diff --git a/apt/utils/datasets/datasets.py b/apt/utils/datasets/datasets.py
index 26ffdef..72efd5a 100644
--- a/apt/utils/datasets/datasets.py
+++ b/apt/utils/datasets/datasets.py
@@ -24,7 +24,7 @@ OUTPUT_DATA_ARRAY_TYPE = np.ndarray
 DATA_PANDAS_NUMPY_TYPE = Union[np.ndarray, pd.DataFrame]
 
 
-def array2numpy(self, arr: INPUT_DATA_ARRAY_TYPE) -> OUTPUT_DATA_ARRAY_TYPE:
+def array2numpy(arr: INPUT_DATA_ARRAY_TYPE) -> OUTPUT_DATA_ARRAY_TYPE:
 
     """
     converts from INPUT_DATA_ARRAY_TYPE to numpy array
@@ -32,7 +32,6 @@ def array2numpy(self, arr: INPUT_DATA_ARRAY_TYPE) -> OUTPUT_DATA_ARRAY_TYPE:
     if type(arr) == np.ndarray:
         return arr
     if type(arr) == pd.DataFrame or type(arr) == pd.Series:
-        self.is_pandas = True
         return arr.to_numpy()
     if isinstance(arr, list):
         return np.array(arr)
@@ -42,14 +41,13 @@ def array2numpy(self, arr: INPUT_DATA_ARRAY_TYPE) -> OUTPUT_DATA_ARRAY_TYPE:
     raise ValueError("Non supported type: ", type(arr).__name__)
 
 
-def array2torch_tensor(self, arr: INPUT_DATA_ARRAY_TYPE) -> Tensor:
+def array2torch_tensor(arr: INPUT_DATA_ARRAY_TYPE) -> Tensor:
     """
     converts from INPUT_DATA_ARRAY_TYPE to torch tensor array
     """
     if type(arr) == np.ndarray:
         return torch.from_numpy(arr)
     if type(arr) == pd.DataFrame or type(arr) == pd.Series:
-        self.is_pandas = True
         return torch.from_numpy(arr.to_numpy())
     if isinstance(arr, list):
         return torch.tensor(arr)
@@ -178,10 +176,12 @@ class ArrayDataset(Dataset):
         :param feature_names: list of str, The feature names, in the order that they appear in the data (optional)
         :param kwargs: dataset parameters
         """
-        self.is_pandas = False
+        self.is_pandas = self.is_pandas = type(x) == pd.DataFrame or type(x) == pd.Series
+
         self.features_names = features_names
-        self._y = array2numpy(self, y) if y is not None else None
-        self._x = array2numpy(self, x)
+        self._y = array2numpy(y) if y is not None else None
+        self._x = array2numpy(x)
+
         if self.is_pandas:
             if features_names and not np.array_equal(features_names, x.columns):
                 raise ValueError("The supplied features are not the same as in the data features")
@@ -207,9 +207,11 @@ class PytorchData(Dataset):
         :param y: collection of labels (optional)
         :param kwargs: dataset parameters
         """
-        self.is_pandas = False
-        self._y = array2torch_tensor(self, y) if y is not None else None
-        self._x = array2torch_tensor(self, x)
+        self._y = array2torch_tensor(y) if y is not None else None
+        self._x = array2torch_tensor(x)
+
+        self.is_pandas = type(x) == pd.DataFrame or type(x) == pd.Series
+
         if self.is_pandas:
             self.features_names = x.columns
 
@@ -223,11 +225,11 @@ class PytorchData(Dataset):
 
     def get_samples(self) -> OUTPUT_DATA_ARRAY_TYPE:
         """Return data samples as numpy array"""
-        return array2numpy(self, self._x)
+        return array2numpy(self._x)
 
     def get_labels(self) -> OUTPUT_DATA_ARRAY_TYPE:
         """Return labels as numpy array"""
-        return array2numpy(self, self._y) if self._y is not None else None
+        return array2numpy(self._y) if self._y is not None else None
 
     def get_sample_item(self, idx) -> Tensor:
         return self._x[idx]