fixed plotting issues and issues in examples

nannyml/data_quality/missing/calculator.py

@@ -76,8 +76,7 @@ def __init__(
         ...     timestamp_column_name='timestamp',
         ... ).fit(reference_df)
         >>> res = calc.calculate(analysis_df)
-        >>> for column_name in res.column_names:
-        ...     _ = res.filter(period='analysis', column_name=column_name).plot().show()
+        >>> res.filter(period='analysis').plot().show()
         """
         super(MissingValuesCalculator, self).__init__(
             chunk_size, chunk_number, chunk_period, chunker, timestamp_column_name

nannyml/data_quality/missing/result.py

@@ -79,8 +79,7 @@ def plot(
         ...     timestamp_column_name='timestamp',
         ... ).fit(reference)
         >>> res = calc.calculate(analysis)
-        >>> for column_name in res.column_names:
-        ...     _ = res.filter(period='analysis', column_name=column_name).plot().show()
+        >>> res.filter(period='analysis').plot().show()
 
         """
         return plot_metrics(

nannyml/data_quality/range/calculator.py

@@ -74,8 +74,7 @@ def __init__(
         ...     timestamp_column_name='timestamp',
         ... ).fit(reference_df)
         >>> res = calc.calculate(analysis_df)
-        >>> for column_name in res.column_names:
-        ...     _ = res.filter(period='analysis', column_name=column_name).plot().show()
+        >>> res.filter(period='analysis').plot().show()
         """
         super(NumericalRangeCalculator, self).__init__(
             chunk_size, chunk_number, chunk_period, chunker, timestamp_column_name

nannyml/data_quality/range/result.py

@@ -18,16 +18,16 @@
 from nannyml._typing import Key
 from nannyml.base import PerColumnResult
 from nannyml.chunk import Chunker
+
+# from nannyml.exceptions import InvalidArgumentsException
 from nannyml.plots.blueprints.comparisons import ResultCompareMixin
 from nannyml.plots.blueprints.metrics import plot_metrics
+from nannyml.plots.components import Hover
 from nannyml.usage_logging import UsageEvent, log_usage
 
 
 class Result(PerColumnResult, ResultCompareMixin):
-    """Values Out Of Range Result Class.
-
-    Contains calculation results and provides plotting functionality.
-    """
+    """Contains the results of the univariate statistical drift calculation and provides plotting functionality."""
 
     def __init__(
         self,
@@ -37,14 +37,13 @@ def __init__(
         timestamp_column_name: Optional[str],
         chunker: Chunker,
     ):
-        """Values Out Of Range Result Class."""
         super().__init__(results_data, column_names)
 
         self.timestamp_column_name = timestamp_column_name
         self.data_quality_metric = data_quality_metric
         self.chunker = chunker
 
-    def keys(self) -> List[Key]:  # noqa: D102
+    def keys(self) -> List[Key]:
         return [
             Key(
                 properties=(column_name,),
@@ -53,13 +52,16 @@ def keys(self) -> List[Key]:  # noqa: D102
             for column_name in self.column_names
         ]
 
-    @log_usage(UsageEvent.DQ_CALC_VALUES_OUT_OF_RANGE_PLOT)
+    @log_usage(UsageEvent.DQ_CALC_UNSEEN_VALUES_PLOT)
     def plot(
         self,
         *args,
         **kwargs,
     ) -> go.Figure:
-        """Values Out Of Range results.
+        """
+
+        Parameters
+        ----------
 
         Returns
         -------
@@ -73,16 +75,16 @@ def plot(
         --------
         >>> import nannyml as nml
         >>> reference, analysis, _ = nml.load_synthetic_car_price_dataset()
-        >>> column_names = [col for col in reference.columns if col not in ['fuel','transmission','timestamp', 'y_pred', 'y_true']]
-        >>> calc = nml.NumericalRangeCalculator(
+        >>> column_names = [col for col in reference.columns if col not in ['car_age', 'km_driven', 'price_new', 'accident_count', 'door_count','timestamp', 'y_pred', 'y_true']]
+        >>> calc = nml.UnseenValuesCalculator(
         ...     column_names=column_names,
         ...     timestamp_column_name='timestamp',
         ... ).fit(reference)
         >>> res = calc.calculate(analysis)
-        >>> for column_name in res.column_names:
-        ...     _ = res.filter(period='analysis', column_name=column_name).plot().show()
+        >>> res.filter(period='analysis').plot().show()
 
         """
+
         return plot_metrics(
             self,
             title='Data Quality ',

nannyml/data_quality/unseen/calculator.py

@@ -75,8 +75,7 @@ def __init__(
         ...     timestamp_column_name='timestamp',
         ... ).fit(reference)
         >>> res = calc.calculate(analysis)
-        >>> for column_name in res.column_names:
-        ...     _ = res.filter(period='analysis', column_name=column_name).plot().show()
+        >>> res.filter(period='analysis').plot().show()
         """
         super(UnseenValuesCalculator, self).__init__(
             chunk_size, chunk_number, chunk_period, chunker, timestamp_column_name

nannyml/data_quality/unseen/result.py

@@ -81,8 +81,7 @@ def plot(
         ...     timestamp_column_name='timestamp',
         ... ).fit(reference)
         >>> res = calc.calculate(analysis)
-        >>> for column_name in res.column_names:
-        ...     _ = res.filter(period='analysis', column_name=column_name).plot().show()
+        >>> res.filter(period='analysis').plot().show()
 
         """