| # |
| # 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 |
| |
| from collections import OrderedDict |
| from decimal import Decimal |
| from typing import cast |
| |
| from pyspark.sql import Row |
| from pyspark.sql.functions import ( |
| array, |
| explode, |
| col, |
| lit, |
| udf, |
| sum, |
| pandas_udf, |
| PandasUDFType, |
| window, |
| ) |
| from pyspark.sql.types import ( |
| IntegerType, |
| DoubleType, |
| ArrayType, |
| BinaryType, |
| ByteType, |
| LongType, |
| DecimalType, |
| ShortType, |
| FloatType, |
| StringType, |
| BooleanType, |
| StructType, |
| StructField, |
| NullType, |
| MapType, |
| YearMonthIntervalType, |
| ) |
| from pyspark.errors import PythonException, PySparkTypeError, PySparkValueError |
| from pyspark.testing.sqlutils import ( |
| ReusedSQLTestCase, |
| have_pandas, |
| have_pyarrow, |
| pandas_requirement_message, |
| pyarrow_requirement_message, |
| ) |
| |
| if have_pandas: |
| import pandas as pd |
| from pandas.testing import assert_frame_equal |
| |
| if have_pyarrow: |
| import pyarrow as pa # noqa: F401 |
| |
| |
| @unittest.skipIf( |
| not have_pandas or not have_pyarrow, |
| cast(str, pandas_requirement_message or pyarrow_requirement_message), |
| ) |
| class GroupedApplyInPandasTestsMixin: |
| @property |
| def data(self): |
| return ( |
| self.spark.range(10) |
| .withColumn("vs", array([lit(i) for i in range(20, 30)])) |
| .withColumn("v", explode(col("vs"))) |
| .drop("vs") |
| ) |
| |
| def test_supported_types(self): |
| values = [ |
| 1, |
| 2, |
| 3, |
| 4, |
| 5, |
| 1.1, |
| 2.2, |
| Decimal(1.123), |
| [1, 2, 2], |
| True, |
| "hello", |
| bytearray([0x01, 0x02]), |
| None, |
| ] |
| output_fields = [ |
| ("id", IntegerType()), |
| ("byte", ByteType()), |
| ("short", ShortType()), |
| ("int", IntegerType()), |
| ("long", LongType()), |
| ("float", FloatType()), |
| ("double", DoubleType()), |
| ("decim", DecimalType(10, 3)), |
| ("array", ArrayType(IntegerType())), |
| ("bool", BooleanType()), |
| ("str", StringType()), |
| ("bin", BinaryType()), |
| ("null", NullType()), |
| ] |
| |
| output_schema = StructType([StructField(*x) for x in output_fields]) |
| df = self.spark.createDataFrame([values], schema=output_schema) |
| |
| # Different forms of group map pandas UDF, results of these are the same |
| udf1 = pandas_udf( |
| lambda pdf: pdf.assign( |
| byte=pdf.byte * 2, |
| short=pdf.short * 2, |
| int=pdf.int * 2, |
| long=pdf.long * 2, |
| float=pdf.float * 2, |
| double=pdf.double * 2, |
| decim=pdf.decim * 2, |
| bool=False if pdf.bool else True, |
| str=pdf.str + "there", |
| array=pdf.array, |
| bin=pdf.bin, |
| null=pdf.null, |
| ), |
| output_schema, |
| PandasUDFType.GROUPED_MAP, |
| ) |
| |
| udf2 = pandas_udf( |
| lambda _, pdf: pdf.assign( |
| byte=pdf.byte * 2, |
| short=pdf.short * 2, |
| int=pdf.int * 2, |
| long=pdf.long * 2, |
| float=pdf.float * 2, |
| double=pdf.double * 2, |
| decim=pdf.decim * 2, |
| bool=False if pdf.bool else True, |
| str=pdf.str + "there", |
| array=pdf.array, |
| bin=pdf.bin, |
| null=pdf.null, |
| ), |
| output_schema, |
| PandasUDFType.GROUPED_MAP, |
| ) |
| |
| udf3 = pandas_udf( |
| lambda key, pdf: pdf.assign( |
| id=key[0], |
| byte=pdf.byte * 2, |
| short=pdf.short * 2, |
| int=pdf.int * 2, |
| long=pdf.long * 2, |
| float=pdf.float * 2, |
| double=pdf.double * 2, |
| decim=pdf.decim * 2, |
| bool=False if pdf.bool else True, |
| str=pdf.str + "there", |
| array=pdf.array, |
| bin=pdf.bin, |
| null=pdf.null, |
| ), |
