07a9885f27
### What changes were proposed in this pull request? Fix PySpark test failures for using Pandas >= 1.0.0. ### Why are the changes needed? Pandas 1.0.0 has recently been released and has API changes that result in PySpark test failures, this PR fixes the broken tests. ### Does this PR introduce any user-facing change? No ### How was this patch tested? Manually tested with Pandas 1.0.1 and PyArrow 0.16.0 Closes #27529 from BryanCutler/pandas-fix-tests-1.0-SPARK-30777. Authored-by: Bryan Cutler <cutlerb@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
504 lines
20 KiB
Python
504 lines
20 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import unittest
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from pyspark.rdd import PythonEvalType
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from pyspark.sql import Row
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from pyspark.sql.functions import array, explode, col, lit, mean, sum, \
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udf, pandas_udf, PandasUDFType
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from pyspark.sql.types import *
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from pyspark.sql.utils import AnalysisException
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from pyspark.testing.sqlutils import ReusedSQLTestCase, have_pandas, have_pyarrow, \
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pandas_requirement_message, pyarrow_requirement_message
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from pyspark.testing.utils import QuietTest
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if have_pandas:
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import pandas as pd
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from pandas.util.testing import assert_frame_equal
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@unittest.skipIf(
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not have_pandas or not have_pyarrow,
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pandas_requirement_message or pyarrow_requirement_message)
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class GroupedAggPandasUDFTests(ReusedSQLTestCase):
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@property
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def data(self):
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return self.spark.range(10).toDF('id') \
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.withColumn("vs", array([lit(i * 1.0) + col('id') for i in range(20, 30)])) \
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.withColumn("v", explode(col('vs'))) \
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.drop('vs') \
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.withColumn('w', lit(1.0))
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@property
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def python_plus_one(self):
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@udf('double')
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def plus_one(v):
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assert isinstance(v, (int, float))
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return v + 1
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return plus_one
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@property
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def pandas_scalar_plus_two(self):
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@pandas_udf('double', PandasUDFType.SCALAR)
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def plus_two(v):
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assert isinstance(v, pd.Series)
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return v + 2
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return plus_two
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@property
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def pandas_agg_mean_udf(self):
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@pandas_udf('double', PandasUDFType.GROUPED_AGG)
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def avg(v):
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return v.mean()
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return avg
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@property
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def pandas_agg_sum_udf(self):
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@pandas_udf('double', PandasUDFType.GROUPED_AGG)
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def sum(v):
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return v.sum()
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return sum
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@property
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def pandas_agg_weighted_mean_udf(self):
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import numpy as np
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@pandas_udf('double', PandasUDFType.GROUPED_AGG)
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def weighted_mean(v, w):
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return np.average(v, weights=w)
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return weighted_mean
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def test_manual(self):
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df = self.data
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sum_udf = self.pandas_agg_sum_udf
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mean_udf = self.pandas_agg_mean_udf
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mean_arr_udf = pandas_udf(
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self.pandas_agg_mean_udf.func,
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ArrayType(self.pandas_agg_mean_udf.returnType),
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self.pandas_agg_mean_udf.evalType)
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result1 = df.groupby('id').agg(
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sum_udf(df.v),
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mean_udf(df.v),
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mean_arr_udf(array(df.v))).sort('id')
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expected1 = self.spark.createDataFrame(
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[[0, 245.0, 24.5, [24.5]],
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[1, 255.0, 25.5, [25.5]],
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[2, 265.0, 26.5, [26.5]],
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[3, 275.0, 27.5, [27.5]],
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[4, 285.0, 28.5, [28.5]],
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[5, 295.0, 29.5, [29.5]],
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[6, 305.0, 30.5, [30.5]],
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[7, 315.0, 31.5, [31.5]],
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[8, 325.0, 32.5, [32.5]],
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[9, 335.0, 33.5, [33.5]]],
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['id', 'sum(v)', 'avg(v)', 'avg(array(v))'])
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assert_frame_equal(expected1.toPandas(), result1.toPandas())
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def test_basic(self):
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df = self.data
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weighted_mean_udf = self.pandas_agg_weighted_mean_udf
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# Groupby one column and aggregate one UDF with literal
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result1 = df.groupby('id').agg(weighted_mean_udf(df.v, lit(1.0))).sort('id')
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expected1 = df.groupby('id').agg(mean(df.v).alias('weighted_mean(v, 1.0)')).sort('id')
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assert_frame_equal(expected1.toPandas(), result1.toPandas())
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# Groupby one expression and aggregate one UDF with literal
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result2 = df.groupby((col('id') + 1)).agg(weighted_mean_udf(df.v, lit(1.0)))\
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.sort(df.id + 1)
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expected2 = df.groupby((col('id') + 1))\
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.agg(mean(df.v).alias('weighted_mean(v, 1.0)')).sort(df.id + 1)
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assert_frame_equal(expected2.toPandas(), result2.toPandas())
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# Groupby one column and aggregate one UDF without literal
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result3 = df.groupby('id').agg(weighted_mean_udf(df.v, df.w)).sort('id')
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expected3 = df.groupby('id').agg(mean(df.v).alias('weighted_mean(v, w)')).sort('id')
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assert_frame_equal(expected3.toPandas(), result3.toPandas())
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# Groupby one expression and aggregate one UDF without literal
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result4 = df.groupby((col('id') + 1).alias('id'))\
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.agg(weighted_mean_udf(df.v, df.w))\
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.sort('id')
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expected4 = df.groupby((col('id') + 1).alias('id'))\
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.agg(mean(df.v).alias('weighted_mean(v, w)'))\
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.sort('id')
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assert_frame_equal(expected4.toPandas(), result4.toPandas())
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def test_unsupported_types(self):
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with QuietTest(self.sc):
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with self.assertRaisesRegexp(NotImplementedError, 'not supported'):
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pandas_udf(
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lambda x: x,
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ArrayType(ArrayType(TimestampType())),
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PandasUDFType.GROUPED_AGG)
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with QuietTest(self.sc):
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with self.assertRaisesRegexp(NotImplementedError, 'not supported'):
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@pandas_udf('mean double, std double', PandasUDFType.GROUPED_AGG)
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def mean_and_std_udf(v):
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return v.mean(), v.std()
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with QuietTest(self.sc):
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with self.assertRaisesRegexp(NotImplementedError, 'not supported'):
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@pandas_udf(MapType(DoubleType(), DoubleType()), PandasUDFType.GROUPED_AGG)
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def mean_and_std_udf(v):
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return {v.mean(): v.std()}
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def test_alias(self):
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df = self.data
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mean_udf = self.pandas_agg_mean_udf
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result1 = df.groupby('id').agg(mean_udf(df.v).alias('mean_alias'))
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expected1 = df.groupby('id').agg(mean(df.v).alias('mean_alias'))
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assert_frame_equal(expected1.toPandas(), result1.toPandas())
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def test_mixed_sql(self):
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"""
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Test mixing group aggregate pandas UDF with sql expression.
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"""
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df = self.data
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sum_udf = self.pandas_agg_sum_udf
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# Mix group aggregate pandas UDF with sql expression
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result1 = (df.groupby('id')
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.agg(sum_udf(df.v) + 1)
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.sort('id'))
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expected1 = (df.groupby('id')
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.agg(sum(df.v) + 1)
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.sort('id'))
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# Mix group aggregate pandas UDF with sql expression (order swapped)
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result2 = (df.groupby('id')
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.agg(sum_udf(df.v + 1))
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.sort('id'))
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expected2 = (df.groupby('id')
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.agg(sum(df.v + 1))
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.sort('id'))
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# Wrap group aggregate pandas UDF with two sql expressions
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result3 = (df.groupby('id')
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.agg(sum_udf(df.v + 1) + 2)
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.sort('id'))
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expected3 = (df.groupby('id')
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.agg(sum(df.v + 1) + 2)
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.sort('id'))
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assert_frame_equal(expected1.toPandas(), result1.toPandas())
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assert_frame_equal(expected2.toPandas(), result2.toPandas())
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assert_frame_equal(expected3.toPandas(), result3.toPandas())
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def test_mixed_udfs(self):
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"""
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Test mixing group aggregate pandas UDF with python UDF and scalar pandas UDF.
