diff --git a/python-package/xgboost/core.py b/python-package/xgboost/core.py
index 93a73152c..a91019a8c 100644
--- a/python-package/xgboost/core.py
+++ b/python-package/xgboost/core.py
@@ -148,20 +148,19 @@ def _maybe_from_pandas(data, label, feature_names, feature_types):
     if not isinstance(data, pd.DataFrame):
         return data, label, feature_names, feature_types
 
+    mapper = {'int8': 'int', 'int16': 'int', 'int32': 'int', 'int64': 'int',
+              'uint8': 'int', 'uint16': 'int', 'uint32': 'int', 'uint64': 'int',
+              'float16': 'float', 'float32': 'float', 'float64': 'float',
+              'bool': 'i'}
+
     data_dtypes = data.dtypes
-    if not all(dtype.name in ('int8', 'int16', 'int32', 'int64',
-                              'uint8', 'uint16', 'uint32', 'uint64',
-                              'float16', 'float32', 'float64',
-                              'bool') for dtype in data_dtypes):
+    if not all(dtype.name in (mapper.keys()) for dtype in data_dtypes):
         raise ValueError('DataFrame.dtypes for data must be int, float or bool')
 
     if label is not None:
         if isinstance(label, pd.DataFrame):
             label_dtypes = label.dtypes
-            if not all(dtype.name in ('int8', 'int16', 'int32', 'int64',
-                                      'uint8', 'uint16', 'uint32', 'uint64',
-                                      'float16', 'float32', 'float64',
-                                      'bool') for dtype in label_dtypes):
+            if not all(dtype.name in (mapper.keys()) for dtype in label_dtypes):
                 raise ValueError('DataFrame.dtypes for label must be int, float or bool')
             else:
                 label = label.values.astype('float')
@@ -170,10 +169,6 @@ def _maybe_from_pandas(data, label, feature_names, feature_types):
         feature_names = data.columns.format()
 
     if feature_types is None:
-        mapper = {'int8': 'int', 'int16': 'int', 'int32': 'int', 'int64': 'int',
-                  'uint8': 'int', 'uint16': 'int', 'uint32': 'int', 'uint64': 'int',
-                  'float16': 'float', 'float32': 'float', 'float64': 'float',
-                  'bool': 'i'}
         feature_types = [mapper[dtype.name] for dtype in data_dtypes]
 
     data = data.values.astype('float')