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3416 lines (2708 loc) · 90.5 KB
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# 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 cpython.pycapsule cimport PyCapsule_CheckExact, PyCapsule_GetPointer
import atexit
from collections.abc import Mapping
import re
import sys
import warnings
# These are imprecise because the type (in pandas 0.x) depends on the presence
# of nulls
cdef dict _pandas_type_map = {
_Type_NA: np.object_, # NaNs
_Type_BOOL: np.bool_,
_Type_INT8: np.int8,
_Type_INT16: np.int16,
_Type_INT32: np.int32,
_Type_INT64: np.int64,
_Type_UINT8: np.uint8,
_Type_UINT16: np.uint16,
_Type_UINT32: np.uint32,
_Type_UINT64: np.uint64,
_Type_HALF_FLOAT: np.float16,
_Type_FLOAT: np.float32,
_Type_DOUBLE: np.float64,
_Type_DATE32: np.dtype('datetime64[ns]'),
_Type_DATE64: np.dtype('datetime64[ns]'),
_Type_TIMESTAMP: np.dtype('datetime64[ns]'),
_Type_DURATION: np.dtype('timedelta64[ns]'),
_Type_BINARY: np.object_,
_Type_FIXED_SIZE_BINARY: np.object_,
_Type_STRING: np.object_,
_Type_LIST: np.object_,
_Type_MAP: np.object_,
_Type_DECIMAL128: np.object_,
}
cdef dict _pep3118_type_map = {
_Type_INT8: b'b',
_Type_INT16: b'h',
_Type_INT32: b'i',
_Type_INT64: b'q',
_Type_UINT8: b'B',
_Type_UINT16: b'H',
_Type_UINT32: b'I',
_Type_UINT64: b'Q',
_Type_HALF_FLOAT: b'e',
_Type_FLOAT: b'f',
_Type_DOUBLE: b'd',
}
cdef bytes _datatype_to_pep3118(CDataType* type):
"""
Construct a PEP 3118 format string describing the given datatype.
None is returned for unsupported types.
"""
try:
char = _pep3118_type_map[type.id()]
except KeyError:
return None
else:
if char in b'bBhHiIqQ':
# Use "standard" int widths, not native
return b'=' + char
else:
return char
cdef void* _as_c_pointer(v, allow_null=False) except *:
"""
Convert a Python object to a raw C pointer.
Used mainly for the C data interface.
Integers are accepted as well as capsule objects with a NULL name.
(the latter for compatibility with raw pointers exported by reticulate)
"""
cdef void* c_ptr
if isinstance(v, int):
c_ptr = <void*> <uintptr_t > v
elif isinstance(v, float):
warnings.warn(
"Passing a pointer value as a float is unsafe and only "
"supported for compatibility with older versions of the R "
"Arrow library", UserWarning, stacklevel=2)
c_ptr = <void*> <uintptr_t > v
elif PyCapsule_CheckExact(v):
c_ptr = PyCapsule_GetPointer(v, NULL)
else:
raise TypeError(f"Expected a pointer value, got {type(v)!r}")
if not allow_null and c_ptr == NULL:
raise ValueError(f"Null pointer (value before cast = {v!r})")
return c_ptr
def _is_primitive(Type type):
# This is simply a redirect, the official API is in pyarrow.types.
return is_primitive(type)
# Workaround for Cython parsing bug
# https://github.com/cython/cython/issues/2143
ctypedef CFixedWidthType* _CFixedWidthTypePtr
cdef class DataType(_Weakrefable):
"""
Base class of all Arrow data types.
Each data type is an *instance* of this class.
