Base Validators¶
Build model for dataset by identifying col type along with its respective params.
- dataprofiler.validators.base_validators.is_in_range(x: Union[float, int], config: dict) bool ¶
Check to see x is in the range of the config.
- Parameters
x (int/float) – number
config (dict) – configuration
- Returns
bool
- dataprofiler.validators.base_validators.is_in_list(x: str, config: dict) bool ¶
Check to see x is in the config list.
- Parameters
x (string) – item
config (dict) – configuration
- Returns
bool
- class dataprofiler.validators.base_validators.Validator¶
Bases:
object
For validating a data set.
Initialize Validator object.
- validate(data: Union[pd.DataFrame, dd.DataFrame], config: dict) None ¶
Validate a data set.
No option for validating a partial data set.
Set configuration on run not on instantiation of the class such that you have the option to run multiple times with different configurations without having to also reinstantiate the class.
- Parameters
data (DataFrame Dask/Pandas) – The data to be processed by the validator. Processing occurs in a column-wise fashion.
config (dict) – configuration for how the validator should run across the given data. Validator will only run over columns specified in the configuration.
- Example
This is an example of the config:
config = { <column_name>: { range: { 'start': 1, 'end':2 }, list: [1,2,3] } }
- get() dict ¶
Get the results of the validation run.