Split Column
For many recipes, the first step is to split data from a single column into multiple columns. This section describes the various methods that can be used for splitting a single column into one or more columns, based on character- or pattern-matching or position within the column's values.
Tip
When this transform appears in a suggestion card, the maximum number of suggested columns to split is 250, which may prevent the browser from crashing. If your dataset requires additional column splits, you can edit the transformation and increase the maximum number of splits. Avoid creating datasets that are wider than 1000 columns.
Split by Delimiter
When data is initially imported into Designer Cloud, data in each row may be split on a single delimiter. In the following example, you can see that the tab key is a single clear delimiter:
<IMSI^MSIDN^IMEI> DATETTIME/TIMEZONE OFFSET/DURATION MSWCNT:BASCNT^BASTRA CALL_TYPE/CORRESP_IDN/DISCONNECT REASON <310170097665881^13011330554^011808005351311> 2014-12-12T00:06:13/-5/1.55 MSC001:BSC002^BTS783 MOT/00000000000:11 <310170097665881^13011330554^011808005351311> 2014-12-12T02:27:26/-5/0.00 MSC001:BSC002^BTS783 SMS/00000000000: <310-170-097665881^13011330554^011808005351311> 2014-12-12T03:24:20/-5/0 MSC001:BSC001^BTS783 SMS/00000000000:
However, when this data is imported, it may be rendered in the data grid in the following structure:
column2 | column3 | column4 | column5 |
|---|---|---|---|
<IMSI^MSIDN^IMEI> | DATETTIME/TIMEZONE OFFSET/DURATION | MSWCNT:BASCNT^BASTRA | CALL_TYPE/CORRESP_IDN:DISCONNECT REASON |
<310170097665881^13011330554^011808005351311> | 2014-12-12T00:06:13/-5/1.55 | MSC001:BSC002^BTS783 | MOT/00000000000:11 |
<310170097665881^13011330554^011808005351311> | 2014-12-12T02:27:26/-5/0.00 | MSC001:BSC002^BTS783 | SMS/00000000000: |
<310-170-097665881^13011330554^011808005351311> | 2014-12-12T03:24:20/-5/0 | MSC001:BSC001^BTS783 | SMS/00000000000: |
Notes:
When the data is first imported, all of it is contained in a single column named column1. The application automatically splits the columns on the tab character for you and removes the original column1.
Tip
This auto-split does not appear in your recipe by default. For most formats, a set of initial steps is automatically applied to the dataset. Optionally, you can review and modify these steps, but you must deselect Detect Structure during the import.
Because the application was unable to determine clear headers for each column's data, generic ones are used. So, before you apply a header to your data, you must split out the data within each column.
The delimiters within each column vary.
column2 uses the caret, while column3 uses the forward slash.
column4 and column5 use multiple delimiters.
There is sparseness in the data. Note that in column5, the second row contains the value
11at the end, while the other two data rows do not have this value.
Split on single delimiter
For column2, you can split the column into separate columns based on the caret delimiter:
Transformation Name |
|
|---|---|
Parameter: Column | column2 |
Parameter: Option | By delimiter |
Parameter: Delimiter | '^' |
Parameter: Number of columns to create | 2 |
Note
The Number of columns to create value reflects the total number of new columns to generate.
Results:
Below is how the data in column2 is transformed:
column1 | column6 | column7 |
|---|---|---|
<IMSI | MSIDN | IMEI> |
<310170097665881 | 13011330554 | 011808005351311> |
<310170097665881 | 13011330554 | 011808005351311> |
<310-170-097665881 | 13011330554 | 011808005351311> |
Since column1 was unused as a name, it re-appears here. column6 and column7 are the next available generic column names.
There is a small bit of cleanup to do in column1 and column7 to remove the symbols at the beginning and end of these column values. You can do this cleanup before the split in the original column2 if desired.
For column3, suppose that you want to keep the DATETIME and TIMEZONE OFFSET values in the same column, preserving the forward slash to demarcate these two values. The DURATION values are to be split into a separate column:
Transformation Name |
|
|---|---|
Parameter: Column | column2 |
Parameter: Option | By delimiter |
Parameter: Delimiter | '/' |
Parameter: Start to split after | `/(-{digit}|{digit})` |
The above uses Wrangle , which are simplified versions of regular expressions for matching patterns.
In this case, the expression is the following:
`/(-{digit}|{digit})`For the Start to split after value, the above indicates that the application should start to look for matches on the delimiter (forward slash) only after the above pattern has been detected in the column values.
In this case, the pattern describes values that appear after a forward slash and could be a negative digit or a positive digit, which matches the pattern for the TIMEZONE OFFSET values in the column.
