We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Use cleaner solution for non-truncating division in python 2
Prefer `from __future__ import division` to `1.*x/y`

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_min.py`
Content:
```
1 import pandas as pd
2 
3 from great_expectations.execution_engine import (
4     PandasExecutionEngine,
5     SparkDFExecutionEngine,
6     SqlAlchemyExecutionEngine,
7 )
8 from great_expectations.expectations.metrics.column_aggregate_metric_provider import (
9     ColumnAggregateMetricProvider,
10     column_aggregate_partial,
11     column_aggregate_value,
12 )
13 from great_expectations.expectations.metrics.import_manager import F, sa
14 
15 
16 class ColumnValuesLengthMin(ColumnAggregateMetricProvider):
17     metric_name = "column_values.length.min"
18 
19     @column_aggregate_value(engine=PandasExecutionEngine, filter_column_isnull=True)
20     def _pandas(cls, column: pd.Series, **kwargs: dict) -> int:
21         return column.map(len).min()
22 
23     @column_aggregate_partial(
24         engine=SqlAlchemyExecutionEngine, filter_column_isnull=True
25     )
26     def _sqlalchemy(cls, column, **kwargs: dict):  # type: ignore[no-untyped-def]
27         return sa.func.min(sa.func.length(column))
28 
29     @column_aggregate_partial(engine=SparkDFExecutionEngine, filter_column_isnull=True)
30     def _spark(cls, column: str, **kwargs: dict):  # type: ignore[no-untyped-def]
31         return F.min(F.length(F.col(column)))
32
```

Path: `great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_max.py`
Content:
```
1 import pandas as pd
2 
3 from great_expectations.execution_engine import (
4     PandasExecutionEngine,
5     SparkDFExecutionEngine,
6     SqlAlchemyExecutionEngine,
7 )
8 from great_expectations.expectations.metrics.column_aggregate_metric_provider import (
9     ColumnAggregateMetricProvider,
10     column_aggregate_partial,
11     column_aggregate_value,
12 )
13 from great_expectations.expectations.metrics.import_manager import F, sa
14 
15 
16 class ColumnValuesLengthMax(ColumnAggregateMetricProvider):
17     metric_name = "column_values.length.max"
18 
19     @column_aggregate_value(engine=PandasExecutionEngine, filter_column_isnull=True)
20     def _pandas(cls, column: pd.Series, **kwargs: dict) -> int:
21         return column.map(len).max()
22 
23     @column_aggregate_partial(
24         engine=SqlAlchemyExecutionEngine, filter_column_isnull=True
25     )
26     def _sqlalchemy(cls, column, **kwargs: dict):  # type: ignore[no-untyped-def]
27         return sa.func.max(sa.func.length(column))
28 
29     @column_aggregate_partial(engine=SparkDFExecutionEngine, filter_column_isnull=True)
30     def _spark(cls, column: str, **kwargs: dict):  # type: ignore[no-untyped-def]
31         return F.max(F.length(F.col(column)))
32
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_max.py b/great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_max.py
--- a/great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_max.py
+++ b/great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_max.py
@@ -27,5 +27,5 @@
         return sa.func.max(sa.func.length(column))
 
     @column_aggregate_partial(engine=SparkDFExecutionEngine, filter_column_isnull=True)
-    def _spark(cls, column: str, **kwargs: dict):  # type: ignore[no-untyped-def]
-        return F.max(F.length(F.col(column)))
+    def _spark(cls, column, **kwargs: dict):  # type: ignore[no-untyped-def]
+        return F.max(F.length(column))
diff --git a/great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_min.py b/great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_min.py
--- a/great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_min.py
+++ b/great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_min.py
@@ -27,5 +27,5 @@
         return sa.func.min(sa.func.length(column))
 
     @column_aggregate_partial(engine=SparkDFExecutionEngine, filter_column_isnull=True)
-    def _spark(cls, column: str, **kwargs: dict):  # type: ignore[no-untyped-def]
-        return F.min(F.length(F.col(column)))
+    def _spark(cls, column, **kwargs: dict):  # type: ignore[no-untyped-def]
+        return F.min(F.length(column))

{"golden_diff": "diff --git a/great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_max.py b/great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_max.py\n--- a/great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_max.py\n+++ b/great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_max.py\n@@ -27,5 +27,5 @@\n         return sa.func.max(sa.func.length(column))\n \n     @column_aggregate_partial(engine=SparkDFExecutionEngine, filter_column_isnull=True)\n-    def _spark(cls, column: str, **kwargs: dict):  # type: ignore[no-untyped-def]\n-        return F.max(F.length(F.col(column)))\n+    def _spark(cls, column, **kwargs: dict):  # type: ignore[no-untyped-def]\n+        return F.max(F.length(column))\ndiff --git a/great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_min.py b/great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_min.py\n--- a/great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_min.py\n+++ b/great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_min.py\n@@ -27,5 +27,5 @@\n         return sa.func.min(sa.func.length(column))\n \n     @column_aggregate_partial(engine=SparkDFExecutionEngine, filter_column_isnull=True)\n-    def _spark(cls, column: str, **kwargs: dict):  # type: ignore[no-untyped-def]\n-        return F.min(F.length(F.col(column)))\n+    def _spark(cls, column, **kwargs: dict):  # type: ignore[no-untyped-def]\n+        return F.min(F.length(column))\n", "issue": "Use cleaner solution for non-truncating division in python 2\nPrefer `from __future__ import division` to `1.