"""
def __cinit__(self):
pass
def __init__(self):
raise TypeError("Do not call {}'s constructor directly, use public "
"functions like pyarrow.int64, pyarrow.list_, etc. "
"instead.".format(self.__class__.__name__))
cdef void init(self, const shared_ptr[CDataType]& type) except *:
assert type != nullptr
self.sp_type = type
self.type = type.get()
self.pep3118_format = _datatype_to_pep3118(self.type)
cpdef Field field(self, i):
if not isinstance(i, int):
raise TypeError(f"Expected int index, got type '{type(i)}'")
cdef int index = <int> _normalize_index(i, self.type.num_fields())
return pyarrow_wrap_field(self.type.field(index))
@property
def id(self):
return self.type.id()
@property
def bit_width(self):
cdef _CFixedWidthTypePtr ty
ty = dynamic_cast[_CFixedWidthTypePtr](self.type)
if ty == nullptr:
raise ValueError("Non-fixed width type")
return ty.bit_width()
@property
def num_fields(self):
"""
The number of child fields.
"""
return self.type.num_fields()
@property
def num_buffers(self):
"""
Number of data buffers required to construct Array type
excluding children.
"""
return self.type.layout().buffers.size()
def __str__(self):
return frombytes(self.type.ToString(), safe=True)
def __hash__(self):
return hash(str(self))
def __reduce__(self):
return type_for_alias, (str(self),)
def __repr__(self):
return '{0.__class__.__name__}({0})'.format(self)
def __eq__(self, other):
try:
return self.equals(other)
except (TypeError, ValueError):
return NotImplemented
def equals(self, other, *, check_metadata=False):
"""
Return true if type is equivalent to passed value.
Parameters
----------
other : DataType or string convertible to DataType
check_metadata : bool
Whether nested Field metadata equality should be checked as well.
Returns
-------
is_equal : bool
"""
cdef:
DataType other_type
c_bool c_check_metadata
other_type = ensure_type(other)
c_check_metadata = check_metadata
return self.type.Equals(deref(other_type.type), c_check_metadata)
def to_pandas_dtype(self):
"""
Return the equivalent NumPy / Pandas dtype.
"""
cdef Type type_id = self.type.id()
if type_id in _pandas_type_map:
return _pandas_type_map[type_id]
else:
raise NotImplementedError(str(self))
def _export_to_c(self, out_ptr):
"""
Export to a C ArrowSchema struct, given its pointer.
Be careful: if you don't pass the ArrowSchema struct to a consumer,
its memory will leak. This is a low-level function intended for
expert users.
"""
check_status(ExportType(deref(self.type),
<ArrowSchema*> _as_c_pointer(out_ptr)))
@staticmethod
def _import_from_c(in_ptr):
"""
Import DataType from a C ArrowSchema struct, given its pointer.
This is a low-level function intended for expert users.
"""
result = GetResultValue(ImportType(<ArrowSchema*>
_as_c_pointer(in_ptr)))
return pyarrow_wrap_data_type(result)
cdef class DictionaryMemo(_Weakrefable):
"""
Tracking container for dictionary-encoded fields.
"""
def __cinit__(self):
self.sp_memo.reset(new CDictionaryMemo())
self.memo = self.sp_memo.get()
cdef class DictionaryType(DataType):
"""
Concrete class for dictionary data types.
"""
cdef void init(self, const shared_ptr[CDataType]& type) except *:
DataType.init(self, type)
self.dict_type = <const CDictionaryType*> type.get()
def __reduce__(self):
return dictionary, (self.index_type, self.value_type, self.ordered)
@property
def ordered(self):
"""
Whether the dictionary is ordered, i.e. whether the ordering of values
in the dictionary is important.
"""
return self.dict_type.ordered()
@property
def index_type(self):
"""
The data type of dictionary indices (a signed integer type).
"""
return pyarrow_wrap_data_type(self.dict_type.index_type())
@property
def value_type(self):
"""
The dictionary value type.
The dictionary values are found in an instance of DictionaryArray.
"""
return pyarrow_wrap_data_type(self.dict_type.value_type())
cdef class ListType(DataType):
"""
Concrete class for list data types.
"""
cdef void init(self, const shared_ptr[CDataType]& type) except *:
DataType.init(self, type)
self.list_type = <const CListType*> type.get()
def __reduce__(self):
return list_, (self.value_field,)
@property
def value_field(self):
return pyarrow_wrap_field(self.list_type.value_field())
@property
def value_type(self):
"""
The data type of list values.
"""
return pyarrow_wrap_data_type(self.list_type.value_type())
cdef class LargeListType(DataType):
"""
Concrete class for large list data types
(like ListType, but with 64-bit offsets).
"""
cdef void init(self, const shared_ptr[CDataType]& type) except *:
DataType.init(self, type)
self.list_type = <const CLargeListType*> type.get()
def __reduce__(self):
return large_list, (self.value_field,)
@property
def value_field(self):
return pyarrow_wrap_field(self.list_type.value_field())
@property
def value_type(self):
"""
The data type of large list values.
"""
return pyarrow_wrap_data_type(self.list_type.value_type())
cdef class MapType(DataType):
"""
Concrete class for map data types.
"""
cdef void init(self, const shared_ptr[CDataType]& type) except *:
DataType.init(self, type)
self.map_type = <const CMapType*> type.get()
def __reduce__(self):
return map_, (self.key_field, self.item_field)
@property
def key_field(self):
"""
The field for keys in the map entries.
"""
return pyarrow_wrap_field(self.map_type.key_field())
@property
def key_type(self):
"""
The data type of keys in the map entries.
"""
return pyarrow_wrap_data_type(self.map_type.key_type())
@property
def item_field(self):
"""
The field for items in the map entries.
"""
return pyarrow_wrap_field(self.map_type.item_field())
@property
def item_type(self):
"""
The data type of items in the map entries.
"""
return pyarrow_wrap_data_type(self.map_type.item_type())
cdef class FixedSizeListType(DataType):
"""
Concrete class for fixed size list data types.
"""
cdef void init(self, const shared_ptr[CDataType]& type) except *:
DataType.init(self, type)
self.list_type = <const CFixedSizeListType*> type.get()
def __reduce__(self):
return list_, (self.value_type, self.list_size)
@property
def value_field(self):
return pyarrow_wrap_field(self.list_type.value_field())
@property
def value_type(self):
"""
The data type of large list values.
"""
return pyarrow_wrap_data_type(self.list_type.value_type())
@property
def list_size(self):
"""
The size of the fixed size lists.
"""
return self.list_type.list_size()
cdef class StructType(DataType):
"""
Concrete class for struct data types.
``StructType`` supports direct indexing using ``[...]`` (implemented via
``__getitem__``) to access its fields.
It will return the struct field with the given index or name.
Examples
--------
>>> import pyarrow as pa
Accessing fields using direct indexing:
>>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()})
>>> struct_type[0]
pyarrow.Field<x: int32>
>>> struct_type['y']
pyarrow.Field<y: string>
Accessing fields using ``field()``:
>>> struct_type.field(1)
pyarrow.Field<y: string>
>>> struct_type.field('x')
pyarrow.Field<x: int32>
# Creating a schema from the struct type's fields:
>>> pa.schema(list(struct_type))
x: int32
y: string
"""
cdef void init(self, const shared_ptr[CDataType]& type) except *:
DataType.init(self, type)
self.struct_type = <const CStructType*> type.get()
cdef Field field_by_name(self, name):
"""
Return a child field by its name.
Parameters
----------
name : str
The name of the field to look up.
Returns
-------
field : Field
The child field with the given name.
Raises
------
KeyError
If the name isn't found, or if several fields have the given
name.
"""
cdef vector[shared_ptr[CField]] fields
fields = self.struct_type.GetAllFieldsByName(tobytes(name))
if fields.size() == 0:
raise KeyError(name)
elif fields.size() > 1:
warnings.warn("Struct field name corresponds to more "
"than one field", UserWarning)
raise KeyError(name)
else:
return pyarrow_wrap_field(fields[0])
def get_field_index(self, name):
"""
Return index of the unique field with the given name.
Parameters
----------
name : str
The name of the field to look up.
Returns
-------
index : int
The index of the field with the given name; -1 if the
name isn't found or there are several fields with the given
name.
"""
return self.struct_type.GetFieldIndex(tobytes(name))
cpdef Field field(self, i):
"""
Select a field by its column name or numeric index.
Parameters
----------
i : int or str
Returns
-------
pyarrow.Field
Examples
--------
>>> import pyarrow as pa
>>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()})
Select the second field:
>>> struct_type.field(1)
pyarrow.Field<y: string>
Select the field named 'x':
>>> struct_type.field('x')
pyarrow.Field<x: int32>
"""
if isinstance(i, (bytes, str)):
return self.field_by_name(i)
elif isinstance(i, int):
return DataType.field(self, i)
else:
raise TypeError('Expected integer or string index')
def get_all_field_indices(self, name):
"""
Return sorted list of indices for the fields with the given name.
Parameters
----------
name : str
The name of the field to look up.
Returns
-------
indices : List[int]
"""
return self.struct_type.GetAllFieldIndices(tobytes(name))
def __len__(self):
"""
Like num_fields().
"""
return self.type.num_fields()
def __iter__(self):
"""
Iterate over struct fields, in order.
"""
for i in range(len(self)):
yield self[i]
def __getitem__(self, i):
"""
Return the struct field with the given index or name.
Alias of ``field``.
"""
return self.field(i)
def __reduce__(self):
return struct, (list(self),)
cdef class UnionType(DataType):
"""
Base class for union data types.
"""
cdef void init(self, const shared_ptr[CDataType]& type) except *:
DataType.init(self, type)
@property
def mode(self):
"""
The mode of the union ("dense" or "sparse").
"""
cdef CUnionType* type = <CUnionType*> self.sp_type.get()
cdef int mode = type.mode()
if mode == _UnionMode_DENSE:
return 'dense'
if mode == _UnionMode_SPARSE:
return 'sparse'
assert 0
@property
def type_codes(self):
"""
The type code to indicate each data type in this union.
"""
cdef CUnionType* type = <CUnionType*> self.sp_type.get()
return type.type_codes()
def __len__(self):
"""
Like num_fields().
"""
return self.type.num_fields()
def __iter__(self):
"""
Iterate over union members, in order.
"""
for i in range(len(self)):
yield self[i]
cpdef Field field(self, i):
"""
Return a child field by its numeric index.
Parameters
----------
i : int
Returns
-------
pyarrow.Field
"""
if isinstance(i, int):
return DataType.field(self, i)
else:
raise TypeError('Expected integer')
def __getitem__(self, i):
"""
Return a child field by its index.
Alias of ``field``.
"""
return self.field(i)
def __reduce__(self):
return union, (list(self), self.mode, self.type_codes)
cdef class SparseUnionType(UnionType):
"""
Concrete class for sparse union types.
"""
cdef class DenseUnionType(UnionType):
"""
Concrete class for dense union types.
"""
cdef class TimestampType(DataType):
"""
Concrete class for timestamp data types.
"""
cdef void init(self, const shared_ptr[CDataType]& type) except *:
DataType.init(self, type)
self.ts_type = <const CTimestampType*> type.get()
@property
def unit(self):
"""
The timestamp unit ('s', 'ms', 'us' or 'ns').
"""
return timeunit_to_string(self.ts_type.unit())
@property
def tz(self):
"""
The timestamp time zone, if any, or None.
"""
if self.ts_type.timezone().size() > 0:
return frombytes(self.ts_type.timezone())
else:
return None
def to_pandas_dtype(self):
"""
Return the equivalent NumPy / Pandas dtype.
"""
if self.tz is None:
return _pandas_type_map[_Type_TIMESTAMP]
else:
# Return DatetimeTZ
from pyarrow.pandas_compat import make_datetimetz
return make_datetimetz(self.tz)
def __reduce__(self):
return timestamp, (self.unit, self.tz)
cdef class Time32Type(DataType):
"""
Concrete class for time32 data types.
"""
cdef void init(self, const shared_ptr[CDataType]& type) except *:
DataType.init(self, type)
self.time_type = <const CTime32Type*> type.get()
@property
def unit(self):
"""
The time unit ('s', 'ms', 'us' or 'ns').
"""
return timeunit_to_string(self.time_type.unit())
cdef class Time64Type(DataType):
"""
Concrete class for time64 data types.
"""
cdef void init(self, const shared_ptr[CDataType]& type) except *:
DataType.init(self, type)
self.time_type = <const CTime64Type*> type.get()
@property
def unit(self):
"""
The time unit ('s', 'ms', 'us' or 'ns').
"""
return timeunit_to_string(self.time_type.unit())
cdef class DurationType(DataType):
"""
Concrete class for duration data types.
"""
cdef void init(self, const shared_ptr[CDataType]& type) except *:
DataType.init(self, type)
self.duration_type = <const CDurationType*> type.get()
@property
def unit(self):
"""
The duration unit ('s', 'ms', 'us' or 'ns').
"""
return timeunit_to_string(self.duration_type.unit())
cdef class FixedSizeBinaryType(DataType):
"""
Concrete class for fixed-size binary data types.
"""
cdef void init(self, const shared_ptr[CDataType]& type) except *:
DataType.init(self, type)
self.fixed_size_binary_type = (
<const CFixedSizeBinaryType*> type.get())
def __reduce__(self):
return binary, (self.byte_width,)
@property
def byte_width(self):
"""
The binary size in bytes.
"""
return self.fixed_size_binary_type.byte_width()
cdef class Decimal128Type(FixedSizeBinaryType):
"""
Concrete class for decimal128 data types.
"""
cdef void init(self, const shared_ptr[CDataType]& type) except *:
FixedSizeBinaryType.init(self, type)
self.decimal128_type = <const CDecimal128Type*> type.get()
def __reduce__(self):
return decimal128, (self.precision, self.scale)
@property
def precision(self):
"""
The decimal precision, in number of decimal digits (an integer).
"""
return self.decimal128_type.precision()
@property
def scale(self):
"""
The decimal scale (an integer).
"""
return self.decimal128_type.scale()
cdef class Decimal256Type(FixedSizeBinaryType):
"""
Concrete class for Decimal256 data types.
"""
cdef void init(self, const shared_ptr[CDataType]& type) except *:
FixedSizeBinaryType.init(self, type)
self.decimal256_type = <const CDecimal256Type*> type.get()
def __reduce__(self):
return decimal256, (self.precision, self.scale)
@property
def precision(self):
"""
The decimal precision, in number of decimal digits (an integer).
"""
return self.decimal256_type.precision()
@property
def scale(self):
"""
The decimal scale (an integer).
"""
return self.decimal256_type.scale()
cdef class BaseExtensionType(DataType):
"""
Concrete base class for extension types.
"""
cdef void init(self, const shared_ptr[CDataType]& type) except *:
DataType.init(self, type)
self.ext_type = <const CExtensionType*> type.get()
@property
def extension_name(self):
"""
The extension type name.
"""
return frombytes(self.ext_type.extension_name())
@property
def storage_type(self):
"""
The underlying storage type.
"""
return pyarrow_wrap_data_type(self.ext_type.storage_type())
def wrap_array(self, storage):
"""
Wrap the given storage array as an extension array.
