[GitHub] [spark] d80tb7 commented on a change in pull request #24965: [WIP][SPARK-27463][PYTHON] Support Dataframe Cogroup via Pandas UDFs

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d80tb7 commented on a change in pull request #24965: [WIP][SPARK-27463][PYTHON] Support Dataframe Cogroup via Pandas UDFs
URL: https://github.com/apache/spark/pull/24965#discussion_r299371751
 
 

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 File path: python/pyspark/sql/tests/test_pandas_udf_cogrouped_map.py
 ##########
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+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import datetime
+import unittest
+import sys
+
+from collections import OrderedDict
+from decimal import Decimal
+
+from pyspark.sql import Row
+from pyspark.sql.functions import array, explode, col, lit, udf, sum, pandas_udf, PandasUDFType
+from pyspark.sql.types import *
+from pyspark.testing.sqlutils import ReusedSQLTestCase, have_pandas, have_pyarrow, \
+    pandas_requirement_message, pyarrow_requirement_message
+from pyspark.testing.utils import QuietTest
+
+if have_pandas:
+    import pandas as pd
+    from pandas.util.testing import assert_frame_equal
+
+if have_pyarrow:
+    import pyarrow as pa
+
+
+"""
+Tests below use pd.DataFrame.assign that will infer mixed types (unicode/str) for column names
+from kwargs w/ Python 2, so need to set check_column_type=False and avoid this check
+"""
+if sys.version < '3':
+    _check_column_type = False
+else:
+    _check_column_type = True
+
+
+@unittest.skipIf(
+    not have_pandas or not have_pyarrow,
+    pandas_requirement_message or pyarrow_requirement_message)
+class CoGroupedMapPandasUDFTests(ReusedSQLTestCase):
+
+    @property
+    def data1(self):
+        return self.spark.range(10).toDF('id') \
+            .withColumn("ks", array([lit(i) for i in range(20, 30)])) \
+            .withColumn("k", explode(col('ks')))\
+            .withColumn("v", col('k') * 10)\
+            .drop('ks')
+
+    @property
+    def data2(self):
+        return self.spark.range(10).toDF('id') \
+            .withColumn("ks", array([lit(i) for i in range(20, 30)])) \
+            .withColumn("k", explode(col('ks'))) \
+            .withColumn("v2", col('k') * 100) \
+            .drop('ks')
+
+    def test_simple(self):
+        import pandas as pd
+
+        l = self.data1
+        r = self.data2
+
+        @pandas_udf('id long, k int, v int, v2 int', PandasUDFType.COGROUPED_MAP)
+        def merge_pandas(left, right):
+            return pd.merge(left, right, how='outer', on=['k', 'id'])
+
+        # TODO: Grouping by a string fails to resolve here as analyzer cannot determine side
+        result = l\
+            .groupby(l.id)\
+            .cogroup(r.groupby(r.id))\
+            .apply(merge_pandas)\
+            .sort(['id', 'k'])\
+            .toPandas()
+
+        expected = pd\
+            .merge(l.toPandas(), r.toPandas(), how='outer', on=['k', 'id'])
+
+        assert_frame_equal(expected, result, check_column_type=_check_column_type)
+
 
 Review comment:
   Hi @hjoo 
   
   So far I've just been running it via PyCharm's unit test runner under python 3.  I suspect the problem you had was that the iterator I added wasn't compatible with python 2 (sorry!).  I've fixed the iterator and added a similar snippet to the one you provided above.  Now I can run using `python/run-tests --testnames pyspark.sql.tests.test_pandas_udf_cogrouped_map`
   
   If you still have problems let me know the error you get and I'll take a look.

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