prompt: 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()
  
  ```
  
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
  --- 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))
  
verification_info: {"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"}]}