[python-package] Better column dtype logging when column has "bad dtype" (#5065)

* better logging of column datatypes

* update to checking function

* fix typo

* Update python-package/lightgbm/basic.py
Co-authored-by: José Morales <jmoralz92@gmail.com>

* Update python-package/lightgbm/basic.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: José Morales <jmoralz92@gmail.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

python-package/lightgbm/basic.py

@@ -183,8 +183,7 @@ def list_to_1d_numpy(data, dtype=np.float32, name='list'):
     elif is_1d_list(data):
         return np.array(data, dtype=dtype, copy=False)
     elif isinstance(data, pd_Series):
-        if _get_bad_pandas_dtypes([data.dtypes]):
-            raise ValueError('Series.dtypes must be int, float or bool')
+        _check_for_bad_pandas_dtypes(data.to_frame().dtypes)
         return np.array(data, dtype=dtype, copy=False)  # SparseArray should be supported as well
     else:
         raise TypeError(f"Wrong type({type(data).__name__}) for {name}.\n"
@@ -217,8 +216,7 @@ def _data_to_2d_numpy(data: Any, dtype: type = np.float32, name: str = 'list') -
     if _is_2d_list(data):
         return np.array(data, dtype=dtype)
     if isinstance(data, pd_DataFrame):
-        if _get_bad_pandas_dtypes(data.dtypes):
-            raise ValueError('DataFrame.dtypes must be int, float or bool')
+        _check_for_bad_pandas_dtypes(data.dtypes)
         return cast_numpy_array_to_dtype(data.values, dtype)
     raise TypeError(f"Wrong type({type(data).__name__}) for {name}.\n"
                     "It should be list of lists, numpy 2-D array or pandas DataFrame")
@@ -500,7 +498,7 @@ def c_int_array(data):
     return (ptr_data, type_data, data)  # return `data` to avoid the temporary copy is freed
 
 
-def _get_bad_pandas_dtypes(dtypes):
+def _check_for_bad_pandas_dtypes(pandas_dtypes_series):
     float128 = getattr(np, 'float128', type(None))
 
     def is_allowed_numpy_dtype(dtype):
@@ -509,7 +507,14 @@ def _get_bad_pandas_dtypes(dtypes):
             and not issubclass(dtype, (np.timedelta64, float128))
         )
 
-    return [i for i, dtype in enumerate(dtypes) if not is_allowed_numpy_dtype(dtype.type)]
+    bad_pandas_dtypes = [
+        f'{column_name}: {pandas_dtype}'
+        for column_name, pandas_dtype in pandas_dtypes_series.iteritems()
+        if not is_allowed_numpy_dtype(pandas_dtype.type)
+    ]
+    if bad_pandas_dtypes:
+        raise ValueError('pandas dtypes must be int, float or bool.\n'
+                         f'Fields with bad pandas dtypes: {", ".join(bad_pandas_dtypes)}')
 
 
 def _data_from_pandas(data, feature_name, categorical_feature, pandas_categorical):
@@ -540,12 +545,7 @@ def _data_from_pandas(data, feature_name, categorical_feature, pandas_categorica
                 categorical_feature = list(categorical_feature)
         if feature_name == 'auto':
             feature_name = list(data.columns)
-        bad_indices = _get_bad_pandas_dtypes(data.dtypes)
-        if bad_indices:
-            bad_index_cols_str = ', '.join(data.columns[bad_indices])
-            raise ValueError("DataFrame.dtypes for data must be int, float or bool.\n"
-                             "Did not expect the data types in the following fields: "
-                             f"{bad_index_cols_str}")
+        _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
         target_dtype = np.find_common_type(df_dtypes, [])
@@ -562,8 +562,7 @@ def _label_from_pandas(label):
     if isinstance(label, pd_DataFrame):
         if len(label.columns) > 1:
             raise ValueError('DataFrame for label cannot have multiple columns')
-        if _get_bad_pandas_dtypes(label.dtypes):
-            raise ValueError('DataFrame.dtypes for label must be int, float or bool')
+        _check_for_bad_pandas_dtypes(label.dtypes)
         label = np.ravel(label.values.astype(np.float32, copy=False))
     return label