2a335f2d7d
### What changes were proposed in this pull request? Fixes `mypy` errors and enables `mypy` check for pandas-on-Spark. ### Why are the changes needed? The `mypy` check for pandas-on-Spark was disabled when the initial porting. It should be enabled again; otherwise we will miss type checking errors. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? The enabled `mypy` check and existing unit tests should pass. Closes #32540 from ueshin/issues/SPARK-34941/pandas_mypy. Authored-by: Takuya UESHIN <ueshin@databricks.com> Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
196 lines
6.3 KiB
Python
196 lines
6.3 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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"""
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Helpers and utilities to deal with PySpark instances
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"""
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from typing import overload
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from pyspark.sql.types import DecimalType, StructType, MapType, ArrayType, StructField, DataType
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@overload
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def as_nullable_spark_type(dt: StructType) -> StructType:
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...
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@overload
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def as_nullable_spark_type(dt: ArrayType) -> ArrayType:
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...
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@overload
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def as_nullable_spark_type(dt: MapType) -> MapType:
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...
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@overload
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def as_nullable_spark_type(dt: DataType) -> DataType:
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...
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def as_nullable_spark_type(dt: DataType) -> DataType:
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"""
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Returns a nullable schema or data types.
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Examples
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--------
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>>> from pyspark.sql.types import *
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>>> as_nullable_spark_type(StructType([
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... StructField("A", IntegerType(), True),
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... StructField("B", FloatType(), False)])) # doctest: +NORMALIZE_WHITESPACE
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StructType(List(StructField(A,IntegerType,true),StructField(B,FloatType,true)))
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>>> as_nullable_spark_type(StructType([
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... StructField("A",
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... StructType([
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... StructField('a',
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... MapType(IntegerType(),
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... ArrayType(IntegerType(), False), False), False),
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... StructField('b', StringType(), True)])),
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... StructField("B", FloatType(), False)])) # doctest: +NORMALIZE_WHITESPACE
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StructType(List(StructField(A,StructType(List(StructField(a,MapType(IntegerType,ArrayType\
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(IntegerType,true),true),true),StructField(b,StringType,true))),true),\
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StructField(B,FloatType,true)))
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"""
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if isinstance(dt, StructType):
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new_fields = []
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for field in dt.fields:
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new_fields.append(
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StructField(
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field.name,
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as_nullable_spark_type(field.dataType),
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nullable=True,
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metadata=field.metadata,
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)
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)
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return StructType(new_fields)
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elif isinstance(dt, ArrayType):
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return ArrayType(as_nullable_spark_type(dt.elementType), containsNull=True)
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elif isinstance(dt, MapType):
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return MapType(
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as_nullable_spark_type(dt.keyType),
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as_nullable_spark_type(dt.valueType),
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valueContainsNull=True,
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)
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else:
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return dt
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@overload
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def force_decimal_precision_scale(
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dt: StructType, *, precision: int = ..., scale: int = ...
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) -> StructType:
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...
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@overload
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def force_decimal_precision_scale(
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dt: ArrayType, *, precision: int = ..., scale: int = ...
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) -> ArrayType:
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...
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@overload
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def force_decimal_precision_scale(
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dt: MapType, *, precision: int = ..., scale: int = ...
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) -> MapType:
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...
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@overload
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def force_decimal_precision_scale(
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dt: DataType, *, precision: int = ..., scale: int = ...
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) -> DataType:
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...
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def force_decimal_precision_scale(
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dt: DataType, *, precision: int = 38, scale: int = 18
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) -> DataType:
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"""
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Returns a data type with a fixed decimal type.
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The precision and scale of the decimal type are fixed with the given values.
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Examples
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--------
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>>> from pyspark.sql.types import *
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>>> force_decimal_precision_scale(StructType([
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... StructField("A", DecimalType(10, 0), True),
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... StructField("B", DecimalType(14, 7), False)])) # doctest: +NORMALIZE_WHITESPACE
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StructType(List(StructField(A,DecimalType(38,18),true),StructField(B,DecimalType(38,18),false)))
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>>> force_decimal_precision_scale(StructType([
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... StructField("A",
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... StructType([
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... StructField('a',
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... MapType(DecimalType(5, 0),
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... ArrayType(DecimalType(20, 0), False), False), False),
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... StructField('b', StringType(), True)])),
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... StructField("B", DecimalType(30, 15), False)]),
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... precision=30, scale=15) # doctest: +NORMALIZE_WHITESPACE
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StructType(List(StructField(A,StructType(List(StructField(a,MapType(DecimalType(30,15),\
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ArrayType(DecimalType(30,15),false),false),false),StructField(b,StringType,true))),true),\
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StructField(B,DecimalType(30,15),false)))
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"""
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if isinstance(dt, StructType):
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new_fields = []
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for field in dt.fields:
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new_fields.append(
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StructField(
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field.name,
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force_decimal_precision_scale(field.dataType, precision=precision, scale=scale),
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nullable=field.nullable,
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metadata=field.metadata,
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)
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)
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return StructType(new_fields)
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elif isinstance(dt, ArrayType):
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return ArrayType(
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force_decimal_precision_scale(dt.elementType, precision=precision, scale=scale),
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containsNull=dt.containsNull,
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)
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elif isinstance(dt, MapType):
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return MapType(
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force_decimal_precision_scale(dt.keyType, precision=precision, scale=scale),
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force_decimal_precision_scale(dt.valueType, precision=precision, scale=scale),
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valueContainsNull=dt.valueContainsNull,
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)
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elif isinstance(dt, DecimalType):
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return DecimalType(precision=precision, scale=scale)
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else:
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return dt
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def _test():
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import doctest
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import sys
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import pyspark.pandas.spark.utils
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globs = pyspark.pandas.spark.utils.__dict__.copy()
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(failure_count, test_count) = doctest.testmod(
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pyspark.pandas.spark.utils,
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globs=globs,
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optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE,
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)
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if failure_count:
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sys.exit(-1)
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if __name__ == "__main__":
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_test()
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