SEGMENT genre / ['Fantasy', 'Science Fiction']
col_vals_gt()
col_vals_gt()
0.88
0.12
col_vals_gt(), col_vals_regex(), etc.). When passing in a column, or a tuple with a column and certain values, a segment will be created for each individual value within the column or given values. The [seg_group()](seg_group.md#pointblank.seg_group) selector enables values to be grouped together into a segment. For example, if you were to create a segment for a column "region", investigating just "North" and "South" regions, a typical segment would look like:
`segments=("region", ["North", "South"])`
This would create two validation steps, one for each of the regions. If you wanted to group these two regions into a single segment, you could use the [seg_group()](seg_group.md#pointblank.seg_group) function like this:
`segments=("region", pb.seg_group(["North", "South"]))`
You could create a second segment for "East" and "West" regions like this:
`segments=("region", pb.seg_group([["North", "South"], ["East", "West"]]))`
There will be a validation step created for every segment. Note that if there aren't any segments created using [seg_group()](seg_group.md#pointblank.seg_group) (or any other segment expression), the validation step will fail to be evaluated during the interrogation process. Such a failure to evaluate will be reported in the validation results but it won't affect the interrogation process overall (i.e., the process won't be halted).
## Parameters
`values: list[Any]`
A list of values to be grouped into a segment. This can be a single list or a list of lists.
## Returns
`Segment`
A `Segment` object, which can be used to combine values into a segment.
## Examples
Let's say we're analyzing sales from our local bookstore, and want to check the number of books sold for the month exceeds a certain threshold. We could pass in the argument `segments="genre"`, which would return a segment for each unique genre in the datasets. We could also pass in `segments=("genre", ["Fantasy", "Science Fiction"])`, to only create segments for those two genres. However, if we wanted to group these two genres into a single segment, we could use the [seg_group()](seg_group.md#pointblank.seg_group) function.
``` python
import pointblank as pb
import polars as pl
tbl = pl.DataFrame(
{
"title": [
"The Hobbit",
"Harry Potter and the Sorcerer's Stone",
"The Lord of the Rings",
"A Game of Thrones",
"The Name of the Wind",
"The Girl with the Dragon Tattoo",
"The Da Vinci Code",
"The Hitchhiker's Guide to the Galaxy",
"The Martian",
"Brave New World"
],
"genre": [
"Fantasy",
"Fantasy",
"Fantasy",
"Fantasy",
"Fantasy",
"Mystery",
"Mystery",
"Science Fiction",
"Science Fiction",
"Science Fiction",
],
"units_sold": [875, 932, 756, 623, 445, 389, 678, 534, 712, 598],
}
)
validation = (
pb.Validate(data=tbl)
.col_vals_gt(
columns="units_sold",
value=500,
segments=("genre", pb.seg_group(["Fantasy", "Science Fiction"]))
)
.interrogate()
)
validation
```
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | E | C | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| #4CA64C66 | 1 |
SEGMENT genre / ['Fantasy', 'Science Fiction']
col_vals_gt()
|
|
✓ | 8 | 7 0.88 |
1 0.12 |
-- | -- | -- | CSV |
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | E | C | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| #4CA64C66 | 1 |
SEGMENT genre / ['Fantasy', 'Science Fiction']
col_vals_gt()
|
|
✓ | 8 | 7 0.88 |
1 0.12 |
-- | -- | -- | CSV | ||
| #4CA64C66 | 2 |
SEGMENT genre / ['Fantasy', 'Mystery']
col_vals_gt()
|
|
✓ | 7 | 5 0.71 |
2 0.29 |
-- | -- | -- | CSV | ||
| #4CA64C66 | 3 |
SEGMENT genre / ['Mystery', 'Science Fiction']
col_vals_gt()
|
|
✓ | 5 | 4 0.80 |
1 0.20 |
-- | -- | -- | CSV |