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_pair_values_lat_lng_matches_geohash.py b/contrib/experimental/great_expectations_experimental/expectations/expect_column_pair_values_lat_lng_matches_geohash.py
new file mode 100644
--- /dev/null
+++ b/contrib/experimental/great_expectations_experimental/expectations/expect_column_pair_values_lat_lng_matches_geohash.py
@@ -0,0 +1,129 @@
+from typing import Optional
+
+import geohash as gh
+import pandas as pd
+
+from great_expectations.core.expectation_configuration import ExpectationConfiguration
+from great_expectations.execution_engine import PandasExecutionEngine
+from great_expectations.expectations.expectation import (
+    ColumnPairMapExpectation,
+    InvalidExpectationConfigurationError,
+)
+from great_expectations.expectations.metrics.map_metric_provider import (
+    ColumnPairMapMetricProvider,
+    column_pair_condition_partial,
+)
+
+
+def compare(row):
+    """"""
+    lat, lng = row[0]
+    geohash = row[1]
+    return gh.encode(lat, lng).startswith(geohash)
+
+
+class ColumnPairValuesLatLngMatchesGeohash(ColumnPairMapMetricProvider):
+    condition_metric_name = "column_pair_values.lat_lng_matches_geohash"
+
+    # noinspection PyPep8Naming
+    @column_pair_condition_partial(engine=PandasExecutionEngine)
+    def _pandas(cls, column_A, column_B, **kwargs):
+        df = pd.DataFrame(column_A).join(column_B)
+        return df.apply(lambda x: compare(x), axis=1)
+
+
+class ExpectColumnPairValuesLatLngMatchesGeohash(ColumnPairMapExpectation):
+    """
+    Expect latlngs in column_A to match with geohashes in column_B.
+
+    The more digits a geohash has, the smaller and more precise of an area it
+    represents. When converting a latlng to a geohash, we are only asserting
+    that it falls somewhere within the other geohash we're comparing it with.
+    To verify this, we only need to make sure that they share their left-most
+    digits. For example, dpz8 contains dpz80 (in addition to any other geohash
+    that begins with "dpz8".
+    """
+
+    examples = [
+        {
+            "data": {
+                "latlngs": [
+                    (43.681640625, -79.27734375),
+                    (43.681640620, -79.27734370),
+                    (43.681640615, -79.27734385),
+                ],
+                "geohashes_same": ["dpz8", "dpz8", "dpz8"],
+                "geohashes_different": ["d", "dpz8", "dpz87zzzzzzz"],
+                "geohashes_incorrect": ["dpz8", "dpz7", "dpz6"],
+            },
+            "tests": [
+                {
+                    "title": "basic_positive",
+                    "exact_match_out": False,
+                    "include_in_gallery": True,
+                    "in": {"column_A": "latlngs", "column_B": "geohashes_same"},
+                    "out": {
+                        "success": True,
+                    },
+                },
+                {
+                    "title": "basic_negative",
+                    "exact_match_out": False,
+                    "include_in_gallery": True,
+                    "in": {"column_A": "latlngs", "column_B": "geohashes_incorrect"},
+                    "out": {
+                        "success": False,
+                    },
+                },
+                {
+                    "title": "basic_negative",
+                    "exact_match_out": False,
+                    "include_in_gallery": True,
+                    "in": {"column_A": "latlngs", "column_B": "geohashes_different"},
+                    "out": {
+                        "success": True,
+                    },
+                },
+            ],
+        }
+    ]
+
+    map_metric = "column_pair_values.lat_lng_matches_geohash"
+    success_keys = (
+        "column_A",
+        "column_B",
+        "ignore_row_if",
+        "mostly",
+    )
+    default_kwarg_values = {"mostly": 1.0, "ignore_row_if": "both_values_are_missing"}
+
+    # This dictionary contains metadata for display in the public gallery
+    library_metadata = {
+        "tags": [
+            "geospatial",
+            "hackathon-22",
+            "multi-column expectation",
+        ],  # Tags for this Expectation in the Gallery
+        "contributors": [  # Github handles for all contributors to this Expectation.
+            "@chrisarnold91",  # Don't forget to add your github handle here!
+        ],
+        "requirements": ["python-geohash", "pandas"],
+    }
+
+    def validate_configuration(
+        self, configuration: Optional[ExpectationConfiguration]
+    ) -> None:
+        super().validate_configuration(configuration)
+        if configuration is None:
+            configuration = self.configuration
+        try:
+            assert (
+                "column_A" in configuration.kwargs
+                and "column_B" in configuration.kwargs
+            ), "both columns must be provided"
+        except AssertionError as e:
+            raise InvalidExpectationConfigurationError(str(e))
+
+
+if __name__ == "__main__":
+    ExpectColumnPairValuesLatLngMatchesGeohash().print_diagnostic_checklist()


Test patch: