.

time_sc:

The PARSEFLOAT function writes the source value 25.00 as 25 in output.
The source value -25.22 remains. However, since this is time-based data, it needs to be fixed.
Invalid values are written as nulls.

date column:

All values are written in the standardized format: yyyy-MM-dd HH:mm:ss. Time data has been stripped.