This checks for duplicate rows. You need to take into account that checking for duplicate rows can lead to high memory consumption on big files. Here is an example.
from pprint import pprint
from frictionless import validate, checks
source = b"header\nvalue\nvalue"
report = validate(source, format="csv", checks=[checks.duplicate_row()])
pprint(report.flatten(["type", "message"]))
[['duplicate-row',
'Row at position 3 is duplicated: the same as row at position "2"']]Check for duplicate rows This check can be enabled using the `checks` parameter for the `validate` function.
(*, name: Optional[str] = None, title: Optional[str] = None, description: Optional[str] = None) -> None
This check is the most powerful one as it uses the external simpleeval package allowing you to evaluate arbitrary Python expressions on data rows. Let's show on an example.
from pprint import pprint
from frictionless import validate, checks
source = [
["row", "salary", "bonus"],
[2, 1000, 200],
[3, 2500, 500],
[4, 1300, 500],
[5, 5000, 1000],
]
report = validate(source, checks=[checks.row_constraint(formula="salary == bonus * 5")])
pprint(report.flatten(["type", "message"]))
[['row-constraint',
'The row at position 4 has an error: the row constraint to conform is '
'"salary == bonus * 5"']]Check that every row satisfies a provided Python expression.
(*, name: Optional[str] = None, title: Optional[str] = None, description: Optional[str] = None, formula: str) -> None
Python expression to apply to all rows. To evaluate the formula simpleeval library is used.
str