For more information on how to use Wrangle , see Text Matching.
Since you are splitting the column into two columns, you do not need to specify the number of new columns to create. The default is
1.
Split column by multiple delimiters
After splitting column3, the data resembled the following:
column3 |
|---|
DATETTIME/TIMEZONE OFFSET |
2014-12-12T00:06:13/-5 |
2014-12-12T02:27:26/-5 |
2014-12-12T03:24:20/-5 |
Suppose you want to break down the components of this date-time data into separate columns for year, month, day, hour, minute, second, and offset. The following could be use to do so:
Transformation Name |
|
|---|---|
Parameter: Column | column2 |
Parameter: Option | By multiple delimiters |
Parameter: Delimiter 1 | '-' |
Parameter: Delimiter 2 | '-' |
Parameter: Delimiter 3 | 'T' |
Parameter: Delimiter 4 | ':' |
Parameter: Delimiter 5 | ':' |
Parameter: Delimiter 6 | '/' |
Each delimiter is entered on a separate row.
Delimiters are processed in the listed order.
Split column between delimiters
Suppose that for column4, you want to split the column such that the middle part section is removed. You could use the previous transformation and then delete the middle column. You can also use the following transformation, which identifies that starting and editing delimiters that demarcate the separator between fields, effectively removing the middle column:
Transformation Name |
|
|---|---|
Parameter: Column | column4 |
Parameter: Option | By two delimiters |
Parameter: Start delimiter | ':' |
Parameter: Include as part of split | Selected |
Parameter: End delimiter | '^' |
Parameter: Include as part of split | Selected |
The separator between the columns is all of the content between the forward slashes. This content is removed from the dataset.
The two selected options include the forward slashes as part of the separator, which removes them from the dataset.
Split by Position
You can also perform column splits based on numerical positions in column values. These splitting options are useful for highly regular data that is of consistent length.
Tip
When specifying numeric positions, you do not have to list the positions in numeric order. You can now do faster iteration since you can add new positions as needed when previewing the transformation.
Suppose you have the following coordination information in three dimensions (x, y, and z). Note that the data is very regular, with leading zeroes for values that are less than 1000.
column1 |
|---|
POSXPOSYPOSZ |
000100040001 |
012405210555 |
100220046554 |
202056789011 |
379274329832 |
Split column by positions
The above data could be split based on positions within a column's value:
Transformation Name |
|
|---|---|
Parameter: Column | column1 |
Parameter: Option | By positions |
Parameter: Position 1 | 4 |
Parameter: Position 2 | 8 |
Results:
column2 | column3 | column4 |
|---|---|---|
POSX | POSY | POSZ |
0001 | 0004 | 0001 |
0124 | 0521 | 0555 |
1002 | 2004 | 6554 |
2020 | 5678 | 9011 |
3792 | 7432 | 9832 |
Split columns between positions
Suppose that you wish to split the above source data such that the middle column is removed:
Transformation Name |
|
|---|---|
Parameter: Column | column1 |
Parameter: Option | Between two positions |
Parameter: Position 1 | 4 |
Parameter: Position 2 | 8 |
Results:
column2 | column3 |
|---|---|
POSX | POSZ |
0001 | 0001 |
0124 | 0555 |
1002 | 6554 |
2020 | 9011 |
3792 | 9832 |
Split column at regular interval
The above transformation could be simplified even further, since the splits happen at regular intervals:
Transformation Name |
|
|---|---|
Parameter: Column | column1 |
Parameter: Option | At regular interval |
Parameter: Interval | 4 |
Parameter: Number of times to split | 2 |
Results:
The results would be the same as the first example.
Encoding Issues
If you are attempting to split columns based on non-ASCII characters that appear in the dataset, your transformations may fail.
In these cases, you should change the encoding that is applied to the dataset.
Steps:
In the Import Data page, select the dataset to import.
When the dataset card appears in the right column, click the Edit Settings link.
From the drop-down, select a more appropriate encoding to apply to the file.
Import the data and wrangle.
Try your split transformation on the dataset.
Splitting Rows
When a dataset is imported, the application attempts to split the data into individual rows, based on any available end of line delimiters. This transformation is performed automatically and is not included in your initial set of steps.
If the data is not consistently formatted, the rows may not be properly split. If so, you can disable the automatic splitting of rows.
Steps:
In the Import Data page, select the dataset to import.
When the dataset card appears in the right column, click the Edit Settings link.
Deselect the Detect Structure checkbox.
Import the data and wrangle.
The steps used to detect structure are listed as the first steps of your recipe, which allows you to modify them as needed.