| output_schema, |
| PandasUDFType.GROUPED_MAP, |
| ) |
| |
| result1 = df.groupby("id").apply(udf1).sort("id").toPandas() |
| expected1 = df.toPandas().groupby("id").apply(udf1.func).reset_index(drop=True) |
| |
| result2 = df.groupby("id").apply(udf2).sort("id").toPandas() |
| expected2 = expected1 |
| |
| result3 = df.groupby("id").apply(udf3).sort("id").toPandas() |
| expected3 = expected1 |
| |
| assert_frame_equal(expected1, result1) |
| assert_frame_equal(expected2, result2) |
| assert_frame_equal(expected3, result3) |
| |
| def test_array_type_correct(self): |
| df = self.data.withColumn("arr", array(col("id"))).repartition(1, "id") |
| |
| output_schema = StructType( |
| [ |
| StructField("id", LongType()), |
| StructField("v", IntegerType()), |
| StructField("arr", ArrayType(LongType())), |
| ] |
| ) |
| |
| udf = pandas_udf(lambda pdf: pdf, output_schema, PandasUDFType.GROUPED_MAP) |
| |
| result = df.groupby("id").apply(udf).sort("id").toPandas() |
| expected = df.toPandas().groupby("id").apply(udf.func).reset_index(drop=True) |
| assert_frame_equal(expected, result) |
| |
| def test_register_grouped_map_udf(self): |
| with self.quiet(): |
| self.check_register_grouped_map_udf() |
| |
| def check_register_grouped_map_udf(self): |
| foo_udf = pandas_udf(lambda x: x, "id long", PandasUDFType.GROUPED_MAP) |
| |
| with self.assertRaises(PySparkTypeError) as pe: |
| self.spark.catalog.registerFunction("foo_udf", foo_udf) |
| |
| self.check_error( |
| exception=pe.exception, |
| error_class="INVALID_UDF_EVAL_TYPE", |
| message_parameters={ |
| "eval_type": "SQL_BATCHED_UDF, SQL_ARROW_BATCHED_UDF, SQL_SCALAR_PANDAS_UDF, " |
| "SQL_SCALAR_PANDAS_ITER_UDF or SQL_GROUPED_AGG_PANDAS_UDF" |
| }, |
| ) |
| |
| def test_decorator(self): |
| df = self.data |
| |
| @pandas_udf("id long, v int, v1 double, v2 long", PandasUDFType.GROUPED_MAP) |
| def foo(pdf): |
| return pdf.assign(v1=pdf.v * pdf.id * 1.0, v2=pdf.v + pdf.id) |
| |
| result = df.groupby("id").apply(foo).sort("id").toPandas() |
| expected = df.toPandas().groupby("id").apply(foo.func).reset_index(drop=True) |
| assert_frame_equal(expected, result) |
| |
| def test_coerce(self): |
| df = self.data |
| |
| foo = pandas_udf(lambda pdf: pdf, "id long, v double", PandasUDFType.GROUPED_MAP) |
| |
| result = df.groupby("id").apply(foo).sort("id").toPandas() |
| expected = df.toPandas().groupby("id").apply(foo.func).reset_index(drop=True) |
| expected = expected.assign(v=expected.v.astype("float64")) |
| assert_frame_equal(expected, result) |
| |
| def test_complex_groupby(self): |
| df = self.data |
| |
| @pandas_udf("id long, v int, norm double", PandasUDFType.GROUPED_MAP) |
| def normalize(pdf): |
| v = pdf.v |
| return pdf.assign(norm=(v - v.mean()) / v.std()) |
| |
| result = df.groupby(col("id") % 2 == 0).apply(normalize).sort("id", "v").toPandas() |
| pdf = df.toPandas() |
| expected = pdf.groupby(pdf["id"] % 2 == 0, as_index=False).apply(normalize.func) |
| expected = expected.sort_values(["id", "v"]).reset_index(drop=True) |
| expected = expected.assign(norm=expected.norm.astype("float64")) |
| assert_frame_equal(expected, result) |
| |
| def test_empty_groupby(self): |
| df = self.data |
| |
| @pandas_udf("id long, v int, norm double", PandasUDFType.GROUPED_MAP) |
| def normalize(pdf): |
| v = pdf.v |
| return pdf.assign(norm=(v - v.mean()) / v.std()) |
| |
| result = df.groupby().apply(normalize).sort("id", "v").toPandas() |
| pdf = df.toPandas() |
| expected = normalize.func(pdf) |
| expected = expected.sort_values(["id", "v"]).reset_index(drop=True) |
| expected = expected.assign(norm=expected.norm.astype("float64")) |
| assert_frame_equal(expected, result) |
| |
| def test_apply_in_pandas_not_returning_pandas_dataframe(self): |
| with self.quiet(): |
| self.check_apply_in_pandas_not_returning_pandas_dataframe() |
| |
| def check_apply_in_pandas_not_returning_pandas_dataframe(self): |
| with self.assertRaisesRegex( |
| PythonException, |
| "Return type of the user-defined function should be pandas.DataFrame, but is tuple.", |
| ): |
| self._test_apply_in_pandas(lambda key, pdf: key) |
| |
| @staticmethod |
| def stats_with_column_names(key, pdf): |
| # order of column can be different to applyInPandas schema when column names are given |
| return pd.DataFrame([(pdf.v.mean(),) + key], columns=["mean", "id"]) |
| |
| @staticmethod |
| def stats_with_no_column_names(key, pdf): |
| # columns must be in order of applyInPandas schema when no columns given |
| return pd.DataFrame([key + (pdf.v.mean(),)]) |
| |
| def test_apply_in_pandas_returning_column_names(self): |
| self._test_apply_in_pandas(GroupedApplyInPandasTestsMixin.stats_with_column_names) |
| |
| def test_apply_in_pandas_returning_no_column_names(self): |
| self._test_apply_in_pandas(GroupedApplyInPandasTestsMixin.stats_with_no_column_names) |
| |
| def test_apply_in_pandas_returning_column_names_sometimes(self): |
| def stats(key, pdf): |
| if key[0] % 2: |
| return GroupedApplyInPandasTestsMixin.stats_with_column_names(key, pdf) |
| else: |
| return GroupedApplyInPandasTestsMixin.stats_with_no_column_names(key, pdf) |
| |
| self._test_apply_in_pandas(stats) |
| |
| def test_apply_in_pandas_returning_wrong_column_names(self): |
| with self.quiet(): |
| self.check_apply_in_pandas_returning_wrong_column_names() |
| |
| def check_apply_in_pandas_returning_wrong_column_names(self): |
| with self.assertRaisesRegex( |
| PythonException, |
| "Column names of the returned pandas.DataFrame do not match specified schema. " |
| "Missing: mean. Unexpected: median, std.\n", |
| ): |
| self._test_apply_in_pandas( |
| lambda key, pdf: pd.DataFrame( |
| [key + (pdf.v.median(), pdf.v.std())], columns=["id", "median", "std"] |
| ) |
| ) |
| |
| def test_apply_in_pandas_returning_no_column_names_and_wrong_amount(self): |
| with self.quiet(): |
| self.check_apply_in_pandas_returning_no_column_names_and_wrong_amount() |
| |
| def check_apply_in_pandas_returning_no_column_names_and_wrong_amount(self): |
| with self.assertRaisesRegex( |
| PythonException, |
| "Number of columns of the returned pandas.DataFrame doesn't match " |
| "specified schema. Expected: 2 Actual: 3\n", |
| ): |
| self._test_apply_in_pandas( |
| lambda key, pdf: pd.DataFrame([key + (pdf.v.mean(), pdf.v.std())]) |
| ) |
| |
| def test_apply_in_pandas_returning_empty_dataframe(self): |
| self._test_apply_in_pandas_returning_empty_dataframe(pd.DataFrame()) |
| |
| def test_apply_in_pandas_returning_incompatible_type(self): |
| with self.quiet(): |
| self.check_apply_in_pandas_returning_incompatible_type() |
| |
| def check_apply_in_pandas_returning_incompatible_type(self): |
| for safely in [True, False]: |
| with self.subTest(convertToArrowArraySafely=safely), self.sql_conf( |
| {"spark.sql.execution.pandas.convertToArrowArraySafely": safely} |
| ): |
| # sometimes we see ValueErrors |
| with self.subTest(convert="string to double"): |
| expected = ( |
| r"ValueError: Exception thrown when converting pandas.Series \(object\) " |
| r"with name 'mean' to Arrow Array \(double\)." |
| ) |
| if safely: |
| expected = expected + ( |
| " It can be caused by overflows or other " |
| "unsafe conversions warned by Arrow. Arrow safe type check " |
| "can be disabled by using SQL config " |
| "`spark.sql.execution.pandas.convertToArrowArraySafely`." |
| ) |
| with self.assertRaisesRegex(PythonException, expected + "\n"): |
| self._test_apply_in_pandas( |
| lambda key, pdf: pd.DataFrame([key + (str(pdf.v.mean()),)]), |
| output_schema="id long, mean double", |
| ) |
| |
| # sometimes we see TypeErrors |
| with self.subTest(convert="double to string"): |
| with self.assertRaisesRegex( |
| PythonException, |
| r"TypeError: Exception thrown when converting pandas.Series \(float64\) " |
| r"with name 'mean' to Arrow Array \(string\).\n", |
| ): |
| self._test_apply_in_pandas( |
| lambda key, pdf: pd.DataFrame([key + (pdf.v.mean(),)]), |
| output_schema="id long, mean string", |
| ) |
| |
| def test_datatype_string(self): |
| df = self.data |
| |
| foo_udf = pandas_udf( |
| lambda pdf: pdf.assign(v1=pdf.v * pdf.id * 1.0, v2=pdf.v + pdf.id), |
| "id long, v int, v1 double, v2 long", |
| PandasUDFType.GROUPED_MAP, |
| ) |
| |
| result = df.groupby("id").apply(foo_udf).sort("id").toPandas() |
| expected = df.toPandas().groupby("id").apply(foo_udf.func).reset_index(drop=True) |
| assert_frame_equal(expected, result) |
| |
| def test_wrong_return_type(self): |
| with self.quiet(): |
| self.check_wrong_return_type() |
| |
| def check_wrong_return_type(self): |
| with self.assertRaisesRegex( |
| NotImplementedError, |
| "Invalid return type.*grouped map Pandas UDF.*ArrayType.*YearMonthIntervalType", |
| ): |
| pandas_udf( |
| lambda pdf: pdf, |
| StructType().add("id", LongType()).add("v", ArrayType(YearMonthIntervalType())), |
| PandasUDFType.GROUPED_MAP, |
| ) |
| |
| def test_wrong_args(self): |
| with self.quiet(): |
| self.check_wrong_args() |
| |
| def check_wrong_args(self): |
| df = self.data |
| |
| with self.assertRaisesRegex(PySparkTypeError, "INVALID_UDF_EVAL_TYPE"): |
| df.groupby("id").apply(lambda x: x) |
| with self.assertRaisesRegex(PySparkTypeError, "INVALID_UDF_EVAL_TYPE"): |
| df.groupby("id").apply(udf(lambda x: x, DoubleType())) |
| with self.assertRaisesRegex(PySparkTypeError, "INVALID_UDF_EVAL_TYPE"): |
| df.groupby("id").apply(sum(df.v)) |
| with self.assertRaisesRegex(PySparkTypeError, "INVALID_UDF_EVAL_TYPE"): |
| df.groupby("id").apply(df.v + 1) |
| with self.assertRaisesRegex(PySparkTypeError, "INVALID_UDF_EVAL_TYPE"): |
| df.groupby("id").apply(pandas_udf(lambda x, y: x, DoubleType())) |
| with self.assertRaisesRegex(PySparkTypeError, "INVALID_UDF_EVAL_TYPE"): |
| df.groupby("id").apply(pandas_udf(lambda x, y: x, DoubleType(), PandasUDFType.SCALAR)) |
| |
| with self.assertRaisesRegex(PySparkValueError, "INVALID_PANDAS_UDF"): |
| df.groupby("id").apply( |
| pandas_udf(lambda: 1, StructType([StructField("d", DoubleType())])) |
| ) |
| |
| def test_unsupported_types(self): |
| with self.quiet(): |
| self.check_unsupported_types() |
| |
| def check_unsupported_types(self): |
| common_err_msg = "Invalid return type.*grouped map Pandas UDF.*" |
| unsupported_types = [ |
| StructField("array_struct", ArrayType(YearMonthIntervalType())), |
| StructField("map", MapType(StringType(), YearMonthIntervalType())), |
| ] |
| |
| for unsupported_type in unsupported_types: |
| with self.subTest(unsupported_type=unsupported_type.name): |
| schema = StructType([StructField("id", LongType(), True), unsupported_type]) |
| |
| with self.assertRaisesRegex(NotImplementedError, common_err_msg): |
| pandas_udf(lambda x: x, schema, PandasUDFType.GROUPED_MAP) |
| |
| # Regression test for SPARK-23314 |
| def test_timestamp_dst(self): |
| # Daylight saving time for Los Angeles for 2015 is Sun, Nov 1 at 2:00 am |
| dt = [ |
| datetime.datetime(2015, 11, 1, 0, 30), |
| datetime.datetime(2015, 11, 1, 1, 30), |
| datetime.datetime(2015, 11, 1, 2, 30), |
| ] |
| df = self.spark.createDataFrame(dt, "timestamp").toDF("time") |
| foo_udf = pandas_udf(lambda pdf: pdf, "time timestamp", PandasUDFType.GROUPED_MAP) |
| result = df.groupby("time").apply(foo_udf).sort("time") |
| assert_frame_equal(df.toPandas(), result.toPandas()) |
| |
| def test_udf_with_key(self): |
| import numpy as np |
| |
| df = self.data |
| pdf = df.toPandas() |
| |
| def foo1(key, pdf): |
| assert type(key) == tuple |
| assert type(key[0]) == np.int64 |
| |
| return pdf.assign( |
| v1=key[0], v2=pdf.v * key[0], v3=pdf.v * pdf.id, v4=pdf.v * pdf.id.mean() |
| ) |
| |
| def foo2(key, pdf): |
| assert type(key) == tuple |
| assert type(key[0]) == np.int64 |
| assert type(key[1]) == np.int32 |
| |
| return pdf.assign(v1=key[0], v2=key[1], v3=pdf.v * key[0], v4=pdf.v + key[1]) |
| |
| def foo3(key, pdf): |
| assert type(key) == tuple |
| assert len(key) == 0 |
| return pdf.assign(v1=pdf.v * pdf.id) |
| |
| # v2 is int because numpy.int64 * pd.Series<int32> results in pd.Series<int32> |
| # v3 is long because pd.Series<int64> * pd.Series<int32> results in pd.Series<int64> |
| udf1 = pandas_udf( |
| foo1, "id long, v int, v1 long, v2 int, v3 long, v4 double", PandasUDFType.GROUPED_MAP |
| ) |
| |
| udf2 = pandas_udf( |
| foo2, "id long, v int, v1 long, v2 int, v3 int, v4 int", PandasUDFType.GROUPED_MAP |
| ) |
| |
| udf3 = pandas_udf(foo3, "id long, v int, v1 long", PandasUDFType.GROUPED_MAP) |
| |
| # Test groupby column |
| result1 = df.groupby("id").apply(udf1).sort("id", "v").toPandas() |
| expected1 = ( |
| pdf.groupby("id", as_index=False) |
| .apply(lambda x: udf1.func((x.id.iloc[0],), x)) |
| .sort_values(["id", "v"]) |
| .reset_index(drop=True) |
| ) |
| assert_frame_equal(expected1, result1) |
| |
| # Test groupby expression |
| result2 = df.groupby(df.id % 2).apply(udf1).sort("id", "v").toPandas() |
| expected2 = ( |
| pdf.groupby(pdf.id % 2, as_index=False) |
| .apply(lambda x: udf1.func((x.id.iloc[0] % 2,), x)) |
| .sort_values(["id", "v"]) |
| .reset_index(drop=True) |
| ) |
| assert_frame_equal(expected2, result2) |
| |
| # Test complex groupby |
| result3 = df.groupby(df.id, df.v % 2).apply(udf2).sort("id", "v").toPandas() |
| expected3 = ( |
| pdf.groupby([pdf.id, pdf.v % 2], as_index=False) |
| .apply( |
| lambda x: udf2.func( |
| ( |
| x.id.iloc[0], |
| (x.v % 2).iloc[0], |
| ), |
| x, |
| ) |
| ) |
| .sort_values(["id", "v"]) |
| .reset_index(drop=True) |
| ) |
| assert_frame_equal(expected3, result3) |
| |
| # Test empty groupby |
| result4 = df.groupby().apply(udf3).sort("id", "v").toPandas() |
| expected4 = udf3.func((), pdf) |
| assert_frame_equal(expected4, result4) |
| |
| def test_column_order(self): |
| with self.quiet(): |
| self.check_column_order() |
| |
| def check_column_order(self): |
| # Helper function to set column names from a list |
| def rename_pdf(pdf, names): |
| pdf.rename( |
| columns={old: new for old, new in zip(pd_result.columns, names)}, inplace=True |
| ) |
| |
| df = self.data |
| grouped_df = df.groupby("id") |
| grouped_pdf = df.toPandas().groupby("id", as_index=False) |
| |
| # Function returns a pdf with required column names, but order could be arbitrary using dict |
| def change_col_order(pdf): |
| # Constructing a DataFrame from a dict should result in the same order, |
| # but use OrderedDict to ensure the pdf column order is different than schema |
| return pd.DataFrame.from_dict( |
| OrderedDict([("id", pdf.id), ("u", pdf.v * 2), ("v", pdf.v)]) |
| ) |
| |
| ordered_udf = pandas_udf( |
| change_col_order, "id long, v int, u int", PandasUDFType.GROUPED_MAP |
| ) |
| |
| # The UDF result should assign columns by name from the pdf |
| result = grouped_df.apply(ordered_udf).sort("id", "v").select("id", "u", "v").toPandas() |
| pd_result = grouped_pdf.apply(change_col_order) |
| expected = pd_result.sort_values(["id", "v"]).reset_index(drop=True) |
| assert_frame_equal(expected, result) |
| |
| # Function returns a pdf with positional columns, indexed by range |
| def range_col_order(pdf): |
| # Create a DataFrame with positional columns, fix types to long |
| return pd.DataFrame(list(zip(pdf.id, pdf.v * 3, pdf.v)), dtype="int64") |
| |
| range_udf = pandas_udf( |
| range_col_order, "id long, u long, v long", PandasUDFType.GROUPED_MAP |
| ) |
| |
| # The UDF result uses positional columns from the pdf |
| result = grouped_df.apply(range_udf).sort("id", "v").select("id", "u", "v").toPandas() |
| pd_result = grouped_pdf.apply(range_col_order) |
| rename_pdf(pd_result, ["id", "u", "v"]) |
| expected = pd_result.sort_values(["id", "v"]).reset_index(drop=True) |
| assert_frame_equal(expected, result) |
| |
| # Function returns a pdf with columns indexed with integers |
| def int_index(pdf): |
| return pd.DataFrame(OrderedDict([(0, pdf.id), (1, pdf.v * 4), (2, pdf.v)])) |
| |
| int_index_udf = pandas_udf(int_index, "id long, u int, v int", PandasUDFType.GROUPED_MAP) |
| |
| # The UDF result should assign columns by position of integer index |
| result = grouped_df.apply(int_index_udf).sort("id", "v").select("id", "u", "v").toPandas() |
| pd_result = grouped_pdf.apply(int_index) |
| rename_pdf(pd_result, ["id", "u", "v"]) |
| expected = pd_result.sort_values(["id", "v"]).reset_index(drop=True) |
| assert_frame_equal(expected, result) |
| |
| @pandas_udf("id long, v int", PandasUDFType.GROUPED_MAP) |
| def column_name_typo(pdf): |
| return pd.DataFrame({"iid": pdf.id, "v": pdf.v}) |
| |
| @pandas_udf("id long, v decimal", PandasUDFType.GROUPED_MAP) |
| def invalid_positional_types(pdf): |
| return pd.DataFrame([(1, datetime.date(2020, 10, 5))]) |
| |
| with self.sql_conf({"spark.sql.execution.pandas.convertToArrowArraySafely": False}): |
| with self.assertRaisesRegex( |
| PythonException, |
| "Column names of the returned pandas.DataFrame do not match " |
| "specified schema. Missing: id. Unexpected: iid.\n", |
| ): |
| grouped_df.apply(column_name_typo).collect() |
| with self.assertRaisesRegex(Exception, "[D|d]ecimal.*got.*date"): |
| grouped_df.apply(invalid_positional_types).collect() |
| |
| def test_positional_assignment_conf(self): |
| with self.sql_conf( |
| {"spark.sql.legacy.execution.pandas.groupedMap.assignColumnsByName": False} |
| ): |
| |
| @pandas_udf("a string, b float", PandasUDFType.GROUPED_MAP) |
| def foo(_): |
| return pd.DataFrame([("hi", 1)], columns=["x", "y"]) |
| |
| df = self.data |
| result = df.groupBy("id").apply(foo).select("a", "b").collect() |
| for r in result: |
| self.assertEqual(r.a, "hi") |
| self.assertEqual(r.b, 1) |
