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table.pxi
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table.pxi
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import json
from collections import OrderedDict
try:
import pandas as pd
except ImportError:
# The pure-Python based API works without a pandas installation
pass
else:
import pyarrow.pandas_compat as pdcompat
cdef class ChunkedArray(_PandasConvertible):
"""
Array backed via one or more memory chunks.
Warning
-------
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 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 format(self, int indent=0, int window=10):
cdef:
c_string result
with nogil:
check_status(
PrettyPrint(
deref(self.chunked_array),
PrettyPrintOptions(indent, window),
&result
)
)
return frombytes(result)
def __str__(self):
return self.format()
@property
def null_count(self):
"""
Number of null entires
Returns
-------
int
"""
return self.chunked_array.null_count()
def __iter__(self):
for chunk in self.iterchunks():
for item in chunk:
yield item
def __getitem__(self, key):
if isinstance(key, slice):
return _normalize_slice(self, key)
elif isinstance(key, six.integer_types):
return self.getitem(key)
else:
raise TypeError("key must either be a slice or integer")
cdef getitem(self, int64_t i):
cdef int j
index = _normalize_index(i, self.chunked_array.length())
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 __eq__(self, other):
try:
return self.equals(other)
except TypeError:
return NotImplemented
def equals(self, ChunkedArray other):
"""
Return whether the contents of two chunked arrays are equal
Parameters
----------
other : pyarrow.ChunkedArray
Returns
-------
are_equal : boolean
"""
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):
cdef:
PyObject* out
PandasOptions c_options = options
with nogil:
check_status(libarrow.ConvertChunkedArrayToPandas(
c_options,
self.sp_chunked_array,
self, &out))
return wrap_array_output(out)
def __array__(self, dtype=None):
if dtype is None:
return self.to_pandas()
return self.to_pandas().astype(dtype)
def dictionary_encode(self):
"""
Compute dictionary-encoded representation of array
Returns
-------
pyarrow.ChunkedArray
Same chunking as the input, all chunks share a common dictionary.
"""
cdef CDatum out
with nogil:
check_status(
DictionaryEncode(_context(), CDatum(self.sp_chunked_array),
&out))
return wrap_datum(out)
def unique(self):
"""
Compute distinct elements in array
Returns
-------
pyarrow.Array
"""
cdef shared_ptr[CArray] result
with nogil:
check_status(
Unique(_context(), CDatum(self.sp_chunked_array), &result))
return pyarrow_wrap_array(result)
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')
if length is None:
result = self.chunked_array.Slice(offset)
else:
result = self.chunked_array.Slice(offset, length)
return pyarrow_wrap_chunked_array(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 : list of Array or values coercible to arrays
type : DataType or string coercible to DataType
Returns
-------
ChunkedArray
"""
cdef:
Array arr
vector[shared_ptr[CArray]] c_arrays
shared_ptr[CChunkedArray] sp_chunked_array
shared_ptr[CDataType] sp_data_type
for x in arrays:
if isinstance(x, Array):
arr = x
if type is not None:
assert x.type == type
else:
arr = array(x, type=type)
c_arrays.push_back(arr.sp_array)
if type:
sp_data_type = pyarrow_unwrap_data_type(type)
sp_chunked_array.reset(new CChunkedArray(c_arrays, sp_data_type))
else:
if c_arrays.size() == 0:
raise ValueError("Cannot construct a chunked array with neither "
"arrays nor type")
sp_chunked_array.reset(new CChunkedArray(c_arrays))
return pyarrow_wrap_chunked_array(sp_chunked_array)
def column(object field_or_name, arr):
"""
Create Column object from field/string and array-like data
Parameters
----------
field_or_name : string or Field
arr : Array, list of Arrays, or ChunkedArray
Returns
-------
column : Column
"""
cdef:
Field boxed_field
Array _arr
ChunkedArray _carr
shared_ptr[CColumn] sp_column
if isinstance(arr, list):
arr = chunked_array(arr)
elif not isinstance(arr, (Array, ChunkedArray)):
arr = array(arr)
if isinstance(field_or_name, Field):
boxed_field = field_or_name
if arr.type != boxed_field.type:
raise ValueError('Passed field type does not match array')
else:
boxed_field = field(field_or_name, arr.type)
if isinstance(arr, Array):
_arr = arr
sp_column.reset(new CColumn(boxed_field.sp_field, _arr.sp_array))
elif isinstance(arr, ChunkedArray):
_carr = arr
sp_column.reset(new CColumn(boxed_field.sp_field,
_carr.sp_chunked_array))
else:
raise ValueError("Unsupported type for column(...): {}"
.format(type(arr)))
return pyarrow_wrap_column(sp_column)
cdef class Column(_PandasConvertible):
"""
Named vector of elements of equal type.
Warning
-------
Do not call this class's constructor directly.
