[python-package] consolidate pandas-to-numpy conversion code (#6156)

python-package/lightgbm/basic.py

@@ -758,6 +758,23 @@ def _check_for_bad_pandas_dtypes(pandas_dtypes_series: pd_Series) -> None:
                          f'Fields with bad pandas dtypes: {", ".join(bad_pandas_dtypes)}')
 
 
+def _pandas_to_numpy(
+    data: pd_DataFrame,
+    target_dtype: "np.typing.DTypeLike"
+) -> np.ndarray:
+    _check_for_bad_pandas_dtypes(data.dtypes)
+    try:
+        # most common case (no nullable dtypes)
+        return 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
+        return 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
+        return data.to_numpy(dtype=target_dtype, na_value=np.nan)
+
+
 def _data_from_pandas(
     data: pd_DataFrame,
     feature_name: _LGBM_FeatureNameConfiguration,
@@ -790,22 +807,17 @@ def _data_from_pandas(
     else:  # use cat cols specified by user
         categorical_feature = list(categorical_feature)  # type: ignore[assignment]
 
-    # get numpy representation of the data
-    _check_for_bad_pandas_dtypes(data.dtypes)
     df_dtypes = [dtype.type for dtype in data.dtypes]
-    df_dtypes.append(np.float32)  # so that the target dtype considers floats
+    # so that the target dtype considers floats
+    df_dtypes.append(np.float32)
     target_dtype = np.result_type(*df_dtypes)
-    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)
-    return data, feature_name, categorical_feature, pandas_categorical
+
+    return (
+        _pandas_to_numpy(data, target_dtype=target_dtype),
+        feature_name,
+        categorical_feature,
+        pandas_categorical
+    )
 
 
 def _dump_pandas_categorical(
@@ -2805,18 +2817,7 @@ class Dataset:
             if isinstance(label, pd_DataFrame):
                 if len(label.columns) > 1:
                     raise ValueError('DataFrame for label cannot have multiple columns')
-                _check_for_bad_pandas_dtypes(label.dtypes)
-                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)
+                label_array = np.ravel(_pandas_to_numpy(label, target_dtype=np.float32))
             elif _is_pyarrow_array(label):
                 label_array = label
             else: