add InputData from pd and numpy

fedot/core/data/data.py

@@ -53,6 +53,57 @@ class Data:
     # Object with supplementary info
     supplementary_data: SupplementaryData = field(default_factory=SupplementaryData)
 
+    @classmethod
+    def from_numpy(cls,
+                   features_array: np.ndarray,
+                   target_array: np.ndarray,
+                   idx: Optional[np.ndarray] = None,
+                   task: Task = Task(TaskTypesEnum.classification),
+                   data_type: Optional[DataTypesEnum] = None) -> InputData:
+        """Import data from numpy array.
+
+                        Args:
+                            features_array: numpy array with features.
+                            target_array: numpy array with target.
+                            task: the :obj:`Task` to solve with the data.
+                            data_type: the type of the data. Possible values are listed at :class:`DataTypesEnum`.
+
+                        Returns:
+                            data
+                        """
+        return array_to_input_data(features_array, target_array, idx, task, data_type)
+
+    @classmethod
+    def from_dataframe(cls,
+                       df: pd.DataFrame,
+                       task: Union[Task, str] = 'classification',
+                       data_type: DataTypesEnum = DataTypesEnum.table,
+                       target_columns: Union[str, List[Union[str, int]]] = '') -> InputData:
+        """Import data from pandas DataFrame.
+
+                Args:
+                    df: loaded pandas DataFrame.
+                    task: the :obj:`Task` to solve with the data.
+                    data_type: the type of the data. Possible values are listed at :class:`DataTypesEnum`.
+                    target_columns: name of the target column (the last column if empty and no target if ``None``).
+
+                Returns:
+                    data
+                """
+
+        if isinstance(task, str):
+            task = Task(TaskTypesEnum(task))
+
+        idx = df.index.to_numpy()
+        if not target_columns:
+            features_names = df.columns.to_numpy()[:-1]
+        else:
+            features_names = df.drop(target_columns, axis=1).columns.to_numpy()
+        features, target = process_target_and_features(df, target_columns)
+
+        return InputData(idx=idx, features=features, target=target, task=task, data_type=data_type,
+                         features_names=features_names)
+
     @classmethod
     def from_csv(cls,
                  file_path: PathType,

test/unit/data/test_data.py

@@ -59,9 +59,12 @@ def test_data_from_csv():
                                   idx=idx,
                                   task=task,
                                   data_type=DataTypesEnum.table).features
-    actual_features = InputData.from_csv(
+    actual_features_from_csv = InputData.from_csv(
         os.path.join(test_file_path, file)).features
-    assert np.array_equal(expected_features, actual_features)
+    assert np.array_equal(expected_features, actual_features_from_csv)
+    df.set_index('ID', drop=True, inplace=True)
+    actual_features_from_df = InputData.from_dataframe(df).features
+    assert np.array_equal(expected_features, actual_features_from_df)
 
 
 def test_with_custom_target():