diff --git a/python-package/xgboost/data.py b/python-package/xgboost/data.py
index 7df768934c2b..48d1884ef12c 100644
--- a/python-package/xgboost/data.py
+++ b/python-package/xgboost/data.py
@@ -227,15 +227,13 @@ def _transform_pandas_df(
     meta_type=None,
 ):
     import pandas as pd
-    from pandas import MultiIndex, Int64Index, RangeIndex
     from pandas.api.types import is_sparse, is_categorical_dtype
 
-    data_dtypes = data.dtypes
     if not all(dtype.name in _pandas_dtype_mapper or is_sparse(dtype) or
                (is_categorical_dtype(dtype) and enable_categorical)
-               for dtype in data_dtypes):
+               for dtype in data.dtypes):
         bad_fields = [
-            str(data.columns[i]) for i, dtype in enumerate(data_dtypes)
+            str(data.columns[i]) for i, dtype in enumerate(data.dtypes)
             if dtype.name not in _pandas_dtype_mapper
         ]
 
@@ -246,11 +244,11 @@ def _transform_pandas_df(
 
     # handle feature names
     if feature_names is None and meta is None:
-        if isinstance(data.columns, MultiIndex):
+        if isinstance(data.columns, pd.MultiIndex):
             feature_names = [
                 ' '.join([str(x) for x in i]) for i in data.columns
             ]
-        elif isinstance(data.columns, (Int64Index, RangeIndex)):
+        elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):
             feature_names = list(map(str, data.columns))
         else:
             feature_names = data.columns.format()
@@ -258,7 +256,7 @@ def _transform_pandas_df(
     # handle feature types
     if feature_types is None and meta is None:
         feature_types = []
-        for i, dtype in enumerate(data_dtypes):
+        for i, dtype in enumerate(data.dtypes):
             if is_sparse(dtype):
                 feature_types.append(_pandas_dtype_mapper[dtype.subtype.name])
             elif is_categorical_dtype(dtype) and enable_categorical:
@@ -269,7 +267,7 @@ def _transform_pandas_df(
     # handle categorical codes.
     transformed = pd.DataFrame()
     if enable_categorical:
-        for i, dtype in enumerate(data_dtypes):
+        for i, dtype in enumerate(data.dtypes):
             if is_categorical_dtype(dtype):
                 transformed[data.columns[i]] = data[data.columns[i]].cat.codes
             else: