# 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.
import warnings
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.
"""
def __cinit__(self):
self.chunked_array = NULL
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):
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 pyarrow_wrap_data_type(self.sp_chunked_array.get().type())
def length(self):
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=10,
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 at the begin and end
of the array when the arrays is bigger than the window.
The other elements will be ellipsed.
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.
"""
cdef:
c_string result
PrettyPrintOptions options
with nogil:
options = PrettyPrintOptions(indent, window)
options.skip_new_lines = skip_new_lines
check_status(
PrettyPrint(
deref(self.chunked_array),
options,
&result
)
)
return frombytes(result, safe=True)
def format(self, **kwargs):
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:
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
"""
return self.chunked_array.null_count()
@property
def nbytes(self):
"""
Total number of bytes consumed by the elements of the chunked array.
"""
size = 0
for chunk in self.iterchunks():
size += chunk.nbytes
return 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)
"""
if isinstance(key, slice):
return _normalize_slice(self, key)
return self.getitem(_normalize_index(key, self.chunked_array.length()))
cdef getitem(self, int64_t index):
cdef int j
for j in range(self.num_chunks):
if index < self.chunked_array.chunk(j).get().length():
return self.chunk(j)[index]
else:
index -= self.chunked_array.chunk(j).get().length()
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
"""
options = _pc().NullOptions(nan_is_null=nan_is_null)
return _pc().call_function('is_null', [self], options)
def is_valid(self):
"""
Return boolean array indicating the non-null values.
"""
return _pc().is_valid(self)
def __eq__(self, other):
try:
return self.equals(other)
except TypeError:
return NotImplemented
def fill_null(self, fill_value):
"""
See pyarrow.compute.fill_null docstring for usage.
"""
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
"""
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, **kwargs):
return _array_like_to_pandas(self, options)
def to_numpy(self):
"""
Return a NumPy copy of this array (experimental).
Returns
-------
array : numpy.ndarray
"""
cdef:
PyObject* out
PandasOptions c_options
object values
if self.type.id == _Type_EXTENSION:
storage_array = chunked_array(
[chunk.storage for chunk in self.iterchunks()],
type=self.type.storage_type
)
return storage_array.to_numpy()
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):
values = self.to_numpy()
if dtype is None:
return values
return values.astype(dtype)
def cast(self, object target_type, safe=True):
"""
Cast array values to another data type
See pyarrow.compute.cast for usage
"""
return _pc().cast(self, target_type, safe=safe)
def dictionary_encode(self, null_encoding='mask'):
"""
Compute dictionary-encoded representation of array
Returns
-------
pyarrow.ChunkedArray
Same chunking as the input, all chunks share a common dictionary.
"""
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[ChunkedArray]
"""
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
"""
return concat_arrays(self.chunks)
def unique(self):
"""
Compute distinct elements in array
Returns
-------
pyarrow.Array
"""
return _pc().call_function('unique', [self])
def value_counts(self):
"""
Compute counts of unique elements in array.
Returns
-------
An array of structs
"""
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
"""
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 a chunked array. See pyarrow.compute.filter for full
usage.
"""
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 pyarrow.compute.index for full usage.
"""
return _pc().index(self, value, start, end, memory_pool=memory_pool)
def take(self, object indices):
"""
Select values from a chunked array. See pyarrow.compute.take for full
usage.
"""
return _pc().take(self, indices)
def drop_null(self):
"""
Remove missing values from a chunked array.
See pyarrow.compute.drop_null for full description.
"""
return _pc().drop_null(self)
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
"""
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
"""
return self.chunked_array.num_chunks()
def chunk(self, i):
"""
Select a chunk by its index
Parameters
----------
i : int
Returns
-------
pyarrow.Array
"""
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):
return list(self.iterchunks())
def iterchunks(self):
for i in range(self.num_chunks):
yield self.chunk(i)
def to_pylist(self):
"""
Convert to a list of native Python objects.
"""
result = []
for i in range(self.num_chunks):
result += self.chunk(i).to_pylist()
return result
def chunked_array(arrays, type=None):
"""
Construct chunked array from list of array-like objects
Parameters
----------
arrays : Array, list of Array, or values coercible to arrays
Must all be the same data type. Can be empty only if type also passed.
type : DataType or string coercible to DataType
Returns
-------
ChunkedArray
"""
cdef:
Array arr
vector[shared_ptr[CArray]] c_arrays
shared_ptr[CChunkedArray] sp_chunked_array
type = ensure_type(type, allow_none=True)
if isinstance(arrays, Array):
arrays = [arrays]
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
else:
if arr.type != type:
raise TypeError(
"All array chunks must have type {}".format(type)
)
c_arrays.push_back(arr.sp_array)
if c_arrays.size() == 0 and type is None:
raise ValueError("When passing an empty collection of arrays "
"you must also pass the data type")
sp_chunked_array.reset(
new CChunkedArray(c_arrays, pyarrow_unwrap_data_type(type))
)
with nogil:
check_status(sp_chunked_array.get().Validate())
return pyarrow_wrap_chunked_array(sp_chunked_array)
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 RecordBatch(_PandasConvertible):
"""
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.
"""
def __cinit__(self):
self.batch = NULL
self._schema = None
def __init__(self):
raise TypeError("Do not call RecordBatch's constructor directly, use "
"one of the `RecordBatch.from_*` functions instead.")
cdef void init(self, const shared_ptr[CRecordBatch]& batch):
self.sp_batch = batch
self.batch = batch.get()
@staticmethod
def from_pydict(mapping, schema=None, metadata=None):
"""
Construct a 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
-------
RecordBatch
"""
return _from_pydict(cls=RecordBatch,
mapping=mapping,
schema=schema,
metadata=metadata)
def __reduce__(self):
return _reconstruct_record_batch, (self.columns, self.schema)
def __len__(self):
return self.batch.num_rows()
def __eq__(self, other):
try:
return self.equals(other)
except TypeError:
return NotImplemented
def to_string(self, show_metadata=False):
# Use less verbose schema output.
schema_as_string = self.schema.to_string(
show_field_metadata=show_metadata,
show_schema_metadata=show_metadata
)
return 'pyarrow.{}\n{}'.format(type(self).__name__, schema_as_string)
def __repr__(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:
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
"""
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
"""
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
"""
return len(self)
@property
def schema(self):
"""
Schema of the RecordBatch and its columns
Returns
-------
pyarrow.Schema
"""
if self._schema is None:
self._schema = pyarrow_wrap_schema(self.batch.schema())
return self._schema
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
-------
pyarrow.Field
"""
return self.schema.field(i)
@property
def columns(self):
"""
List of all columns in numerical order
Returns
-------
list of pa.Array
"""
return [self.column(i) for i in range(self.num_columns)]
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 record batch schema"
.format(i))
elif len(field_indices) > 1:
raise KeyError(
"Field \"{}\" exists {} times in record batch 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 column(self, i):
"""
Select single column from record batch
Parameters
----------
i : int or string
The index or name of the column to retrieve.
Returns
-------
column : pyarrow.Array
"""
return self._column(self._ensure_integer_index(i))
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.
"""
size = 0
for i in range(self.num_columns):
size += self.column(i).nbytes
return size
def __sizeof__(self):
return super(RecordBatch, self).__sizeof__() + self.nbytes
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
-------
value : Array (index/column) or RecordBatch (slice)
"""
if isinstance(key, slice):
return _normalize_slice(self, key)
else:
return self.column(key)
def serialize(self, memory_pool=None):
"""
Write RecordBatch to Buffer as encapsulated IPC message.
Parameters
----------
memory_pool : MemoryPool, default None
Uses default memory pool if not specified
Returns
-------
serialized : Buffer
"""
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
"""
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 filter(self, mask, object null_selection_behavior="drop"):
"""
Select record from a record batch. See pyarrow.compute.filter for full
usage.
"""
return _pc().filter(self, mask, null_selection_behavior)
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
"""
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 take(self, object indices):
"""
Select records from a RecordBatch. See pyarrow.compute.take for full
usage.
"""
return _pc().take(self, indices)
def drop_null(self):
"""
Remove missing values from a RecordBatch.
See pyarrow.compute.drop_null for full usage.
"""
return _pc().drop_null(self)
def to_pydict(self):
"""
Convert the RecordBatch to a dict or OrderedDict.
Returns
-------
dict
"""
entries = []
for i in range(self.batch.num_columns()):
name = bytes(self.batch.column_name(i)).decode('utf8')
column = self[i].to_pylist()
entries.append((name, column))
return ordered_dict(entries)
def _to_pandas(self, options, **kwargs):
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 (may use up to system CPU count threads)
If greater than 1, convert columns to Arrow in parallel using
indicated number of threads
columns : list, optional
List of column to be converted. If None, use all columns.
Returns
-------
pyarrow.RecordBatch
"""
from pyarrow.pandas_compat import dataframe_to_arrays
arrays, schema = dataframe_to_arrays(
df, schema, preserve_index, nthreads=nthreads, columns=columns
)
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
"""
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
"""
cdef:
shared_ptr[CRecordBatch] c_record_batch
with nogil:
c_record_batch = GetResultValue(
CRecordBatch.FromStructArray(struct_array.sp_array))
return pyarrow_wrap_batch(c_record_batch)
def _export_to_c(self, uintptr_t out_ptr, uintptr_t 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.
"""
with nogil:
check_status(ExportRecordBatch(deref(self.sp_batch),
out_ptr,
out_schema_ptr))
@staticmethod
def _import_from_c(uintptr_t 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:
shared_ptr[CRecordBatch] c_batch
c_schema = pyarrow_unwrap_schema(schema)
if c_schema == nullptr:
# Not a Schema object, perhaps a raw ArrowSchema pointer
schema_ptr = schema
with nogil:
c_batch = GetResultValue(ImportRecordBatch(
in_ptr, schema_ptr))
else:
with nogil:
c_batch = GetResultValue(ImportRecordBatch(
in_ptr, c_schema))
return pyarrow_wrap_batch(c_batch)
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}
# ARROW-3789(wesm); Convert 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.ConvertTableToPandas(c_options, move(c_table),
&result_obj)
)
return PyObject_to_object(result_obj)
cdef class Table(_PandasConvertible):
"""
A collection of top-level named, equal length Arrow arrays.
Warning
-------
Do not call this class's constructor directly, use one of the ``from_*``
methods instead.
"""
def __cinit__(self):
self.table = NULL
def __init__(self):
raise TypeError("Do not call Table's constructor directly, use one of "
"the `Table.from_*` functions instead.")
def to_string(self, *, show_metadata=False, preview_cols=0):
"""
Return human-readable string representation of Table.
Parameters
----------
show_metadata : bool, default True
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 __repr__(self):
if self.table == NULL:
raise ValueError("Table's internal pointer is NULL, do not use "
"any methods or attributes on this object")
return self.to_string(preview_cols=10)
cdef void init(self, const shared_ptr[CTable]& table):
self.sp_table = table
self.table = table.get()
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:
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
columns = [col for col in self.columns]
return _reconstruct_table, (columns, self.schema)
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
-------
ChunkedArray (index/column) or Table (slice)
"""
if isinstance(key, slice):
return _normalize_slice(self, key)
else:
return self.column(key)
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
"""
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 filter(self, mask, object null_selection_behavior="drop"):
"""
Select records from a Table. See :func:`pyarrow.compute.filter` for
full usage.
"""
return _pc().filter(self, mask, null_selection_behavior)
def take(self, object indices):
"""
Select records from a Table. See :func:`pyarrow.compute.take` for full
usage.
"""
return _pc().take(self, indices)
def drop_null(self):
"""
Remove missing values from a Table.
See :func:`pyarrow.compute.drop_null` for full usage.
"""
return _pc().drop_null(self)
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
"""
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
"""
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
"""
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
"""
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
"""
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 __eq__(self, other):
try:
return self.equals(other)
except TypeError:
return NotImplemented
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
"""
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, bint safe=True):
"""
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.
Returns
-------
Table
"""
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):
casted = column.cast(field.type, safe=safe)
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 (may use up to system CPU count threads)
If greater than 1, convert columns to Arrow in parallel using
indicated number of 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 pandas as pd
>>> import pyarrow as pa
>>> df = pd.DataFrame({
... 'int': [1, 2],
... 'str': ['a', 'b']
... })
>>> pa.Table.from_pandas(df)
"""
from pyarrow.pandas_compat import dataframe_to_arrays
arrays, schema = dataframe_to_arrays(
df,
schema=schema,
preserve_index=preserve_index,
nthreads=nthreads,
columns=columns,
safe=safe
)
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
"""
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_pydict(mapping, schema=None, metadata=None):
"""
Construct a Table 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
"""
return _from_pydict(cls=Table,
mapping=mapping,
schema=schema,
metadata=metadata)
@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
"""
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, **kwargs):
"""
Convert Table to list of (contiguous) RecordBatch objects.
Parameters
----------
max_chunksize : int, default None
Maximum size for RecordBatch chunks. Individual chunks may be
smaller depending on the chunk layout of individual columns.
Returns
-------
list of RecordBatch
"""
cdef:
unique_ptr[TableBatchReader] reader
int64_t c_max_chunksize
list result = []
shared_ptr[CRecordBatch] batch
reader.reset(new TableBatchReader(deref(self.table)))
if 'chunksize' in kwargs:
max_chunksize = kwargs['chunksize']
msg = ('The parameter chunksize is deprecated for '
'pyarrow.Table.to_batches as of 0.15, please use '
'the parameter max_chunksize instead')
warnings.warn(msg, FutureWarning)
if max_chunksize is not None:
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_pandas(self, options, categories=None, ignore_metadata=False,
types_mapper=None):
from pyarrow.pandas_compat import table_to_blockmanager
mgr = table_to_blockmanager(
options, self, categories,
ignore_metadata=ignore_metadata,
types_mapper=types_mapper)
return pandas_api.data_frame(mgr)
def to_pydict(self):
"""
Convert the Table to a dict or OrderedDict.
Returns
-------
dict
"""
cdef:
size_t i
size_t num_columns = self.table.num_columns()
list entries = []
ChunkedArray column
for i in range(num_columns):
column = self.column(i)
entries.append((self.field(i).name, column.to_pylist()))
return ordered_dict(entries)
@property
def schema(self):
"""
Schema of the table and its columns.
Returns
-------
Schema
"""
return pyarrow_wrap_schema(self.table.schema())
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
"""
return self.schema.field(i)
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 table schema"
.format(i))
elif len(field_indices) > 1:
raise KeyError("Field \"{}\" exists {} times in table 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 column(self, i):
"""
Select a column by its column name, or numeric index.
Parameters
----------
i : int or string
The index or name of the column to retrieve.
Returns
-------
ChunkedArray
"""
return self._column(self._ensure_integer_index(i))
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
def itercolumns(self):
"""
Iterator over all columns in their numerical order.
Yields
------
ChunkedArray
"""
for i in range(self.num_columns):
yield self._column(i)
@property
def columns(self):
"""
List of all columns in numerical order.
Returns
-------
list of ChunkedArray
"""
return [self._column(i) for i in range(self.num_columns)]
@property
def num_columns(self):
"""
Number of columns in this table.
Returns
-------
int
"""
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
"""
return self.table.num_rows()
def __len__(self):
return self.num_rows
@property
def shape(self):
"""
Dimensions of the table: (#rows, #columns).
Returns
-------
(int, int)
Number of rows and number of columns.
"""
return (self.num_rows, self.num_columns)
@property
def nbytes(self):
"""
Total number of bytes consumed by the elements of the table.
Returns
-------
int
"""
size = 0
for column in self.itercolumns():
size += column.nbytes
return 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.
"""
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 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, list of Array, or values coercible to arrays
Column data.
Returns
-------
Table
New table with the passed column added.
"""
return self.add_column(self.num_columns, field_, column)
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.
"""
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.
"""
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)
@property
def column_names(self):
"""
Names of the table's columns.
Returns
-------
list of str
"""
names = self.table.ColumnNames()
return [frombytes(name) for name in names]
def rename_columns(self, names):
"""
Create new table with columns renamed to provided names.
Parameters
----------
names : list of str
List of new column names.
Returns
-------
Table
"""
cdef:
shared_ptr[CTable] c_table
vector[c_string] c_names
for name in names:
c_names.push_back(tobytes(name))
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.
Parameters
----------
columns : list of str
List of field names referencing existing columns.
Raises
------
KeyError
If any of the passed columns name are not existing.
Returns
-------
Table
New table without the 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()
table = self
for idx in indices:
table = table.remove_column(idx)
return table
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 : pandas.DataFrame, list
A DataFrame or list of arrays or chunked arrays.
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
"""
# 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 _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 : pandas.DataFrame, dict, list
A DataFrame, mapping of strings to Arrays or Python lists, or list of
arrays or chunked arrays.
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 (may use up to system CPU count threads)
For pandas.DataFrame inputs: if greater than 1, convert columns to
Arrow in parallel using indicated number of threads.
Returns
-------
Table
See Also
--------
Table.from_arrays, Table.from_pandas, Table.from_pydict
"""
# 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)
else:
raise TypeError(
"Expected pandas DataFrame, python dictionary or list of arrays")
def concat_tables(tables, c_bool promote=False, MemoryPool memory_pool=None):
"""
Concatenate pyarrow.Table objects.
If promote==False, 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==True, 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``.
Parameters
----------
tables : iterable of pyarrow.Table objects
Pyarrow tables to concatenate into a single Table.
promote : bool, default False
If True, concatenate tables with null-filling and null type promotion.
memory_pool : MemoryPool, default None
For memory allocations, if required, otherwise use default pool.
"""
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())
for table in tables:
c_tables.push_back(table.sp_table)
with nogil:
options.unify_schemas = promote
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')