Repository: great-expectations/great_expectations

Issue:
Use cleaner solution for non-truncating division in python 2
Prefer `from __future__ import division` to `1.*x/y`


Discussion:
Done inline during improve_partitions push.

Resolving patch:
diff --git a/contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_be_valid_pronic_number.py b/contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_be_valid_pronic_number.py
new file mode 100644
--- /dev/null
+++ b/contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_be_valid_pronic_number.py
@@ -0,0 +1,138 @@
+from math import sqrt
+from typing import Optional
+
+from great_expectations.core.expectation_configuration import ExpectationConfiguration
+from great_expectations.execution_engine import PandasExecutionEngine
+from great_expectations.expectations.expectation import ColumnMapExpectation
+from great_expectations.expectations.metrics import (
+    ColumnMapMetricProvider,
+    column_condition_partial,
+)
+
+
+# This method compares a string to the valid pronic number
+def is_valid_pronic_number(num: str) -> bool:
+    try:
+        n = int(num)
+        n_sr = (int)(sqrt(n))
+        return n_sr * (n_sr + 1) == n
+    except ValueError:
+        return False
+
+
+# This class defines a Metric to support your Expectation.
+# For most ColumnMapExpectations, the main business logic for calculation will live in this class.
+class ColumnValuesToBeValidPronicNumber(ColumnMapMetricProvider):
+
+    # This is the id string that will be used to reference your metric.
+    condition_metric_name = "column_values.valid_pronic_number"
+
+    # This method implements the core logic for the PandasExecutionEngine
+    @column_condition_partial(engine=PandasExecutionEngine)
+    def _pandas(cls, column, **kwargs):
+        return column.apply(lambda x: is_valid_pronic_number(x))
+
+    # This method defines the business logic for evaluating your metric when using a SqlAlchemyExecutionEngine
+    # @column_condition_partial(engine=SqlAlchemyExecutionEngine)
+    # def _sqlalchemy(cls, column, _dialect, **kwargs):
+    #     raise NotImplementedError
+
+    # This method defines the business logic for evaluating your metric when using a SparkDFExecutionEngine
+    # @column_condition_partial(engine=SparkDFExecutionEngine)
+    # def _spark(cls, column, **kwargs):
+    #     raise NotImplementedError
+
+
+# This class defines the Expectation itself
+class ExpectColumnValuesToBeValidPronicNumber(ColumnMapExpectation):
+    """This Expectation validates data as conforming to the valid pronic number."""
+
+    # These examples will be shown in the public gallery.
+    # They will also be executed as unit tests for your Expectation.
+    examples = [
+        {
+            "data": {
+                "well_formed_pronic_number": [
+                    "0",
+                    "2",
+                    "72",
+                    "462",
+                    "10100",
+                ],
+                "malformed_pronic_number": [
+                    "1",
+                    "11",
+                    "112",
+                    "10123",
+                    "This is not a valid pronic number",
+                ],
+            },
+            "tests": [
+                {
+                    "title": "basic_positive_test",
+                    "exact_match_out": False,
+                    "include_in_gallery": True,
+                    "in": {"column": "well_formed_pronic_number"},
+                    "out": {"success": True},
+                },
+                {
+                    "title": "basic_negative_test",
+                    "exact_match_out": False,
+                    "include_in_gallery": True,
+                    "in": {"column": "malformed_pronic_number"},
+                    "out": {"success": False},
+                },
+            ],
+        }
+    ]
+
+    # This is the id string of the Metric used by this Expectation.
+    # For most Expectations, it will be the same as the `condition_metric_name` defined in your Metric class above.
+    map_metric = "column_values.valid_pronic_number"
+
+    # This is a list of parameter names that can affect whether the Expectation evaluates to True or False
+    success_keys = ("mostly",)
+
+    # This dictionary contains default values for any parameters that should have default values
+    default_kwarg_values = {}
+
+    def validate_configuration(
+        self, configuration: Optional[ExpectationConfiguration]
+    ) -> None:
+        """
+        Validates that a configuration has been set, and sets a configuration if it has yet to be set. Ensures that
+        necessary configuration arguments have been provided for the validation of the expectation.
+        Args:
+            configuration (OPTIONAL[ExpectationConfiguration]): \
+                An optional Expectation Configuration entry that will be used to configure the expectation
+        Returns:
+            None. Raises InvalidExpectationConfigurationError if the config is not validated successfully
+        """
+
+        super().validate_configuration(configuration)
+        if configuration is None:
+            configuration = self.configuration
+
+        # # Check other things in configuration.kwargs and raise Exceptions if needed
+        # try:
+        #     assert (
+        #         ...
+        #     ), "message"
+        #     assert (
+        #         ...
+        #     ), "message"
+        # except AssertionError as e:
+        #     raise InvalidExpectationConfigurationError(str(e))
+
+    # This object contains metadata for display in the public Gallery
+    library_metadata = {
+        "maturity": "experimental",
+        "tags": ["experimental", "hackathon", "typed-entities"],
+        "contributors": [
+            "@voidforall",
+        ],
+    }
+
+
+if __name__ == "__main__":
+    ExpectColumnValuesToBeValidPronicNumber().print_diagnostic_checklist()


Test patch: