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_degree_decimal_coordinates.py b/contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_be_valid_degree_decimal_coordinates.py
new file mode 100644
--- /dev/null
+++ b/contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_be_valid_degree_decimal_coordinates.py
@@ -0,0 +1,130 @@
+"""
+This is a template for creating custom ColumnMapExpectations.
+For detailed instructions on how to use it, please see:
+    https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations
+"""
+
+import json
+
+from shapely.geometry import Point, Polygon
+
+from great_expectations.core.expectation_configuration import ExpectationConfiguration
+from great_expectations.exceptions import InvalidExpectationConfigurationError
+from great_expectations.execution_engine import (
+    PandasExecutionEngine,
+    SparkDFExecutionEngine,
+    SqlAlchemyExecutionEngine,
+)
+from great_expectations.expectations.expectation import ColumnMapExpectation
+from great_expectations.expectations.metrics import (
+    ColumnMapMetricProvider,
+    column_condition_partial,
+)
+
+
+# This class defines a Metric to support your Expectation.
+# For most ColumnMapExpectations, the main business logic for calculation will live in this class.
+class ColumnValuesCoordinatesDegreeDecimal(ColumnMapMetricProvider):
+
+    # This is the id string that will be used to reference your metric.
+    condition_metric_name = "column_values.coordinates.degree_decimal"
+
+    # This method implements the core logic for the PandasExecutionEngine
+    @column_condition_partial(engine=PandasExecutionEngine)
+    def _pandas(cls, column, **kwargs):
+        bounds = Polygon.from_bounds(xmin=-180.0, ymin=-90.0, xmax=180.0, ymax=90.0)
+        return column.apply(lambda x, y=bounds: cls._point_in_bounds(x, y))
+
+    @staticmethod
+    def _point_in_bounds(point, bounds):
+        try:
+            result = bounds.intersects(
+                Point(float(eval(point)[1]), float(eval(point)[0]))
+            )
+        except TypeError:
+            try:
+                result = bounds.intersects(Point(float(point[1]), float(point[0])))
+            except TypeError:
+                return False
+
+        return result
+
+    # 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 ExpectColumnValuesToBeValidDegreeDecimalCoordinates(ColumnMapExpectation):
+    """Expect column values to contain degree-decimal, lat/lon coordinates."""
+
+    # These examples will be shown in the public gallery.
+    # They will also be executed as unit tests for your Expectation.
+    examples = [
+        {
+            "data": {
+                "tuple_float": [
+                    (62.75955799999999, -164.483752),
+                    (62.7673475, -164.4996625),
+                    (62.7698675, -164.5034575),
+                    (62.76901333333333, -164.50339),
+                    (62.76906333333334, -164.50353333333337),
+                ],
+                "mixed_types": [
+                    "[62.75955799999999, -164.483752]",
+                    [62.7673475, -164.4996625],
+                    (62.7698675, -164.5034575),
+                    "(62.76901333333333, -164.50339)",
+                    True,
+                ],
+            },
+            "tests": [
+                {
+                    "title": "basic_positive_test",
+                    "exact_match_out": False,
+                    "include_in_gallery": True,
+                    "in": {"column": "tuple_float"},
+                    "out": {"success": True},
+                },
+                {
+                    "title": "basic_negative_test",
+                    "exact_match_out": False,
+                    "include_in_gallery": True,
+                    "in": {"column": "mixed_types", "mostly": 1},
+                    "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.coordinates.degree_decimal"
+
+    # 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 = {}
+
+    # This object contains metadata for display in the public Gallery
+    library_metadata = {
+        "tags": [
+            "geospatial",
+            "hackathon-22",
+        ],  # Tags for this Expectation in the Gallery
+        "contributors": [  # Github handles for all contributors to this Expectation.
+            "@austiezr",  # Don't forget to add your github handle here!
+        ],
+        "requirements": ["shapely"],
+    }
+
+
+if __name__ == "__main__":
+    ExpectColumnValuesToBeValidDegreeDecimalCoordinates().print_diagnostic_checklist()


Test patch: