mirror of
https://github.com/apache/arrow.git
synced 2026-10-29 00:56:31 +08:00
Joris Van den Bossche
9583eb787b
### Rationale for this change `zoneinfo` is available starting with Python 3.9, so we can now assume that it is available, and so we can switch from returning `pytz` timezones by default to return `zoneinfo` timezones (or `datetime.timezone` for fixed offsets). Only keeping pytz as fallback for strings that are not supported by `zoneinfo` but were supported by `pytz`. Later, we should maybe deprecate that fallback. Generally we should move away from using `pytz`, since the core functionality of having time zones is now available in the standard library (`zoneinfo`), and because the pytz package has several warts / incompatibilities with stdlib datetime (https://blog.ganssle.io/articles/2018/03/pytz-fastest-footgun.html) ### What changes are included in this PR? Whenever we create a python timezone object, which is when converting to pandas or when converting to a `datetime.datetime` object: - always prefer `zoneinfo` for `datetime.datetime` objects - prefer `zoneinfo` for pandas objects _if_ pandas >= 3, to align with the change on the pandas side (https://github.com/pandas-dev/pandas/issues/34916) In either case, when preferring `zoneinfo`, we still fall back to `pytz` for named timezones if `zoneinfo` does not recognize the zone name (apparently pytz can have some common (older) aliases that might not always work with zoneinfo). This fallback is something we could deprecate and remove later on (so we can eventually remove all usage of pytz) ### Are these changes tested? Yes ### Are there any user-facing changes? **This PR includes breaking changes to public APIs.** It is a different object that we return (different class, i.e. `zoneinfo.ZoneInfo` instead of a `pytz.tzinfo.BaseTzInfo`, both are still subclasses of `datetime.tzinfo`), which has some differences in the API, so for people relying on that, this is a breaking change. For the conversion to pandas, pandas itself has made this breaking change anyhow, so for those cases it aligns with that change of pandas. * GitHub Issue: #15047
298 lines
11 KiB
Cython
298 lines
11 KiB
Cython
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with 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,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# distutils: language = c++
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from pyarrow.includes.common cimport *
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from pyarrow.includes.libarrow cimport *
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ctypedef CInvalidRowResult PyInvalidRowCallback(object,
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const CCSVInvalidRow&)
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cdef extern from "arrow/python/csv.h" namespace "arrow::py::csv":
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function[CInvalidRowHandler] MakeInvalidRowHandler(
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function[PyInvalidRowCallback], object handler)
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cdef extern from "arrow/python/api.h" namespace "arrow::py":
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# Requires GIL
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CResult[shared_ptr[CDataType]] InferArrowType(
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object obj, object mask, c_bool pandas_null_sentinels)
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cdef extern from "arrow/python/api.h" namespace "arrow::py::internal":
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object NewMonthDayNanoTupleType()
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CResult[PyObject*] MonthDayNanoIntervalArrayToPyList(
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const CMonthDayNanoIntervalArray& array)
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CResult[PyObject*] MonthDayNanoIntervalScalarToPyObject(
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const CMonthDayNanoIntervalScalar& scalar)
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cdef extern from "arrow/python/arrow_to_pandas.h" namespace "arrow::py::MapConversionType":
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cdef enum MapConversionType "arrow::py::MapConversionType":
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DEFAULT,
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LOSSY,
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STRICT_
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cdef extern from "arrow/python/api.h" namespace "arrow::py" nogil:
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shared_ptr[CDataType] GetPrimitiveType(Type type)
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object PyFloat_FromHalf(uint16_t value)
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cdef cppclass PyConversionOptions:
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PyConversionOptions()
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shared_ptr[CDataType] type
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int64_t size
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CMemoryPool* pool
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c_bool from_pandas
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c_bool ignore_timezone
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c_bool strict
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# TODO Some functions below are not actually "nogil"
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CResult[shared_ptr[CChunkedArray]] ConvertPySequence(
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object obj, object mask, const PyConversionOptions& options,
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CMemoryPool* pool)
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CResult[shared_ptr[CArray]] Arange(int64_t start, int64_t stop,
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int64_t step, CMemoryPool* pool)
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CResult[shared_ptr[CDataType]] NumPyDtypeToArrow(object dtype)
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CStatus NdarrayToArrow(CMemoryPool* pool, object ao, object mo,
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c_bool from_pandas,
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const shared_ptr[CDataType]& type,
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shared_ptr[CChunkedArray]* out)
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CStatus NdarrayToArrow(CMemoryPool* pool, object ao, object mo,
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c_bool from_pandas,
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const shared_ptr[CDataType]& type,
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const CCastOptions& cast_options,
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shared_ptr[CChunkedArray]* out)
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CStatus NdarrayToTensor(CMemoryPool* pool, object ao,
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const vector[c_string]& dim_names,
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shared_ptr[CTensor]* out)
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CStatus TensorToNdarray(const shared_ptr[CTensor]& tensor, object base,
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PyObject** out)
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CStatus SparseCOOTensorToNdarray(
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const shared_ptr[CSparseCOOTensor]& sparse_tensor, object base,
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PyObject** out_data, PyObject** out_coords)
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CStatus SparseCSRMatrixToNdarray(
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const shared_ptr[CSparseCSRMatrix]& sparse_tensor, object base,
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PyObject** out_data, PyObject** out_indptr, PyObject** out_indices)
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CStatus SparseCSCMatrixToNdarray(
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const shared_ptr[CSparseCSCMatrix]& sparse_tensor, object base,
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PyObject** out_data, PyObject** out_indptr, PyObject** out_indices)
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CStatus SparseCSFTensorToNdarray(
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const shared_ptr[CSparseCSFTensor]& sparse_tensor, object base,
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PyObject** out_data, PyObject** out_indptr, PyObject** out_indices)
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CStatus NdarraysToSparseCOOTensor(CMemoryPool* pool, object data_ao,
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object coords_ao,
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const vector[int64_t]& shape,
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const vector[c_string]& dim_names,
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shared_ptr[CSparseCOOTensor]* out)
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CStatus NdarraysToSparseCSRMatrix(CMemoryPool* pool, object data_ao,
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object indptr_ao, object indices_ao,
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const vector[int64_t]& shape,
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const vector[c_string]& dim_names,
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shared_ptr[CSparseCSRMatrix]* out)
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CStatus NdarraysToSparseCSCMatrix(CMemoryPool* pool, object data_ao,
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object indptr_ao, object indices_ao,
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const vector[int64_t]& shape,
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const vector[c_string]& dim_names,
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shared_ptr[CSparseCSCMatrix]* out)
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CStatus NdarraysToSparseCSFTensor(CMemoryPool* pool, object data_ao,
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object indptr_ao, object indices_ao,
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const vector[int64_t]& shape,
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const vector[int64_t]& axis_order,
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const vector[c_string]& dim_names,
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shared_ptr[CSparseCSFTensor]* out)
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CStatus TensorToSparseCOOTensor(shared_ptr[CTensor],
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shared_ptr[CSparseCOOTensor]* out)
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CStatus TensorToSparseCSRMatrix(shared_ptr[CTensor],
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shared_ptr[CSparseCSRMatrix]* out)
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CStatus TensorToSparseCSCMatrix(shared_ptr[CTensor],
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shared_ptr[CSparseCSCMatrix]* out)
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CStatus TensorToSparseCSFTensor(shared_ptr[CTensor],
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shared_ptr[CSparseCSFTensor]* out)
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CStatus ConvertArrayToPandas(const PandasOptions& options,
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shared_ptr[CArray] arr,
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object py_ref, PyObject** out)
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CStatus ConvertChunkedArrayToPandas(const PandasOptions& options,
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shared_ptr[CChunkedArray] arr,
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object py_ref, PyObject** out)
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CStatus ConvertTableToPandas(const PandasOptions& options,
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shared_ptr[CTable] table,
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PyObject** out)
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void c_set_default_memory_pool \
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" arrow::py::set_default_memory_pool"(CMemoryPool* pool)\
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CMemoryPool* c_get_memory_pool \
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" arrow::py::get_memory_pool"()
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cdef cppclass PyBuffer(CBuffer):
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@staticmethod
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CResult[shared_ptr[CBuffer]] FromPyObject(object obj)
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cdef cppclass PyForeignBuffer(CBuffer):
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@staticmethod
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CStatus Make(const uint8_t* data, int64_t size, object base,
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shared_ptr[CBuffer]* out)
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cdef cppclass PyReadableFile(CRandomAccessFile):
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PyReadableFile(object fo)
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cdef cppclass PyOutputStream(COutputStream):
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PyOutputStream(object fo)
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cdef cppclass PandasOptions:
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CMemoryPool* pool
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c_bool strings_to_categorical
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c_bool zero_copy_only
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c_bool integer_object_nulls
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c_bool date_as_object
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c_bool timestamp_as_object
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c_bool use_threads
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c_bool coerce_temporal_nanoseconds
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c_bool ignore_timezone
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c_bool deduplicate_objects
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c_bool safe_cast
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c_bool split_blocks
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c_bool self_destruct
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MapConversionType maps_as_pydicts
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c_bool decode_dictionaries
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shared_ptr[const unordered_set[c_string]] categorical_columns
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shared_ptr[const unordered_set[c_string]] extension_columns
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c_bool to_numpy
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cdef extern from "arrow/python/api.h" namespace "arrow::py::internal" nogil:
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cdef cppclass CTimePoint "arrow::py::internal::TimePoint":
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pass
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CTimePoint PyDateTime_to_TimePoint(PyDateTime_DateTime* pydatetime)
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int64_t TimePoint_to_ns(CTimePoint val)
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CTimePoint TimePoint_from_s(double val)
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CTimePoint TimePoint_from_ns(int64_t val)
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CResult[c_string] TzinfoToString(PyObject* pytzinfo)
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CResult[PyObject*] StringToTzinfo(c_string, c_bool)
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cdef extern from "arrow/python/numpy_init.h" namespace "arrow::py":
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int arrow_init_numpy() except -1
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cdef extern from "arrow/python/pyarrow.h" namespace "arrow::py":
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int import_pyarrow() except -1
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cdef extern from "arrow/python/common.h" namespace "arrow::py":
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c_bool IsPyError(const CStatus& status)
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void RestorePyError(const CStatus& status) except *
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cdef extern from "arrow/python/common.h" namespace "arrow::py" nogil:
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cdef cppclass SharedPtrNoGIL[T](shared_ptr[T]):
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# This looks like the only way to satisfy both Cython 2 and Cython 3
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SharedPtrNoGIL& operator=(...)
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cdef cppclass UniquePtrNoGIL[T, DELETER=*](unique_ptr[T, DELETER]):
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UniquePtrNoGIL& operator=(...)
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cdef extern from "arrow/python/inference.h" namespace "arrow::py":
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c_bool IsPyBool(object o)
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c_bool IsPyInt(object o)
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c_bool IsPyFloat(object o)
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cdef extern from "arrow/python/ipc.h" namespace "arrow::py":
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cdef cppclass CPyRecordBatchReader" arrow::py::PyRecordBatchReader" \
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(CRecordBatchReader):
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@staticmethod
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CResult[shared_ptr[CRecordBatchReader]] Make(shared_ptr[CSchema],
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object)
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cdef extern from "arrow/python/ipc.h" namespace "arrow::py" nogil:
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cdef cppclass CCastingRecordBatchReader" arrow::py::CastingRecordBatchReader" \
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(CRecordBatchReader):
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@staticmethod
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CResult[shared_ptr[CRecordBatchReader]] Make(shared_ptr[CRecordBatchReader],
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shared_ptr[CSchema])
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cdef extern from "arrow/python/extension_type.h" namespace "arrow::py":
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cdef cppclass CPyExtensionType \
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" arrow::py::PyExtensionType"(CExtensionType):
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@staticmethod
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CStatus FromClass(const shared_ptr[CDataType] storage_type,
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const c_string extension_name, object typ,
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shared_ptr[CExtensionType]* out)
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@staticmethod
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CStatus FromInstance(shared_ptr[CDataType] storage_type,
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object inst, shared_ptr[CExtensionType]* out)
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object GetInstance()
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CStatus SetInstance(object)
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c_string PyExtensionName()
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CStatus RegisterPyExtensionType(shared_ptr[CDataType])
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CStatus UnregisterPyExtensionType(c_string type_name)
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cdef extern from "arrow/python/benchmark.h" namespace "arrow::py::benchmark":
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void Benchmark_PandasObjectIsNull(object lst) except *
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cdef extern from "arrow/python/gdb.h" namespace "arrow::gdb" nogil:
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void GdbTestSession "arrow::gdb::TestSession"()
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cdef extern from "arrow/python/config.h" namespace "arrow::py":
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cdef cppclass CBuildInfo "arrow::py::BuildInfo":
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c_string build_type
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const CBuildInfo& GetBuildInfo "arrow::py::GetBuildInfo"()
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cdef extern from "arrow/python/config.h" namespace "arrow::py::internal":
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c_bool IsOpenTelemetryEnabled()
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c_bool IsThreadingEnabled()
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