Dependencies Validation Using Cerberus

Am validating a CSV file with Cerberus but am struggling with what I'd assume is some basic logic Scenario: A CSV file has 2 columns. Column 2 requires to have a value only if Colu

Solution 1:

Note The evaluation of this rule (dependencies) does not consider any constraints defined with the required rule.

Whatever the "required" would be:

from cerberus import Validator
v = Validator()

document = {
    "col1": "a",
    "col2": ""
}

schema = {
    "col1": {"required": False},
    "col2": {"required": True, "dependencies": "col1"},
}

print(v.validate(document, schema))  # Trueprint(v.errors) # {}

---

schema = {
    "col1": {"required": True},
    "col2": {"required": True, "dependencies": "col1"},
}


print(v.validate(document, schema))  # Trueprint(v.errors)  # {}

---

schema = {
    "col1": {"required": True},
    "col2": {"required": False, "dependencies": "col1"},
}


print(v.validate(document, schema))  # Trueprint(v.errors)  # {}

http://docs.python-cerberus.org/en/stable/validation-rules.html#dependencies

---

Update:

Solution for your condition "Make col2 mandatory if col1 has a value in it.". To apply a sophisticated rules - create a custom Validator as shown below:

from cerberus import Validator


classMyValidator(Validator):
    def_validate_depends_on_col1(self, depends_on_col1, field, value):
        """ Test if a field value is set depending on `col1` field value.
        """if depends_on_col1 and self.document.get('col1', None) andnot value:
            self._error(field, f"`{field}` cannot be empty given that `col1` has a value")


v = MyValidator()

schema = {
    "col1": {"required": False},
    "col2": {"required": True, "depends_on_col1": True},
}

print(v.validate({"col1": "a", "col2": ""}, schema))  # Falseprint(v.errors) # {'col2': ['`col2` cannot be empty given that `col1` has a value']}print(v.validate({"col1": "", "col2": ""}, schema))  # Trueprint(v.errors) # {}print(v.validate({"col1": 0, "col2": "aaa"}, schema))  # Trueprint(v.errors) # {}

Note, you need to run into convention of what column col1 values should be treated as empty (to adjust a custom validator rules).

---

Extended version to specify a "dependancy" field name:

classMyValidator(Validator):
    def_validate_depends_on_col(self, col_name, field, value):
        """ Test if a field value is set depending on `col_name` field value.
        """if col_name and self.document.get(col_name, None) andnot value:
            self._error(field, f"`{field}` cannot be empty given that `{col_name}` has a value")


v = MyValidator()

document = {"col1": "a", "col2": ""}

schema = {
    "col1": {"required": False},
    "col2": {"required": True, "depends_on_col": "col1"},
}

http://docs.python-cerberus.org/en/stable/customize.html

Solution 2:

Assuming that you transformed your csv input into a list of documents, you could first preprocess the documents in order to remove the col2 field where it is empty:

fordocumentindocuments:
    if not document["col2"]:
        document.pop("col2")

Then this schema would do the job:

{"col1": {
    "oneof": [
        {"empty": True},
        {"empty": False, "dependencies": "col2"}
    ]
}}

Mind that the the dependencies and required rules don't consider the value of a field, but only the presence of the field in the document.