2025-summer/team-10
10
Fork 0
Code Issues Pull requests Projects Releases Activity
team-10/env/Lib/site-packages/pyarrow/table.pxi

6626 lines
204 KiB
Cython
Raw Normal View History

integrata generazione immagini
2025-08-02 07:34:44 +02:00
# 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, PyCapsule_New
import warnings
from cython import sizeof
cdef class ChunkedArray(_PandasConvertible):
"""
An array-like composed from a (possibly empty) collection of pyarrow.Arrays
Warnings
--------
Do not call this class's constructor directly.
Examples
--------
To construct a ChunkedArray object use :func:`pyarrow.chunked_array`:
>>> import pyarrow as pa
>>> pa.chunked_array([], type=pa.int8())
<pyarrow.lib.ChunkedArray object at ...>
[
...
]
>>> pa.chunked_array([[2, 2, 4], [4, 5, 100]])
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
2,
4
],
[
4,
5,
100
]
]
>>> isinstance(pa.chunked_array([[2, 2, 4], [4, 5, 100]]), pa.ChunkedArray)
True
"""
def __cinit__(self):
self.chunked_array = NULL
self._init_is_cpu = False
def __init__(self):
raise TypeError("Do not call ChunkedArray's constructor directly, use "
"`chunked_array` function instead.")
cdef void init(self, const shared_ptr[CChunkedArray]& chunked_array):
self.sp_chunked_array = chunked_array
self.chunked_array = chunked_array.get()
def __reduce__(self):
self._assert_cpu()
return chunked_array, (self.chunks, self.type)
@property
def data(self):
import warnings
warnings.warn("Calling .data on ChunkedArray is provided for "
"compatibility after Column was removed, simply drop "
"this attribute", FutureWarning)
return self
@property
def type(self):
"""
Return data type of a ChunkedArray.
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> n_legs.type
DataType(int64)
"""
return pyarrow_wrap_data_type(self.sp_chunked_array.get().type())
def length(self):
"""
Return length of a ChunkedArray.
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> n_legs.length()
6
"""
return self.chunked_array.length()
def __len__(self):
return self.length()
def __repr__(self):
type_format = object.__repr__(self)
return f"{type_format}\n{self}"
def to_string(self, *, int indent=0, int window=5, int container_window=2,
c_bool skip_new_lines=False, int element_size_limit=100):
"""
Render a "pretty-printed" string representation of the ChunkedArray
Parameters
----------
indent : int
How much to indent right the content of the array,
by default ``0``.
window : int
How many items to preview within each chunk at the begin and end
of the chunk when the chunk is bigger than the window.
The other elements will be ellipsed.
container_window : int
How many chunks to preview at the begin and end
of the array when the array is bigger than the window.
The other elements will be ellipsed.
This setting also applies to list columns.
skip_new_lines : bool
If the array should be rendered as a single line of text
or if each element should be on its own line.
element_size_limit : int, default 100
Maximum number of characters of a single element before it is truncated.
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> n_legs.to_string(skip_new_lines=True)
'[[2,2,4],[4,5,100]]'
"""
cdef:
c_string result
PrettyPrintOptions options
with nogil:
options = PrettyPrintOptions(indent, window)
options.skip_new_lines = skip_new_lines
options.container_window = container_window
options.element_size_limit = element_size_limit
check_status(
PrettyPrint(
deref(self.chunked_array),
options,
&result
)
)
return frombytes(result, safe=True)
def format(self, **kwargs):
"""
DEPRECATED, use pyarrow.ChunkedArray.to_string
Parameters
----------
**kwargs : dict
Returns
-------
str
"""
import warnings
warnings.warn('ChunkedArray.format is deprecated, '
'use ChunkedArray.to_string')
return self.to_string(**kwargs)
def __str__(self):
return self.to_string()
def validate(self, *, full=False):
"""
Perform validation checks. An exception is raised if validation fails.
By default only cheap validation checks are run. Pass `full=True`
for thorough validation checks (potentially O(n)).
Parameters
----------
full : bool, default False
If True, run expensive checks, otherwise cheap checks only.
Raises
------
ArrowInvalid
"""
if full:
self._assert_cpu()
with nogil:
check_status(self.sp_chunked_array.get().ValidateFull())
else:
with nogil:
check_status(self.sp_chunked_array.get().Validate())
@property
def null_count(self):
"""
Number of null entries
Returns
-------
int
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
>>> n_legs.null_count
1
"""
self._assert_cpu()
return self.chunked_array.null_count()
@property
def nbytes(self):
"""
Total number of bytes consumed by the elements of the chunked array.
In other words, the sum of bytes from all buffer ranges referenced.
Unlike `get_total_buffer_size` this method will account for array
offsets.
If buffers are shared between arrays then the shared
portion will only be counted multiple times.
The dictionary of dictionary arrays will always be counted in their
entirety even if the array only references a portion of the dictionary.
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
>>> n_legs.nbytes
49
"""
self._assert_cpu()
cdef:
CResult[int64_t] c_res_buffer
with nogil:
c_res_buffer = ReferencedBufferSize(deref(self.chunked_array))
size = GetResultValue(c_res_buffer)
return size
def get_total_buffer_size(self):
"""
The sum of bytes in each buffer referenced by the chunked array.
An array may only reference a portion of a buffer.
This method will overestimate in this case and return the
byte size of the entire buffer.
If a buffer is referenced multiple times then it will
only be counted once.
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
>>> n_legs.get_total_buffer_size()
49
"""
self._assert_cpu()
cdef:
int64_t total_buffer_size
total_buffer_size = TotalBufferSize(deref(self.chunked_array))
return total_buffer_size
def __sizeof__(self):
return super(ChunkedArray, self).__sizeof__() + self.nbytes
def __iter__(self):
for chunk in self.iterchunks():
for item in chunk:
yield item
def __getitem__(self, key):
"""
Slice or return value at given index
Parameters
----------
key : integer or slice
Slices with step not equal to 1 (or None) will produce a copy
rather than a zero-copy view
Returns
-------
value : Scalar (index) or ChunkedArray (slice)
"""
self._assert_cpu()
if isinstance(key, slice):
return _normalize_slice(self, key)
return self.getitem(_normalize_index(key, self.chunked_array.length()))
cdef getitem(self, int64_t i):
self._assert_cpu()
return Scalar.wrap(GetResultValue(self.chunked_array.GetScalar(i)))
def is_null(self, *, nan_is_null=False):
"""
Return boolean array indicating the null values.
Parameters
----------
nan_is_null : bool (optional, default False)
Whether floating-point NaN values should also be considered null.
Returns
-------
array : boolean Array or ChunkedArray
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
>>> n_legs.is_null()
<pyarrow.lib.ChunkedArray object at ...>
[
[
false,
false,
false,
false,
true,
false
]
]
"""
self._assert_cpu()
options = _pc().NullOptions(nan_is_null=nan_is_null)
return _pc().call_function('is_null', [self], options)
def is_nan(self):
"""
Return boolean array indicating the NaN values.
Examples
--------
>>> import pyarrow as pa
>>> import numpy as np
>>> arr = pa.chunked_array([[2, np.nan, 4], [4, None, 100]])
>>> arr.is_nan()
<pyarrow.lib.ChunkedArray object at ...>
[
[
false,
true,
false,
false,
null,
false
]
]
"""
self._assert_cpu()
return _pc().is_nan(self)
def is_valid(self):
"""
Return boolean array indicating the non-null values.
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
>>> n_legs.is_valid()
<pyarrow.lib.ChunkedArray object at ...>
[
[
true,
true,
true
],
[
true,
false,
true
]
]
"""
self._assert_cpu()
return _pc().is_valid(self)
def __eq__(self, other):
try:
return self.equals(other)
except TypeError:
return NotImplemented
def fill_null(self, fill_value):
"""
Replace each null element in values with fill_value.
See :func:`pyarrow.compute.fill_null` for full usage.
Parameters
----------
fill_value : any
The replacement value for null entries.
Returns
-------
result : Array or ChunkedArray
A new array with nulls replaced by the given value.
Examples
--------
>>> import pyarrow as pa
>>> fill_value = pa.scalar(5, type=pa.int8())
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
>>> n_legs.fill_null(fill_value)
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
2,
4,
4,
5,
100
]
]
"""
self._assert_cpu()
return _pc().fill_null(self, fill_value)
def equals(self, ChunkedArray other):
"""
Return whether the contents of two chunked arrays are equal.
Parameters
----------
other : pyarrow.ChunkedArray
Chunked array to compare against.
Returns
-------
are_equal : bool
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> animals = pa.chunked_array((
... ["Flamingo", "Parrot", "Dog"],
... ["Horse", "Brittle stars", "Centipede"]
... ))
>>> n_legs.equals(n_legs)
True
>>> n_legs.equals(animals)
False
"""
self._assert_cpu()
if other is None:
return False
cdef:
CChunkedArray* this_arr = self.chunked_array
CChunkedArray* other_arr = other.chunked_array
c_bool result
with nogil:
result = this_arr.Equals(deref(other_arr))
return result
def _to_pandas(self, options, types_mapper=None, **kwargs):
self._assert_cpu()
return _array_like_to_pandas(self, options, types_mapper=types_mapper)
def to_numpy(self, zero_copy_only=False):
"""
Return a NumPy copy of this array (experimental).
Parameters
----------
zero_copy_only : bool, default False
Introduced for signature consistence with pyarrow.Array.to_numpy.
This must be False here since NumPy arrays' buffer must be contiguous.
Returns
-------
array : numpy.ndarray
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> n_legs.to_numpy()
array([ 2, 2, 4, 4, 5, 100])
"""
self._assert_cpu()
if np is None:
raise ImportError(
"Cannot return a numpy.ndarray if NumPy is not present")
if zero_copy_only:
raise ValueError(
"zero_copy_only must be False for pyarrow.ChunkedArray.to_numpy"
)
cdef:
PyObject* out
PandasOptions c_options
object values
c_options.to_numpy = True
with nogil:
check_status(
ConvertChunkedArrayToPandas(
c_options,
self.sp_chunked_array,
self,
&out
)
)
# wrap_array_output uses pandas to convert to Categorical, here
# always convert to numpy array
values = PyObject_to_object(out)
if isinstance(values, dict):
values = np.take(values['dictionary'], values['indices'])
return values
def __array__(self, dtype=None, copy=None):
self._assert_cpu()
if copy is False:
raise ValueError(
"Unable to avoid a copy while creating a numpy array as requested "
"(converting a pyarrow.ChunkedArray always results in a copy).\n"
"If using `np.array(obj, copy=False)` replace it with "
"`np.asarray(obj)` to allow a copy when needed"
)
# 'copy' can further be ignored because to_numpy() already returns a copy
values = self.to_numpy()
if dtype is None:
return values
return values.astype(dtype, copy=False)
def cast(self, object target_type=None, safe=None, options=None):
"""
Cast array values to another data type
See :func:`pyarrow.compute.cast` for usage.
Parameters
----------
target_type : DataType, None
Type to cast array to.
safe : boolean, default True
Whether to check for conversion errors such as overflow.
options : CastOptions, default None
Additional checks pass by CastOptions
Returns
-------
cast : Array or ChunkedArray
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> n_legs.type
DataType(int64)
Change the data type of an array:
>>> n_legs_seconds = n_legs.cast(pa.duration('s'))
>>> n_legs_seconds.type
DurationType(duration[s])
"""
self._assert_cpu()
return _pc().cast(self, target_type, safe=safe, options=options)
def dictionary_encode(self, null_encoding='mask'):
"""
Compute dictionary-encoded representation of array.
See :func:`pyarrow.compute.dictionary_encode` for full usage.
Parameters
----------
null_encoding : str, default "mask"
How to handle null entries.
Returns
-------
encoded : ChunkedArray
A dictionary-encoded version of this array.
Examples
--------
>>> import pyarrow as pa
>>> animals = pa.chunked_array((
... ["Flamingo", "Parrot", "Dog"],
... ["Horse", "Brittle stars", "Centipede"]
... ))
>>> animals.dictionary_encode()
<pyarrow.lib.ChunkedArray object at ...>
[
...
-- dictionary:
[
"Flamingo",
"Parrot",
"Dog",
"Horse",
"Brittle stars",
"Centipede"
]
-- indices:
[
0,
1,
2
],
...
-- dictionary:
[
"Flamingo",
"Parrot",
"Dog",
"Horse",
"Brittle stars",
"Centipede"
]
-- indices:
[
3,
4,
5
]
]
"""
self._assert_cpu()
options = _pc().DictionaryEncodeOptions(null_encoding)
return _pc().call_function('dictionary_encode', [self], options)
def flatten(self, MemoryPool memory_pool=None):
"""
Flatten this ChunkedArray. If it has a struct type, the column is
flattened into one array per struct field.
Parameters
----------
memory_pool : MemoryPool, default None
For memory allocations, if required, otherwise use default pool
Returns
-------
result : list of ChunkedArray
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> c_arr = pa.chunked_array(n_legs.value_counts())
>>> c_arr
<pyarrow.lib.ChunkedArray object at ...>
[
-- is_valid: all not null
-- child 0 type: int64
[
2,
4,
5,
100
]
-- child 1 type: int64
[
2,
2,
1,
1
]
]
>>> c_arr.flatten()
[<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
4,
5,
100
]
], <pyarrow.lib.ChunkedArray object at ...>
[
[
2,
2,
1,
1
]
]]
>>> c_arr.type
StructType(struct<values: int64, counts: int64>)
>>> n_legs.type
DataType(int64)
"""
self._assert_cpu()
cdef:
vector[shared_ptr[CChunkedArray]] flattened
CMemoryPool* pool = maybe_unbox_memory_pool(memory_pool)
with nogil:
flattened = GetResultValue(self.chunked_array.Flatten(pool))
return [pyarrow_wrap_chunked_array(col) for col in flattened]
def combine_chunks(self, MemoryPool memory_pool=None):
"""
Flatten this ChunkedArray into a single non-chunked array.
Parameters
----------
memory_pool : MemoryPool, default None
For memory allocations, if required, otherwise use default pool
Returns
-------
result : Array
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> n_legs
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
2,
4
],
[
4,
5,
100
]
]
>>> n_legs.combine_chunks()
<pyarrow.lib.Int64Array object at ...>
[
2,
2,
4,
4,
5,
100
]
"""
self._assert_cpu()
if self.num_chunks == 0:
return array([], type=self.type)
else:
return concat_arrays(self.chunks)
def unique(self):
"""
Compute distinct elements in array
Returns
-------
pyarrow.Array
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> n_legs
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
2,
4
],
[
4,
5,
100
]
]
>>> n_legs.unique()
<pyarrow.lib.Int64Array object at ...>
[
2,
4,
5,
100
]
"""
self._assert_cpu()
return _pc().call_function('unique', [self])
def value_counts(self):
"""
Compute counts of unique elements in array.
Returns
-------
An array of <input type "Values", int64_t "Counts"> structs
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> n_legs
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
2,
4
],
[
4,
5,
100
]
]
>>> n_legs.value_counts()
<pyarrow.lib.StructArray object at ...>
-- is_valid: all not null
-- child 0 type: int64
[
2,
4,
5,
100
]
-- child 1 type: int64
[
2,
2,
1,
1
]
"""
self._assert_cpu()
return _pc().call_function('value_counts', [self])
def slice(self, offset=0, length=None):
"""
Compute zero-copy slice of this ChunkedArray
Parameters
----------
offset : int, default 0
Offset from start of array to slice
length : int, default None
Length of slice (default is until end of batch starting from
offset)
Returns
-------
sliced : ChunkedArray
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> n_legs
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
2,
4
],
[
4,
5,
100
]
]
>>> n_legs.slice(2,2)
<pyarrow.lib.ChunkedArray object at ...>
[
[
4
],
[
4
]
]
"""
cdef shared_ptr[CChunkedArray] result
if offset < 0:
raise IndexError('Offset must be non-negative')
offset = min(len(self), offset)
if length is None:
result = self.chunked_array.Slice(offset)
else:
result = self.chunked_array.Slice(offset, length)
return pyarrow_wrap_chunked_array(result)
def filter(self, mask, object null_selection_behavior="drop"):
"""
Select values from the chunked array.
See :func:`pyarrow.compute.filter` for full usage.
Parameters
----------
mask : Array or array-like
The boolean mask to filter the chunked array with.
null_selection_behavior : str, default "drop"
How nulls in the mask should be handled.
Returns
-------
filtered : Array or ChunkedArray
An array of the same type, with only the elements selected by
the boolean mask.
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> n_legs
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
2,
4
],
[
4,
5,
100
]
]
>>> mask = pa.array([True, False, None, True, False, True])
>>> n_legs.filter(mask)
<pyarrow.lib.ChunkedArray object at ...>
[
[
2
],
[
4,
100
]
]
>>> n_legs.filter(mask, null_selection_behavior="emit_null")
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
null
],
[
4,
100
]
]
"""
self._assert_cpu()
return _pc().filter(self, mask, null_selection_behavior)
def index(self, value, start=None, end=None, *, memory_pool=None):
"""
Find the first index of a value.
See :func:`pyarrow.compute.index` for full usage.
Parameters
----------
value : Scalar or object
The value to look for in the array.
start : int, optional
The start index where to look for `value`.
end : int, optional
The end index where to look for `value`.
memory_pool : MemoryPool, optional
A memory pool for potential memory allocations.
Returns
-------
index : Int64Scalar
The index of the value in the array (-1 if not found).
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> n_legs
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
2,
4
],
[
4,
5,
100
]
]
>>> n_legs.index(4)
<pyarrow.Int64Scalar: 2>
>>> n_legs.index(4, start=3)
<pyarrow.Int64Scalar: 3>
"""
self._assert_cpu()
return _pc().index(self, value, start, end, memory_pool=memory_pool)
def take(self, object indices):
"""
Select values from the chunked array.
See :func:`pyarrow.compute.take` for full usage.
Parameters
----------
indices : Array or array-like
The indices in the array whose values will be returned.
Returns
-------
taken : Array or ChunkedArray
An array with the same datatype, containing the taken values.
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> n_legs
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
2,
4
],
[
4,
5,
100
]
]
>>> n_legs.take([1,4,5])
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
5,
100
]
]
"""
self._assert_cpu()
return _pc().take(self, indices)
def drop_null(self):
"""
Remove missing values from a chunked array.
See :func:`pyarrow.compute.drop_null` for full description.
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]])
>>> n_legs
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
2,
null
],
[
4,
5,
100
]
]
>>> n_legs.drop_null()
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
2
],
[
4,
5,
100
]
]
"""
self._assert_cpu()
return _pc().drop_null(self)
def sort(self, order="ascending", **kwargs):
"""
Sort the ChunkedArray
Parameters
----------
order : str, default "ascending"
Which order to sort values in.
Accepted values are "ascending", "descending".
**kwargs : dict, optional
Additional sorting options.
As allowed by :class:`SortOptions`
Returns
-------
result : ChunkedArray
"""
self._assert_cpu()
indices = _pc().sort_indices(
self,
options=_pc().SortOptions(sort_keys=[("", order)], **kwargs)
)
return self.take(indices)
def unify_dictionaries(self, MemoryPool memory_pool=None):
"""
Unify dictionaries across all chunks.
This method returns an equivalent chunked array, but where all
chunks share the same dictionary values. Dictionary indices are
transposed accordingly.
If there are no dictionaries in the chunked array, it is returned
unchanged.
Parameters
----------
memory_pool : MemoryPool, default None
For memory allocations, if required, otherwise use default pool
Returns
-------
result : ChunkedArray
Examples
--------
>>> import pyarrow as pa
>>> arr_1 = pa.array(["Flamingo", "Parrot", "Dog"]).dictionary_encode()
>>> arr_2 = pa.array(["Horse", "Brittle stars", "Centipede"]).dictionary_encode()
>>> c_arr = pa.chunked_array([arr_1, arr_2])
>>> c_arr
<pyarrow.lib.ChunkedArray object at ...>
[
...
-- dictionary:
[
"Flamingo",
"Parrot",
"Dog"
]
-- indices:
[
0,
1,
2
],
...
-- dictionary:
[
"Horse",
"Brittle stars",
"Centipede"
]
-- indices:
[
0,
1,
2
]
]
>>> c_arr.unify_dictionaries()
<pyarrow.lib.ChunkedArray object at ...>
[
...
-- dictionary:
[
"Flamingo",
"Parrot",
"Dog",
"Horse",
"Brittle stars",
"Centipede"
]
-- indices:
[
0,
1,
2
],
...
-- dictionary:
[
"Flamingo",
"Parrot",
"Dog",
"Horse",
"Brittle stars",
"Centipede"
]
-- indices:
[
3,
4,
5
]
]
"""
self._assert_cpu()
cdef:
CMemoryPool* pool = maybe_unbox_memory_pool(memory_pool)
shared_ptr[CChunkedArray] c_result
with nogil:
c_result = GetResultValue(CDictionaryUnifier.UnifyChunkedArray(
self.sp_chunked_array, pool))
return pyarrow_wrap_chunked_array(c_result)
@property
def num_chunks(self):
"""
Number of underlying chunks.
Returns
-------
int
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]])
>>> n_legs.num_chunks
2
"""
return self.chunked_array.num_chunks()
def chunk(self, i):
"""
Select a chunk by its index.
Parameters
----------
i : int
Returns
-------
pyarrow.Array
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]])
>>> n_legs.chunk(1)
<pyarrow.lib.Int64Array object at ...>
[
4,
5,
100
]
"""
if i >= self.num_chunks or i < 0:
raise IndexError('Chunk index out of range.')
return pyarrow_wrap_array(self.chunked_array.chunk(i))
@property
def chunks(self):
"""
Convert to a list of single-chunked arrays.
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]])
>>> n_legs
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
2,
null
],
[
4,
5,
100
]
]
>>> n_legs.chunks
[<pyarrow.lib.Int64Array object at ...>
[
2,
2,
null
], <pyarrow.lib.Int64Array object at ...>
[
4,
5,
100
]]
"""
return list(self.iterchunks())
def iterchunks(self):
"""
Convert to an iterator of ChunkArrays.
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
>>> for i in n_legs.iterchunks():
... print(i.null_count)
...
0
1
"""
for i in range(self.num_chunks):
yield self.chunk(i)
def to_pylist(self, *, maps_as_pydicts=None):
"""
Convert to a list of native Python objects.
Parameters
----------
maps_as_pydicts : str, optional, default `None`
Valid values are `None`, 'lossy', or 'strict'.
The default behavior (`None`), is to convert Arrow Map arrays to
Python association lists (list-of-tuples) in the same order as the
Arrow Map, as in [(key1, value1), (key2, value2), ...].
If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
If 'lossy', whenever duplicate keys are detected, a warning will be printed.
The last seen value of a duplicate key will be in the Python dictionary.
If 'strict', this instead results in an exception being raised when detected.
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]])
>>> n_legs.to_pylist()
[2, 2, 4, 4, None, 100]
"""
self._assert_cpu()
result = []
for i in range(self.num_chunks):
result += self.chunk(i).to_pylist(maps_as_pydicts=maps_as_pydicts)
return result
def __arrow_c_stream__(self, requested_schema=None):
"""
Export to a C ArrowArrayStream PyCapsule.
Parameters
----------
requested_schema : PyCapsule, default None
The schema to which the stream should be casted, passed as a
PyCapsule containing a C ArrowSchema representation of the
requested schema.
Returns
-------
PyCapsule
A capsule containing a C ArrowArrayStream struct.
"""
self._assert_cpu()
cdef:
ChunkedArray chunked
ArrowArrayStream* c_stream = NULL
if requested_schema is not None:
target_type = DataType._import_from_c_capsule(requested_schema)
if target_type != self.type:
try:
chunked = self.cast(target_type, safe=True)
except ArrowInvalid as e:
raise ValueError(
f"Could not cast {self.type} to requested type {target_type}: {e}"
)
else:
chunked = self
else:
chunked = self
stream_capsule = alloc_c_stream(&c_stream)
with nogil:
check_status(ExportChunkedArray(chunked.sp_chunked_array, c_stream))
return stream_capsule
@staticmethod
def _import_from_c_capsule(stream):
"""
Import ChunkedArray from a C ArrowArrayStream PyCapsule.
Parameters
----------
stream: PyCapsule
A capsule containing a C ArrowArrayStream PyCapsule.
Returns
-------
ChunkedArray
"""
cdef:
ArrowArrayStream* c_stream
shared_ptr[CChunkedArray] c_chunked_array
ChunkedArray self
c_stream = <ArrowArrayStream*>PyCapsule_GetPointer(
stream, 'arrow_array_stream'
)
with nogil:
c_chunked_array = GetResultValue(ImportChunkedArray(c_stream))
self = ChunkedArray.__new__(ChunkedArray)
self.init(c_chunked_array)
return self
@property
def is_cpu(self):
"""
Whether all chunks in the ChunkedArray are CPU-accessible.
"""
if not self._init_is_cpu:
self._is_cpu = self.chunked_array.is_cpu()
self._init_is_cpu = True
return self._is_cpu
def _assert_cpu(self):
if not self.is_cpu:
raise NotImplementedError("Implemented only for data on CPU device")
def chunked_array(arrays, type=None):
"""
Construct chunked array from list of array-like objects
Parameters
----------
arrays : Array, list of Array, or array-like
Must all be the same data type. Can be empty only if type also passed.
Any Arrow-compatible array that implements the Arrow PyCapsule Protocol
(has an ``__arrow_c_array__`` or ``__arrow_c_stream__`` method) can be
passed as well.
type : DataType or string coercible to DataType
Returns
-------
ChunkedArray
Examples
--------
>>> import pyarrow as pa
>>> pa.chunked_array([], type=pa.int8())
<pyarrow.lib.ChunkedArray object at ...>
[
...
]
>>> pa.chunked_array([[2, 2, 4], [4, 5, 100]])
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
2,
4
],
[
4,
5,
100
]
]
"""
cdef:
Array arr
vector[shared_ptr[CArray]] c_arrays
shared_ptr[CChunkedArray] c_result
shared_ptr[CDataType] c_type
type = ensure_type(type, allow_none=True)
if isinstance(arrays, Array):
arrays = [arrays]
elif hasattr(arrays, "__arrow_c_stream__"):
if type is not None:
requested_type = type.__arrow_c_schema__()
else:
requested_type = None
capsule = arrays.__arrow_c_stream__(requested_type)
result = ChunkedArray._import_from_c_capsule(capsule)
if type is not None and result.type != type:
# __arrow_c_stream__ coerces schema with best effort, so we might
# need to cast it if the producer wasn't able to cast to exact schema.
result = result.cast(type)
return result
elif hasattr(arrays, "__arrow_c_array__"):
arr = array(arrays, type=type)
arrays = [arr]
for x in arrays:
arr = x if isinstance(x, Array) else array(x, type=type)
if type is None:
# it allows more flexible chunked array construction from to coerce
# subsequent arrays to the firstly inferred array type
# it also spares the inference overhead after the first chunk
type = arr.type
c_arrays.push_back(arr.sp_array)
c_type = pyarrow_unwrap_data_type(type)
with nogil:
c_result = GetResultValue(CChunkedArray.Make(c_arrays, c_type))
return pyarrow_wrap_chunked_array(c_result)
cdef _schema_from_arrays(arrays, names, metadata, shared_ptr[CSchema]* schema):
cdef:
Py_ssize_t K = len(arrays)
c_string c_name
shared_ptr[CDataType] c_type
shared_ptr[const CKeyValueMetadata] c_meta
vector[shared_ptr[CField]] c_fields
if metadata is not None:
c_meta = KeyValueMetadata(metadata).unwrap()
if K == 0:
if names is None or len(names) == 0:
schema.reset(new CSchema(c_fields, c_meta))
return arrays
else:
raise ValueError(f'Length of names ({len(names)}) does not match '
f'length of arrays ({K})')
c_fields.resize(K)
if names is None:
raise ValueError('Must pass names or schema when constructing '
'Table or RecordBatch.')
if len(names) != K:
raise ValueError(f'Length of names ({len(names)}) does not match '
f'length of arrays ({K})')
converted_arrays = []
for i in range(K):
val = arrays[i]
if not isinstance(val, (Array, ChunkedArray)):
val = array(val)
c_type = (<DataType> val.type).sp_type
if names[i] is None:
c_name = b'None'
else:
c_name = tobytes(names[i])
c_fields[i].reset(new CField(c_name, c_type, True))
converted_arrays.append(val)
schema.reset(new CSchema(c_fields, c_meta))
return converted_arrays
cdef _sanitize_arrays(arrays, names, schema, metadata,
shared_ptr[CSchema]* c_schema):
cdef Schema cy_schema
if schema is None:
converted_arrays = _schema_from_arrays(arrays, names, metadata,
c_schema)
else:
if names is not None:
raise ValueError('Cannot pass both schema and names')
if metadata is not None:
raise ValueError('Cannot pass both schema and metadata')
cy_schema = schema
if len(schema) != len(arrays):
raise ValueError('Schema and number of arrays unequal')
c_schema[0] = cy_schema.sp_schema
converted_arrays = []
for i, item in enumerate(arrays):
item = asarray(item, type=schema[i].type)
converted_arrays.append(item)
return converted_arrays
cdef class _Tabular(_PandasConvertible):
"""Internal: An interface for common operations on tabular objects."""
def __init__(self):
raise TypeError(f"Do not call {self.__class__.__name__}'s constructor directly, use "
f"one of the `{self.__class__.__name__}.from_*` functions instead.")
def __array__(self, dtype=None, copy=None):
self._assert_cpu()
if copy is False:
raise ValueError(
"Unable to avoid a copy while creating a numpy array as requested "
f"(converting a pyarrow.{self.__class__.__name__} always results "
"in a copy).\n"
"If using `np.array(obj, copy=False)` replace it with "
"`np.asarray(obj)` to allow a copy when needed"
)
# 'copy' can further be ignored because stacking will result in a copy
column_arrays = [
np.asarray(self.column(i), dtype=dtype) for i in range(self.num_columns)
]
if column_arrays:
arr = np.stack(column_arrays, axis=1)
else:
arr = np.empty((self.num_rows, 0), dtype=dtype)
return arr
def __dataframe__(self, nan_as_null: bool = False, allow_copy: bool = True):
"""
Return the dataframe interchange object implementing the interchange protocol.
Parameters
----------
nan_as_null : bool, default False
Whether to tell the DataFrame to overwrite null values in the data
with ``NaN`` (or ``NaT``).
allow_copy : bool, default True
Whether to allow memory copying when exporting. If set to False
it would cause non-zero-copy exports to fail.
Returns
-------
DataFrame interchange object
The object which consuming library can use to ingress the dataframe.
Notes
-----
Details on the interchange protocol:
https://data-apis.org/dataframe-protocol/latest/index.html
`nan_as_null` currently has no effect; once support for nullable extension
dtypes is added, this value should be propagated to columns.
"""
from pyarrow.interchange.dataframe import _PyArrowDataFrame
return _PyArrowDataFrame(self, nan_as_null, allow_copy)
def __eq__(self, other):
try:
return self.equals(other)
except TypeError:
return NotImplemented
def __getitem__(self, key):
"""
Slice or return column at given index or column name
Parameters
----------
key : integer, str, or slice
Slices with step not equal to 1 (or None) will produce a copy
rather than a zero-copy view
Returns
-------
Array (from RecordBatch) or ChunkedArray (from Table) for column input.
RecordBatch or Table for slice input.
"""
if isinstance(key, slice):
return _normalize_slice(self, key)
return self.column(key)
def __len__(self):
return self.num_rows
def __repr__(self):
if not self._is_initialized():
raise ValueError("This object's internal pointer is NULL, do not "
"use any methods or attributes on this object")
return self.to_string(preview_cols=10)
def _column(self, int i):
raise NotImplementedError
def _ensure_integer_index(self, i):
"""
Ensure integer index (convert string column name to integer if needed).
"""
if isinstance(i, (bytes, str)):
field_indices = self.schema.get_all_field_indices(i)
if len(field_indices) == 0:
raise KeyError(f'Field "{i}" does not exist in schema')
elif len(field_indices) > 1:
raise KeyError(
f'Field "{i}" exists {len(field_indices)} times in schema')
else:
return field_indices[0]
elif isinstance(i, int):
return i
else:
raise TypeError("Index must either be string or integer")
def _is_initialized(self):
raise NotImplementedError
def column(self, i):
"""
Select single column from Table or RecordBatch.
Parameters
----------
i : int or string
The index or name of the column to retrieve.
Returns
-------
column : Array (for RecordBatch) or ChunkedArray (for Table)
Examples
--------
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
Select a column by numeric index:
>>> table.column(0)
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
4,
5,
100
]
]
Select a column by its name:
>>> table.column("animals")
<pyarrow.lib.ChunkedArray object at ...>
[
[
"Flamingo",
"Horse",
"Brittle stars",
"Centipede"
]
]
"""
return self._column(self._ensure_integer_index(i))
@property
def column_names(self):
"""
Names of the Table or RecordBatch columns.
Returns
-------
list of str
Examples
--------
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> table = pa.Table.from_arrays([[2, 4, 5, 100],
... ["Flamingo", "Horse", "Brittle stars", "Centipede"]],
... names=['n_legs', 'animals'])
>>> table.column_names
['n_legs', 'animals']
"""
return [self.field(i).name for i in range(self.num_columns)]
@property
def columns(self):
"""
List of all columns in numerical order.
Returns
-------
columns : list of Array (for RecordBatch) or list of ChunkedArray (for Table)
Examples
--------
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [None, 4, 5, None],
... 'animals': ["Flamingo", "Horse", None, "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.columns
[<pyarrow.lib.ChunkedArray object at ...>
[
[
null,
4,
5,
null
]
], <pyarrow.lib.ChunkedArray object at ...>
[
[
"Flamingo",
"Horse",
null,
"Centipede"
]
]]
"""
return [self._column(i) for i in range(self.num_columns)]
def drop_null(self):
"""
Remove rows that contain missing values from a Table or RecordBatch.
See :func:`pyarrow.compute.drop_null` for full usage.
Returns
-------
Table or RecordBatch
A tabular object with the same schema, with rows containing
no missing values.
Examples
--------
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'year': [None, 2022, 2019, 2021],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", None, "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.drop_null()
pyarrow.Table
year: double
n_legs: int64
animals: string
----
year: [[2022,2021]]
n_legs: [[4,100]]
animals: [["Horse","Centipede"]]
"""
self._assert_cpu()
return _pc().drop_null(self)
def field(self, i):
"""
Select a schema field by its column name or numeric index.
Parameters
----------
i : int or string
The index or name of the field to retrieve.
Returns
-------
Field
Examples
--------
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.field(0)
pyarrow.Field<n_legs: int64>
>>> table.field(1)
pyarrow.Field<animals: string>
"""
return self.schema.field(i)
@classmethod
def from_pydict(cls, mapping, schema=None, metadata=None):
"""
Construct a Table or RecordBatch from Arrow arrays or columns.
Parameters
----------
mapping : dict or Mapping
A mapping of strings to Arrays or Python lists.
schema : Schema, default None
If not passed, will be inferred from the Mapping values.
metadata : dict or Mapping, default None
Optional metadata for the schema (if inferred).
Returns
-------
Table or RecordBatch
Examples
--------
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
>>> pydict = {'n_legs': n_legs, 'animals': animals}
Construct a Table from a dictionary of arrays:
>>> pa.Table.from_pydict(pydict)
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
>>> pa.Table.from_pydict(pydict).schema
n_legs: int64
animals: string
Construct a Table from a dictionary of arrays with metadata:
>>> my_metadata={"n_legs": "Number of legs per animal"}
>>> pa.Table.from_pydict(pydict, metadata=my_metadata).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
Construct a Table from a dictionary of arrays with pyarrow schema:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.int64()),
... pa.field('animals', pa.string())],
... metadata={"n_legs": "Number of legs per animal"})
>>> pa.Table.from_pydict(pydict, schema=my_schema).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
"""
return _from_pydict(cls=cls,
mapping=mapping,
schema=schema,
metadata=metadata)
@classmethod
def from_pylist(cls, mapping, schema=None, metadata=None):
"""
Construct a Table or RecordBatch from list of rows / dictionaries.
Parameters
----------
mapping : list of dicts of rows
A mapping of strings to row values.
schema : Schema, default None
If not passed, will be inferred from the first row of the
mapping values.
metadata : dict or Mapping, default None
Optional metadata for the schema (if inferred).
Returns
-------
Table or RecordBatch
Examples
--------
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'},
... {'n_legs': 4, 'animals': 'Dog'}]
Construct a Table from a list of rows:
>>> pa.Table.from_pylist(pylist)
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4]]
animals: [["Flamingo","Dog"]]
Construct a Table from a list of rows with metadata:
>>> my_metadata={"n_legs": "Number of legs per animal"}
>>> pa.Table.from_pylist(pylist, metadata=my_metadata).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
Construct a Table from a list of rows with pyarrow schema:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.int64()),
... pa.field('animals', pa.string())],
... metadata={"n_legs": "Number of legs per animal"})
>>> pa.Table.from_pylist(pylist, schema=my_schema).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
"""
return _from_pylist(cls=cls,
mapping=mapping,
schema=schema,
metadata=metadata)
def itercolumns(self):
"""
Iterator over all columns in their numerical order.
Yields
------
Array (for RecordBatch) or ChunkedArray (for Table)
Examples
--------
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [None, 4, 5, None],
... 'animals': ["Flamingo", "Horse", None, "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> for i in table.itercolumns():
... print(i.null_count)
...
2
1
"""
for i in range(self.num_columns):
yield self._column(i)
@property
def num_columns(self):
raise NotImplementedError
@property
def num_rows(self):
raise NotImplementedError
@property
def shape(self):
"""
Dimensions of the table or record batch: (#rows, #columns).
Returns
-------
(int, int)
Number of rows and number of columns.
Examples
--------
>>> import pyarrow as pa
>>> table = pa.table({'n_legs': [None, 4, 5, None],
... 'animals': ["Flamingo", "Horse", None, "Centipede"]})
>>> table.shape
(4, 2)
"""
return (self.num_rows, self.num_columns)
@property
def schema(self):
raise NotImplementedError
def sort_by(self, sorting, **kwargs):
"""
Sort the Table or RecordBatch by one or multiple columns.
Parameters
----------
sorting : str or list[tuple(name, order)]
Name of the column to use to sort (ascending), or
a list of multiple sorting conditions where
each entry is a tuple with column name
and sorting order ("ascending" or "descending")
**kwargs : dict, optional
Additional sorting options.
As allowed by :class:`SortOptions`
Returns
-------
Table or RecordBatch
A new tabular object sorted according to the sort keys.
Examples
--------
Table (works similarly for RecordBatch)
>>> import pandas as pd
>>> import pyarrow as pa
>>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022, 2019, 2021],
... 'n_legs': [2, 2, 4, 4, 5, 100],
... 'animal': ["Flamingo", "Parrot", "Dog", "Horse",
... "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.sort_by('animal')
pyarrow.Table
year: int64
n_legs: int64
animal: string
----
year: [[2019,2021,2021,2020,2022,2022]]
n_legs: [[5,100,4,2,4,2]]
animal: [["Brittle stars","Centipede","Dog","Flamingo","Horse","Parrot"]]
"""
self._assert_cpu()
if isinstance(sorting, str):
sorting = [(sorting, "ascending")]
indices = _pc().sort_indices(
self,
options=_pc().SortOptions(sort_keys=sorting, **kwargs)
)
return self.take(indices)
def take(self, object indices):
"""
Select rows from a Table or RecordBatch.
See :func:`pyarrow.compute.take` for full usage.
Parameters
----------
indices : Array or array-like
The indices in the tabular object whose rows will be returned.
Returns
-------
Table or RecordBatch
A tabular object with the same schema, containing the taken rows.
Examples
--------
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.take([1,3])
pyarrow.Table
year: int64
n_legs: int64
animals: string
----
year: [[2022,2021]]
n_legs: [[4,100]]
animals: [["Horse","Centipede"]]
"""
self._assert_cpu()
return _pc().take(self, indices)
def filter(self, mask, object null_selection_behavior="drop"):
"""
Select rows from the table or record batch based on a boolean mask.
The Table can be filtered based on a mask, which will be passed to
:func:`pyarrow.compute.filter` to perform the filtering, or it can
be filtered through a boolean :class:`.Expression`
Parameters
----------
mask : Array or array-like or .Expression
The boolean mask or the :class:`.Expression` to filter the table with.
null_selection_behavior : str, default "drop"
How nulls in the mask should be handled, does nothing if
an :class:`.Expression` is used.
Returns
-------
filtered : Table or RecordBatch
A tabular object of the same schema, with only the rows selected
by applied filtering
Examples
--------
Using a Table (works similarly for RecordBatch):
>>> import pyarrow as pa
>>> table = pa.table({'year': [2020, 2022, 2019, 2021],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
Define an expression and select rows:
>>> import pyarrow.compute as pc
>>> expr = pc.field("year") <= 2020
>>> table.filter(expr)
pyarrow.Table
year: int64
n_legs: int64
animals: string
----
year: [[2020,2019]]
n_legs: [[2,5]]
animals: [["Flamingo","Brittle stars"]]
Define a mask and select rows:
>>> mask=[True, True, False, None]
>>> table.filter(mask)
pyarrow.Table
year: int64
n_legs: int64
animals: string
----
year: [[2020,2022]]
n_legs: [[2,4]]
animals: [["Flamingo","Horse"]]
>>> table.filter(mask, null_selection_behavior='emit_null')
pyarrow.Table
year: int64
n_legs: int64
animals: string
----
year: [[2020,2022,null]]
n_legs: [[2,4,null]]
animals: [["Flamingo","Horse",null]]
"""
self._assert_cpu()
if isinstance(mask, _pc().Expression):
return _pac()._filter_table(self, mask)
else:
return _pc().filter(self, mask, null_selection_behavior)
def to_pydict(self, *, maps_as_pydicts=None):
"""
Convert the Table or RecordBatch to a dict or OrderedDict.
Parameters
----------
maps_as_pydicts : str, optional, default `None`
Valid values are `None`, 'lossy', or 'strict'.
The default behavior (`None`), is to convert Arrow Map arrays to
Python association lists (list-of-tuples) in the same order as the
Arrow Map, as in [(key1, value1), (key2, value2), ...].
If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
If 'lossy', whenever duplicate keys are detected, a warning will be printed.
The last seen value of a duplicate key will be in the Python dictionary.
If 'strict', this instead results in an exception being raised when detected.
Returns
-------
dict
Examples
--------
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
>>> table = pa.Table.from_arrays([n_legs, animals], names=["n_legs", "animals"])
>>> table.to_pydict()
{'n_legs': [2, 2, 4, 4, 5, 100], 'animals': ['Flamingo', 'Parrot', ..., 'Centipede']}
"""
entries = []
for i in range(self.num_columns):
name = self.field(i).name
column = self[i].to_pylist(maps_as_pydicts=maps_as_pydicts)
entries.append((name, column))
return ordered_dict(entries)
def to_pylist(self, *, maps_as_pydicts=None):
"""
Convert the Table or RecordBatch to a list of rows / dictionaries.
Parameters
----------
maps_as_pydicts : str, optional, default `None`
Valid values are `None`, 'lossy', or 'strict'.
The default behavior (`None`), is to convert Arrow Map arrays to
Python association lists (list-of-tuples) in the same order as the
Arrow Map, as in [(key1, value1), (key2, value2), ...].
If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts.
If 'lossy', whenever duplicate keys are detected, a warning will be printed.
The last seen value of a duplicate key will be in the Python dictionary.
If 'strict', this instead results in an exception being raised when detected.
Returns
-------
list
Examples
--------
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> data = [[2, 4, 5, 100],
... ["Flamingo", "Horse", "Brittle stars", "Centipede"]]
>>> table = pa.table(data, names=["n_legs", "animals"])
>>> table.to_pylist()
[{'n_legs': 2, 'animals': 'Flamingo'}, {'n_legs': 4, 'animals': 'Horse'}, ...
"""
pydict = self.to_pydict(maps_as_pydicts=maps_as_pydicts)
names = self.schema.names
pylist = [{column: pydict[column][row] for column in names}
for row in range(self.num_rows)]
return pylist
def to_string(self, *, show_metadata=False, preview_cols=0):
"""
Return human-readable string representation of Table or RecordBatch.
Parameters
----------
show_metadata : bool, default False
Display Field-level and Schema-level KeyValueMetadata.
preview_cols : int, default 0
Display values of the columns for the first N columns.
Returns
-------
str
"""
# Use less verbose schema output.
schema_as_string = self.schema.to_string(
show_field_metadata=show_metadata,
show_schema_metadata=show_metadata
)
title = f'pyarrow.{type(self).__name__}\n{schema_as_string}'
pieces = [title]
if preview_cols:
pieces.append('----')
for i in range(min(self.num_columns, preview_cols)):
pieces.append(
f'{self.field(i).name}: {self.column(i).to_string(indent=0, skip_new_lines=True)}')
if preview_cols < self.num_columns:
pieces.append('...')
return '\n'.join(pieces)
def remove_column(self, int i):
# implemented in RecordBatch/Table subclasses
raise NotImplementedError
def drop_columns(self, columns):
"""
Drop one or more columns and return a new Table or RecordBatch.
Parameters
----------
columns : str or list[str]
Field name(s) referencing existing column(s).
Raises
------
KeyError
If any of the passed column names do not exist.
Returns
-------
Table or RecordBatch
A tabular object without the column(s).
Examples
--------
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
Drop one column:
>>> table.drop_columns("animals")
pyarrow.Table
n_legs: int64
----
n_legs: [[2,4,5,100]]
Drop one or more columns:
>>> table.drop_columns(["n_legs", "animals"])
pyarrow.Table
...
----
"""
if isinstance(columns, str):
columns = [columns]
indices = []
for col in columns:
idx = self.schema.get_field_index(col)
if idx == -1:
raise KeyError(f"Column {col!r} not found")
indices.append(idx)
indices.sort()
indices.reverse()
res = self
for idx in indices:
res = res.remove_column(idx)
return res
def add_column(self, int i, field_, column):
# implemented in RecordBatch/Table subclasses
raise NotImplementedError
def append_column(self, field_, column):
"""
Append column at end of columns.
Parameters
----------
field_ : str or Field
If a string is passed then the type is deduced from the column
data.
column : Array or value coercible to array
Column data.
Returns
-------
Table or RecordBatch
New table or record batch with the passed column added.
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
Append column at the end:
>>> year = [2021, 2022, 2019, 2021]
>>> table.append_column('year', [year])
pyarrow.Table
n_legs: int64
animals: string
year: int64
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
year: [[2021,2022,2019,2021]]
"""
return self.add_column(self.num_columns, field_, column)
cdef void _assert_cpu(self) except *:
return
cdef class RecordBatch(_Tabular):
"""
Batch of rows of columns of equal length
Warnings
--------
Do not call this class's constructor directly, use one of the
``RecordBatch.from_*`` functions instead.
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
>>> names = ["n_legs", "animals"]
Constructing a RecordBatch from arrays:
>>> pa.RecordBatch.from_arrays([n_legs, animals], names=names)
pyarrow.RecordBatch
n_legs: int64
animals: string
----
n_legs: [2,2,4,4,5,100]
animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
>>> pa.RecordBatch.from_arrays([n_legs, animals], names=names).to_pandas()
n_legs animals
0 2 Flamingo
1 2 Parrot
2 4 Dog
3 4 Horse
4 5 Brittle stars
5 100 Centipede
Constructing a RecordBatch from pandas DataFrame:
>>> import pandas as pd
>>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022],
... 'month': [3, 5, 7, 9],
... 'day': [1, 5, 9, 13],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> pa.RecordBatch.from_pandas(df)
pyarrow.RecordBatch
year: int64
month: int64
day: int64
n_legs: int64
animals: string
----
year: [2020,2022,2021,2022]
month: [3,5,7,9]
day: [1,5,9,13]
n_legs: [2,4,5,100]
animals: ["Flamingo","Horse","Brittle stars","Centipede"]
>>> pa.RecordBatch.from_pandas(df).to_pandas()
year month day n_legs animals
0 2020 3 1 2 Flamingo
1 2022 5 5 4 Horse
2 2021 7 9 5 Brittle stars
3 2022 9 13 100 Centipede
Constructing a RecordBatch from pylist:
>>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'},
... {'n_legs': 4, 'animals': 'Dog'}]
>>> pa.RecordBatch.from_pylist(pylist).to_pandas()
n_legs animals
0 2 Flamingo
1 4 Dog
You can also construct a RecordBatch using :func:`pyarrow.record_batch`:
>>> pa.record_batch([n_legs, animals], names=names).to_pandas()
n_legs animals
0 2 Flamingo
1 2 Parrot
2 4 Dog
3 4 Horse
4 5 Brittle stars
5 100 Centipede
>>> pa.record_batch(df)
pyarrow.RecordBatch
year: int64
month: int64
day: int64
n_legs: int64
animals: string
----
year: [2020,2022,2021,2022]
month: [3,5,7,9]
day: [1,5,9,13]
n_legs: [2,4,5,100]
animals: ["Flamingo","Horse","Brittle stars","Centipede"]
"""
def __cinit__(self):
self.batch = NULL
self._schema = None
cdef void init(self, const shared_ptr[CRecordBatch]& batch):
self.sp_batch = batch
self.batch = batch.get()
def _is_initialized(self):
return self.batch != NULL
def __reduce__(self):
self._assert_cpu()
return _reconstruct_record_batch, (self.columns, self.schema)
def validate(self, *, full=False):
"""
Perform validation checks. An exception is raised if validation fails.
By default only cheap validation checks are run. Pass `full=True`
for thorough validation checks (potentially O(n)).
Parameters
----------
full : bool, default False
If True, run expensive checks, otherwise cheap checks only.
Raises
------
ArrowInvalid
"""
if full:
self._assert_cpu()
with nogil:
check_status(self.batch.ValidateFull())
else:
with nogil:
check_status(self.batch.Validate())
def replace_schema_metadata(self, metadata=None):
"""
Create shallow copy of record batch by replacing schema
key-value metadata with the indicated new metadata (which may be None,
which deletes any existing metadata
Parameters
----------
metadata : dict, default None
Returns
-------
shallow_copy : RecordBatch
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
Constructing a RecordBatch with schema and metadata:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.int64())],
... metadata={"n_legs": "Number of legs per animal"})
>>> batch = pa.RecordBatch.from_arrays([n_legs], schema=my_schema)
>>> batch.schema
n_legs: int64
-- schema metadata --
n_legs: 'Number of legs per animal'
Shallow copy of a RecordBatch with deleted schema metadata:
>>> batch.replace_schema_metadata().schema
n_legs: int64
"""
cdef:
shared_ptr[const CKeyValueMetadata] c_meta
shared_ptr[CRecordBatch] c_batch
metadata = ensure_metadata(metadata, allow_none=True)
c_meta = pyarrow_unwrap_metadata(metadata)
with nogil:
c_batch = self.batch.ReplaceSchemaMetadata(c_meta)
return pyarrow_wrap_batch(c_batch)
@property
def num_columns(self):
"""
Number of columns
Returns
-------
int
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
>>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
... names=["n_legs", "animals"])
>>> batch.num_columns
2
"""
return self.batch.num_columns()
@property
def num_rows(self):
"""
Number of rows
Due to the definition of a RecordBatch, all columns have the same
number of rows.
Returns
-------
int
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
>>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
... names=["n_legs", "animals"])
>>> batch.num_rows
6
"""
return self.batch.num_rows()
@property
def schema(self):
"""
Schema of the RecordBatch and its columns
Returns
-------
pyarrow.Schema
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
>>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
... names=["n_legs", "animals"])
>>> batch.schema
n_legs: int64
animals: string
"""
if self._schema is None:
self._schema = pyarrow_wrap_schema(self.batch.schema())
return self._schema
def _column(self, int i):
"""
Select single column from record batch by its numeric index.
Parameters
----------
i : int
The index of the column to retrieve.
Returns
-------
column : pyarrow.Array
"""
cdef int index = <int> _normalize_index(i, self.num_columns)
cdef Array result = pyarrow_wrap_array(self.batch.column(index))
result._name = self.schema[index].name
return result
@property
def nbytes(self):
"""
Total number of bytes consumed by the elements of the record batch.
In other words, the sum of bytes from all buffer ranges referenced.
Unlike `get_total_buffer_size` this method will account for array
offsets.
If buffers are shared between arrays then the shared
portion will only be counted multiple times.
The dictionary of dictionary arrays will always be counted in their
entirety even if the array only references a portion of the dictionary.
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
>>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
... names=["n_legs", "animals"])
>>> batch.nbytes
116
"""
self._assert_cpu()
cdef:
CResult[int64_t] c_res_buffer
with nogil:
c_res_buffer = ReferencedBufferSize(deref(self.batch))
size = GetResultValue(c_res_buffer)
return size
def get_total_buffer_size(self):
"""
The sum of bytes in each buffer referenced by the record batch
An array may only reference a portion of a buffer.
This method will overestimate in this case and return the
byte size of the entire buffer.
If a buffer is referenced multiple times then it will
only be counted once.
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
>>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
... names=["n_legs", "animals"])
>>> batch.get_total_buffer_size()
120
"""
self._assert_cpu()
cdef:
int64_t total_buffer_size
total_buffer_size = TotalBufferSize(deref(self.batch))
return total_buffer_size
def __sizeof__(self):
return super(RecordBatch, self).__sizeof__() + self.nbytes
def add_column(self, int i, field_, column):
"""
Add column to RecordBatch at position i.
A new record batch is returned with the column added, the original record batch
object is left unchanged.
Parameters
----------
i : int
Index to place the column at.
field_ : str or Field
If a string is passed then the type is deduced from the column
data.
column : Array or value coercible to array
Column data.
Returns
-------
RecordBatch
New record batch with the passed column added.
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> batch = pa.RecordBatch.from_pandas(df)
Add column:
>>> year = [2021, 2022, 2019, 2021]
>>> batch.add_column(0,"year", year)
pyarrow.RecordBatch
year: int64
n_legs: int64
animals: string
----
year: [2021,2022,2019,2021]
n_legs: [2,4,5,100]
animals: ["Flamingo","Horse","Brittle stars","Centipede"]
Original record batch is left unchanged:
>>> batch
pyarrow.RecordBatch
n_legs: int64
animals: string
----
n_legs: [2,4,5,100]
animals: ["Flamingo","Horse","Brittle stars","Centipede"]
"""
cdef:
shared_ptr[CRecordBatch] c_batch
Field c_field
Array c_arr
CDeviceAllocationType device_type = self.sp_batch.get().device_type()
if isinstance(column, Array):
c_arr = column
else:
c_arr = array(column)
if device_type != c_arr.sp_array.get().device_type():
raise TypeError("The column must be allocated on the same "
"device as the RecordBatch. Got column on "
f"device {c_arr.device_type!r}, but expected "
f"{self.device_type!r}.")
if isinstance(field_, Field):
c_field = field_
else:
c_field = field(field_, c_arr.type)
with nogil:
c_batch = GetResultValue(self.batch.AddColumn(
i, c_field.sp_field, c_arr.sp_array))
return pyarrow_wrap_batch(c_batch)
def remove_column(self, int i):
"""
Create new RecordBatch with the indicated column removed.
Parameters
----------
i : int
Index of column to remove.
Returns
-------
Table
New record batch without the column.
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> batch = pa.RecordBatch.from_pandas(df)
>>> batch.remove_column(1)
pyarrow.RecordBatch
n_legs: int64
----
n_legs: [2,4,5,100]
"""
cdef shared_ptr[CRecordBatch] c_batch
with nogil:
c_batch = GetResultValue(self.batch.RemoveColumn(i))
return pyarrow_wrap_batch(c_batch)
def set_column(self, int i, field_, column):
"""
Replace column in RecordBatch at position.
Parameters
----------
i : int
Index to place the column at.
field_ : str or Field
If a string is passed then the type is deduced from the column
data.
column : Array or value coercible to array
Column data.
Returns
-------
RecordBatch
New record batch with the passed column set.
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> batch = pa.RecordBatch.from_pandas(df)
Replace a column:
>>> year = [2021, 2022, 2019, 2021]
>>> batch.set_column(1,'year', year)
pyarrow.RecordBatch
n_legs: int64
year: int64
----
n_legs: [2,4,5,100]
year: [2021,2022,2019,2021]
"""
cdef:
shared_ptr[CRecordBatch] c_batch
Field c_field
Array c_arr
CDeviceAllocationType device_type = self.sp_batch.get().device_type()
if isinstance(column, Array):
c_arr = column
else:
c_arr = array(column)
if device_type != c_arr.sp_array.get().device_type():
raise TypeError("The column must be allocated on the same "
"device as the RecordBatch. Got column on "
f"device {c_arr.device_type!r}, but expected "
f"{self.device_type!r}.")
if isinstance(field_, Field):
c_field = field_
else:
c_field = field(field_, c_arr.type)
with nogil:
c_batch = GetResultValue(self.batch.SetColumn(
i, c_field.sp_field, c_arr.sp_array))
return pyarrow_wrap_batch(c_batch)
def rename_columns(self, names):
"""
Create new record batch with columns renamed to provided names.
Parameters
----------
names : list[str] or dict[str, str]
List of new column names or mapping of old column names to new column names.
If a mapping of old to new column names is passed, then all columns which are
found to match a provided old column name will be renamed to the new column name.
If any column names are not found in the mapping, a KeyError will be raised.
Raises
------
KeyError
If any of the column names passed in the names mapping do not exist.
Returns
-------
RecordBatch
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> batch = pa.RecordBatch.from_pandas(df)
>>> new_names = ["n", "name"]
>>> batch.rename_columns(new_names)
pyarrow.RecordBatch
n: int64
name: string
----
n: [2,4,5,100]
name: ["Flamingo","Horse","Brittle stars","Centipede"]
>>> new_names = {"n_legs": "n", "animals": "name"}
>>> batch.rename_columns(new_names)
pyarrow.RecordBatch
n: int64
name: string
----
n: [2,4,5,100]
name: ["Flamingo","Horse","Brittle stars","Centipede"]
"""
cdef:
shared_ptr[CRecordBatch] c_batch
vector[c_string] c_names
if isinstance(names, (list, tuple)):
for name in names:
c_names.push_back(tobytes(name))
elif isinstance(names, dict):
idx_to_new_name = {}
for name, new_name in names.items():
indices = self.schema.get_all_field_indices(name)
if not indices:
raise KeyError(f"Column {name!r} not found")
for index in indices:
idx_to_new_name[index] = new_name
for i in range(self.num_columns):
new_name = idx_to_new_name.get(i, self.column_names[i])
c_names.push_back(tobytes(new_name))
else:
raise TypeError(f"names must be a list or dict not {type(names)!r}")
with nogil:
c_batch = GetResultValue(self.batch.RenameColumns(move(c_names)))
return pyarrow_wrap_batch(c_batch)
def serialize(self, memory_pool=None):
"""
Write RecordBatch to Buffer as encapsulated IPC message, which does not
include a Schema.
To reconstruct a RecordBatch from the encapsulated IPC message Buffer
returned by this function, a Schema must be passed separately. See
Examples.
Parameters
----------
memory_pool : MemoryPool, default None
Uses default memory pool if not specified
Returns
-------
serialized : Buffer
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
>>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
... names=["n_legs", "animals"])
>>> buf = batch.serialize()
>>> buf
<pyarrow.Buffer address=0x... size=... is_cpu=True is_mutable=True>
Reconstruct RecordBatch from IPC message Buffer and original Schema
>>> pa.ipc.read_record_batch(buf, batch.schema)
pyarrow.RecordBatch
n_legs: int64
animals: string
----
n_legs: [2,2,4,4,5,100]
animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
"""
self._assert_cpu()
cdef shared_ptr[CBuffer] buffer
cdef CIpcWriteOptions options = CIpcWriteOptions.Defaults()
options.memory_pool = maybe_unbox_memory_pool(memory_pool)
with nogil:
buffer = GetResultValue(
SerializeRecordBatch(deref(self.batch), options))
return pyarrow_wrap_buffer(buffer)
def slice(self, offset=0, length=None):
"""
Compute zero-copy slice of this RecordBatch
Parameters
----------
offset : int, default 0
Offset from start of record batch to slice
length : int, default None
Length of slice (default is until end of batch starting from
offset)
Returns
-------
sliced : RecordBatch
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
>>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
... names=["n_legs", "animals"])
>>> batch.to_pandas()
n_legs animals
0 2 Flamingo
1 2 Parrot
2 4 Dog
3 4 Horse
4 5 Brittle stars
5 100 Centipede
>>> batch.slice(offset=3).to_pandas()
n_legs animals
0 4 Horse
1 5 Brittle stars
2 100 Centipede
>>> batch.slice(length=2).to_pandas()
n_legs animals
0 2 Flamingo
1 2 Parrot
>>> batch.slice(offset=3, length=1).to_pandas()
n_legs animals
0 4 Horse
"""
cdef shared_ptr[CRecordBatch] result
if offset < 0:
raise IndexError('Offset must be non-negative')
offset = min(len(self), offset)
if length is None:
result = self.batch.Slice(offset)
else:
result = self.batch.Slice(offset, length)
return pyarrow_wrap_batch(result)
def equals(self, object other, bint check_metadata=False):
"""
Check if contents of two record batches are equal.
Parameters
----------
other : pyarrow.RecordBatch
RecordBatch to compare against.
check_metadata : bool, default False
Whether schema metadata equality should be checked as well.
Returns
-------
are_equal : bool
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
>>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
... names=["n_legs", "animals"])
>>> batch_0 = pa.record_batch([])
>>> batch_1 = pa.RecordBatch.from_arrays([n_legs, animals],
... names=["n_legs", "animals"],
... metadata={"n_legs": "Number of legs per animal"})
>>> batch.equals(batch)
True
>>> batch.equals(batch_0)
False
>>> batch.equals(batch_1)
True
>>> batch.equals(batch_1, check_metadata=True)
False
"""
self._assert_cpu()
cdef:
CRecordBatch* this_batch = self.batch
shared_ptr[CRecordBatch] other_batch = pyarrow_unwrap_batch(other)
c_bool result
if not other_batch:
return False
with nogil:
result = this_batch.Equals(deref(other_batch), check_metadata)
return result
def select(self, object columns):
"""
Select columns of the RecordBatch.
Returns a new RecordBatch with the specified columns, and metadata
preserved.
Parameters
----------
columns : list-like
The column names or integer indices to select.
Returns
-------
RecordBatch
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
>>> batch = pa.record_batch([n_legs, animals],
... names=["n_legs", "animals"])
Select columns my indices:
>>> batch.select([1])
pyarrow.RecordBatch
animals: string
----
animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
Select columns by names:
>>> batch.select(["n_legs"])
pyarrow.RecordBatch
n_legs: int64
----
n_legs: [2,2,4,4,5,100]
"""
cdef:
shared_ptr[CRecordBatch] c_batch
vector[int] c_indices
for idx in columns:
idx = self._ensure_integer_index(idx)
idx = _normalize_index(idx, self.num_columns)
c_indices.push_back(<int> idx)
with nogil:
c_batch = GetResultValue(self.batch.SelectColumns(move(c_indices)))
return pyarrow_wrap_batch(c_batch)
def cast(self, Schema target_schema, safe=None, options=None):
"""
Cast record batch values to another schema.
Parameters
----------
target_schema : Schema
Schema to cast to, the names and order of fields must match.
safe : bool, default True
Check for overflows or other unsafe conversions.
options : CastOptions, default None
Additional checks pass by CastOptions
Returns
-------
RecordBatch
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> batch = pa.RecordBatch.from_pandas(df)
>>> batch.schema
n_legs: int64
animals: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ...
Define new schema and cast batch values:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.duration('s')),
... pa.field('animals', pa.string())]
... )
>>> batch.cast(target_schema=my_schema)
pyarrow.RecordBatch
n_legs: duration[s]
animals: string
----
n_legs: [2,4,5,100]
animals: ["Flamingo","Horse","Brittle stars","Centipede"]
"""
cdef:
Array column, casted
Field field
list newcols = []
if self.schema.names != target_schema.names:
raise ValueError(f"Target schema's field names are not matching "
f"the record batch's field names: {self.schema.names!r}, {target_schema.names!r}")
for column, field in zip(self.itercolumns(), target_schema):
if not field.nullable and column.null_count > 0:
raise ValueError(
f"Casting field {field.name!r} with null values to non-nullable")
casted = column.cast(field.type, safe=safe, options=options)
newcols.append(casted)
return RecordBatch.from_arrays(newcols, schema=target_schema)
def _to_pandas(self, options, **kwargs):
self._assert_cpu()
return Table.from_batches([self])._to_pandas(options, **kwargs)
@classmethod
def from_pandas(cls, df, Schema schema=None, preserve_index=None,
nthreads=None, columns=None):
"""
Convert pandas.DataFrame to an Arrow RecordBatch
Parameters
----------
df : pandas.DataFrame
schema : pyarrow.Schema, optional
The expected schema of the RecordBatch. This can be used to
indicate the type of columns if we cannot infer it automatically.
If passed, the output will have exactly this schema. Columns
specified in the schema that are not found in the DataFrame columns
or its index will raise an error. Additional columns or index
levels in the DataFrame which are not specified in the schema will
be ignored.
preserve_index : bool, optional
Whether to store the index as an additional column in the resulting
``RecordBatch``. The default of None will store the index as a
column, except for RangeIndex which is stored as metadata only. Use
``preserve_index=True`` to force it to be stored as a column.
nthreads : int, default None
If greater than 1, convert columns to Arrow in parallel using
indicated number of threads. By default, this follows
:func:`pyarrow.cpu_count` (may use up to system CPU count threads).
columns : list, optional
List of column to be converted. If None, use all columns.
Returns
-------
pyarrow.RecordBatch
Examples
--------
>>> import pandas as pd
>>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022],
... 'month': [3, 5, 7, 9],
... 'day': [1, 5, 9, 13],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
Convert pandas DataFrame to RecordBatch:
>>> import pyarrow as pa
>>> pa.RecordBatch.from_pandas(df)
pyarrow.RecordBatch
year: int64
month: int64
day: int64
n_legs: int64
animals: string
----
year: [2020,2022,2021,2022]
month: [3,5,7,9]
day: [1,5,9,13]
n_legs: [2,4,5,100]
animals: ["Flamingo","Horse","Brittle stars","Centipede"]
Convert pandas DataFrame to RecordBatch using schema:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.int64()),
... pa.field('animals', pa.string())],
... metadata={"n_legs": "Number of legs per animal"})
>>> pa.RecordBatch.from_pandas(df, schema=my_schema)
pyarrow.RecordBatch
n_legs: int64
animals: string
----
n_legs: [2,4,5,100]
animals: ["Flamingo","Horse","Brittle stars","Centipede"]
Convert pandas DataFrame to RecordBatch specifying columns:
>>> pa.RecordBatch.from_pandas(df, columns=["n_legs"])
pyarrow.RecordBatch
n_legs: int64
----
n_legs: [2,4,5,100]
"""
from pyarrow.pandas_compat import dataframe_to_arrays
arrays, schema, n_rows = dataframe_to_arrays(
df, schema, preserve_index, nthreads=nthreads, columns=columns
)
# If df is empty but row index is not, create empty RecordBatch with rows >0
cdef vector[shared_ptr[CArray]] c_arrays
if n_rows:
return pyarrow_wrap_batch(CRecordBatch.Make((<Schema> schema).sp_schema,
n_rows, c_arrays))
else:
return cls.from_arrays(arrays, schema=schema)
@staticmethod
def from_arrays(list arrays, names=None, schema=None, metadata=None):
"""
Construct a RecordBatch from multiple pyarrow.Arrays
Parameters
----------
arrays : list of pyarrow.Array
One for each field in RecordBatch
names : list of str, optional
Names for the batch fields. If not passed, schema must be passed
schema : Schema, default None
Schema for the created batch. If not passed, names must be passed
metadata : dict or Mapping, default None
Optional metadata for the schema (if inferred).
Returns
-------
pyarrow.RecordBatch
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
>>> names = ["n_legs", "animals"]
Construct a RecordBatch from pyarrow Arrays using names:
>>> pa.RecordBatch.from_arrays([n_legs, animals], names=names)
pyarrow.RecordBatch
n_legs: int64
animals: string
----
n_legs: [2,2,4,4,5,100]
animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
>>> pa.RecordBatch.from_arrays([n_legs, animals], names=names).to_pandas()
n_legs animals
0 2 Flamingo
1 2 Parrot
2 4 Dog
3 4 Horse
4 5 Brittle stars
5 100 Centipede
Construct a RecordBatch from pyarrow Arrays using schema:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.int64()),
... pa.field('animals', pa.string())],
... metadata={"n_legs": "Number of legs per animal"})
>>> pa.RecordBatch.from_arrays([n_legs, animals], schema=my_schema).to_pandas()
n_legs animals
0 2 Flamingo
1 2 Parrot
2 4 Dog
3 4 Horse
4 5 Brittle stars
5 100 Centipede
>>> pa.RecordBatch.from_arrays([n_legs, animals], schema=my_schema).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
"""
cdef:
Array arr
shared_ptr[CSchema] c_schema
vector[shared_ptr[CArray]] c_arrays
int64_t num_rows
if len(arrays) > 0:
num_rows = len(arrays[0])
else:
num_rows = 0
if isinstance(names, Schema):
import warnings
warnings.warn("Schema passed to names= option, please "
"pass schema= explicitly. "
"Will raise exception in future", FutureWarning)
schema = names
names = None
converted_arrays = _sanitize_arrays(arrays, names, schema, metadata,
&c_schema)
c_arrays.reserve(len(arrays))
for arr in converted_arrays:
if len(arr) != num_rows:
raise ValueError('Arrays were not all the same length: '
f'{len(arr)} vs {num_rows}')
c_arrays.push_back(arr.sp_array)
result = pyarrow_wrap_batch(CRecordBatch.Make(c_schema, num_rows,
c_arrays))
result.validate()
return result
@staticmethod
def from_struct_array(StructArray struct_array):
"""
Construct a RecordBatch from a StructArray.
Each field in the StructArray will become a column in the resulting
``RecordBatch``.
Parameters
----------
struct_array : StructArray
Array to construct the record batch from.
Returns
-------
pyarrow.RecordBatch
Examples
--------
>>> import pyarrow as pa
>>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'},
... {'year': 2022, 'n_legs': 4}])
>>> pa.RecordBatch.from_struct_array(struct).to_pandas()
animals n_legs year
0 Parrot 2 NaN
1 None 4 2022.0
"""
cdef:
shared_ptr[CRecordBatch] c_record_batch
if struct_array.sp_array.get().device_type() != CDeviceAllocationType_kCPU:
raise NotImplementedError("Implemented only for data on CPU device")
with nogil:
c_record_batch = GetResultValue(
CRecordBatch.FromStructArray(struct_array.sp_array))
return pyarrow_wrap_batch(c_record_batch)
def to_struct_array(self):
"""
Convert to a struct array.
"""
self._assert_cpu()
cdef:
shared_ptr[CRecordBatch] c_record_batch
shared_ptr[CArray] c_array
c_record_batch = pyarrow_unwrap_batch(self)
with nogil:
c_array = GetResultValue(
<CResult[shared_ptr[CArray]]>deref(c_record_batch).ToStructArray())
return pyarrow_wrap_array(c_array)
def to_tensor(self, c_bool null_to_nan=False, c_bool row_major=True, MemoryPool memory_pool=None):
"""
Convert to a :class:`~pyarrow.Tensor`.
RecordBatches that can be converted have fields of type signed or unsigned
integer or float, including all bit-widths.
``null_to_nan`` is ``False`` by default and this method will raise an error in case
any nulls are present. RecordBatches with nulls can be converted with ``null_to_nan``
set to ``True``. In this case null values are converted to ``NaN`` and integer type
arrays are promoted to the appropriate float type.
Parameters
----------
null_to_nan : bool, default False
Whether to write null values in the result as ``NaN``.
row_major : bool, default True
Whether resulting Tensor is row-major or column-major
memory_pool : MemoryPool, default None
For memory allocations, if required, otherwise use default pool
Examples
--------
>>> import pyarrow as pa
>>> batch = pa.record_batch(
... [
... pa.array([1, 2, 3, 4, None], type=pa.int32()),
... pa.array([10, 20, 30, 40, None], type=pa.float32()),
... ], names = ["a", "b"]
... )
>>> batch
pyarrow.RecordBatch
a: int32
b: float
----
a: [1,2,3,4,null]
b: [10,20,30,40,null]
Convert a RecordBatch to row-major Tensor with null values
written as ``NaN``s
>>> batch.to_tensor(null_to_nan=True)
<pyarrow.Tensor>
type: double
shape: (5, 2)
strides: (16, 8)
>>> batch.to_tensor(null_to_nan=True).to_numpy()
array([[ 1., 10.],
[ 2., 20.],
[ 3., 30.],
[ 4., 40.],
[nan, nan]])
Convert a RecordBatch to column-major Tensor
>>> batch.to_tensor(null_to_nan=True, row_major=False)
<pyarrow.Tensor>
type: double
shape: (5, 2)
strides: (8, 40)
>>> batch.to_tensor(null_to_nan=True, row_major=False).to_numpy()
array([[ 1., 10.],
[ 2., 20.],
[ 3., 30.],
[ 4., 40.],
[nan, nan]])
"""
self._assert_cpu()
cdef:
shared_ptr[CRecordBatch] c_record_batch
shared_ptr[CTensor] c_tensor
CMemoryPool* pool = maybe_unbox_memory_pool(memory_pool)
c_record_batch = pyarrow_unwrap_batch(self)
with nogil:
c_tensor = GetResultValue(
<CResult[shared_ptr[CTensor]]>deref(c_record_batch).ToTensor(null_to_nan,
row_major, pool))
return pyarrow_wrap_tensor(c_tensor)
def copy_to(self, destination):
"""
Copy the entire RecordBatch to destination device.
This copies each column of the record batch to create
a new record batch where all underlying buffers for the columns have
been copied to the destination MemoryManager.
Parameters
----------
destination : pyarrow.MemoryManager or pyarrow.Device
The destination device to copy the array to.
Returns
-------
RecordBatch
"""
cdef:
shared_ptr[CRecordBatch] c_batch
shared_ptr[CMemoryManager] c_memory_manager
if isinstance(destination, Device):
c_memory_manager = (<Device>destination).unwrap().get().default_memory_manager()
elif isinstance(destination, MemoryManager):
c_memory_manager = (<MemoryManager>destination).unwrap()
else:
raise TypeError(
"Argument 'destination' has incorrect type (expected a "
f"pyarrow Device or MemoryManager, got {type(destination)})"
)
with nogil:
c_batch = GetResultValue(self.batch.CopyTo(c_memory_manager))
return pyarrow_wrap_batch(c_batch)
def _export_to_c(self, out_ptr, out_schema_ptr=0):
"""
Export to a C ArrowArray struct, given its pointer.
If a C ArrowSchema struct pointer is also given, the record batch
schema is exported to it at the same time.
Parameters
----------
out_ptr: int
The raw pointer to a C ArrowArray struct.
out_schema_ptr: int (optional)
The raw pointer to a C ArrowSchema struct.
Be careful: if you don't pass the ArrowArray struct to a consumer,
array memory will leak. This is a low-level function intended for
expert users.
"""
cdef:
void* c_ptr = _as_c_pointer(out_ptr)
void* c_schema_ptr = _as_c_pointer(out_schema_ptr,
allow_null=True)
with nogil:
check_status(ExportRecordBatch(deref(self.sp_batch),
<ArrowArray*> c_ptr,
<ArrowSchema*> c_schema_ptr))
@staticmethod
def _import_from_c(in_ptr, schema):
"""
Import RecordBatch from a C ArrowArray struct, given its pointer
and the imported schema.
Parameters
----------
in_ptr: int
The raw pointer to a C ArrowArray struct.
type: Schema or int
Either a Schema object, or the raw pointer to a C ArrowSchema
struct.
This is a low-level function intended for expert users.
"""
cdef:
void* c_ptr = _as_c_pointer(in_ptr)
void* c_schema_ptr
shared_ptr[CRecordBatch] c_batch
c_schema = pyarrow_unwrap_schema(schema)
if c_schema == nullptr:
# Not a Schema object, perhaps a raw ArrowSchema pointer
c_schema_ptr = _as_c_pointer(schema, allow_null=True)
with nogil:
c_batch = GetResultValue(ImportRecordBatch(
<ArrowArray*> c_ptr, <ArrowSchema*> c_schema_ptr))
else:
with nogil:
c_batch = GetResultValue(ImportRecordBatch(
<ArrowArray*> c_ptr, c_schema))
return pyarrow_wrap_batch(c_batch)
def __arrow_c_array__(self, requested_schema=None):
"""
Get a pair of PyCapsules containing a C ArrowArray representation of the object.
Parameters
----------
requested_schema : PyCapsule | None
A PyCapsule containing a C ArrowSchema representation of a requested
schema. PyArrow will attempt to cast the batch to this schema.
If None, the batch will be returned as-is, with a schema matching the
one returned by :meth:`__arrow_c_schema__()`.
Returns
-------
Tuple[PyCapsule, PyCapsule]
A pair of PyCapsules containing a C ArrowSchema and ArrowArray,
respectively.
"""
self._assert_cpu()
cdef:
ArrowArray* c_array
ArrowSchema* c_schema
if requested_schema is not None:
target_schema = Schema._import_from_c_capsule(requested_schema)
if target_schema != self.schema:
try:
casted_batch = self.cast(target_schema, safe=True)
inner_batch = pyarrow_unwrap_batch(casted_batch)
except ArrowInvalid as e:
raise ValueError(
f"Could not cast {self.schema} to requested schema {target_schema}: {e}"
)
else:
inner_batch = self.sp_batch
else:
inner_batch = self.sp_batch
schema_capsule = alloc_c_schema(&c_schema)
array_capsule = alloc_c_array(&c_array)
with nogil:
check_status(ExportRecordBatch(deref(inner_batch), c_array, c_schema))
return schema_capsule, array_capsule
def __arrow_c_stream__(self, requested_schema=None):
"""
Export the batch as an Arrow C stream PyCapsule.
Parameters
----------
requested_schema : PyCapsule, default None
The schema to which the stream should be casted, passed as a
PyCapsule containing a C ArrowSchema representation of the
requested schema.
Currently, this is not supported and will raise a
NotImplementedError if the schema doesn't match the current schema.
Returns
-------
PyCapsule
"""
self._assert_cpu()
return Table.from_batches([self]).__arrow_c_stream__(requested_schema)
@staticmethod
def _import_from_c_capsule(schema_capsule, array_capsule):
"""
Import RecordBatch from a pair of PyCapsules containing a C ArrowSchema
and ArrowArray, respectively.
Parameters
----------
schema_capsule : PyCapsule
A PyCapsule containing a C ArrowSchema representation of the schema.
array_capsule : PyCapsule
A PyCapsule containing a C ArrowArray representation of the array.
Returns
-------
pyarrow.RecordBatch
"""
cdef:
ArrowSchema* c_schema
ArrowArray* c_array
shared_ptr[CRecordBatch] c_batch
c_schema = <ArrowSchema*> PyCapsule_GetPointer(schema_capsule, 'arrow_schema')
c_array = <ArrowArray*> PyCapsule_GetPointer(array_capsule, 'arrow_array')
with nogil:
c_batch = GetResultValue(ImportRecordBatch(c_array, c_schema))
return pyarrow_wrap_batch(c_batch)
def _export_to_c_device(self, out_ptr, out_schema_ptr=0):
"""
Export to a C ArrowDeviceArray struct, given its pointer.
If a C ArrowSchema struct pointer is also given, the record batch
schema is exported to it at the same time.
Parameters
----------
out_ptr: int
The raw pointer to a C ArrowDeviceArray struct.
out_schema_ptr: int (optional)
The raw pointer to a C ArrowSchema struct.
Be careful: if you don't pass the ArrowDeviceArray struct to a consumer,
array memory will leak. This is a low-level function intended for
expert users.
"""
cdef:
void* c_ptr = _as_c_pointer(out_ptr)
void* c_schema_ptr = _as_c_pointer(out_schema_ptr,
allow_null=True)
with nogil:
check_status(ExportDeviceRecordBatch(
deref(self.sp_batch), <shared_ptr[CSyncEvent]>NULL,
<ArrowDeviceArray*> c_ptr, <ArrowSchema*> c_schema_ptr)
)
@staticmethod
def _import_from_c_device(in_ptr, schema):
"""
Import RecordBatch from a C ArrowDeviceArray struct, given its pointer
and the imported schema.
Parameters
----------
in_ptr: int
The raw pointer to a C ArrowDeviceArray struct.
type: Schema or int
Either a Schema object, or the raw pointer to a C ArrowSchema
struct.
This is a low-level function intended for expert users.
"""
cdef:
ArrowDeviceArray* c_device_array = <ArrowDeviceArray*>_as_c_pointer(in_ptr)
void* c_schema_ptr
shared_ptr[CRecordBatch] c_batch
if c_device_array.device_type == ARROW_DEVICE_CUDA:
_ensure_cuda_loaded()
c_schema = pyarrow_unwrap_schema(schema)
if c_schema == nullptr:
# Not a Schema object, perhaps a raw ArrowSchema pointer
c_schema_ptr = _as_c_pointer(schema, allow_null=True)
with nogil:
c_batch = GetResultValue(ImportDeviceRecordBatch(
c_device_array, <ArrowSchema*> c_schema_ptr))
else:
with nogil:
c_batch = GetResultValue(ImportDeviceRecordBatch(
c_device_array, c_schema))
return pyarrow_wrap_batch(c_batch)
def __arrow_c_device_array__(self, requested_schema=None, **kwargs):
"""
Get a pair of PyCapsules containing a C ArrowDeviceArray representation
of the object.
Parameters
----------
requested_schema : PyCapsule | None
A PyCapsule containing a C ArrowSchema representation of a requested
schema. PyArrow will attempt to cast the batch to this data type.
If None, the batch will be returned as-is, with a type matching the
one returned by :meth:`__arrow_c_schema__()`.
kwargs
Currently no additional keyword arguments are supported, but
this method will accept any keyword with a value of ``None``
for compatibility with future keywords.
Returns
-------
Tuple[PyCapsule, PyCapsule]
A pair of PyCapsules containing a C ArrowSchema and ArrowDeviceArray,
respectively.
"""
cdef:
ArrowDeviceArray* c_array
ArrowSchema* c_schema
shared_ptr[CRecordBatch] inner_batch
non_default_kwargs = [
name for name, value in kwargs.items() if value is not None
]
if non_default_kwargs:
raise NotImplementedError(
f"Received unsupported keyword argument(s): {non_default_kwargs}"
)
if requested_schema is not None:
target_schema = Schema._import_from_c_capsule(requested_schema)
if target_schema != self.schema:
if not self.is_cpu:
raise NotImplementedError(
"Casting to a requested schema is only supported for CPU data"
)
try:
casted_batch = self.cast(target_schema, safe=True)
inner_batch = pyarrow_unwrap_batch(casted_batch)
except ArrowInvalid as e:
raise ValueError(
f"Could not cast {self.schema} to requested schema {target_schema}: {e}"
)
else:
inner_batch = self.sp_batch
else:
inner_batch = self.sp_batch
schema_capsule = alloc_c_schema(&c_schema)
array_capsule = alloc_c_device_array(&c_array)
with nogil:
check_status(ExportDeviceRecordBatch(
deref(inner_batch), <shared_ptr[CSyncEvent]>NULL, c_array, c_schema))
return schema_capsule, array_capsule
@staticmethod
def _import_from_c_device_capsule(schema_capsule, array_capsule):
"""
Import RecordBatch from a pair of PyCapsules containing a
C ArrowSchema and ArrowDeviceArray, respectively.
Parameters
----------
schema_capsule : PyCapsule
A PyCapsule containing a C ArrowSchema representation of the schema.
array_capsule : PyCapsule
A PyCapsule containing a C ArrowDeviceArray representation of the array.
Returns
-------
pyarrow.RecordBatch
"""
cdef:
ArrowSchema* c_schema
ArrowDeviceArray* c_array
shared_ptr[CRecordBatch] batch
c_schema = <ArrowSchema*> PyCapsule_GetPointer(schema_capsule, 'arrow_schema')
c_array = <ArrowDeviceArray*> PyCapsule_GetPointer(
array_capsule, 'arrow_device_array'
)
with nogil:
batch = GetResultValue(ImportDeviceRecordBatch(c_array, c_schema))
return pyarrow_wrap_batch(batch)
@property
def device_type(self):
"""
The device type where the arrays in the RecordBatch reside.
Returns
-------
DeviceAllocationType
"""
return _wrap_device_allocation_type(self.sp_batch.get().device_type())
@property
def is_cpu(self):
"""
Whether the RecordBatch's arrays are CPU-accessible.
"""
return self.device_type == DeviceAllocationType.CPU
cdef void _assert_cpu(self) except *:
if self.sp_batch.get().device_type() != CDeviceAllocationType_kCPU:
raise NotImplementedError("Implemented only for data on CPU device")
def _reconstruct_record_batch(columns, schema):
"""
Internal: reconstruct RecordBatch from pickled components.
"""
return RecordBatch.from_arrays(columns, schema=schema)
def table_to_blocks(options, Table table, categories, extension_columns):
cdef:
PyObject* result_obj
shared_ptr[CTable] c_table
CMemoryPool* pool
PandasOptions c_options = _convert_pandas_options(options)
if categories is not None:
c_options.categorical_columns = {tobytes(cat) for cat in categories}
if extension_columns is not None:
c_options.extension_columns = {tobytes(col)
for col in extension_columns}
if pandas_api.is_v1():
# ARROW-3789: Coerce date/timestamp types to datetime64[ns]
c_options.coerce_temporal_nanoseconds = True
if c_options.self_destruct:
# Move the shared_ptr, table is now unsafe to use further
c_table = move(table.sp_table)
table.table = NULL
else:
c_table = table.sp_table
with nogil:
check_status(
libarrow_python.ConvertTableToPandas(c_options, move(c_table),
&result_obj)
)
return PyObject_to_object(result_obj)
cdef class Table(_Tabular):
"""
A collection of top-level named, equal length Arrow arrays.
Warnings
--------
Do not call this class's constructor directly, use one of the ``from_*``
methods instead.
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
>>> names = ["n_legs", "animals"]
Construct a Table from arrays:
>>> pa.Table.from_arrays([n_legs, animals], names=names)
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
Construct a Table from a RecordBatch:
>>> batch = pa.record_batch([n_legs, animals], names=names)
>>> pa.Table.from_batches([batch])
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
Construct a Table from pandas DataFrame:
>>> import pandas as pd
>>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> pa.Table.from_pandas(df)
pyarrow.Table
year: int64
n_legs: int64
animals: string
----
year: [[2020,2022,2019,2021]]
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
Construct a Table from a dictionary of arrays:
>>> pydict = {'n_legs': n_legs, 'animals': animals}
>>> pa.Table.from_pydict(pydict)
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
>>> pa.Table.from_pydict(pydict).schema
n_legs: int64
animals: string
Construct a Table from a dictionary of arrays with metadata:
>>> my_metadata={"n_legs": "Number of legs per animal"}
>>> pa.Table.from_pydict(pydict, metadata=my_metadata).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
Construct a Table from a list of rows:
>>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'}, {'year': 2021, 'animals': 'Centipede'}]
>>> pa.Table.from_pylist(pylist)
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,null]]
animals: [["Flamingo","Centipede"]]
Construct a Table from a list of rows with pyarrow schema:
>>> my_schema = pa.schema([
... pa.field('year', pa.int64()),
... pa.field('n_legs', pa.int64()),
... pa.field('animals', pa.string())],
... metadata={"year": "Year of entry"})
>>> pa.Table.from_pylist(pylist, schema=my_schema).schema
year: int64
n_legs: int64
animals: string
-- schema metadata --
year: 'Year of entry'
Construct a Table with :func:`pyarrow.table`:
>>> pa.table([n_legs, animals], names=names)
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
"""
def __cinit__(self):
self.table = NULL
self._init_is_cpu = False
cdef void init(self, const shared_ptr[CTable]& table):
self.sp_table = table
self.table = table.get()
def _is_initialized(self):
return self.table != NULL
def validate(self, *, full=False):
"""
Perform validation checks. An exception is raised if validation fails.
By default only cheap validation checks are run. Pass `full=True`
for thorough validation checks (potentially O(n)).
Parameters
----------
full : bool, default False
If True, run expensive checks, otherwise cheap checks only.
Raises
------
ArrowInvalid
"""
if full:
self._assert_cpu()
with nogil:
check_status(self.table.ValidateFull())
else:
with nogil:
check_status(self.table.Validate())
def __reduce__(self):
# Reduce the columns as ChunkedArrays to avoid serializing schema
# data twice
self._assert_cpu()
columns = [col for col in self.columns]
return _reconstruct_table, (columns, self.schema)
def slice(self, offset=0, length=None):
"""
Compute zero-copy slice of this Table.
Parameters
----------
offset : int, default 0
Offset from start of table to slice.
length : int, default None
Length of slice (default is until end of table starting from
offset).