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"""
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df = self.data
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plus_one = self.python_plus_one
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plus_two = self.pandas_scalar_plus_two
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sum_udf = self.pandas_agg_sum_udf
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# Mix group aggregate pandas UDF and python UDF
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result1 = (df.groupby('id')
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.agg(plus_one(sum_udf(df.v)))
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.sort('id'))
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expected1 = (df.groupby('id')
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.agg(plus_one(sum(df.v)))
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.sort('id'))
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# Mix group aggregate pandas UDF and python UDF (order swapped)
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result2 = (df.groupby('id')
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.agg(sum_udf(plus_one(df.v)))
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.sort('id'))
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expected2 = (df.groupby('id')
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.agg(sum(plus_one(df.v)))
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.sort('id'))
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# Mix group aggregate pandas UDF and scalar pandas UDF
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result3 = (df.groupby('id')
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.agg(sum_udf(plus_two(df.v)))
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.sort('id'))
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expected3 = (df.groupby('id')
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.agg(sum(plus_two(df.v)))
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.sort('id'))
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# Mix group aggregate pandas UDF and scalar pandas UDF (order swapped)
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result4 = (df.groupby('id')
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.agg(plus_two(sum_udf(df.v)))
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.sort('id'))
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expected4 = (df.groupby('id')
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.agg(plus_two(sum(df.v)))
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.sort('id'))
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# Wrap group aggregate pandas UDF with two python UDFs and use python UDF in groupby
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result5 = (df.groupby(plus_one(df.id))
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.agg(plus_one(sum_udf(plus_one(df.v))))
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.sort('plus_one(id)'))
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expected5 = (df.groupby(plus_one(df.id))
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.agg(plus_one(sum(plus_one(df.v))))
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.sort('plus_one(id)'))
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# Wrap group aggregate pandas UDF with two scala pandas UDF and user scala pandas UDF in
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# groupby
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result6 = (df.groupby(plus_two(df.id))
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.agg(plus_two(sum_udf(plus_two(df.v))))
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.sort('plus_two(id)'))
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expected6 = (df.groupby(plus_two(df.id))
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.agg(plus_two(sum(plus_two(df.v))))
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.sort('plus_two(id)'))
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assert_frame_equal(expected1.toPandas(), result1.toPandas())
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assert_frame_equal(expected2.toPandas(), result2.toPandas())
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assert_frame_equal(expected3.toPandas(), result3.toPandas())
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assert_frame_equal(expected4.toPandas(), result4.toPandas())
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assert_frame_equal(expected5.toPandas(), result5.toPandas())
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assert_frame_equal(expected6.toPandas(), result6.toPandas())
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def test_multiple_udfs(self):
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"""
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Test multiple group aggregate pandas UDFs in one agg function.
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"""
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df = self.data
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mean_udf = self.pandas_agg_mean_udf
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sum_udf = self.pandas_agg_sum_udf
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weighted_mean_udf = self.pandas_agg_weighted_mean_udf
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result1 = (df.groupBy('id')
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.agg(mean_udf(df.v),
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sum_udf(df.v),
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weighted_mean_udf(df.v, df.w))
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.sort('id')
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.toPandas())
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expected1 = (df.groupBy('id')
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.agg(mean(df.v),
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sum(df.v),
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mean(df.v).alias('weighted_mean(v, w)'))
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.sort('id')
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.toPandas())
