v0 (#1304)

deepchecks/core/check_result.py

@@ -501,7 +501,7 @@ def to_json(self, with_display: bool = True):
         """
         result_json = self._get_metadata()
         if with_display:
-            result_json['display'] = [('str', str(self.exception))]
+            result_json['display'] = [('html', f'<p style="color:red">{self.exception}</p>')]
         return jsonpickle.dumps(result_json, unpicklable=False)
 
     def to_wandb(self, dedicated_run: bool = True, **kwargs: Any):
@@ -546,7 +546,7 @@ def _ipython_display_(self):
         if hasattr(self.check.__class__, '__doc__'):
             summary = get_docs_summary(self.check)
             check_html += f'<p>{summary}</p>'
-        check_html += f'<p style="color:red"> {self.exception}</p>'
+        check_html += f'<p style="color:red">{self.exception}</p>'
         display_html(check_html, raw=True)
 
     def print_traceback(self):

deepchecks/tabular/dataset.py

@@ -41,8 +41,9 @@ class Dataset:
 
     Parameters
     ----------
-    df : pd.DataFrame
-        A pandas DataFrame containing data relevant for the training or validating of a ML models.
+    df : Any
+        An object that can be casted to a pandas DataFrame
+         - containing data relevant for the training or validating of a ML models.
     label : t.Union[Hashable, pd.Series, pd.DataFrame, np.ndarray] , default: None
         label column provided either as a string with the name of an existing column in the DataFrame or a label
         object including the label data (pandas Series/DataFrame or a numpy array) that will be concatenated to the
@@ -103,7 +104,7 @@ class Dataset:
 
     def __init__(
             self,
-            df: pd.DataFrame,
+            df: t.Any,
             label: t.Union[Hashable, pd.Series, pd.DataFrame, np.ndarray] = None,
             features: t.Optional[t.Sequence[Hashable]] = None,
             cat_features: t.Optional[t.Sequence[Hashable]] = None,
@@ -121,7 +122,7 @@ def __init__(
 
         if len(df) == 0:
             raise DeepchecksValueError('Can\'t create a Dataset object with an empty dataframe')
-        self._data = df.copy()
+        self._data = pd.DataFrame(df).copy()
 
         # Validations
         if label is None:

requirements/requirements.txt

@@ -25,5 +25,5 @@ importlib_resources>=1.3; python_version < '3.9'
 statsmodels>=0.11.0
 scipy>=1.4.1
 dataclasses>=0.6; python_version < '3.7'
-plotly>=5.5.0, <5.6
+plotly>=5.5.0
 matplotlib>=3.2.2