diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py index 88d6a191babca..e870325d202ca 100644 --- a/python/pyspark/serializers.py +++ b/python/pyspark/serializers.py @@ -267,12 +267,15 @@ def load_stream(self, stream): """ Deserialize ArrowRecordBatches to an Arrow table and return as a list of pandas.Series. """ - from pyspark.sql.types import _check_dataframe_localize_timestamps + from pyspark.sql.types import from_arrow_schema, _check_dataframe_convert_date, \ + _check_dataframe_localize_timestamps import pyarrow as pa reader = pa.open_stream(stream) + schema = from_arrow_schema(reader.schema) for batch in reader: - # NOTE: changed from pa.Columns.to_pandas, timezone issue in conversion fixed in 0.7.1 - pdf = _check_dataframe_localize_timestamps(batch.to_pandas(), self._timezone) + pdf = batch.to_pandas() + pdf = _check_dataframe_convert_date(pdf, schema) + pdf = _check_dataframe_localize_timestamps(pdf, self._timezone) yield [c for _, c in pdf.iteritems()] def __repr__(self): diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 055b2c4a0ffec..f7fde43b331ca 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -1933,7 +1933,8 @@ 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.types import _check_dataframe_convert_date, \ + _check_dataframe_localize_timestamps from pyspark.sql.utils import require_minimum_pyarrow_version import pyarrow require_minimum_pyarrow_version() @@ -1941,6 +1942,7 @@ def toPandas(self): if tables: table = pyarrow.concat_tables(tables) pdf = table.to_pandas() + pdf = _check_dataframe_convert_date(pdf, self.schema) return _check_dataframe_localize_timestamps(pdf, timezone) else: return pd.DataFrame.from_records([], columns=self.columns) @@ -2019,7 +2021,6 @@ 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. - NOTE: DateType is inferred incorrectly as 'object', TimestampType is correct with datetime64[ns] """ import numpy as np if type(dt) == ByteType: @@ -2030,8 +2031,6 @@ def _to_corrected_pandas_type(dt): return np.int32 elif type(dt) == FloatType: return np.float32 - elif type(dt) == DateType: - return 'datetime64[ns]' else: return None diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index ca7bbf8ffe71c..105be9dd440a1 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -2816,7 +2816,7 @@ def test_to_pandas(self): self.assertEquals(types[1], np.object) self.assertEquals(types[2], np.bool) self.assertEquals(types[3], np.float32) - self.assertEquals(types[4], 'datetime64[ns]') + self.assertEquals(types[4], np.object) # datetime.date self.assertEquals(types[5], 'datetime64[ns]') @unittest.skipIf(not _have_old_pandas, "Old Pandas not installed") @@ -3362,7 +3362,7 @@ class ArrowTests(ReusedSQLTestCase): @classmethod def setUpClass(cls): - from datetime import datetime + from datetime import date, datetime from decimal import Decimal ReusedSQLTestCase.setUpClass() @@ -3384,11 +3384,11 @@ def setUpClass(cls): StructField("7_date_t", DateType(), True), StructField("8_timestamp_t", TimestampType(), True)]) cls.data = [(u"a", 1, 10, 0.2, 2.0, Decimal("2.0"), - datetime(1969, 1, 1), datetime(1969, 1, 1, 1, 1, 1)), + date(1969, 1, 1), datetime(1969, 1, 1, 1, 1, 1)), (u"b", 2, 20, 0.4, 4.0, Decimal("4.0"), - datetime(2012, 2, 2), datetime(2012, 2, 2, 2, 2, 2)), + date(2012, 2, 2), datetime(2012, 2, 2, 2, 2, 2)), (u"c", 3, 30, 0.8, 6.0, Decimal("6.0"), - datetime(2100, 3, 3), datetime(2100, 3, 3, 3, 3, 3))] + date(2100, 3, 3), datetime(2100, 3, 3, 3, 3, 3))] @classmethod def tearDownClass(cls): @@ -3435,7 +3435,9 @@ def _toPandas_arrow_toggle(self, df): def