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assert_frame_equal(expected1, result1)
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def test_complex_groupby(self):
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df = self.data
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sum_udf = self.pandas_agg_sum_udf
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plus_one = self.python_plus_one
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plus_two = self.pandas_scalar_plus_two
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# groupby one expression
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result1 = df.groupby(df.v % 2).agg(sum_udf(df.v))
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expected1 = df.groupby(df.v % 2).agg(sum(df.v))
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# empty groupby
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result2 = df.groupby().agg(sum_udf(df.v))
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expected2 = df.groupby().agg(sum(df.v))
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# groupby one column and one sql expression
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result3 = df.groupby(df.id, df.v % 2).agg(sum_udf(df.v)).orderBy(df.id, df.v % 2)
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expected3 = df.groupby(df.id, df.v % 2).agg(sum(df.v)).orderBy(df.id, df.v % 2)
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# groupby one python UDF
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result4 = df.groupby(plus_one(df.id)).agg(sum_udf(df.v))
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expected4 = df.groupby(plus_one(df.id)).agg(sum(df.v))
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# groupby one scalar pandas UDF
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result5 = df.groupby(plus_two(df.id)).agg(sum_udf(df.v)).sort('sum(v)')
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expected5 = df.groupby(plus_two(df.id)).agg(sum(df.v)).sort('sum(v)')
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# groupby one expression and one python UDF
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result6 = df.groupby(df.v % 2, plus_one(df.id)).agg(sum_udf(df.v))
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expected6 = df.groupby(df.v % 2, plus_one(df.id)).agg(sum(df.v))
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# groupby one expression and one scalar pandas UDF
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result7 = (df.groupby(df.v % 2, plus_two(df.id))
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.agg(sum_udf(df.v)).sort(['sum(v)', 'plus_two(id)']))
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expected7 = (df.groupby(df.v % 2, plus_two(df.id))
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.agg(sum(df.v)).sort(['sum(v)', 'plus_two(id)']))
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assert_frame_equal(expected1.toPandas(), result1.toPandas())
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assert_frame_equal(expected2.toPandas(), result2.toPandas())
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assert_frame_equal(expected3.toPandas(), result3.toPandas())
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assert_frame_equal(expected4.toPandas(), result4.toPandas())
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assert_frame_equal(expected5.toPandas(), result5.toPandas())
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assert_frame_equal(expected6.toPandas(), result6.toPandas())
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assert_frame_equal(expected7.toPandas(), result7.toPandas())
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def test_complex_expressions(self):
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df = self.data
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plus_one = self.python_plus_one
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plus_two = self.pandas_scalar_plus_two
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sum_udf = self.pandas_agg_sum_udf
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# Test complex expressions with sql expression, python UDF and
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# group aggregate pandas UDF
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result1 = (df.withColumn('v1', plus_one(df.v))
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.withColumn('v2', df.v + 2)
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.groupby(df.id, df.v % 2)
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.agg(sum_udf(col('v')),
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sum_udf(col('v1') + 3),
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sum_udf(col('v2')) + 5,
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plus_one(sum_udf(col('v1'))),
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sum_udf(plus_one(col('v2'))))
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.sort(['id', '(v % 2)'])
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.toPandas().sort_values(by=['id', '(v % 2)']))
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expected1 = (df.withColumn('v1', df.v + 1)
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.withColumn('v2', df.v + 2)
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.groupby(df.id, df.v % 2)
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.agg(sum(col('v')),
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sum(col('v1') + 3),
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sum(col('v2')) + 5,
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plus_one(sum(col('v1'))),
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sum(plus_one(col('v2'))))
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.sort(['id', '(v % 2)'])
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.toPandas().sort_values(by=['id', '(v % 2)']))
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# Test complex expressions with sql expression, scala pandas UDF and
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# group aggregate pandas UDF
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result2 = (df.withColumn('v1', plus_one(df.v))