| |
| def test_self_join_with_pandas(self): |
| @pandas_udf("key long, col string", PandasUDFType.GROUPED_MAP) |
| def dummy_pandas_udf(df): |
| return df[["key", "col"]] |
| |
| df = self.spark.createDataFrame( |
| [Row(key=1, col="A"), Row(key=1, col="B"), Row(key=2, col="C")] |
| ) |
| df_with_pandas = df.groupBy("key").apply(dummy_pandas_udf) |
| |
| # this was throwing an AnalysisException before SPARK-24208 |
| res = df_with_pandas.alias("temp0").join( |
| df_with_pandas.alias("temp1"), col("temp0.key") == col("temp1.key") |
| ) |
| self.assertEqual(res.count(), 5) |
| |
| def test_mixed_scalar_udfs_followed_by_groupby_apply(self): |
| df = self.spark.range(0, 10).toDF("v1") |
| df = df.withColumn("v2", udf(lambda x: x + 1, "int")(df["v1"])).withColumn( |
| "v3", pandas_udf(lambda x: x + 2, "int")(df["v1"]) |
| ) |
| |
| result = df.groupby().apply( |
| pandas_udf( |
| lambda x: pd.DataFrame([x.sum().sum()]), "sum int", PandasUDFType.GROUPED_MAP |
| ) |
| ) |
| |
| self.assertEqual(result.collect()[0]["sum"], 165) |
| |
| 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, x=5), Row(id=1, x=5), Row(id=2, x=4)] |
| num_parts = len(data) + 1 |
| df = self.spark.createDataFrame(data).repartition(num_parts) |
| |
| f = pandas_udf( |
| lambda pdf: pdf.assign(x=pdf["x"].sum()), "id long, x int", PandasUDFType.GROUPED_MAP |
| ) |
| |
| result = df.groupBy("id").apply(f).sort("id").collect() |
| self.assertEqual(result, expected) |
| |
| def test_grouped_over_window(self): |
| data = [ |
| (0, 1, "2018-03-10T00:00:00+00:00", [0]), |
| (1, 2, "2018-03-11T00:00:00+00:00", [0]), |
| (2, 2, "2018-03-12T00:00:00+00:00", [0]), |
| (3, 3, "2018-03-15T00:00:00+00:00", [0]), |
| (4, 3, "2018-03-16T00:00:00+00:00", [0]), |
| (5, 3, "2018-03-17T00:00:00+00:00", [0]), |
| (6, 3, "2018-03-21T00:00:00+00:00", [0]), |
| ] |
| |
| expected = {0: [0], 1: [1, 2], 2: [1, 2], 3: [3, 4, 5], 4: [3, 4, 5], 5: [3, 4, 5], 6: [6]} |
| |
| df = self.spark.createDataFrame(data, ["id", "group", "ts", "result"]) |
| df = df.select(col("id"), col("group"), col("ts").cast("timestamp"), col("result")) |
| |
| def f(pdf): |
| # Assign each result element the ids of the windowed group |
| pdf["result"] = [pdf["id"]] * len(pdf) |
| return pdf |
| |
| result = ( |
| df.groupby("group", window("ts", "5 days")) |
| .applyInPandas(f, df.schema) |
| .select("id", "result") |
| .orderBy("id") |
| .collect() |
| ) |
| |
| self.assertListEqual([Row(id=key, result=val) for key, val in expected.items()], result) |
| |
| def test_grouped_over_window_with_key(self): |
| data = [ |
| (0, 1, "2018-03-10T00:00:00+00:00", [0]), |
| (1, 2, "2018-03-11T00:00:00+00:00", [0]), |
| (2, 2, "2018-03-12T00:00:00+00:00", [0]), |
| (3, 3, "2018-03-15T00:00:00+00:00", [0]), |
| (4, 3, "2018-03-16T00:00:00+00:00", [0]), |
| (5, 3, "2018-03-17T00:00:00+00:00", [0]), |
| (6, 3, "2018-03-21T00:00:00+00:00", [0]), |
| ] |
| |
| timezone = self.spark.conf.get("spark.sql.session.timeZone") |
| expected_window = [ |
| { |
| key: ( |
| pd.Timestamp(ts) |
| .tz_localize(datetime.timezone.utc) |
| .tz_convert(timezone) |
| .tz_localize(None) |
| ) |
| for key, ts in w.items() |
| } |
| for w in [ |
| { |
| "start": datetime.datetime(2018, 3, 10, 0, 0), |
| "end": datetime.datetime(2018, 3, 15, 0, 0), |
| }, |
| { |
| "start": datetime.datetime(2018, 3, 15, 0, 0), |
| "end": datetime.datetime(2018, 3, 20, 0, 0), |
| }, |
| { |
| "start": datetime.datetime(2018, 3, 20, 0, 0), |
| "end": datetime.datetime(2018, 3, 25, 0, 0), |