Returns
-------
Table
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.slice(length=3)
pyarrow.Table
year: int64
n_legs: int64
animals: string
----
year: [[2020,2022,2019]]
n_legs: [[2,4,5]]
animals: [["Flamingo","Horse","Brittle stars"]]
>>> table.slice(offset=2)
pyarrow.Table
year: int64
n_legs: int64
animals: string
----
year: [[2019,2021]]
n_legs: [[5,100]]
animals: [["Brittle stars","Centipede"]]
>>> table.slice(offset=2, length=1)
pyarrow.Table
year: int64
n_legs: int64
animals: string
----
year: [[2019]]
n_legs: [[5]]
animals: [["Brittle stars"]]
"""
cdef shared_ptr[CTable] result
if offset < 0:
raise IndexError('Offset must be non-negative')
offset = min(len(self), offset)
if length is None:
result = self.table.Slice(offset)
else:
result = self.table.Slice(offset, length)
return pyarrow_wrap_table(result)
def select(self, object columns):
"""
Select columns of the Table.
Returns a new Table with the specified columns, and metadata
preserved.
Parameters
----------
columns : list-like
The column names or integer indices to select.
Returns
-------
Table
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.select([0,1])
pyarrow.Table
year: int64
n_legs: int64
----
year: [[2020,2022,2019,2021]]
n_legs: [[2,4,5,100]]
>>> table.select(["year"])
pyarrow.Table
year: int64
----
year: [[2020,2022,2019,2021]]
"""
cdef:
shared_ptr[CTable] c_table
vector[int] c_indices
for idx in columns:
idx = self._ensure_integer_index(idx)
idx = _normalize_index(idx, self.num_columns)
c_indices.push_back(<int> idx)
with nogil:
c_table = GetResultValue(self.table.SelectColumns(move(c_indices)))
return pyarrow_wrap_table(c_table)
def replace_schema_metadata(self, metadata=None):
"""
Create shallow copy of table by replacing schema
key-value metadata with the indicated new metadata (which may be None),
which deletes any existing metadata.
Parameters
----------
metadata : dict, default None
Returns
-------
Table
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
Constructing a Table with pyarrow schema and metadata:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.int64()),
... pa.field('animals', pa.string())],
... metadata={"n_legs": "Number of legs per animal"})
>>> table= pa.table(df, my_schema)
>>> table.schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
pandas: ...
Create a shallow copy of a Table with deleted schema metadata:
>>> table.replace_schema_metadata().schema
n_legs: int64
animals: string
Create a shallow copy of a Table with new schema metadata:
>>> metadata={"animals": "Which animal"}
>>> table.replace_schema_metadata(metadata = metadata).schema
n_legs: int64
animals: string
-- schema metadata --
animals: 'Which animal'
"""
cdef:
shared_ptr[const CKeyValueMetadata] c_meta
shared_ptr[CTable] c_table
metadata = ensure_metadata(metadata, allow_none=True)
c_meta = pyarrow_unwrap_metadata(metadata)
with nogil:
c_table = self.table.ReplaceSchemaMetadata(c_meta)
return pyarrow_wrap_table(c_table)
def flatten(self, MemoryPool memory_pool=None):
"""
Flatten this Table.
Each column with a struct type is flattened
into one column per struct field. Other columns are left unchanged.
Parameters
----------
memory_pool : MemoryPool, default None
For memory allocations, if required, otherwise use default pool
Returns
-------
Table
Examples
--------
>>> import pyarrow as pa
>>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'},
... {'year': 2022, 'n_legs': 4}])
>>> month = pa.array([4, 6])
>>> table = pa.Table.from_arrays([struct,month],
... names = ["a", "month"])
>>> table
pyarrow.Table
a: struct<animals: string, n_legs: int64, year: int64>
child 0, animals: string
child 1, n_legs: int64
child 2, year: int64
month: int64
----
a: [
-- is_valid: all not null
-- child 0 type: string
["Parrot",null]
-- child 1 type: int64
[2,4]
-- child 2 type: int64
[null,2022]]
month: [[4,6]]
Flatten the columns with struct field:
>>> table.flatten()
pyarrow.Table
a.animals: string
a.n_legs: int64
a.year: int64
month: int64
----
a.animals: [["Parrot",null]]
a.n_legs: [[2,4]]
a.year: [[null,2022]]
month: [[4,6]]
"""
self._assert_cpu()
cdef:
shared_ptr[CTable] flattened
CMemoryPool* pool = maybe_unbox_memory_pool(memory_pool)
with nogil:
flattened = GetResultValue(self.table.Flatten(pool))
return pyarrow_wrap_table(flattened)
def combine_chunks(self, MemoryPool memory_pool=None):
"""
Make a new table by combining the chunks this table has.
All the underlying chunks in the ChunkedArray of each column are
concatenated into zero or one chunk.
To avoid buffer overflow, binary columns may be combined into
multiple chunks. Chunks will have the maximum possible length.
Parameters
----------
memory_pool : MemoryPool, default None
For memory allocations, if required, otherwise use default pool.
Returns
-------
Table
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> animals = pa.chunked_array([["Flamingo", "Parrot", "Dog"], ["Horse", "Brittle stars", "Centipede"]])
>>> names = ["n_legs", "animals"]
>>> table = pa.table([n_legs, animals], names=names)
>>> table
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,2,4],[4,5,100]]
animals: [["Flamingo","Parrot","Dog"],["Horse","Brittle stars","Centipede"]]
>>> table.combine_chunks()
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,2,4,4,5,100]]
animals: [["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]]
"""
self._assert_cpu()
cdef:
shared_ptr[CTable] combined
CMemoryPool* pool = maybe_unbox_memory_pool(memory_pool)
with nogil:
combined = GetResultValue(self.table.CombineChunks(pool))
return pyarrow_wrap_table(combined)
def unify_dictionaries(self, MemoryPool memory_pool=None):
"""
Unify dictionaries across all chunks.
This method returns an equivalent table, but where all chunks of
each column share the same dictionary values. Dictionary indices
are transposed accordingly.
Columns without dictionaries are returned unchanged.
Parameters
----------
memory_pool : MemoryPool, default None
For memory allocations, if required, otherwise use default pool
Returns
-------
Table
Examples
--------
>>> import pyarrow as pa
>>> arr_1 = pa.array(["Flamingo", "Parrot", "Dog"]).dictionary_encode()
>>> arr_2 = pa.array(["Horse", "Brittle stars", "Centipede"]).dictionary_encode()
>>> c_arr = pa.chunked_array([arr_1, arr_2])
>>> table = pa.table([c_arr], names=["animals"])
>>> table
pyarrow.Table
animals: dictionary<values=string, indices=int32, ordered=0>
----
animals: [ -- dictionary:
["Flamingo","Parrot","Dog"] -- indices:
[0,1,2], -- dictionary:
["Horse","Brittle stars","Centipede"] -- indices:
[0,1,2]]
Unify dictionaries across both chunks:
>>> table.unify_dictionaries()
pyarrow.Table
animals: dictionary<values=string, indices=int32, ordered=0>
----
animals: [ -- dictionary:
["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] -- indices:
[0,1,2], -- dictionary:
["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] -- indices:
[3,4,5]]
"""
self._assert_cpu()
cdef:
CMemoryPool* pool = maybe_unbox_memory_pool(memory_pool)
shared_ptr[CTable] c_result
with nogil:
c_result = GetResultValue(CDictionaryUnifier.UnifyTable(
deref(self.table), pool))
return pyarrow_wrap_table(c_result)
def equals(self, Table other, bint check_metadata=False):
"""
Check if contents of two tables are equal.
Parameters
----------
other : pyarrow.Table
Table to compare against.
check_metadata : bool, default False
Whether schema metadata equality should be checked as well.
Returns
-------
bool
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
>>> names=["n_legs", "animals"]
>>> table = pa.Table.from_arrays([n_legs, animals], names=names)
>>> table_0 = pa.Table.from_arrays([])
>>> table_1 = pa.Table.from_arrays([n_legs, animals],
... names=names,
... metadata={"n_legs": "Number of legs per animal"})
>>> table.equals(table)
True
>>> table.equals(table_0)
False
>>> table.equals(table_1)
True
>>> table.equals(table_1, check_metadata=True)
False
"""
self._assert_cpu()
if other is None:
return False
cdef:
CTable* this_table = self.table
CTable* other_table = other.table
c_bool result
with nogil:
result = this_table.Equals(deref(other_table), check_metadata)
return result
def cast(self, Schema target_schema, safe=None, options=None):
"""
Cast table values to another schema.
Parameters
----------
target_schema : Schema
Schema to cast to, the names and order of fields must match.
safe : bool, default True
Check for overflows or other unsafe conversions.
options : CastOptions, default None
Additional checks pass by CastOptions
Returns
-------
Table
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.schema
n_legs: int64
animals: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ...
Define new schema and cast table values:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.duration('s')),
... pa.field('animals', pa.string())]
... )
>>> table.cast(target_schema=my_schema)
pyarrow.Table
n_legs: duration[s]
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
"""
self._assert_cpu()
cdef:
ChunkedArray column, casted
Field field
list newcols = []
if self.schema.names != target_schema.names:
raise ValueError(f"Target schema's field names are not matching "
f"the table's field names: {self.schema.names!r}, {target_schema.names!r}")
for column, field in zip(self.itercolumns(), target_schema):
if not field.nullable and column.null_count > 0:
raise ValueError(
f"Casting field {field.name!r} with null values to non-nullable")
casted = column.cast(field.type, safe=safe, options=options)
newcols.append(casted)
return Table.from_arrays(newcols, schema=target_schema)
@classmethod
def from_pandas(cls, df, Schema schema=None, preserve_index=None,
nthreads=None, columns=None, bint safe=True):
"""
Convert pandas.DataFrame to an Arrow Table.
The column types in the resulting Arrow Table are inferred from the
dtypes of the pandas.Series in the DataFrame. In the case of non-object
Series, the NumPy dtype is translated to its Arrow equivalent. In the
case of `object`, we need to guess the datatype by looking at the
Python objects in this Series.
Be aware that Series of the `object` dtype don't carry enough
information to always lead to a meaningful Arrow type. In the case that
we cannot infer a type, e.g. because the DataFrame is of length 0 or
the Series only contains None/nan objects, the type is set to
null. This behavior can be avoided by constructing an explicit schema
and passing it to this function.
Parameters
----------
df : pandas.DataFrame
schema : pyarrow.Schema, optional
The expected schema of the Arrow Table. This can be used to
indicate the type of columns if we cannot infer it automatically.
If passed, the output will have exactly this schema. Columns
specified in the schema that are not found in the DataFrame columns
or its index will raise an error. Additional columns or index
levels in the DataFrame which are not specified in the schema will
be ignored.
preserve_index : bool, optional
Whether to store the index as an additional column in the resulting
``Table``. The default of None will store the index as a column,
except for RangeIndex which is stored as metadata only. Use
``preserve_index=True`` to force it to be stored as a column.
nthreads : int, default None
If greater than 1, convert columns to Arrow in parallel using
indicated number of threads. By default, this follows
:func:`pyarrow.cpu_count` (may use up to system CPU count threads).
columns : list, optional
List of column to be converted. If None, use all columns.
safe : bool, default True
Check for overflows or other unsafe conversions.
Returns
-------
Table
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> pa.Table.from_pandas(df)
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
"""
from pyarrow.pandas_compat import dataframe_to_arrays
arrays, schema, n_rows = dataframe_to_arrays(
df,
schema=schema,
preserve_index=preserve_index,
nthreads=nthreads,
columns=columns,
safe=safe
)
# If df is empty but row index is not, create empty Table with rows >0
cdef vector[shared_ptr[CChunkedArray]] c_arrays
if n_rows:
return pyarrow_wrap_table(
CTable.MakeWithRows((<Schema> schema).sp_schema, c_arrays, n_rows))
else:
return cls.from_arrays(arrays, schema=schema)
@staticmethod
def from_arrays(arrays, names=None, schema=None, metadata=None):
"""
Construct a Table from Arrow arrays.
Parameters
----------
arrays : list of pyarrow.Array or pyarrow.ChunkedArray
Equal-length arrays that should form the table.
names : list of str, optional
Names for the table columns. If not passed, schema must be passed.
schema : Schema, default None
Schema for the created table. If not passed, names must be passed.
metadata : dict or Mapping, default None
Optional metadata for the schema (if inferred).
Returns
-------
Table
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
>>> names = ["n_legs", "animals"]
Construct a Table from arrays:
>>> pa.Table.from_arrays([n_legs, animals], names=names)
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
Construct a Table from arrays with metadata:
>>> my_metadata={"n_legs": "Number of legs per animal"}
>>> pa.Table.from_arrays([n_legs, animals],
... names=names,
... metadata=my_metadata)
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
>>> pa.Table.from_arrays([n_legs, animals],
... names=names,
... metadata=my_metadata).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
Construct a Table from arrays with pyarrow schema:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.int64()),
... pa.field('animals', pa.string())],
... metadata={"animals": "Name of the animal species"})
>>> pa.Table.from_arrays([n_legs, animals],
... schema=my_schema)
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
>>> pa.Table.from_arrays([n_legs, animals],
... schema=my_schema).schema
n_legs: int64
animals: string
-- schema metadata --
animals: 'Name of the animal species'
"""
cdef:
vector[shared_ptr[CChunkedArray]] columns
shared_ptr[CSchema] c_schema
int i, K = <int> len(arrays)
converted_arrays = _sanitize_arrays(arrays, names, schema, metadata,
&c_schema)
columns.reserve(K)
for item in converted_arrays:
if isinstance(item, Array):
columns.push_back(
make_shared[CChunkedArray](
(<Array> item).sp_array
)
)
elif isinstance(item, ChunkedArray):
columns.push_back((<ChunkedArray> item).sp_chunked_array)
else:
raise TypeError(type(item))
result = pyarrow_wrap_table(CTable.Make(c_schema, columns))
result.validate()
return result
@staticmethod
def from_struct_array(struct_array):
"""
Construct a Table from a StructArray.
Each field in the StructArray will become a column in the resulting
``Table``.
Parameters
----------
struct_array : StructArray or ChunkedArray
Array to construct the table from.
Returns
-------
pyarrow.Table
Examples
--------
>>> import pyarrow as pa
>>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'},
... {'year': 2022, 'n_legs': 4}])
>>> pa.Table.from_struct_array(struct).to_pandas()
animals n_legs year
0 Parrot 2 NaN
1 None 4 2022.0
"""
if isinstance(struct_array, Array):
return Table.from_batches([RecordBatch.from_struct_array(struct_array)])
else:
return Table.from_batches([
RecordBatch.from_struct_array(chunk)
for chunk in struct_array.chunks
])
def to_struct_array(self, max_chunksize=None):
"""
Convert to a chunked array of struct type.
Parameters
----------
max_chunksize : int, default None
Maximum number of rows for ChunkedArray chunks. Individual chunks
may be smaller depending on the chunk layout of individual columns.
Returns
-------
ChunkedArray
"""
self._assert_cpu()
chunks = [
batch.to_struct_array()
for batch in self.to_batches(max_chunksize=max_chunksize)
]
return chunked_array(chunks, type=struct(self.schema))
@staticmethod
def from_batches(batches, Schema schema=None):
"""
Construct a Table from a sequence or iterator of Arrow RecordBatches.
Parameters
----------
batches : sequence or iterator of RecordBatch
Sequence of RecordBatch to be converted, all schemas must be equal.
schema : Schema, default None
If not passed, will be inferred from the first RecordBatch.
Returns
-------
Table
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
>>> names = ["n_legs", "animals"]
>>> batch = pa.record_batch([n_legs, animals], names=names)
>>> batch.to_pandas()
n_legs animals
0 2 Flamingo
1 4 Horse
2 5 Brittle stars
3 100 Centipede
Construct a Table from a RecordBatch:
>>> pa.Table.from_batches([batch])
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
Construct a Table from a sequence of RecordBatches:
>>> pa.Table.from_batches([batch, batch])
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100],[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"],["Flamingo","Horse","Brittle stars","Centipede"]]
"""
cdef:
vector[shared_ptr[CRecordBatch]] c_batches
shared_ptr[CTable] c_table
shared_ptr[CSchema] c_schema
RecordBatch batch
for batch in batches:
c_batches.push_back(batch.sp_batch)
if schema is None:
if c_batches.size() == 0:
raise ValueError('Must pass schema, or at least '
'one RecordBatch')
c_schema = c_batches[0].get().schema()
else:
c_schema = schema.sp_schema
with nogil:
c_table = GetResultValue(
CTable.FromRecordBatches(c_schema, move(c_batches)))
return pyarrow_wrap_table(c_table)
def to_batches(self, max_chunksize=None):
"""
Convert Table to a list of RecordBatch objects.
Note that this method is zero-copy, it merely exposes the same data
under a different API.
Parameters
----------
max_chunksize : int, default None
Maximum number of rows for each RecordBatch chunk. Individual chunks
may be smaller depending on the chunk layout of individual columns.
Returns
-------
list[RecordBatch]
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
Convert a Table to a RecordBatch:
>>> table.to_batches()[0].to_pandas()
n_legs animals
0 2 Flamingo
1 4 Horse
2 5 Brittle stars
3 100 Centipede
Convert a Table to a list of RecordBatches:
>>> table.to_batches(max_chunksize=2)[0].to_pandas()
n_legs animals
0 2 Flamingo
1 4 Horse
>>> table.to_batches(max_chunksize=2)[1].to_pandas()
n_legs animals
0 5 Brittle stars
1 100 Centipede
"""
cdef:
unique_ptr[TableBatchReader] reader
int64_t c_max_chunksize
list result = []
shared_ptr[CRecordBatch] batch
reader.reset(new TableBatchReader(deref(self.table)))
if max_chunksize is not None:
if not max_chunksize > 0:
raise ValueError("'max_chunksize' should be strictly positive")
c_max_chunksize = max_chunksize
reader.get().set_chunksize(c_max_chunksize)
while True:
with nogil:
check_status(reader.get().ReadNext(&batch))
if batch.get() == NULL:
break
result.append(pyarrow_wrap_batch(batch))
return result
def to_reader(self, max_chunksize=None):
"""
Convert the Table to a RecordBatchReader.
Note that this method is zero-copy, it merely exposes the same data
under a different API.
Parameters
----------
max_chunksize : int, default None
Maximum number of rows for each RecordBatch chunk. Individual chunks
may be smaller depending on the chunk layout of individual columns.
