[SPARK-21766][PYSPARK][SQL] DataFrame toPandas() raises ValueError with nullable int columns

## What changes were proposed in this pull request?

When calling `DataFrame.toPandas()` (without Arrow enabled), if there is a `IntegralType` column (`IntegerType`, `ShortType`, `ByteType`) that has null values the following exception is thrown:

    ValueError: Cannot convert non-finite values (NA or inf) to integer

This is because the null values first get converted to float NaN during the construction of the Pandas DataFrame in `from_records`, and then it is attempted to be converted back to to an integer where it fails.

The fix is going to check if the Pandas DataFrame can cause such failure when converting, if so, we don't do the conversion and use the inferred type by Pandas.

Closes apache#18945

## How was this patch tested?

Added pyspark test.

Author: Liang-Chi Hsieh <[email protected]>

Closes apache#19319 from viirya/SPARK-21766.

Author: viirya

python/pyspark/sql/dataframe.py

@@ -37,6 +37,7 @@
 from pyspark.sql.column import Column, _to_seq, _to_list, _to_java_column
 from pyspark.sql.readwriter import DataFrameWriter
 from pyspark.sql.streaming import DataStreamWriter
+from pyspark.sql.types import IntegralType
 from pyspark.sql.types import *
 
 __all__ = ["DataFrame", "DataFrameNaFunctions", "DataFrameStatFunctions"]
@@ -1891,14 +1892,20 @@ def toPandas(self):
                       "if using spark.sql.execution.arrow.enable=true"
                 raise ImportError("%s\n%s" % (e.message, msg))
         else:
+            pdf = 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:
+                # SPARK-21766: if an integer field is nullable and has null values, it can be
+                # inferred by pandas as float column. Once we convert the column with NaN back
+                # to integer type e.g., np.int16, we will hit exception. So we use the inferred
+                # float type, not the corrected type from the schema in this case.
+                if pandas_type is not None and \
+                    not(isinstance(field.dataType, IntegralType) and field.nullable and
+                        pdf[field.name].isnull().any()):
                     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

python/pyspark/sql/tests.py

@@ -2564,6 +2564,18 @@ def test_to_pandas(self):
         self.assertEquals(types[2], np.bool)
         self.assertEquals(types[3], np.float32)
 
+    @unittest.skipIf(not _have_pandas, "Pandas not installed")
+    def test_to_pandas_avoid_astype(self):
+        import numpy as np
+        schema = StructType().add("a", IntegerType()).add("b", StringType())\
+                             .add("c", IntegerType())
+        data = [(1, "foo", 16777220), (None, "bar", None)]
+        df = self.spark.createDataFrame(data, schema)
+        types = df.toPandas().dtypes
+        self.assertEquals(types[0], np.float64)  # doesn't convert to np.int32 due to NaN value.
+        self.assertEquals(types[1], np.object)
+        self.assertEquals(types[2], np.float64)
+
     def test_create_dataframe_from_array_of_long(self):
         import array
         data = [Row(longarray=array.array('l', [-9223372036854775808, 0, 9223372036854775807]))]