*x/y`\n", "before_files": [{"content": "import pandas as pd\n\nfrom great_expectations.execution_engine import (\n    PandasExecutionEngine,\n    SparkDFExecutionEngine,\n    SqlAlchemyExecutionEngine,\n)\nfrom great_expectations.expectations.metrics.column_aggregate_metric_provider import (\n    ColumnAggregateMetricProvider,\n    column_aggregate_partial,\n    column_aggregate_value,\n)\nfrom great_expectations.expectations.metrics.import_manager import F, sa\n\n\nclass ColumnValuesLengthMin(ColumnAggregateMetricProvider):\n    metric_name = \"column_values.length.min\"\n\n    @column_aggregate_value(engine=PandasExecutionEngine, filter_column_isnull=True)\n    def _pandas(cls, column: pd.Series, **kwargs: dict) -> int:\n        return column.map(len).min()\n\n    @column_aggregate_partial(\n        engine=SqlAlchemyExecutionEngine, filter_column_isnull=True\n    )\n    def _sqlalchemy(cls, column, **kwargs: dict):  # type: ignore[no-untyped-def]\n        return sa.func.min(sa.func.length(column))\n\n    @column_aggregate_partial(engine=SparkDFExecutionEngine, filter_column_isnull=True)\n    def _spark(cls, column: str, **kwargs: dict):  # type: ignore[no-untyped-def]\n        return F.min(F.length(F.col(column)))\n", "path": "great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_min.py"}, {"content": "import pandas as pd\n\nfrom great_expectations.execution_engine import (\n    PandasExecutionEngine,\n    SparkDFExecutionEngine,\n    SqlAlchemyExecutionEngine,\n)\nfrom great_expectations.expectations.metrics.column_aggregate_metric_provider import (\n    ColumnAggregateMetricProvider,\n    column_aggregate_partial,\n    column_aggregate_value,\n)\nfrom great_expectations.expectations.metrics.import_manager import F, sa\n\n\nclass ColumnValuesLengthMax(ColumnAggregateMetricProvider):\n    metric_name = \"column_values.length.max\"\n\n    @column_aggregate_value(engine=PandasExecutionEngine, filter_column_isnull=True)\n    def _pandas(cls, column: pd.Series, **kwargs: dict) -> int:\n        return column.map(len).max()\n\n    @column_aggregate_partial(\n        engine=SqlAlchemyExecutionEngine, filter_column_isnull=True\n    )\n    def _sqlalchemy(cls, column, **kwargs: dict):  # type: ignore[no-untyped-def]\n        return sa.func.max(sa.func.length(column))\n\n    @column_aggregate_partial(engine=SparkDFExecutionEngine, filter_column_isnull=True)\n    def _spark(cls, column: str, **kwargs: dict):  # type: ignore[no-untyped-def]\n        return F.max(F.length(F.col(column)))\n", "path": "great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_max.py"}], "after_files": [{"content": "import pandas as pd\n\nfrom great_expectations.execution_engine import (\n    PandasExecutionEngine,\n    SparkDFExecutionEngine,\n    SqlAlchemyExecutionEngine,\n)\nfrom great_expectations.expectations.metrics.column_aggregate_metric_provider import (\n    ColumnAggregateMetricProvider,\n    column_aggregate_partial,\n    column_aggregate_value,\n)\nfrom great_expectations.expectations.metrics.import_manager import F, sa\n\n\nclass ColumnValuesLengthMin(ColumnAggregateMetricProvider):\n    metric_name = \"column_values.length.min\"\n\n    @column_aggregate_value(engine=PandasExecutionEngine, filter_column_isnull=True)\n    def _pandas(cls, column: pd.Series, **kwargs: dict) -> int:\n        return column.map(len).min()\n\n    @column_aggregate_partial(\n        engine=SqlAlchemyExecutionEngine, filter_column_isnull=True\n    )\n    def _sqlalchemy(cls, column, **kwargs: dict):  # type: ignore[no-untyped-def]\n        return sa.func.min(sa.func.length(column))\n\n    @column_aggregate_partial(engine=SparkDFExecutionEngine, filter_column_isnull=True)\n    def _spark(cls, column, **kwargs: dict):  # type: ignore[no-untyped-def]\n        return F.min(F.length(column))\n", "path": "great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_min.py"}, {"content": "import pandas as pd\n\nfrom great_expectations.execution_engine import (\n    PandasExecutionEngine,\n    SparkDFExecutionEngine,\n    SqlAlchemyExecutionEngine,\n)\nfrom great_expectations.expectations.metrics.column_aggregate_metric_provider import (\n    ColumnAggregateMetricProvider,\n    column_aggregate_partial,\n    column_aggregate_value,\n)\nfrom great_expectations.expectations.metrics.import_manager import F, sa\n\n\nclass ColumnValuesLengthMax(ColumnAggregateMetricProvider):\n    metric_name = \"column_values.length.max\"\n\n    @column_aggregate_value(engine=PandasExecutionEngine, filter_column_isnull=True)\n    def _pandas(cls, column: pd.Series, **kwargs: dict) -> int:\n        return column.map(len).max()\n\n    @column_aggregate_partial(\n        engine=SqlAlchemyExecutionEngine, filter_column_isnull=True\n    )\n    def _sqlalchemy(cls, column, **kwargs: dict):  # type: ignore[no-untyped-def]\n        return sa.func.max(sa.func.length(column))\n\n    @column_aggregate_partial(engine=SparkDFExecutionEngine, filter_column_isnull=True)\n    def _spark(cls, column, **kwargs: dict):  # type: ignore[no-untyped-def]\n        return F.max(F.length(column))\n", "path": "great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_max.py"}]}