PydeequDynamicParser 0.2

Python library which makes it possible to use validation rules in pydeequ based on json structures.

pydeequ-dynamic-parser

Python library which makes it possible to use validation rules in pydeequ based on json/dict structures.

Installing

pip install PydeequDynamicParser

Usage

# User Dynamic Checks
all_checks = [{"name": "isUnique", "parameters": {"column": "COLUMN_NAME", "hint": "Hint here"}},
              {"name": "satisfies", "parameters": {"columnCondition": "(LENGTH(COLUMN_NAME) = 11 OR LENGTH(COLUMN_NAME) = 14) ", "constraintName": "COLUMN_NAME length validate", "assertion": "lambda x: x == 1.0", "hint": None}},
              {"name": "containsEmail", "parameters": {"column": "COLUMN_NAME", "assertion": None, "hint": None}},
              {"name": "isComplete", "parameters": {"column": "COLUMN_NAME", "hint": None}}]

# PyDeequ constraint dynamic constraint based on "all_checks"
from pydeequ.checks import Check
from pydeequ.checks import CheckLevel
from pydeequ.verification import VerificationSuite
from pydeequ.verification import VerificationResult
import PydeequDynamicParser

check = Check(spark, CheckLevel.Error, "Check Name")
check_instance_parsed = PydeequDynamicParser.Parser(check, all_checks).parse()
checkResult = VerificationSuite(spark).onData(df).addCheck(check_instance_parsed).run()
checkResult_df = VerificationResult.checkResultsAsDataFrame(spark, checkResult)

checkResult_df.toPandas()

As we can see the line responsible for executing the parse will translate de user json/dict to PyDeequ Check instance.

import PydeequDynamicParser
check_instance_parsed = PydeequDynamicParser.Parser(check, all_checks).parse()

Currently supported validations

• Constraints
  • isUnique
  • satisfies
  • containsEmail
  • isComplete