ueshin commented on a change in pull request #32469: URL: https://github.com/apache/spark/pull/32469#discussion_r633786522
########## File path: python/pyspark/pandas/data_type_ops/base.py ########## @@ -0,0 +1,121 @@ +# +# 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. +# + +from abc import ABCMeta, abstractmethod + +from pandas.api.types import CategoricalDtype + +from pyspark.sql.types import ( + BooleanType, + DataType, + DateType, + DecimalType, + DoubleType, + FloatType, + IntegralType, + StringType, + TimestampType, +) + +from pyspark.pandas.typedef import Dtype + + +class DataTypeOps(object, metaclass=ABCMeta): + """The base class for binary operations of pandas-on-Spark objects (of different data types).""" + + def __new__(cls, dtype: Dtype, spark_type: DataType): + from pyspark.pandas.data_type_ops.boolean_ops import BooleanOps + from pyspark.pandas.data_type_ops.categorical_ops import CategoricalOps + from pyspark.pandas.data_type_ops.date_ops import DateOps + from pyspark.pandas.data_type_ops.datetime_ops import DatetimeOps + from pyspark.pandas.data_type_ops.num_ops import ( + IntegralOps, + FractionalOps, + ) + from pyspark.pandas.data_type_ops.string_ops import StringOps + + if isinstance(dtype, CategoricalDtype): + return object.__new__(CategoricalOps) + elif ( + isinstance(spark_type, FloatType) + or isinstance(spark_type, DoubleType) + or isinstance(spark_type, DecimalType) Review comment: nit: style. 4 spaces indent. ########## File path: python/pyspark/pandas/data_type_ops/num_ops.py ########## @@ -0,0 +1,297 @@ +# +# 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 numbers + +import numpy as np +from pandas.api.types import CategoricalDtype + +from pyspark.sql import Column, functions as F +from pyspark.sql.types import ( + NumericType, + StringType, + TimestampType, +) + +from pyspark.pandas.base import column_op, IndexOpsMixin, numpy_column_op +from pyspark.pandas.data_type_ops.base import DataTypeOps +from pyspark.pandas.spark import functions as SF + + +class NumericOps(DataTypeOps): + """ + The class for binary operations of numeric pandas-on-Spark objects. + """ + + @property + def pretty_name(self): + return 'numerics' + + def __add__(self, left, right): + if ( + isinstance(right, IndexOpsMixin) and isinstance(right.spark.data_type, StringType) + ) or isinstance(right, str): + raise TypeError("string addition can only be applied to string series or literals.") + + if ( + isinstance(right, IndexOpsMixin) + and ( + isinstance(right.dtype, CategoricalDtype) + or (not isinstance(right.spark.data_type, NumericType)) + ) + ) and not isinstance(right, numbers.Number): + raise TypeError("addition can not be applied to given types.") + + return column_op(Column.__add__)(left, right) + + def __sub__(self, left, right): + if ( + isinstance(right, IndexOpsMixin) and isinstance(right.spark.data_type, StringType) + ) or isinstance(right, str): + raise TypeError("subtraction can not be applied to string series or literals.") + + if ( + isinstance(right, IndexOpsMixin) + and ( + isinstance(right.dtype, CategoricalDtype) + or (not isinstance(right.spark.data_type, NumericType)) + ) + ) and not isinstance(right, numbers.Number): + raise TypeError("subtraction can not be applied to given types.") + + return column_op(Column.__sub__)(left, right) + + def __mul__(self, left, right): Review comment: Is this `__mul__` for `FractionalOps`? If so, we should have this in it? ########## File path: python/pyspark/pandas/data_type_ops/string_ops.py ########## @@ -0,0 +1,98 @@ +# +# 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. +# + +from pandas.api.types import CategoricalDtype + +from pyspark.sql import functions as F +from pyspark.sql.types import IntegralType, StringType + +from pyspark.pandas.base import column_op, IndexOpsMixin +from pyspark.pandas.data_type_ops.base import DataTypeOps +from pyspark.pandas.spark import functions as SF + + +class StringOps(DataTypeOps): + """ + The class for binary operations of pandas-on-Spark objects with spark type: StringType. + """ + + @property + def pretty_name(self): + return 'strings' + + def __add__(self, left, right): + if isinstance(right, IndexOpsMixin) and isinstance(right.spark.data_type, StringType): + return column_op(F.concat)(left, right) + elif isinstance(right, str): + return column_op(F.concat)(left, F.lit(right)) + else: + raise TypeError("string addition can only be applied to string series or literals.") + + def __sub__(self, left, right): + raise TypeError("subtraction can not be applied to string series or literals.") + + def __mul__(self, left, right): + if isinstance(right, str): + raise TypeError("multiplication can not be applied to a string literal.") + + if ( + isinstance(right, IndexOpsMixin) + and isinstance(right.spark.data_type, IntegralType) + and not isinstance(right.dtype, CategoricalDtype) Review comment: nit: style. 4 spaces indent. ########## File path: python/pyspark/pandas/tests/data_type_ops/test_boolean_ops.py ########## @@ -0,0 +1,150 @@ +# +# 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 pandas as pd + +from pyspark import pandas as ps +from pyspark.pandas.config import option_context +from pyspark.pandas.tests.data_type_ops.testing_utils import TestCasesUtils +from pyspark.testing.pandasutils import PandasOnSparkTestCase + + +class BooleanOpsTest(PandasOnSparkTestCase, TestCasesUtils): Review comment: We have to add these new tests in `dev/sparktestsupport/modules.py`? ########## File path: python/pyspark/pandas/data_type_ops/base.py ########## @@ -0,0 +1,121 @@ +# +# 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. +# + +from abc import ABCMeta, abstractmethod + +from pandas.api.types import CategoricalDtype + +from pyspark.sql.types import ( + BooleanType, + DataType, + DateType, + DecimalType, + DoubleType, + FloatType, + IntegralType, + StringType, + TimestampType, +) + +from pyspark.pandas.typedef import Dtype + + +class DataTypeOps(object, metaclass=ABCMeta): + """The base class for binary operations of pandas-on-Spark objects (of different data types).""" + + def __new__(cls, dtype: Dtype, spark_type: DataType): + from pyspark.pandas.data_type_ops.boolean_ops import BooleanOps + from pyspark.pandas.data_type_ops.categorical_ops import CategoricalOps + from pyspark.pandas.data_type_ops.date_ops import DateOps + from pyspark.pandas.data_type_ops.datetime_ops import DatetimeOps + from pyspark.pandas.data_type_ops.num_ops import ( + IntegralOps, + FractionalOps, + ) + from pyspark.pandas.data_type_ops.string_ops import StringOps + + if isinstance(dtype, CategoricalDtype): + return object.__new__(CategoricalOps) + elif ( + isinstance(spark_type, FloatType) + or isinstance(spark_type, DoubleType) + or isinstance(spark_type, DecimalType) Review comment: btw, I guess we can use `FractionalType` instead of those three? ########## File path: python/pyspark/pandas/data_type_ops/num_ops.py ########## @@ -0,0 +1,297 @@ +# +# 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 numbers + +import numpy as np +from pandas.api.types import CategoricalDtype + +from pyspark.sql import Column, functions as F +from pyspark.sql.types import ( + NumericType, + StringType, + TimestampType, +) + +from pyspark.pandas.base import column_op, IndexOpsMixin, numpy_column_op +from pyspark.pandas.data_type_ops.base import DataTypeOps +from pyspark.pandas.spark import functions as SF + + +class NumericOps(DataTypeOps): + """ + The class for binary operations of numeric pandas-on-Spark objects. + """ + + @property + def pretty_name(self): + return 'numerics' + + def __add__(self, left, right): + if ( + isinstance(right, IndexOpsMixin) and isinstance(right.spark.data_type, StringType) + ) or isinstance(right, str): + raise TypeError("string addition can only be applied to string series or literals.") + + if ( + isinstance(right, IndexOpsMixin) + and ( + isinstance(right.dtype, CategoricalDtype) + or (not isinstance(right.spark.data_type, NumericType)) + ) + ) and not isinstance(right, numbers.Number): + raise TypeError("addition can not be applied to given types.") + + return column_op(Column.__add__)(left, right) + + def __sub__(self, left, right): + if ( + isinstance(right, IndexOpsMixin) and isinstance(right.spark.data_type, StringType) + ) or isinstance(right, str): + raise TypeError("subtraction can not be applied to string series or literals.") + + if ( + isinstance(right, IndexOpsMixin) + and ( + isinstance(right.dtype, CategoricalDtype) + or (not isinstance(right.spark.data_type, NumericType)) + ) + ) and not isinstance(right, numbers.Number): + raise TypeError("subtraction can not be applied to given types.") + + return column_op(Column.__sub__)(left, right) + + def __mul__(self, left, right): + if isinstance(right, str): + raise TypeError("multiplication can not be applied to a string literal.") + + if isinstance(right, IndexOpsMixin) and isinstance(right.spark.data_type, TimestampType): + raise TypeError("multiplication can not be applied to date times.") + + if ( + isinstance(right, IndexOpsMixin) + and ( + isinstance(right.dtype, CategoricalDtype) + or not isinstance(right.spark.data_type, NumericType) + ) + ) and not isinstance(right, numbers.Number): + raise TypeError("multiplication can not be applied to given types.") + + return column_op(Column.__mul__)(left, right) + + def __truediv__(self, left, right): + if ( + isinstance(right, IndexOpsMixin) and isinstance(right.spark.data_type, StringType) + ) or isinstance(right, str): + raise TypeError("division can not be applied on string series or literals.") + + if ( + isinstance(right, IndexOpsMixin) + and ( + isinstance(right.dtype, CategoricalDtype) + or (not isinstance(right.spark.data_type, NumericType)) + ) + ) and not isinstance(right, numbers.Number): + raise TypeError("division can not be applied to given types.") + + def truediv(left, right): + return F.when(F.lit(right != 0) | F.lit(right).isNull(), left.__div__(right)).otherwise( + F.when(F.lit(left == np.inf) | F.lit(left == -np.inf), left).otherwise( + F.lit(np.inf).__div__(left) + ) + ) + + return numpy_column_op(truediv)(left, right) + + def __floordiv__(self, left, right): + if ( + isinstance(right, IndexOpsMixin) and isinstance(right.spark.data_type, StringType) + ) or isinstance(right, str): + raise TypeError("division can not be applied on string series or literals.") + + if ( + isinstance(right, IndexOpsMixin) + and ( + isinstance(right.dtype, CategoricalDtype) + or (not isinstance(right.spark.data_type, NumericType)) + ) + ) and not isinstance(right, numbers.Number): + raise TypeError("division can not be applied to given types.") + + def floordiv(left, right): + return F.when(F.lit(right is np.nan), np.nan).otherwise( + F.when( + F.lit(right != 0) | F.lit(right).isNull(), F.floor(left.__div__(right)) + ).otherwise( + F.when(F.lit(left == np.inf) | F.lit(left == -np.inf), left).otherwise( + F.lit(np.inf).__div__(left) + ) + ) + ) + + return numpy_column_op(floordiv)(left, right) + + def __mod__(self, left, right): + if ( + isinstance(right, IndexOpsMixin) and isinstance(right.spark.data_type, StringType) + ) or isinstance(right, str): + raise TypeError("modulo can not be applied on string series or literals.") + + if ( + isinstance(right, IndexOpsMixin) + and ( + isinstance(right.dtype, CategoricalDtype) + or (not isinstance(right.spark.data_type, NumericType)) + ) + ) and not isinstance(right, numbers.Number): + raise TypeError("modulo can not be applied to given types.") + + def mod(left, right): + return ((left % right) + right) % right + + return column_op(mod)(left, right) + + def __pow__(self, left, right): + if ( + isinstance(right, IndexOpsMixin) and isinstance(right.spark.data_type, StringType) + ) or isinstance(right, str): + raise TypeError("exponentiation can not be applied on string series or literals.") + + if ( + isinstance(right, IndexOpsMixin) + and ( + isinstance(right.dtype, CategoricalDtype) + or (not isinstance(right.spark.data_type, NumericType)) + ) + ) and not isinstance(right, numbers.Number): + raise TypeError("exponentiation can not be applied to given types.") + + def pow_func(left, right): + return F.when(left == 1, left).otherwise(Column.__pow__(left, right)) + + return column_op(pow_func)(left, right) + + def __radd__(self, left, right=None): + if isinstance(right, str): + raise TypeError("string addition can only be applied to string series or literals.") + if not isinstance(right, numbers.Number): + raise TypeError("addition can not be applied to given types.") + + return column_op(Column.__radd__)(left, right) + + def __rsub__(self, left, right=None): + if isinstance(right, str): + raise TypeError("subtraction can not be applied to string series or literals.") + if not isinstance(right, numbers.Number): + raise TypeError("subtraction can not be applied to given types.") + return column_op(Column.__rsub__)(left, right) + + def __rmul__(self, left, right=None): + if isinstance(right, str): + raise TypeError("multiplication can not be applied to a string literal.") + if not isinstance(right, numbers.Number): + raise TypeError("multiplication can not be applied to given types.") + return column_op(Column.__rmul__)(left, right) + + def __rtruediv__(self, left, right=None): + if isinstance(right, str): + raise TypeError("division can not be applied on string series or literals.") + if not isinstance(right, numbers.Number): + raise TypeError("division can not be applied to given types.") + + def rtruediv(left, right): + return F.when(left == 0, F.lit(np.inf).__div__(right)).otherwise( + F.lit(right).__truediv__(left) + ) + + return numpy_column_op(rtruediv)(left, right) + + def __rfloordiv__(self, left, right=None): + if isinstance(right, str): + raise TypeError("division can not be applied on string series or literals.") + if not isinstance(right, numbers.Number): + raise TypeError("division can not be applied to given types.") + + def rfloordiv(left, right): + return F.when(F.lit(left == 0), F.lit(np.inf).__div__(right)).otherwise( + F.when(F.lit(left) == np.nan, np.nan).otherwise(F.floor(F.lit(right).__div__(left))) + ) + + return numpy_column_op(rfloordiv)(left, right) + + def __rpow__(self, left, right=None): + if isinstance(right, str): + raise TypeError("exponentiation can not be applied on string series or literals.") + if not isinstance(right, numbers.Number): + raise TypeError("exponentiation can not be applied to given types.") + + def rpow_func(left, right): + return F.when(F.lit(right == 1), right).otherwise(Column.__rpow__(left, right)) + + return column_op(rpow_func)(left, right) + + def __rmod__(self, left, right=None): + if isinstance(right, str): + raise TypeError("modulo can not be applied on string series or literals.") + if not isinstance(right, numbers.Number): + raise TypeError("modulo can not be applied to given types.") + + def rmod(left, right): + return ((right % left) + left) % left + + return column_op(rmod)(left, right) + + +class IntegralOps(NumericOps): + """ + The class for binary operations of pandas-on-Spark objects with spark types: + LongType, IntegerType, ByteType and ShortType. + """ + + @property + def pretty_name(self): + return 'integrals' + + def __mul__(self, left, right): + if isinstance(right, str): + raise TypeError("multiplication can not be applied to a string literal.") + + if isinstance(right, IndexOpsMixin) and isinstance(right.spark.data_type, TimestampType): + raise TypeError("multiplication can not be applied to date times.") + + if isinstance(right, IndexOpsMixin) and isinstance(right.spark.data_type, StringType): + return column_op(SF.repeat)(right, left) + + if ( + isinstance(right, IndexOpsMixin) + and ( + isinstance(right.dtype, CategoricalDtype) + or not isinstance(right.spark.data_type, NumericType) + ) + ) and not isinstance(right, numbers.Number): + raise TypeError("multiplication can not be applied to given types.") + + return column_op(Column.__mul__)(left, right) + + +class FractionalOps(NumericOps): + """ + The class for binary operations of pandas-on-Spark objects with spark types: + FloatType, DoubleType and DecimalType. + """ + + pass Review comment: This should have `pretty_name`? -- This is an automated message from the Apache Git Service. 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