5f0113e3a6
### What changes were proposed in this pull request? The PR is proposed to support creating a Column of numpy literal value in pandas-on-Spark. It consists of three changes mainly: - Enable the `lit` function defined in `pyspark.pandas.spark.functions` to support numpy literals input. ```py >>> from pyspark.pandas.spark import functions as SF >>> SF.lit(np.int64(1)) Column<'CAST(1 AS BIGINT)'> >>> SF.lit(np.int32(1)) Column<'CAST(1 AS INT)'> >>> SF.lit(np.int8(1)) Column<'CAST(1 AS TINYINT)'> >>> SF.lit(np.byte(1)) Column<'CAST(1 AS TINYINT)'> >>> SF.lit(np.float32(1)) Column<'CAST(1.0 AS FLOAT)'> ``` - Substitute `F.lit` by `SF.lit`, that is, use `lit` function defined in `pyspark.pandas.spark.functions` rather than `lit` function defined in `pyspark.sql.functions` to allow creating columns out of numpy literals. - Enable numpy literals input in `isin` method Non-goal: - Some pandas-on-Spark APIs use PySpark column-related APIs internally, and these column-related APIs don't support numpy literals, thus numpy literals are disallowed as input (e.g. `to_replace` parameter in `replace` API). This PR doesn't aim to adjust all of them. This PR adjusts `isin` only, because the PR is inspired by that (as https://github.com/databricks/koalas/issues/2161). - To complete mappings between all kinds of numpy literals and Spark data types should be a followup task. ### Why are the changes needed? Spark (`lit` function defined in `pyspark.sql.functions`) doesn't support creating a Column out of numpy literal value. So `lit` function defined in `pyspark.pandas.spark.functions` is adjusted in order to support that in pandas-on-Spark. ### Does this PR introduce _any_ user-facing change? Yes. Before: ```py >>> a = ps.DataFrame({'source': [1,2,3,4,5]}) >>> a.source.isin([np.int64(1), np.int64(2)]) Traceback (most recent call last): ... AttributeError: 'numpy.int64' object has no attribute '_get_object_id' ``` After: ```py >>> a = ps.DataFrame({'source': [1,2,3,4,5]}) >>> a.source.isin([np.int64(1), np.int64(2)]) 0 True 1 True 2 False 3 False 4 False Name: source, dtype: bool ``` ### How was this patch tested? Unit tests. Closes #32955 from xinrong-databricks/datatypeops_literal. Authored-by: Xinrong Meng <xinrong.meng@databricks.com> Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
75 lines
2.5 KiB
Python
75 lines
2.5 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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Additional Spark functions used in pandas-on-Spark.
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"""
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from typing import Any, Union, no_type_check
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import numpy as np
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from pyspark import SparkContext
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from pyspark.sql import functions as F
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from pyspark.sql.column import Column, _to_java_column, _create_column_from_literal # type: ignore
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from pyspark.sql.types import (
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ByteType,
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FloatType,
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IntegerType,
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LongType,
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)
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def repeat(col: Column, n: Union[int, Column]) -> Column:
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"""
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Repeats a string column n times, and returns it as a new string column.
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"""
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sc = SparkContext._active_spark_context # type: ignore
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n = _to_java_column(n) if isinstance(n, Column) else _create_column_from_literal(n)
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return _call_udf(sc, "repeat", _to_java_column(col), n)
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def lit(literal: Any) -> Column:
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"""
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Creates a Column of literal value.
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"""
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if isinstance(literal, np.generic):
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scol = F.lit(literal.item())
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if isinstance(literal, np.int64):
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return scol.astype(LongType())
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elif isinstance(literal, np.int32):
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return scol.astype(IntegerType())
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elif isinstance(literal, np.int8) or isinstance(literal, np.byte):
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return scol.astype(ByteType())
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elif isinstance(literal, np.float32):
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return scol.astype(FloatType())
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else: # TODO: Complete mappings between numpy literals and Spark data types
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return scol
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else:
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return F.lit(literal)
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@no_type_check
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def _call_udf(sc, name, *cols):
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return Column(sc._jvm.functions.callUDF(name, _make_arguments(sc, *cols)))
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@no_type_check
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def _make_arguments(sc, *cols):
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java_arr = sc._gateway.new_array(sc._jvm.Column, len(cols))
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for i, col in enumerate(cols):
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java_arr[i] = col
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return java_arr
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