[python-package] replace .values usage with .to_numpy() (#5612)

python-package/lightgbm/basic.py

@@ -602,7 +602,16 @@ def _data_from_pandas(data, feature_name, categorical_feature, pandas_categorica
         df_dtypes = [dtype.type for dtype in data.dtypes]
         df_dtypes.append(np.float32)  # so that the target dtype considers floats
         target_dtype = np.find_common_type(df_dtypes, [])
-        data = data.astype(target_dtype, copy=False).values
+        try:
+            # most common case (no nullable dtypes)
+            data = data.to_numpy(dtype=target_dtype, copy=False)
+        except TypeError:
+            # 1.0 <= pd version < 1.1 and nullable dtypes, least common case
+            # raises error because array is casted to type(pd.NA) and there's no na_value argument
+            data = data.astype(target_dtype, copy=False).values
+        except ValueError:
+            # data has nullable dtypes, but we can specify na_value argument and copy will be made
+            data = data.to_numpy(dtype=target_dtype, na_value=np.nan)
     else:
         if feature_name == 'auto':
             feature_name = None
@@ -2291,7 +2300,17 @@ class Dataset:
                 if len(label.columns) > 1:
                     raise ValueError('DataFrame for label cannot have multiple columns')
                 _check_for_bad_pandas_dtypes(label.dtypes)
-                label_array = np.ravel(label.values.astype(np.float32, copy=False))
+                try:
+                    # most common case (no nullable dtypes)
+                    label = label.to_numpy(dtype=np.float32, copy=False)
+                except TypeError:
+                    # 1.0 <= pd version < 1.1 and nullable dtypes, least common case
+                    # raises error because array is casted to type(pd.NA) and there's no na_value argument
+                    label = label.astype(np.float32, copy=False).values
+                except ValueError:
+                    # data has nullable dtypes, but we can specify na_value argument and copy will be made
+                    label = label.to_numpy(dtype=np.float32, na_value=np.nan)
+                label_array = np.ravel(label)
             else:
                 label_array = _list_to_1d_numpy(label, name='label')
             self.set_field('label', label_array)