prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Use cleaner solution for non-truncating division in python 2
  Prefer `from __future__ import division` to `1.*x/y`
  
  </issue>
  <code>
  [start of great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_min.py]
  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
  [end of great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_min.py]
  [start of great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_max.py]
  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 of great_expectations/expectations/metrics/column_aggregate_metrics/column_values_length_max.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
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"}]}