test_toPandas_arrow_toggle(self): df = self.spark.createDataFrame(self.data, schema=self.schema) pdf, pdf_arrow = self._toPandas_arrow_toggle(df) - self.assertPandasEqual(pdf_arrow, pdf) + expected = self.create_pandas_data_frame() + self.assertPandasEqual(expected, pdf) + self.assertPandasEqual(expected, pdf_arrow) def test_toPandas_respect_session_timezone(self): df = self.spark.createDataFrame(self.data, schema=self.schema) @@ -4036,18 +4038,42 @@ def test_vectorized_udf_unsupported_types(self): with self.assertRaisesRegexp(Exception, 'Unsupported data type'): df.select(f(col('map'))).collect() - def test_vectorized_udf_null_date(self): + def test_vectorized_udf_dates(self): from pyspark.sql.functions import pandas_udf, col from datetime import date - schema = StructType().add("date", DateType()) - data = [(date(1969, 1, 1),), - (date(2012, 2, 2),), - (None,), - (date(2100, 4, 4),)] + schema = StructType().add("idx", LongType()).add("date", DateType()) + data = [(0, date(1969, 1, 1),), + (1, date(2012, 2, 2),), + (2, None,), + (3, date(2100, 4, 4),)] df = self.spark.createDataFrame(data, schema=schema) - date_f = pandas_udf(lambda t: t, returnType=DateType()) - res = df.select(date_f(col("date"))) - self.assertEquals(df.collect(), res.collect()) + + date_copy = pandas_udf(lambda t: t, returnType=DateType()) + df = df.withColumn("date_copy", date_copy(col("date"))) + + @pandas_udf(returnType=StringType()) + def check_data(idx, date, date_copy): + import pandas as pd + msgs = [] + is_equal = date.isnull() + for i in range(len(idx)): + if (is_equal[i] and data[idx[i]][1] is None) or \ + date[i] == data[idx[i]][1]: + msgs.append(None) + else: + msgs.append( + "date values are not equal (date='%s': data[%d][1]='%s')" + % (date[i], idx[i], data[idx[i]][1])) + return pd.Series(msgs) + + result = df.withColumn("check_data", + check_data(col("idx"), col("date"), col("date_copy"))).collect() + + self.assertEquals(len(data), len(result)) + for i in range(len(result)): + self.assertEquals(data[i][1], result[i][1]) # "date" col + self.assertEquals(data[i][1], result[i][2]) # "date_copy" col + self.assertIsNone(result[i][3]) # "check_data" col def test_vectorized_udf_timestamps(self): from pyspark.sql.functions import pandas_udf, col @@ -4088,6 +4114,7 @@ def check_data(idx, timestamp, timestamp_copy): self.assertEquals(len(data), len(result)) for i in range(len(result)): self.assertEquals(data[i][1], result[i][1]) # "timestamp" col + self.assertEquals(data[i][1], result[i][2]) # "timestamp_copy" col self.assertIsNone(result[i][3]) # "check_data" col def test_vectorized_udf_return_timestamp_tz(self): diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py index 0dc5823f72a3c..093dae5a22e1f 100644 --- a/python/pyspark/sql/types.py +++ b/python/pyspark/sql/types.py @@ -1694,6 +1694,21 @@ def from_arrow_schema(arrow_schema): for field in arrow_schema]) +def _check_dataframe_convert_date(pdf, schema): + """ Correct date type value to use datetime.date. + + Pandas DataFrame created from PyArrow uses datetime64[ns] for date type values, but we should + use datetime.date to match the behavior with when Arrow optimization is disabled. + + :param pdf: pandas.DataFrame + :param schema: a Spark schema of the pandas.DataFrame + """ + for field in schema: + if type(field.dataType) == DateType: + pdf[field.name] = pdf[field.name].dt.date + return pdf + + def _check_dataframe_localize_timestamps(pdf, timezone): """ Convert timezone aware timestamps to timezone-naive in the specified timezone or local timezone