diff --git a/python-package/lightgbm/basic.py b/python-package/lightgbm/basic.py
index 3b4db41c5..b985292dc 100644
--- a/python-package/lightgbm/basic.py
+++ b/python-package/lightgbm/basic.py
@@ -11,8 +11,9 @@ from tempfile import NamedTemporaryFile
 import numpy as np
 import scipy.sparse
 
-from .compat import (DataFrame, Series, integer_types, json, numeric_types,
-                     range_, string_type)
+from .compat import (DataFrame, Series, integer_types, json,
+                     json_default_with_numpy, numeric_types, range_,
+                     string_type)
 from .libpath import find_lib_path
 
 
@@ -271,6 +272,19 @@ def _label_from_pandas(label):
     return label
 
 
+def _save_pandas_categorical(file_name, pandas_categorical):
+    with open(file_name, 'a') as f:
+        f.write('\npandas_categorical:' + json.dumps(pandas_categorical, default=json_default_with_numpy))
+
+
+def _load_pandas_categorical(file_name):
+    with open(file_name, 'r') as f:
+        last_line = f.readlines()[-1]
+        if last_line.startswith('pandas_categorical:'):
+            return json.loads(last_line[len('pandas_categorical:'):])
+    return None
+
+
 class _InnerPredictor(object):
     """
     A _InnerPredictor of LightGBM.
@@ -302,12 +316,7 @@ class _InnerPredictor(object):
                 ctypes.byref(out_num_class)))
             self.num_class = out_num_class.value
             self.num_total_iteration = out_num_iterations.value
-            with open(model_file, 'r') as f:
-                last_line = f.readlines()[-1]
-                if last_line.startswith('pandas_categorical:'):
-                    self.pandas_categorical = eval(last_line[len('pandas_categorical:'):])
-                else:
-                    self.pandas_categorical = None
+            self.pandas_categorical = _load_pandas_categorical(model_file)
         elif booster_handle is not None:
             self.__is_manage_handle = False
             self.handle = booster_handle
@@ -1207,12 +1216,7 @@ class Booster(object):
                 self.handle,
                 ctypes.byref(out_num_class)))
             self.__num_class = out_num_class.value
-            with open(model_file, 'r') as f:
-                last_line = f.readlines()[-1]
-                if last_line.startswith('pandas_categorical:'):
-                    self.pandas_categorical = eval(last_line[len('pandas_categorical:'):])
-                else:
-                    self.pandas_categorical = None
+            self.pandas_categorical = _load_pandas_categorical(model_file)
         elif 'model_str' in params:
             self.__load_model_from_string(params['model_str'])
         else:
@@ -1468,8 +1472,7 @@ class Booster(object):
             self.handle,
             ctypes.c_int(num_iteration),
             c_str(filename)))
-        with open(filename, 'a') as f:
-            f.write('\npandas_categorical:' + repr(self.pandas_categorical))
+        _save_pandas_categorical(filename, self.pandas_categorical)
 
     def __load_model_from_string(self, model_str):
         """[Private] Load model from string"""
diff --git a/python-package/lightgbm/compat.py b/python-package/lightgbm/compat.py
index 19fe82dee..26af67e43 100644
--- a/python-package/lightgbm/compat.py
+++ b/python-package/lightgbm/compat.py
@@ -6,6 +6,8 @@ from __future__ import absolute_import
 import inspect
 import sys
 
+import numpy as np
+
 is_py3 = (sys.version_info[0] == 3)
 
 """compatibility between python2 and python3"""
@@ -36,6 +38,16 @@ except (ImportError, SyntaxError):
     # because of u'...' Unicode literals.
     import json
 
+
+def json_default_with_numpy(obj):
+    if isinstance(obj, (np.integer, np.floating, np.bool_)):
+        return obj.item()
+    elif isinstance(obj, np.ndarray):
+        return obj.tolist()
+    else:
+        return obj
+
+
 """pandas"""
 try:
     from pandas import Series, DataFrame
diff --git a/tests/python_package_test/test_engine.py b/tests/python_package_test/test_engine.py
index ffc160be4..0a275e0d3 100644
--- a/tests/python_package_test/test_engine.py
+++ b/tests/python_package_test/test_engine.py
@@ -146,15 +146,18 @@ class TestEngine(unittest.TestCase):
 
     @unittest.skipIf(not IS_PANDAS_INSTALLED, 'pandas not installed')
     def test_pandas_categorical(self):
-        X = pd.DataFrame({"A": np.random.permutation(['a', 'b', 'c', 'd'] * 75),
-                          "B": np.random.permutation([1, 2, 3] * 100)})
-        X["A"] = X["A"].astype('category')
-        X["B"] = X["B"].astype('category')
+        X = pd.DataFrame({"A": np.random.permutation(['a', 'b', 'c', 'd'] * 75),  # str
+                          "B": np.random.permutation([1, 2, 3] * 100),  # int
+                          "C": np.random.permutation([0.1, 0.2, -0.1, -0.1, 0.2] * 60),  # float
+                          "D": np.random.permutation([True, False] * 150)})  # bool
         y = np.random.permutation([0, 1] * 150)
         X_test = pd.DataFrame({"A": np.random.permutation(['a', 'b', 'e'] * 20),
-                               "B": np.random.permutation([1, 3] * 30)})
-        X_test["A"] = X_test["A"].astype('category')
-        X_test["B"] = X_test["B"].astype('category')
+                               "B": np.random.permutation([1, 3] * 30),
+                               "C": np.random.permutation([0.1, -0.1, 0.2, 0.2] * 15),
+                               "D": np.random.permutation([True, False] * 30)})
+        for col in ["A", "B", "C", "D"]:
+            X[col] = X[col].astype('category')
+            X_test[col] = X_test[col].astype('category')
         params = {
             'objective': 'binary',
             'metric': 'binary_logloss',
@@ -173,7 +176,7 @@ class TestEngine(unittest.TestCase):
         pred2 = list(gbm2.predict(X_test))
         lgb_train = lgb.Dataset(X, y)
         gbm3 = lgb.train(params, lgb_train, num_boost_round=10, verbose_eval=False,
-                         categorical_feature=['A', 'B'])
+                         categorical_feature=['A', 'B', 'C', 'D'])
         pred3 = list(gbm3.predict(X_test))
         lgb_train = lgb.Dataset(X, y)
         gbm3.save_model('categorical.model')