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Posted to commits@spark.apache.org by we...@apache.org on 2018/02/06 06:52:33 UTC
spark git commit: [SPARK-23290][SQL][PYTHON] Use datetime.date for
date type when converting Spark DataFrame to Pandas DataFrame.
Repository: spark
Updated Branches:
refs/heads/master f3f1e14bb -> a24c03138
[SPARK-23290][SQL][PYTHON] Use datetime.date for date type when converting Spark DataFrame to Pandas DataFrame.
## What changes were proposed in this pull request?
In #18664, there was a change in how `DateType` is being returned to users ([line 1968 in dataframe.py](https://github.com/apache/spark/pull/18664/files#diff-6fc344560230bf0ef711bb9b5573f1faR1968)). This can cause client code which works in Spark 2.2 to fail.
See [SPARK-23290](https://issues.apache.org/jira/browse/SPARK-23290?focusedCommentId=16350917&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-16350917) for an example.
This pr modifies to use `datetime.date` for date type as Spark 2.2 does.
## How was this patch tested?
Tests modified to fit the new behavior and existing tests.
Author: Takuya UESHIN <ue...@databricks.com>
Closes #20506 from ueshin/issues/SPARK-23290.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/a24c0313
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/a24c0313
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/a24c0313
Branch: refs/heads/master
Commit: a24c03138a6935a442b983c8a4c721b26df3f9e2
Parents: f3f1e14
Author: Takuya UESHIN <ue...@databricks.com>
Authored: Tue Feb 6 14:52:25 2018 +0800
Committer: Wenchen Fan <we...@databricks.com>
Committed: Tue Feb 6 14:52:25 2018 +0800
----------------------------------------------------------------------
python/pyspark/serializers.py | 9 ++++--
python/pyspark/sql/dataframe.py | 7 ++---
python/pyspark/sql/tests.py | 57 ++++++++++++++++++++++++++----------
python/pyspark/sql/types.py | 15 ++++++++++
4 files changed, 66 insertions(+), 22 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/a24c0313/python/pyspark/serializers.py
----------------------------------------------------------------------
diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py
index 88d6a19..e870325 100644
--- a/python/pyspark/serializers.py
+++ b/python/pyspark/serializers.py
@@ -267,12 +267,15 @@ class ArrowStreamPandasSerializer(Serializer):
"""
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):
http://git-wip-us.apache.org/repos/asf/spark/blob/a24c0313/python/pyspark/sql/dataframe.py
----------------------------------------------------------------------
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 2e55407..59a4170 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -1923,7 +1923,8 @@ class DataFrame(object):
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()
@@ -1931,6 +1932,7 @@ class DataFrame(object):
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)
@@ -2009,7 +2011,6 @@ 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:
@@ -2020,8 +2021,6 @@ 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
http://git-wip-us.apache.org/repos/asf/spark/blob/a24c0313/python/pyspark/sql/tests.py
----------------------------------------------------------------------
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index b273630..545ec5a 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -2816,7 +2816,7 @@ class SQLTests(ReusedSQLTestCase):
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")
@@ -3388,7 +3388,7 @@ class ArrowTests(ReusedSQLTestCase):
@classmethod
def setUpClass(cls):
- from datetime import datetime
+ from datetime import date, datetime
from decimal import Decimal
ReusedSQLTestCase.setUpClass()
@@ -3410,11 +3410,11 @@ class ArrowTests(ReusedSQLTestCase):
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):
@@ -3461,7 +3461,9 @@ class ArrowTests(ReusedSQLTestCase):
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)
@@ -4062,18 +4064,42 @@ class ScalarPandasUDF(ReusedSQLTestCase):
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
@@ -4114,6 +4140,7 @@ class ScalarPandasUDF(ReusedSQLTestCase):
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):
http://git-wip-us.apache.org/repos/asf/spark/blob/a24c0313/python/pyspark/sql/types.py
----------------------------------------------------------------------
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index 0dc5823..093dae5 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
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