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.withColumn('v2', df.v + 2)
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.groupby(df.id, df.v % 2)
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.agg(sum_udf(col('v')),
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sum_udf(col('v1') + 3),
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sum_udf(col('v2')) + 5,
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plus_two(sum_udf(col('v1'))),
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sum_udf(plus_two(col('v2'))))
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.sort(['id', '(v % 2)'])
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.toPandas().sort_values(by=['id', '(v % 2)']))
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expected2 = (df.withColumn('v1', df.v + 1)
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.withColumn('v2', df.v + 2)
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.groupby(df.id, df.v % 2)
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.agg(sum(col('v')),
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sum(col('v1') + 3),
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sum(col('v2')) + 5,
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plus_two(sum(col('v1'))),
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sum(plus_two(col('v2'))))
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.sort(['id', '(v % 2)'])
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.toPandas().sort_values(by=['id', '(v % 2)']))
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# Test sequential groupby aggregate
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result3 = (df.groupby('id')
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.agg(sum_udf(df.v).alias('v'))
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.groupby('id')
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.agg(sum_udf(col('v')))
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.sort('id')
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.toPandas())
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expected3 = (df.groupby('id')
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.agg(sum(df.v).alias('v'))
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.groupby('id')
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.agg(sum(col('v')))
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.sort('id')
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.toPandas())
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assert_frame_equal(expected1, result1)
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assert_frame_equal(expected2, result2)
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assert_frame_equal(expected3, result3)
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def test_retain_group_columns(self):
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with self.sql_conf({"spark.sql.retainGroupColumns": False}):
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df = self.data
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sum_udf = self.pandas_agg_sum_udf
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result1 = df.groupby(df.id).agg(sum_udf(df.v))
|
|
expected1 = df.groupby(df.id).agg(sum(df.v))
|
|
assert_frame_equal(expected1.toPandas(), result1.toPandas())
|
|
|
|
def test_array_type(self):
|
|
df = self.data
|
|
|
|
array_udf = pandas_udf(lambda x: [1.0, 2.0], 'array<double>', PandasUDFType.GROUPED_AGG)
|
|
result1 = df.groupby('id').agg(array_udf(df['v']).alias('v2'))
|
|
self.assertEquals(result1.first()['v2'], [1.0, 2.0])
|
|
|
|
def test_invalid_args(self):
|
|
df = self.data
|
|
plus_one = self.python_plus_one
|
|
mean_udf = self.pandas_agg_mean_udf
|
|
|
|
with QuietTest(self.sc):
|
|
with self.assertRaisesRegexp(
|
|
AnalysisException,
|
|
'nor.*aggregate function'):
|
|
df.groupby(df.id).agg(plus_one(df.v)).collect()
|
|
|
|
with QuietTest(self.sc):
|
|
with self.assertRaisesRegexp(
|
|
AnalysisException,
|
|
'aggregate function.*argument.*aggregate function'):
|
|
df.groupby(df.id).agg(mean_udf(mean_udf(df.v))).collect()
|
|
|
|
with QuietTest(self.sc):
|
|
with self.assertRaisesRegexp(
|
|
AnalysisException,
|
|
'mixture.*aggregate function.*group aggregate pandas UDF'):
|
|
df.groupby(df.id).agg(mean_udf(df.v), mean(df.v)).collect()
|
|
|
|
def test_register_vectorized_udf_basic(self):
|
|
sum_pandas_udf = pandas_udf(
|
|
lambda v: v.sum(), "integer", PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF)
|
|
|
|
self.assertEqual(sum_pandas_udf.evalType, PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF)
|
|
group_agg_pandas_udf = self.spark.udf.register("sum_pandas_udf", sum_pandas_udf)
|
|
self.assertEqual(group_agg_pandas_udf.evalType, PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF)
|
|
q = "SELECT sum_pandas_udf(v1) FROM VALUES (3, 0), (2, 0), (1, 1) tbl(v1, v2) GROUP BY v2"
|
|
actual = sorted(map(lambda r: r[0], self.spark.sql(q).collect()))
|
|
expected = [1, 5]
|
|
self.assertEqual(actual, expected)
|
|
|
|
def test_grouped_with_empty_partition(self):
|
|
data = [Row(id=1, x=2), Row(id=1, x=3), Row(id=2, x=4)]
|
|
expected = [Row(id=1, sum=5), Row(id=2, x=4)]
|
|
num_parts = len(data) + 1
|
|
df = self.spark.createDataFrame(self.sc.parallelize(data, numSlices=num_parts))
|
|
|
|
f = pandas_udf(lambda x: x.sum(),
|
|
'int', PandasUDFType.GROUPED_AGG)
|
|
|
|
result = df.groupBy('id').agg(f(df['x']).alias('sum')).collect()
|
|
self.assertEqual(result, expected)
|
|
|
|
def test_grouped_without_group_by_clause(self):
|
|
@pandas_udf('double', PandasUDFType.GROUPED_AGG)
|
|
def max_udf(v):
|
|
return v.max()
|
|
|
|
df = self.spark.range(0, 100)
|
|
self.spark.udf.register('max_udf', max_udf)
|
|
|
|
with self.tempView("table"):
|
|
df.createTempView('table')
|
|
|
|
agg1 = df.agg(max_udf(df['id']))
|
|
agg2 = self.spark.sql("select max_udf(id) from table")
|
|
assert_frame_equal(agg1.toPandas(), agg2.toPandas())
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from pyspark.sql.tests.test_pandas_udf_grouped_agg import *
|
|
|
|
try:
|
|
import xmlrunner
|
|
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
|
|
except ImportError:
|
|
testRunner = None
|
|
unittest.main(testRunner=testRunner, verbosity=2)
|