Parameters
----------
storage : Array or ChunkedArray
Returns
-------
array : Array or ChunkedArray
Extension array wrapping the storage array
"""
cdef:
shared_ptr[CDataType] c_storage_type
if isinstance(storage, Array):
c_storage_type = (<Array> storage).ap.type()
elif isinstance(storage, ChunkedArray):
c_storage_type = (<ChunkedArray> storage).chunked_array.type()
else:
raise TypeError(
f"Expected array or chunked array, got {storage.__class__}")
if not c_storage_type.get().Equals(deref(self.ext_type)
.storage_type(), False):
raise TypeError(
f"Incompatible storage type for {self}: "
f"expected {self.storage_type}, got {storage.type}")
if isinstance(storage, Array):
return pyarrow_wrap_array(
self.ext_type.WrapArray(
self.sp_type, (<Array> storage).sp_array))
else:
return pyarrow_wrap_chunked_array(
self.ext_type.WrapArray(
self.sp_type, (<ChunkedArray> storage).sp_chunked_array))
cdef class ExtensionType(BaseExtensionType):
"""
Concrete base class for Python-defined extension types.
Parameters
----------
storage_type : DataType
extension_name : str
"""
def __cinit__(self):
if type(self) is ExtensionType:
raise TypeError("Can only instantiate subclasses of "
"ExtensionType")
def __init__(self, DataType storage_type, extension_name):
"""
Initialize an extension type instance.
This should be called at the end of the subclass'
``__init__`` method.
"""
cdef:
shared_ptr[CExtensionType] cpy_ext_type
c_string c_extension_name
c_extension_name = tobytes(extension_name)
assert storage_type is not None
check_status(CPyExtensionType.FromClass(
storage_type.sp_type, c_extension_name, type(self),
&cpy_ext_type))
self.init(<shared_ptr[CDataType]> cpy_ext_type)
cdef void init(self, const shared_ptr[CDataType]& type) except *:
BaseExtensionType.init(self, type)
self.cpy_ext_type = <const CPyExtensionType*> type.get()
# Store weakref and serialized version of self on C++ type instance
check_status(self.cpy_ext_type.SetInstance(self))
def __eq__(self, other):
# Default implementation to avoid infinite recursion through
# DataType.__eq__ -> ExtensionType::ExtensionEquals -> DataType.__eq__
if isinstance(other, ExtensionType):
return (type(self) == type(other) and
self.extension_name == other.extension_name and
self.storage_type == other.storage_type)
else:
return NotImplemented
def __repr__(self):
fmt = '{0.__class__.__name__}({1})'
return fmt.format(self, repr(self.storage_type))
def __arrow_ext_serialize__(self):
"""
Serialized representation of metadata to reconstruct the type object.
This method should return a bytes object, and those serialized bytes
are stored in the custom metadata of the Field holding an extension
type in an IPC message.
The bytes are passed to ``__arrow_ext_deserialize`` and should hold
sufficient information to reconstruct the data type instance.
"""
return NotImplementedError
@classmethod
def __arrow_ext_deserialize__(self, storage_type, serialized):
"""
Return an extension type instance from the storage type and serialized
metadata.
This method should return an instance of the ExtensionType subclass
that matches the passed storage type and serialized metadata (the
return value of ``__arrow_ext_serialize__``).
"""
return NotImplementedError
def __arrow_ext_class__(self):
"""Return an extension array class to be used for building or
deserializing arrays with this extension type.
This method should return a subclass of the ExtensionArray class. By
default, if not specialized in the extension implementation, an
extension type array will be a built-in ExtensionArray instance.
"""
return ExtensionArray
def __arrow_ext_scalar_class__(self):
"""Return an extension scalar class for building scalars with this
extension type.
This method should return subclass of the ExtensionScalar class. By
default, if not specialized in the extension implementation, an
extension type scalar will be a built-in ExtensionScalar instance.
"""
return ExtensionScalar
cdef class PyExtensionType(ExtensionType):
"""
Concrete base class for Python-defined extension types based on pickle
for (de)serialization.
Parameters
----------
storage_type : DataType
The storage type for which the extension is built.