[SPARK-22874][PYSPARK][SQL] Modify checking pandas version to use LooseVersion.

python/pyspark/sql/dataframe.py

@@ -1906,9 +1906,9 @@ def toPandas(self):
         if self.sql_ctx.getConf("spark.sql.execution.arrow.enabled", "false").lower() == "true":
             try:
                 from pyspark.sql.types import _check_dataframe_localize_timestamps
-                from pyspark.sql.utils import _require_minimum_pyarrow_version
+                from pyspark.sql.utils import require_minimum_pyarrow_version
                 import pyarrow
-                _require_minimum_pyarrow_version()
+                require_minimum_pyarrow_version()
                 tables = self._collectAsArrow()
                 if tables:
                     table = pyarrow.concat_tables(tables)

python/pyspark/sql/session.py

@@ -493,15 +493,14 @@ def _create_from_pandas_with_arrow(self, pdf, schema, timezone):
         data types will be used to coerce the data in Pandas to Arrow conversion.
         """
         from pyspark.serializers import ArrowSerializer, _create_batch
-        from pyspark.sql.types import from_arrow_schema, to_arrow_type, \
-            _old_pandas_exception_message, TimestampType
-        from pyspark.sql.utils import _require_minimum_pyarrow_version
-        try:
-            from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
-        except ImportError as e:
-            raise ImportError(_old_pandas_exception_message(e))
+        from pyspark.sql.types import from_arrow_schema, to_arrow_type, TimestampType
+        from pyspark.sql.utils import require_minimum_pandas_version, \
+            require_minimum_pyarrow_version
+
+        require_minimum_pandas_version()
+        require_minimum_pyarrow_version()
 
-        _require_minimum_pyarrow_version()
+        from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
 
         # Determine arrow types to coerce data when creating batches
         if isinstance(schema, StructType):

python/pyspark/sql/tests.py

@@ -53,7 +53,8 @@
 try:
     import pandas
     try:
-        import pandas.api
+        from pyspark.sql.utils import require_minimum_pandas_version
+        require_minimum_pandas_version()
         _have_pandas = True
     except:
         _have_old_pandas = True
@@ -2600,7 +2601,7 @@ def test_to_pandas(self):
     @unittest.skipIf(not _have_old_pandas, "Old Pandas not installed")
     def test_to_pandas_old(self):
         with QuietTest(self.sc):
-            with self.assertRaisesRegexp(ImportError, 'Pandas \(.*\) must be installed'):
+            with self.assertRaisesRegexp(ImportError, 'Pandas >= .* must be installed'):
                 self._to_pandas()
 
     @unittest.skipIf(not _have_pandas, "Pandas not installed")
@@ -2643,7 +2644,7 @@ def test_create_dataframe_from_old_pandas(self):
         pdf = pd.DataFrame({"ts": [datetime(2017, 10, 31, 1, 1, 1)],
                             "d": [pd.Timestamp.now().date()]})
         with QuietTest(self.sc):
-            with self.assertRaisesRegexp(ImportError, 'Pandas \(.*\) must be installed'):
+            with self.assertRaisesRegexp(ImportError, 'Pandas >= .* must be installed'):
                 self.spark.createDataFrame(pdf)
 
 

python/pyspark/sql/types.py

@@ -1678,13 +1678,6 @@ def from_arrow_schema(arrow_schema):
          for field in arrow_schema])
 
 
-def _old_pandas_exception_message(e):
-    """ Create an error message for importing old Pandas.
-    """
-    msg = "note: Pandas (>=0.19.2) must be installed and available on calling Python process"
-    return "%s\n%s" % (_exception_message(e), msg)
-
-
 def _check_dataframe_localize_timestamps(pdf, timezone):
     """
     Convert timezone aware timestamps to timezone-naive in the specified timezone or local timezone
@@ -1693,10 +1686,10 @@ def _check_dataframe_localize_timestamps(pdf, timezone):
     :param timezone: the timezone to convert. if None then use local timezone
     :return pandas.DataFrame where any timezone aware columns have been converted to tz-naive
     """
-    try:
-        from pandas.api.types import is_datetime64tz_dtype
-    except ImportError as e:
-        raise ImportError(_old_pandas_exception_message(e))
+    from pyspark.sql.utils import require_minimum_pandas_version
+    require_minimum_pandas_version()
+
+    from pandas.api.types import is_datetime64tz_dtype
     tz = timezone or 'tzlocal()'
     for column, series in pdf.iteritems():
         # TODO: handle nested timestamps, such as ArrayType(TimestampType())?
@@ -1714,10 +1707,10 @@ def _check_series_convert_timestamps_internal(s, timezone):
     :param timezone: the timezone to convert. if None then use local timezone
     :return pandas.Series where if it is a timestamp, has been UTC normalized without a time zone
     """
-    try:
-        from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
-    except ImportError as e:
-        raise ImportError(_old_pandas_exception_message(e))
+    from pyspark.sql.utils import require_minimum_pandas_version
+    require_minimum_pandas_version()
+
+    from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
     # TODO: handle nested timestamps, such as ArrayType(TimestampType())?
     if is_datetime64_dtype(s.dtype):
         tz = timezone or 'tzlocal()'
@@ -1737,11 +1730,11 @@ def _check_series_convert_timestamps_localize(s, from_timezone, to_timezone):
     :param to_timezone: the timezone to convert to. if None then use local timezone
     :return pandas.Series where if it is a timestamp, has been converted to tz-naive
     """
-    try:
-        import pandas as pd
-        from pandas.api.types import is_datetime64tz_dtype, is_datetime64_dtype
-    except ImportError as e:
-        raise ImportError(_old_pandas_exception_message(e))
+    from pyspark.sql.utils import require_minimum_pandas_version
+    require_minimum_pandas_version()
+
+    import pandas as pd
+    from pandas.api.types import is_datetime64tz_dtype, is_datetime64_dtype
     from_tz = from_timezone or 'tzlocal()'
     to_tz = to_timezone or 'tzlocal()'
     # TODO: handle nested timestamps, such as ArrayType(TimestampType())?

python/pyspark/sql/udf.py

@@ -37,9 +37,9 @@ def _create_udf(f, returnType, evalType):
     if evalType == PythonEvalType.SQL_PANDAS_SCALAR_UDF or \
             evalType == PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF:
         import inspect
-        from pyspark.sql.utils import _require_minimum_pyarrow_version
+        from pyspark.sql.utils import require_minimum_pyarrow_version
 
-        _require_minimum_pyarrow_version()
+        require_minimum_pyarrow_version()
         argspec = inspect.getargspec(f)
 
         if evalType == PythonEvalType.SQL_PANDAS_SCALAR_UDF and len(argspec.args) == 0 and \

python/pyspark/sql/utils.py

@@ -112,7 +112,16 @@ def toJArray(gateway, jtype, arr):
     return jarr
 
 
-def _require_minimum_pyarrow_version():
+def require_minimum_pandas_version():
+    """ Raise ImportError if minimum version of Pandas is not installed
+    """
+    from distutils.version import LooseVersion
+    import pandas
+    if LooseVersion(pandas.__version__) < LooseVersion('0.19.2'):
+        raise ImportError("Pandas >= 0.19.2 must be installed on calling Python process")
+
+
+def require_minimum_pyarrow_version():
     """ Raise ImportError if minimum version of pyarrow is not installed
     """
     from distutils.version import LooseVersion