Returns
-------
RecordBatchReader
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
Convert a Table to a RecordBatchReader:
>>> table.to_reader()
<pyarrow.lib.RecordBatchReader object at ...>
>>> reader = table.to_reader()
>>> reader.schema
n_legs: int64
animals: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ...
>>> reader.read_all()
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
"""
cdef:
shared_ptr[CRecordBatchReader] c_reader
RecordBatchReader reader
shared_ptr[TableBatchReader] t_reader
t_reader = make_shared[TableBatchReader](self.sp_table)
if max_chunksize is not None:
t_reader.get().set_chunksize(max_chunksize)
c_reader = dynamic_pointer_cast[CRecordBatchReader, TableBatchReader](
t_reader)
reader = RecordBatchReader.__new__(RecordBatchReader)
reader.reader = c_reader
return reader
def _to_pandas(self, options, categories=None, ignore_metadata=False,
types_mapper=None):
self._assert_cpu()
from pyarrow.pandas_compat import table_to_dataframe
df = table_to_dataframe(
options, self, categories,
ignore_metadata=ignore_metadata,
types_mapper=types_mapper)
return df
@property
def schema(self):
"""
Schema of the table and its columns.
Returns
-------
Schema
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.schema
n_legs: int64
animals: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' ...
"""
return pyarrow_wrap_schema(self.table.schema())
def _column(self, int i):
"""
Select a column by its numeric index.
Parameters
----------
i : int
The index of the column to retrieve.
Returns
-------
ChunkedArray
"""
cdef int index = <int> _normalize_index(i, self.num_columns)
cdef ChunkedArray result = pyarrow_wrap_chunked_array(
self.table.column(index))
result._name = self.schema[index].name
return result
@property
def num_columns(self):
"""
Number of columns in this table.
Returns
-------
int
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [None, 4, 5, None],
... 'animals': ["Flamingo", "Horse", None, "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.num_columns
2
"""
return self.table.num_columns()
@property
def num_rows(self):
"""
Number of rows in this table.
Due to the definition of a table, all columns have the same number of
rows.
Returns
-------
int
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [None, 4, 5, None],
... 'animals': ["Flamingo", "Horse", None, "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.num_rows
4
"""
return self.table.num_rows()
@property
def nbytes(self):
"""
Total number of bytes consumed by the elements of the table.
In other words, the sum of bytes from all buffer ranges referenced.
Unlike `get_total_buffer_size` this method will account for array
offsets.
If buffers are shared between arrays then the shared
portion will only be counted multiple times.
The dictionary of dictionary arrays will always be counted in their
entirety even if the array only references a portion of the dictionary.
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [None, 4, 5, None],
... 'animals': ["Flamingo", "Horse", None, "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.nbytes
72
"""
self._assert_cpu()
cdef:
CResult[int64_t] c_res_buffer
with nogil:
c_res_buffer = ReferencedBufferSize(deref(self.table))
size = GetResultValue(c_res_buffer)
return size
def get_total_buffer_size(self):
"""
The sum of bytes in each buffer referenced by the table.
An array may only reference a portion of a buffer.
This method will overestimate in this case and return the
byte size of the entire buffer.
If a buffer is referenced multiple times then it will
only be counted once.
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [None, 4, 5, None],
... 'animals': ["Flamingo", "Horse", None, "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.get_total_buffer_size()
76
"""
self._assert_cpu()
cdef:
int64_t total_buffer_size
total_buffer_size = TotalBufferSize(deref(self.table))
return total_buffer_size
def __sizeof__(self):
return super(Table, self).__sizeof__() + self.nbytes
def add_column(self, int i, field_, column):
"""
Add column to Table at position.
A new table is returned with the column added, the original table
object is left unchanged.
Parameters
----------
i : int
Index to place the column at.
field_ : str or Field
If a string is passed then the type is deduced from the column
data.
column : Array, list of Array, or values coercible to arrays
Column data.
Returns
-------
Table
New table with the passed column added.
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
Add column:
>>> year = [2021, 2022, 2019, 2021]
>>> table.add_column(0,"year", [year])
pyarrow.Table
year: int64
n_legs: int64
animals: string
----
year: [[2021,2022,2019,2021]]
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
Original table is left unchanged:
>>> table
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
"""
cdef:
shared_ptr[CTable] c_table
Field c_field
ChunkedArray c_arr
if isinstance(column, ChunkedArray):
c_arr = column
else:
c_arr = chunked_array(column)
if isinstance(field_, Field):
c_field = field_
else:
c_field = field(field_, c_arr.type)
with nogil:
c_table = GetResultValue(self.table.AddColumn(
i, c_field.sp_field, c_arr.sp_chunked_array))
return pyarrow_wrap_table(c_table)
def remove_column(self, int i):
"""
Create new Table with the indicated column removed.
Parameters
----------
i : int
Index of column to remove.
Returns
-------
Table
New table without the column.
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.remove_column(1)
pyarrow.Table
n_legs: int64
----
n_legs: [[2,4,5,100]]
"""
cdef shared_ptr[CTable] c_table
with nogil:
c_table = GetResultValue(self.table.RemoveColumn(i))
return pyarrow_wrap_table(c_table)
def set_column(self, int i, field_, column):
"""
Replace column in Table at position.
Parameters
----------
i : int
Index to place the column at.
field_ : str or Field
If a string is passed then the type is deduced from the column
data.
column : Array, list of Array, or values coercible to arrays
Column data.
Returns
-------
Table
New table with the passed column set.
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
Replace a column:
>>> year = [2021, 2022, 2019, 2021]
>>> table.set_column(1,'year', [year])
pyarrow.Table
n_legs: int64
year: int64
----
n_legs: [[2,4,5,100]]
year: [[2021,2022,2019,2021]]
"""
cdef:
shared_ptr[CTable] c_table
Field c_field
ChunkedArray c_arr
if isinstance(column, ChunkedArray):
c_arr = column
else:
c_arr = chunked_array(column)
if isinstance(field_, Field):
c_field = field_
else:
c_field = field(field_, c_arr.type)
with nogil:
c_table = GetResultValue(self.table.SetColumn(
i, c_field.sp_field, c_arr.sp_chunked_array))
return pyarrow_wrap_table(c_table)
def rename_columns(self, names):
"""
Create new table with columns renamed to provided names.
Parameters
----------
names : list[str] or dict[str, str]
List of new column names or mapping of old column names to new column names.
If a mapping of old to new column names is passed, then all columns which are
found to match a provided old column name will be renamed to the new column name.
If any column names are not found in the mapping, a KeyError will be raised.
Raises
------
KeyError
If any of the column names passed in the names mapping do not exist.
Returns
-------
Table
Examples
--------
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> new_names = ["n", "name"]
>>> table.rename_columns(new_names)
pyarrow.Table
n: int64
name: string
----
n: [[2,4,5,100]]
name: [["Flamingo","Horse","Brittle stars","Centipede"]]
>>> new_names = {"n_legs": "n", "animals": "name"}
>>> table.rename_columns(new_names)
pyarrow.Table
n: int64
name: string
----
n: [[2,4,5,100]]
name: [["Flamingo","Horse","Brittle stars","Centipede"]]
"""
cdef:
shared_ptr[CTable] c_table
vector[c_string] c_names
if isinstance(names, (list, tuple)):
for name in names:
c_names.push_back(tobytes(name))
elif isinstance(names, dict):
idx_to_new_name = {}
for name, new_name in names.items():
indices = self.schema.get_all_field_indices(name)
if not indices:
raise KeyError(f"Column {name!r} not found")
for index in indices:
idx_to_new_name[index] = new_name
for i in range(self.num_columns):
c_names.push_back(tobytes(idx_to_new_name.get(i, self.schema[i].name)))
else:
raise TypeError(f"names must be a list or dict not {type(names)!r}")
with nogil:
c_table = GetResultValue(self.table.RenameColumns(move(c_names)))
return pyarrow_wrap_table(c_table)
def drop(self, columns):
"""
Drop one or more columns and return a new table.
Alias of Table.drop_columns, but kept for backwards compatibility.
Parameters
----------
columns : str or list[str]
Field name(s) referencing existing column(s).
Returns
-------
Table
New table without the column(s).
"""
return self.drop_columns(columns)
def group_by(self, keys, use_threads=True):
"""
Declare a grouping over the columns of the table.
Resulting grouping can then be used to perform aggregations
with a subsequent ``aggregate()`` method.
Parameters
----------
keys : str or list[str]
Name of the columns that should be used as the grouping key.
use_threads : bool, default True
Whether to use multithreading or not. When set to True (the
default), no stable ordering of the output is guaranteed.
Returns
-------
TableGroupBy
See Also
--------
TableGroupBy.aggregate
Examples
--------
>>> import pandas as pd
>>> import pyarrow as pa
>>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022, 2019, 2021],
... 'n_legs': [2, 2, 4, 4, 5, 100],
... 'animal': ["Flamingo", "Parrot", "Dog", "Horse",
... "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.group_by('year').aggregate([('n_legs', 'sum')])
pyarrow.Table
year: int64
n_legs_sum: int64
----
year: [[2020,2022,2021,2019]]
n_legs_sum: [[2,6,104,5]]
"""
self._assert_cpu()
return TableGroupBy(self, keys, use_threads=use_threads)
def join(self, right_table, keys, right_keys=None, join_type="left outer",
left_suffix=None, right_suffix=None, coalesce_keys=True,
use_threads=True, filter_expression=None):
"""
Perform a join between this table and another one.
Result of the join will be a new Table, where further
operations can be applied.
Parameters
----------
right_table : Table
The table to join to the current one, acting as the right table
in the join operation.
keys : str or list[str]
The columns from current table that should be used as keys
of the join operation left side.
right_keys : str or list[str], default None
The columns from the right_table that should be used as keys
on the join operation right side.
When ``None`` use the same key names as the left table.
join_type : str, default "left outer"
The kind of join that should be performed, one of
("left semi", "right semi", "left anti", "right anti",
"inner", "left outer", "right outer", "full outer")
left_suffix : str, default None
Which suffix to add to left column names. This prevents confusion
when the columns in left and right tables have colliding names.
right_suffix : str, default None
Which suffix to add to the right column names. This prevents confusion
when the columns in left and right tables have colliding names.
coalesce_keys : bool, default True
If the duplicated keys should be omitted from one of the sides
in the join result.
use_threads : bool, default True
Whether to use multithreading or not.
filter_expression : pyarrow.compute.Expression
Residual filter which is applied to matching row.
Returns
-------
Table
Examples
--------
>>> import pandas as pd
>>> import pyarrow as pa
>>> import pyarrow.compute as pc
>>> df1 = pd.DataFrame({'id': [1, 2, 3],
... 'year': [2020, 2022, 2019]})
>>> df2 = pd.DataFrame({'id': [3, 4],
... 'n_legs': [5, 100],
... 'animal': ["Brittle stars", "Centipede"]})
>>> t1 = pa.Table.from_pandas(df1)
>>> t2 = pa.Table.from_pandas(df2)
Left outer join:
>>> t1.join(t2, 'id').combine_chunks().sort_by('year')
pyarrow.Table
id: int64
year: int64
n_legs: int64
animal: string
----
id: [[3,1,2]]
year: [[2019,2020,2022]]
n_legs: [[5,null,null]]
animal: [["Brittle stars",null,null]]
Full outer join:
>>> t1.join(t2, 'id', join_type="full outer").combine_chunks().sort_by('year')
pyarrow.Table
id: int64
year: int64
n_legs: int64
animal: string
----
id: [[3,1,2,4]]
year: [[2019,2020,2022,null]]
n_legs: [[5,null,null,100]]
animal: [["Brittle stars",null,null,"Centipede"]]
Right outer join:
>>> t1.join(t2, 'id', join_type="right outer").combine_chunks().sort_by('year')
pyarrow.Table
year: int64
id: int64
n_legs: int64
animal: string
----
year: [[2019,null]]
id: [[3,4]]
n_legs: [[5,100]]
animal: [["Brittle stars","Centipede"]]
Right anti join:
>>> t1.join(t2, 'id', join_type="right anti")
pyarrow.Table
id: int64
n_legs: int64
animal: string
----
id: [[4]]
n_legs: [[100]]
animal: [["Centipede"]]
Inner join with intended mismatch filter expression:
>>> t1.join(t2, 'id', join_type="inner", filter_expression=pc.equal(pc.field("n_legs"), 100))
pyarrow.Table
id: int64
year: int64
n_legs: int64
animal: string
----
id: []
year: []
n_legs: []
animal: []
"""
self._assert_cpu()
if right_keys is None:
right_keys = keys
return _pac()._perform_join(
join_type, self, keys, right_table, right_keys,
left_suffix=left_suffix, right_suffix=right_suffix,
use_threads=use_threads, coalesce_keys=coalesce_keys,
output_type=Table,
filter_expression=filter_expression,
)
def join_asof(self, right_table, on, by, tolerance, right_on=None, right_by=None):
"""
Perform an asof join between this table and another one.
This is similar to a left-join except that we match on nearest key rather
than equal keys. Both tables must be sorted by the key. This type of join
is most useful for time series data that are not perfectly aligned.
Optionally match on equivalent keys with "by" before searching with "on".
Result of the join will be a new Table, where further
operations can be applied.
Parameters
----------
right_table : Table
The table to join to the current one, acting as the right table
in the join operation.
on : str
The column from current table that should be used as the "on" key
of the join operation left side.
An inexact match is used on the "on" key, i.e. a row is considered a
match if and only if left_on - tolerance <= right_on <= left_on.
The input dataset must be sorted by the "on" key. Must be a single
field of a common type.
Currently, the "on" key must be an integer, date, or timestamp type.
by : str or list[str]
The columns from current table that should be used as the keys
of the join operation left side. The join operation is then done
only for the matches in these columns.
tolerance : int
The tolerance for inexact "on" key matching. A right row is considered
a match with the left row ``right.on - left.on <= tolerance``. The
``tolerance`` may be:
- negative, in which case a past-as-of-join occurs;
- or positive, in which case a future-as-of-join occurs;
- or zero, in which case an exact-as-of-join occurs.
The tolerance is interpreted in the same units as the "on" key.
right_on : str or list[str], default None
The columns from the right_table that should be used as the on key
on the join operation right side.
When ``None`` use the same key name as the left table.
right_by : str or list[str], default None
The columns from the right_table that should be used as keys
on the join operation right side.
When ``None`` use the same key names as the left table.
Returns
-------
Table
Example
--------
>>> import pyarrow as pa
>>> t1 = pa.table({'id': [1, 3, 2, 3, 3],
... 'year': [2020, 2021, 2022, 2022, 2023]})
>>> t2 = pa.table({'id': [3, 4],
... 'year': [2020, 2021],
... 'n_legs': [5, 100],
... 'animal': ["Brittle stars", "Centipede"]})
>>> t1.join_asof(t2, on='year', by='id', tolerance=-2)
pyarrow.Table
id: int64
year: int64
n_legs: int64
animal: string
----
id: [[1,3,2,3,3]]
year: [[2020,2021,2022,2022,2023]]
n_legs: [[null,5,null,5,null]]
animal: [[null,"Brittle stars",null,"Brittle stars",null]]
"""
self._assert_cpu()
if right_on is None:
right_on = on
if right_by is None:
right_by = by
return _pac()._perform_join_asof(self, on, by,
right_table, right_on, right_by,
tolerance, output_type=Table)
def __arrow_c_stream__(self, requested_schema=None):
"""
Export the table as an Arrow C stream PyCapsule.
Parameters
----------
requested_schema : PyCapsule, default None
The schema to which the stream should be casted, passed as a
PyCapsule containing a C ArrowSchema representation of the
requested schema.
Currently, this is not supported and will raise a
NotImplementedError if the schema doesn't match the current schema.
Returns
-------
PyCapsule
"""
self._assert_cpu()
return self.to_reader().__arrow_c_stream__(requested_schema)
@property
def is_cpu(self):
"""
Whether all ChunkedArrays are CPU-accessible.
"""
if not self._init_is_cpu:
self._is_cpu = all(c.is_cpu for c in self.itercolumns())
self._init_is_cpu = True
return self._is_cpu
cdef void _assert_cpu(self) except *:
if not self.is_cpu:
raise NotImplementedError("Implemented only for data on CPU device")
def _reconstruct_table(arrays, schema):
"""
Internal: reconstruct pa.Table from pickled components.
"""
return Table.from_arrays(arrays, schema=schema)
def record_batch(data, names=None, schema=None, metadata=None):
"""
Create a pyarrow.RecordBatch from another Python data structure or sequence
of arrays.
Parameters
----------
data : dict, list, pandas.DataFrame, Arrow-compatible table
A mapping of strings to Arrays or Python lists, a list of Arrays,
a pandas DataFame, or any tabular object implementing the
Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` or
``__arrow_c_device_array__`` method).
names : list, default None
Column names if list of arrays passed as data. Mutually exclusive with
'schema' argument.
schema : Schema, default None
The expected schema of the RecordBatch. If not passed, will be inferred
from the data. Mutually exclusive with 'names' argument.
metadata : dict or Mapping, default None
Optional metadata for the schema (if schema not passed).
Returns
-------
RecordBatch
See Also
--------
RecordBatch.from_arrays, RecordBatch.from_pandas, table
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
>>> names = ["n_legs", "animals"]
Construct a RecordBatch from a python dictionary:
>>> pa.record_batch({"n_legs": n_legs, "animals": animals})
pyarrow.RecordBatch
n_legs: int64
animals: string
----
n_legs: [2,2,4,4,5,100]
animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
>>> pa.record_batch({"n_legs": n_legs, "animals": animals}).to_pandas()
n_legs animals
0 2 Flamingo
1 2 Parrot
2 4 Dog
3 4 Horse
4 5 Brittle stars
5 100 Centipede
Creating a RecordBatch from a list of arrays with names:
>>> pa.record_batch([n_legs, animals], names=names)
pyarrow.RecordBatch
n_legs: int64
animals: string
----
n_legs: [2,2,4,4,5,100]
animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
Creating a RecordBatch from a list of arrays with names and metadata:
>>> my_metadata={"n_legs": "How many legs does an animal have?"}
>>> pa.record_batch([n_legs, animals],
... names=names,
... metadata = my_metadata)
pyarrow.RecordBatch
n_legs: int64
animals: string
----
n_legs: [2,2,4,4,5,100]
animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
>>> pa.record_batch([n_legs, animals],
... names=names,
... metadata = my_metadata).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'How many legs does an animal have?'
