diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 8541403dfe2f..0649271ed224 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -1721,7 +1721,18 @@ def toPandas(self):
         1    5    Bob
         """
         import pandas as pd
-        return pd.DataFrame.from_records(self.collect(), columns=self.columns)
+
+        dtype = {}
+        for field in self.schema:
+            pandas_type = _to_corrected_pandas_type(field.dataType)
+            if pandas_type is not None:
+                dtype[field.name] = pandas_type
+
+        pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns)
+
+        for f, t in dtype.items():
+            pdf[f] = pdf[f].astype(t, copy=False)
+        return pdf
 
     ##########################################################################################
     # Pandas compatibility
@@ -1750,6 +1761,24 @@ def _to_scala_map(sc, jm):
     return sc._jvm.PythonUtils.toScalaMap(jm)
 
 
+def _to_corrected_pandas_type(dt):
+    """
+    When converting Spark SQL records to Pandas DataFrame, the inferred data type may be wrong.
+    This method gets the corrected data type for Pandas if that type may be inferred uncorrectly.
+    """
+    import numpy as np
+    if type(dt) == ByteType:
+        return np.int8
+    elif type(dt) == ShortType:
+        return np.int16
+    elif type(dt) == IntegerType:
+        return np.int32
+    elif type(dt) == FloatType:
+        return np.float32
+    else:
+        return None
+
+
 class DataFrameNaFunctions(object):
     """Functionality for working with missing data in :class:`DataFrame`.
 
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 31f932a36322..daee059a9277 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -46,6 +46,14 @@
 else:
     import unittest
 
+_have_pandas = False
+try:
+    import pandas
+    _have_pandas = True
+except:
+    # No Pandas, but that's okay, we'll skip those tests
+    pass
+
 from pyspark import SparkContext
 from pyspark.sql import SparkSession, SQLContext, HiveContext, Column, Row
 from pyspark.sql.types import *
@@ -2274,6 +2282,22 @@ def count_bucketed_cols(names, table="pyspark_bucket"):
             .mode("overwrite").saveAsTable("pyspark_bucket"))
         self.assertSetEqual(set(data), set(self.spark.table("pyspark_bucket").collect()))
 
+    @unittest.skipIf(not _have_pandas, "Pandas not installed")
+    def test_to_pandas(self):
+        import numpy as np
+        schema = StructType().add("a", IntegerType()).add("b", StringType())\
+                             .add("c", BooleanType()).add("d", FloatType())
+        data = [
+            (1, "foo", True, 3.0), (2, "foo", True, 5.0),
+            (3, "bar", False, -1.0), (4, "bar", False, 6.0),
+        ]
+        df = self.spark.createDataFrame(data, schema)
+        types = df.toPandas().dtypes
+        self.assertEquals(types[0], np.int32)
+        self.assertEquals(types[1], np.object)
+        self.assertEquals(types[2], np.bool)
+        self.assertEquals(types[3], np.float32)
+
 
 class HiveSparkSubmitTests(SparkSubmitTests):