| }, |
| ] |
| ] |
| |
| expected_key = { |
| 0: (1, expected_window[0]), |
| 1: (2, expected_window[0]), |
| 2: (2, expected_window[0]), |
| 3: (3, expected_window[1]), |
| 4: (3, expected_window[1]), |
| 5: (3, expected_window[1]), |
| 6: (3, expected_window[2]), |
| } |
| |
| # id -> array of group with len of num records in window |
| expected = {0: [1], 1: [2, 2], 2: [2, 2], 3: [3, 3, 3], 4: [3, 3, 3], 5: [3, 3, 3], 6: [3]} |
| |
| df = self.spark.createDataFrame(data, ["id", "group", "ts", "result"]) |
| df = df.select(col("id"), col("group"), col("ts").cast("timestamp"), col("result")) |
| |
| def f(key, pdf): |
| group = key[0] |
| window_range = key[1] |
| |
| # Make sure the key with group and window values are correct |
| for _, i in pdf.id.items(): |
| assert expected_key[i][0] == group, "{} != {}".format(expected_key[i][0], group) |
| assert expected_key[i][1] == window_range, "{} != {}".format( |
| expected_key[i][1], window_range |
| ) |
| |
| return pdf.assign(result=[[group] * len(pdf)] * len(pdf)) |
| |
| result = ( |
| df.groupby("group", window("ts", "5 days")) |
| .applyInPandas(f, df.schema) |
| .select("id", "result") |
| .orderBy("id") |
| .collect() |
| ) |
| |
| self.assertListEqual([Row(id=key, result=val) for key, val in expected.items()], result) |
| |
| def test_case_insensitive_grouping_column(self): |
| # SPARK-31915: case-insensitive grouping column should work. |
| def my_pandas_udf(pdf): |
| return pdf.assign(score=0.5) |
| |
| df = self.spark.createDataFrame([[1, 1]], ["column", "score"]) |
| row = ( |
| df.groupby("COLUMN") |
| .applyInPandas(my_pandas_udf, schema="column integer, score float") |
| .first() |
| ) |
| self.assertEqual(row.asDict(), Row(column=1, score=0.5).asDict()) |
| |
| def _test_apply_in_pandas(self, f, output_schema="id long, mean double"): |
| df = self.data |
| |
| result = ( |
| df.groupby("id").applyInPandas(f, schema=output_schema).sort("id", "mean").toPandas() |
| ) |
| expected = df.select("id").distinct().withColumn("mean", lit(24.5)).toPandas() |
| |
| assert_frame_equal(expected, result) |
| |
| def _test_apply_in_pandas_returning_empty_dataframe(self, empty_df): |
| """Tests some returned DataFrames are empty.""" |
| df = self.data |
| |
| def stats(key, pdf): |
| if key[0] % 2 == 0: |
| return GroupedApplyInPandasTestsMixin.stats_with_no_column_names(key, pdf) |
| return empty_df |
| |
| result = ( |
| df.groupby("id") |
| .applyInPandas(stats, schema="id long, mean double") |
| .sort("id", "mean") |
| .collect() |
| ) |
| |
| actual_ids = {row[0] for row in result} |
| expected_ids = {row[0] for row in self.data.collect() if row[0] % 2 == 0} |
| self.assertSetEqual(expected_ids, actual_ids) |
| self.assertEqual(len(expected_ids), len(result)) |
| for row in result: |
| self.assertEqual(24.5, row[1]) |
| |
| def _test_apply_in_pandas_returning_empty_dataframe_error(self, empty_df, error): |
| with self.quiet(): |
| with self.assertRaisesRegex(PythonException, error): |
| self._test_apply_in_pandas_returning_empty_dataframe(empty_df) |
| |
| |
| class GroupedApplyInPandasTests(GroupedApplyInPandasTestsMixin, ReusedSQLTestCase): |
| pass |
| |
| |
| if __name__ == "__main__": |
| from pyspark.sql.tests.pandas.test_pandas_grouped_map import * # noqa: F401 |
| |
| try: |
| import xmlrunner |
| |
| testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2) |
| except ImportError: |
| testRunner = None |
| unittest.main(testRunner=testRunner, verbosity=2) |
