# 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()) [ ... ] >>> pa.chunked_array([[2, 2, 4], [4, 5, 100]]) [ [ 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 '{0}\n{1}'.format(type_format, str(self)) def to_string(self, *, int indent=0, int window=5, int container_window=2, c_bool skip_new_lines=False): """ 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. 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 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() [ [ 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() [ [ 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() [ [ 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) [ [ 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() [ ... -- 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 [ -- is_valid: all not null -- child 0 type: int64 [ 2, 4, 5, 100 ] -- child 1 type: int64 [ 2, 2, 1, 1 ] ] >>> c_arr.flatten() [ [ [ 2, 4, 5, 100 ] ], [ [ 2, 2, 1, 1 ] ]] >>> c_arr.type StructType(struct) >>> 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 [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.combine_chunks() [ 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 [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.unique() [ 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 structs Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.value_counts() -- 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 [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.slice(2,2) [ [ 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 [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> mask = pa.array([True, False, None, True, False, True]) >>> n_legs.filter(mask) [ [ 2 ], [ 4, 100 ] ] >>> n_legs.filter(mask, null_selection_behavior="emit_null") [ [ 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 [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.index(4) >>> n_legs.index(4, start=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 [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.take([1,4,5]) [ [ 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 [ [ 2, 2, null ], [ 4, 5, 100 ] ] >>> n_legs.drop_null() [ [ 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 [ ... -- dictionary: [ "Flamingo", "Parrot", "Dog" ] -- indices: [ 0, 1, 2 ], ... -- dictionary: [ "Horse", "Brittle stars", "Centipede" ] -- indices: [ 0, 1, 2 ] ] >>> c_arr.unify_dictionaries() [ ... -- 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) [ 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 [ [ 2, 2, null ], [ 4, 5, 100 ] ] >>> n_legs.chunks [ [ 2, 2, null ], [ 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): """ Convert to a list of native Python objects. 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() 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 = 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()) [ ... ] >>> pa.chunked_array([[2, 2, 4], [4, 5, 100]]) [ [ 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('Length of names ({}) does not match ' 'length of arrays ({})'.format(len(names), 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('Length of names ({}) does not match ' 'length of arrays ({})'.format(len(names), K)) converted_arrays = [] for i in range(K): val = arrays[i] if not isinstance(val, (Array, ChunkedArray)): val = array(val) c_type = ( 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("Field \"{}\" does not exist in schema" .format(i)) elif len(field_indices) > 1: raise KeyError("Field \"{}\" exists {} times in schema" .format(i, len(field_indices))) 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) [ [ 2, 4, 5, 100 ] ] Select a column by its name: >>> table.column("animals") [ [ "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 [ [ [ null, 4, 5, null ] ], [ [ "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 >>> table.field(1) pyarrow.Field """ 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): """ Convert the Table or RecordBatch to a dict or OrderedDict. 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() entries.append((name, column)) return ordered_dict(entries) def to_pylist(self): """ Convert the Table or RecordBatch to a list of rows / dictionaries. 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() 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 = 'pyarrow.{}\n{}'.format(type(self).__name__, schema_as_string) pieces = [title] if preview_cols: pieces.append('----') for i in range(min(self.num_columns, preview_cols)): pieces.append('{}: {}'.format( 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("Column {!r} not found".format(col)) 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 = _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("Column {!r} not found".format(name)) 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 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( 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("Target schema's field names are not matching " "the record batch's field names: {!r}, {!r}" .format(self.schema.names, target_schema.names)) for column, field in zip(self.itercolumns(), target_schema): if not field.nullable and column.null_count > 0: raise ValueError("Casting field {!r} with null values to non-nullable" .format(field.name)) 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).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: ' '{0} vs {1}'.format(len(arr), 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( 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) 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) 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( 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 = (destination).unwrap().get().default_memory_manager() elif isinstance(destination, MemoryManager): c_memory_manager = (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), c_ptr, 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( c_ptr, c_schema_ptr)) else: with nogil: c_batch = GetResultValue(ImportRecordBatch( 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 = PyCapsule_GetPointer(schema_capsule, 'arrow_schema') c_array = 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), NULL, c_ptr, 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 = _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, 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), 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 = PyCapsule_GetPointer(schema_capsule, 'arrow_schema') c_array = 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( 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 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. 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 ---- 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 ---- 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("Target schema's field names are not matching " "the table's field names: {!r}, {!r}" .format(self.schema.names, target_schema.names)) for column, field in zip(self.itercolumns(), target_schema): if not field.nullable and column.null_count > 0: raise ValueError("Casting field {!r} with null values to non-nullable" .format(field.name)) 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).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 = 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]( ( item).sp_array ) ) elif isinstance(item, ChunkedArray): columns.push_back(( 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() return chunked_array([ batch.to_struct_array() for batch in self.to_batches(max_chunksize=max_chunksize) ]) @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() >>> 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 = _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("Column {!r} not found".format(name)) 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): """ 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. Returns ------- Table Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> 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"]] """ 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 ) 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 _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") 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 )