Creating a RecordBatch from a pandas DataFrame:
>>> import pandas as pd
>>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022],
... 'month': [3, 5, 7, 9],
... 'day': [1, 5, 9, 13],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> pa.record_batch(df)
pyarrow.RecordBatch
year: int64
month: int64
day: int64
n_legs: int64
animals: string
----
year: [2020,2022,2021,2022]
month: [3,5,7,9]
day: [1,5,9,13]
n_legs: [2,4,5,100]
animals: ["Flamingo","Horse","Brittle stars","Centipede"]
>>> pa.record_batch(df).to_pandas()
year month day n_legs animals
0 2020 3 1 2 Flamingo
1 2022 5 5 4 Horse
2 2021 7 9 5 Brittle stars
3 2022 9 13 100 Centipede
Creating a RecordBatch from a pandas DataFrame with schema:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.int64()),
... pa.field('animals', pa.string())],
... metadata={"n_legs": "Number of legs per animal"})
>>> pa.record_batch(df, my_schema).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
pandas: ...
>>> pa.record_batch(df, my_schema).to_pandas()
n_legs animals
0 2 Flamingo
1 4 Horse
2 5 Brittle stars
3 100 Centipede
"""
# accept schema as first argument for backwards compatibility / usability
if isinstance(names, Schema) and schema is None:
schema = names
names = None
if isinstance(data, (list, tuple)):
return RecordBatch.from_arrays(data, names=names, schema=schema,
metadata=metadata)
elif isinstance(data, dict):
if names is not None:
raise ValueError(
"The 'names' argument is not valid when passing a dictionary")
return RecordBatch.from_pydict(data, schema=schema, metadata=metadata)
elif hasattr(data, "__arrow_c_device_array__"):
if schema is not None:
requested_schema = schema.__arrow_c_schema__()
else:
requested_schema = None
schema_capsule, array_capsule = data.__arrow_c_device_array__(requested_schema)
batch = RecordBatch._import_from_c_device_capsule(schema_capsule, array_capsule)
if schema is not None and batch.schema != schema:
# __arrow_c_device_array__ coerces schema with best effort, so we might
# need to cast it if the producer wasn't able to cast to exact schema.
batch = batch.cast(schema)
return batch
elif hasattr(data, "__arrow_c_array__"):
if schema is not None:
requested_schema = schema.__arrow_c_schema__()
else:
requested_schema = None
schema_capsule, array_capsule = data.__arrow_c_array__(requested_schema)
batch = RecordBatch._import_from_c_capsule(schema_capsule, array_capsule)
if schema is not None and batch.schema != schema:
# __arrow_c_array__ coerces schema with best effort, so we might
# need to cast it if the producer wasn't able to cast to exact schema.
batch = batch.cast(schema)
return batch
elif _pandas_api.is_data_frame(data):
return RecordBatch.from_pandas(data, schema=schema)
else:
raise TypeError("Expected pandas DataFrame or list of arrays")
def table(data, names=None, schema=None, metadata=None, nthreads=None):
"""
Create a pyarrow.Table from a Python data structure or sequence of arrays.
Parameters
----------
data : dict, list, pandas.DataFrame, Arrow-compatible table
A mapping of strings to Arrays or Python lists, a list of arrays or
chunked arrays, a pandas DataFame, or any tabular object implementing
the Arrow PyCapsule Protocol (has an ``__arrow_c_array__``,
``__arrow_c_device_array__`` or ``__arrow_c_stream__`` method).
names : list, default None
Column names if list of arrays passed as data. Mutually exclusive with
'schema' argument.
schema : Schema, default None
The expected schema of the Arrow Table. If not passed, will be inferred
from the data. Mutually exclusive with 'names' argument.
If passed, the output will have exactly this schema (raising an error
when columns are not found in the data and ignoring additional data not
specified in the schema, when data is a dict or DataFrame).
metadata : dict or Mapping, default None
Optional metadata for the schema (if schema not passed).
nthreads : int, default None
For pandas.DataFrame inputs: if greater than 1, convert columns to
Arrow in parallel using indicated number of threads. By default,
this follows :func:`pyarrow.cpu_count` (may use up to system CPU count
threads).
Returns
-------
Table
See Also
--------
Table.from_arrays, Table.from_pandas, Table.from_pydict
Examples
--------
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
>>> names = ["n_legs", "animals"]
Construct a Table from a python dictionary:
>>> pa.table({"n_legs": n_legs, "animals": animals})
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
Construct a Table from arrays:
>>> pa.table([n_legs, animals], names=names)
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
Construct a Table from arrays with metadata:
>>> my_metadata={"n_legs": "Number of legs per animal"}
>>> pa.table([n_legs, animals], names=names, metadata = my_metadata).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
Construct a Table from pandas DataFrame:
>>> import pandas as pd
>>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> pa.table(df)
pyarrow.Table
year: int64
n_legs: int64
animals: string
----
year: [[2020,2022,2019,2021]]
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
Construct a Table from pandas DataFrame with pyarrow schema:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.int64()),
... pa.field('animals', pa.string())],
... metadata={"n_legs": "Number of legs per animal"})
>>> pa.table(df, my_schema).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
pandas: '{"index_columns": [], "column_indexes": [{"name": null, ...
Construct a Table from chunked arrays:
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> animals = pa.chunked_array([["Flamingo", "Parrot", "Dog"], ["Horse", "Brittle stars", "Centipede"]])
>>> table = pa.table([n_legs, animals], names=names)
>>> table
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,2,4],[4,5,100]]
animals: [["Flamingo","Parrot","Dog"],["Horse","Brittle stars","Centipede"]]
"""
# accept schema as first argument for backwards compatibility / usability
if isinstance(names, Schema) and schema is None:
schema = names
names = None
if isinstance(data, (list, tuple)):
return Table.from_arrays(data, names=names, schema=schema,
metadata=metadata)
elif isinstance(data, dict):
if names is not None:
raise ValueError(
"The 'names' argument is not valid when passing a dictionary")
return Table.from_pydict(data, schema=schema, metadata=metadata)
elif _pandas_api.is_data_frame(data):
if names is not None or metadata is not None:
raise ValueError(
"The 'names' and 'metadata' arguments are not valid when "
"passing a pandas DataFrame")
return Table.from_pandas(data, schema=schema, nthreads=nthreads)
elif hasattr(data, "__arrow_c_stream__"):
if names is not None or metadata is not None:
raise ValueError(
"The 'names' and 'metadata' arguments are not valid when "
"using Arrow PyCapsule Interface")
if schema is not None:
requested = schema.__arrow_c_schema__()
else:
requested = None
capsule = data.__arrow_c_stream__(requested)
reader = RecordBatchReader._import_from_c_capsule(capsule)
table = reader.read_all()
if schema is not None and table.schema != schema:
# __arrow_c_array__ coerces schema with best effort, so we might
# need to cast it if the producer wasn't able to cast to exact schema.
table = table.cast(schema)
return table
elif hasattr(data, "__arrow_c_array__") or hasattr(data, "__arrow_c_device_array__"):
if names is not None or metadata is not None:
raise ValueError(
"The 'names' and 'metadata' arguments are not valid when "
"using Arrow PyCapsule Interface")
batch = record_batch(data, schema)
return Table.from_batches([batch])
else:
raise TypeError(
"Expected pandas DataFrame, python dictionary or list of arrays")
def concat_tables(tables, MemoryPool memory_pool=None, str promote_options="none", **kwargs):
"""
Concatenate pyarrow.Table objects.
If promote_options="none", a zero-copy concatenation will be performed. The schemas
of all the Tables must be the same (except the metadata), otherwise an
exception will be raised. The result Table will share the metadata with the
first table.
If promote_options="default", any null type arrays will be casted to the type of other
arrays in the column of the same name. If a table is missing a particular
field, null values of the appropriate type will be generated to take the
place of the missing field. The new schema will share the metadata with the
first table. Each field in the new schema will share the metadata with the
first table which has the field defined. Note that type promotions may
involve additional allocations on the given ``memory_pool``.
If promote_options="permissive", the behavior of default plus types will be promoted
to the common denominator that fits all the fields.
Parameters
----------
tables : iterable of pyarrow.Table objects
Pyarrow tables to concatenate into a single Table.
memory_pool : MemoryPool, default None
For memory allocations, if required, otherwise use default pool.
promote_options : str, default none
Accepts strings "none", "default" and "permissive".
**kwargs : dict, optional
Examples
--------
>>> import pyarrow as pa
>>> t1 = pa.table([
... pa.array([2, 4, 5, 100]),
... pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
... ], names=['n_legs', 'animals'])
>>> t2 = pa.table([
... pa.array([2, 4]),
... pa.array(["Parrot", "Dog"])
... ], names=['n_legs', 'animals'])
>>> pa.concat_tables([t1,t2])
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100],[2,4]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"],["Parrot","Dog"]]
"""
cdef:
vector[shared_ptr[CTable]] c_tables
shared_ptr[CTable] c_result_table
CMemoryPool* pool = maybe_unbox_memory_pool(memory_pool)
Table table
CConcatenateTablesOptions options = (
CConcatenateTablesOptions.Defaults())
if "promote" in kwargs:
warnings.warn(
"promote has been superseded by promote_options='default'.",
FutureWarning, stacklevel=2)
if kwargs['promote'] is True:
promote_options = "default"
for table in tables:
c_tables.push_back(table.sp_table)
if promote_options == "permissive":
options.field_merge_options = CField.CMergeOptions.Permissive()
elif promote_options in {"default", "none"}:
options.field_merge_options = CField.CMergeOptions.Defaults()
else:
raise ValueError(f"Invalid promote options: {promote_options}")
with nogil:
options.unify_schemas = promote_options != "none"
c_result_table = GetResultValue(
ConcatenateTables(c_tables, options, pool))
return pyarrow_wrap_table(c_result_table)
def concat_batches(recordbatches, MemoryPool memory_pool=None):
"""
Concatenate pyarrow.RecordBatch objects.
All recordbatches must share the same Schema,
the operation implies a copy of the data to merge
the arrays of the different RecordBatches.
Parameters
----------
recordbatches : iterable of pyarrow.RecordBatch objects
Pyarrow record batches to concatenate into a single RecordBatch.
memory_pool : MemoryPool, default None
For memory allocations, if required, otherwise use default pool.
Examples
--------
>>> import pyarrow as pa
>>> t1 = pa.record_batch([
... pa.array([2, 4, 5, 100]),
... pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
... ], names=['n_legs', 'animals'])
>>> t2 = pa.record_batch([
... pa.array([2, 4]),
... pa.array(["Parrot", "Dog"])
... ], names=['n_legs', 'animals'])
>>> pa.concat_batches([t1,t2])
pyarrow.RecordBatch
n_legs: int64
animals: string
----
n_legs: [2,4,5,100,2,4]
animals: ["Flamingo","Horse","Brittle stars","Centipede","Parrot","Dog"]
"""
cdef:
vector[shared_ptr[CRecordBatch]] c_recordbatches
shared_ptr[CRecordBatch] c_result_recordbatch
RecordBatch recordbatch
CMemoryPool* pool = maybe_unbox_memory_pool(memory_pool)
for recordbatch in recordbatches:
c_recordbatches.push_back(recordbatch.sp_batch)
with nogil:
c_result_recordbatch = GetResultValue(
ConcatenateRecordBatches(c_recordbatches, pool))
return pyarrow_wrap_batch(c_result_recordbatch)
def _from_pydict(cls, mapping, schema, metadata):
"""
Construct a Table/RecordBatch from Arrow arrays or columns.
Parameters
----------
cls : Class Table/RecordBatch
mapping : dict or Mapping
A mapping of strings to Arrays or Python lists.
schema : Schema, default None
If not passed, will be inferred from the Mapping values.
metadata : dict or Mapping, default None
Optional metadata for the schema (if inferred).
Returns
-------
Table/RecordBatch
"""
arrays = []
if schema is None:
names = []
for k, v in mapping.items():
names.append(k)
arrays.append(asarray(v))
return cls.from_arrays(arrays, names, metadata=metadata)
elif isinstance(schema, Schema):
for field in schema:
try:
v = mapping[field.name]
except KeyError:
try:
v = mapping[tobytes(field.name)]
except KeyError:
present = mapping.keys()
missing = [n for n in schema.names if n not in present]
raise KeyError(
"The passed mapping doesn't contain the "
"following field(s) of the schema: {}".
format(', '.join(missing))
)
arrays.append(asarray(v, type=field.type))
# Will raise if metadata is not None
return cls.from_arrays(arrays, schema=schema, metadata=metadata)
else:
raise TypeError('Schema must be an instance of pyarrow.Schema')
def _from_pylist(cls, mapping, schema, metadata):
"""
Construct a Table/RecordBatch from list of rows / dictionaries.
Parameters
----------
cls : Class Table/RecordBatch
mapping : list of dicts of rows
A mapping of strings to row values.
schema : Schema, default None
If not passed, will be inferred from the first row of the
mapping values.
metadata : dict or Mapping, default None
Optional metadata for the schema (if inferred).
Returns
-------
Table/RecordBatch
"""
arrays = []
if schema is None:
names = []
if mapping:
names = list(mapping[0].keys())
for n in names:
v = [row[n] if n in row else None for row in mapping]
arrays.append(v)
return cls.from_arrays(arrays, names, metadata=metadata)
else:
if isinstance(schema, Schema):
for n in schema.names:
v = [row[n] if n in row else None for row in mapping]
arrays.append(v)
# Will raise if metadata is not None
return cls.from_arrays(arrays, schema=schema, metadata=metadata)
else:
raise TypeError('Schema must be an instance of pyarrow.Schema')
class TableGroupBy:
"""
A grouping of columns in a table on which to perform aggregations.
Parameters
----------
table : pyarrow.Table
Input table to execute the aggregation on.
keys : str or list[str]
Name of the grouped columns.
use_threads : bool, default True
Whether to use multithreading or not. When set to True (the default),
no stable ordering of the output is guaranteed.
Examples
--------
>>> import pyarrow as pa
>>> t = pa.table([
... pa.array(["a", "a", "b", "b", "c"]),
... pa.array([1, 2, 3, 4, 5]),
... ], names=["keys", "values"])
Grouping of columns:
>>> pa.TableGroupBy(t,"keys")
<pyarrow.lib.TableGroupBy object at ...>
Perform aggregations:
>>> pa.TableGroupBy(t,"keys").aggregate([("values", "sum")])
pyarrow.Table
keys: string
values_sum: int64
----
keys: [["a","b","c"]]
values_sum: [[3,7,5]]
"""
def __init__(self, table, keys, use_threads=True):
if isinstance(keys, str):
keys = [keys]
self._table = table
self.keys = keys
self._use_threads = use_threads
def aggregate(self, aggregations):
"""
Perform an aggregation over the grouped columns of the table.
Parameters
----------
aggregations : list[tuple(str, str)] or \
list[tuple(str, str, FunctionOptions)]
List of tuples, where each tuple is one aggregation specification
and consists of: aggregation column name followed
by function name and optionally aggregation function option.
Pass empty list to get a single row for each group.
The column name can be a string, an empty list or a list of
column names, for unary, nullary and n-ary aggregation functions
respectively.
For the list of function names and respective aggregation
function options see :ref:`py-grouped-aggrs`.
Returns
-------
Table
Results of the aggregation functions.
Examples
--------
>>> import pyarrow as pa
>>> t = pa.table([
... pa.array(["a", "a", "b", "b", "c"]),
... pa.array([1, 2, 3, 4, 5]),
... ], names=["keys", "values"])
Sum the column "values" over the grouped column "keys":
>>> t.group_by("keys").aggregate([("values", "sum")])
pyarrow.Table
keys: string
values_sum: int64
----
keys: [["a","b","c"]]
values_sum: [[3,7,5]]
Count the rows over the grouped column "keys":
>>> t.group_by("keys").aggregate([([], "count_all")])
pyarrow.Table
keys: string
count_all: int64
----
keys: [["a","b","c"]]
count_all: [[2,2,1]]
Do multiple aggregations:
>>> t.group_by("keys").aggregate([
... ("values", "sum"),
... ("keys", "count")
... ])
pyarrow.Table
keys: string
values_sum: int64
keys_count: int64
----
keys: [["a","b","c"]]
values_sum: [[3,7,5]]
keys_count: [[2,2,1]]
Count the number of non-null values for column "values"
over the grouped column "keys":
>>> import pyarrow.compute as pc
>>> t.group_by(["keys"]).aggregate([
... ("values", "count", pc.CountOptions(mode="only_valid"))
... ])
pyarrow.Table
keys: string
values_count: int64
----
keys: [["a","b","c"]]
values_count: [[2,2,1]]
Get a single row for each group in column "keys":
>>> t.group_by("keys").aggregate([])
pyarrow.Table
keys: string
----
keys: [["a","b","c"]]
"""
group_by_aggrs = []
for aggr in aggregations:
# Set opt to None if not specified
if len(aggr) == 2:
target, func = aggr
opt = None
else:
target, func, opt = aggr
# Ensure target is a list
if not isinstance(target, (list, tuple)):
target = [target]
# Ensure aggregate function is hash_ if needed
if len(self.keys) > 0 and not func.startswith("hash_"):
func = "hash_" + func
if len(self.keys) == 0 and func.startswith("hash_"):
func = func[5:]
# Determine output field name
func_nohash = func if not func.startswith("hash_") else func[5:]
if len(target) == 0:
aggr_name = func_nohash
else:
aggr_name = "_".join(target) + "_" + func_nohash
group_by_aggrs.append((target, func, opt, aggr_name))
return _pac()._group_by(
self._table, group_by_aggrs, self.keys, use_threads=self._use_threads
)
Reference in a new issue Copy permalink