"""
def __cinit__(self):
self.column = NULL
def __init__(self):
raise TypeError("Do not call Column's constructor directly, use one "
"of the `Column.from_*` functions instead.")
cdef void init(self, const shared_ptr[CColumn]& column):
self.sp_column = column
self.column = column.get()
def __reduce__(self):
return column, (self.field, self.data)
def __repr__(self):
from pyarrow.compat import StringIO
result = StringIO()
result.write('<Column name={0!r} type={1!r}>'
.format(self.name, self.type))
result.write('\n{}'.format(str(self.data)))
return result.getvalue()
def __getitem__(self, key):
return self.data[key]
@staticmethod
def from_array(*args):
return column(*args)
def cast(self, object target_type, bint safe=True):
"""
Cast column values to another data type
Parameters
----------
target_type : DataType
Type to cast to
safe : boolean, default True
Check for overflows or other unsafe conversions
Returns
-------
casted : Column
"""
cdef:
CCastOptions options = CCastOptions(safe)
DataType type = ensure_type(target_type)
shared_ptr[CArray] result
CDatum out
with nogil:
check_status(Cast(_context(), CDatum(self.column.data()),
type.sp_type, options, &out))
casted_data = pyarrow_wrap_chunked_array(out.chunked_array())
return column(self.name, casted_data)
def dictionary_encode(self):
"""
Compute dictionary-encoded representation of array
Returns
-------
pyarrow.Column
Same chunking as the input, all chunks share a common dictionary.
"""
ca = self.data.dictionary_encode()
return column(self.name, ca)
def unique(self):
"""
Compute distinct elements in array
Returns
-------
pyarrow.Array
"""
return self.data.unique()
def flatten(self, MemoryPool memory_pool=None):
"""
Flatten this Column. If it has a struct type, the column is
flattened into one column per struct field.
Parameters
----------
memory_pool : MemoryPool, default None
For memory allocations, if required, otherwise use default pool
Returns
-------
result : List[Column]
"""
cdef:
vector[shared_ptr[CColumn]] flattened
CMemoryPool* pool = maybe_unbox_memory_pool(memory_pool)
with nogil:
check_status(self.column.Flatten(pool, &flattened))
return [pyarrow_wrap_column(col) for col in flattened]
def _to_pandas(self, options, **kwargs):
values = self.data._to_pandas(options)
result = pd.Series(values, name=self.name)
if isinstance(self.type, TimestampType):
tz = self.type.tz
if tz is not None:
tz = string_to_tzinfo(tz)
result = (result.dt.tz_localize('utc')
.dt.tz_convert(tz))
return result
def __array__(self, dtype=None):
return self.data.__array__(dtype=dtype)
def __eq__(self, other):
try:
return self.equals(other)
except TypeError:
return NotImplemented
def equals(self, Column other):
"""
Check if contents of two columns are equal
Parameters
----------
other : pyarrow.Column
Returns
-------
are_equal : boolean
"""
cdef:
CColumn* this_col = self.column
CColumn* other_col = other.column
c_bool result
with nogil:
result = this_col.Equals(deref(other_col))
return result
def to_pylist(self):
"""
Convert to a list of native Python objects.
"""
return self.data.to_pylist()
def __len__(self):
return self.length()
def length(self):
return self.column.length()
@property
def field(self):
return pyarrow_wrap_field(self.column.field())
@property
def shape(self):
"""
Dimensions of this columns
Returns
-------
(int,)
"""
return (self.length(),)
@property
def null_count(self):
"""
Number of null entires
Returns
-------
int
"""
return self.column.null_count()
@property
def name(self):
"""
Label of the column
Returns
-------
str
"""
return bytes(self.column.name()).decode('utf8')
@property
def type(self):
"""
Type information for this column
Returns
-------
pyarrow.DataType
"""
return pyarrow_wrap_data_type(self.column.type())
@property
def data(self):
"""
The underlying data
Returns
-------
pyarrow.ChunkedArray
"""
return pyarrow_wrap_chunked_array(self.column.data())
cdef _schema_from_arrays(arrays, names, metadata, shared_ptr[CSchema]* schema):
cdef:
Py_ssize_t K = len(arrays)
c_string c_name
CColumn* c_column
shared_ptr[CDataType] c_type
shared_ptr[CKeyValueMetadata] c_meta
vector[shared_ptr[CField]] c_fields
if metadata is not None:
if not isinstance(metadata, dict):
raise TypeError('Metadata must be an instance of dict')
c_meta = pyarrow_unwrap_metadata(metadata)
if K == 0:
schema.reset(new CSchema(c_fields, c_meta))
return
c_fields.resize(K)
if isinstance(arrays[0], Column):
for i in range(K):
c_column = (<Column>arrays[i]).column
c_fields[i] = c_column.field()
else:
if names is None:
raise ValueError('Must pass names when constructing '
'from Array objects')
if len(names) != K:
raise ValueError('Length of names ({}) does not match '
'length of arrays ({})'.format(len(names), K))
for i in range(K):
val = arrays[i]
if isinstance(val, (Array, ChunkedArray)):
c_type = (<DataType> val.type).sp_type
else:
raise TypeError(type(val))
if names[i] is None:
c_name = tobytes(u'None')
else:
c_name = tobytes(names[i])
c_fields[i].reset(new CField(c_name, c_type, True))
schema.reset(new CSchema(c_fields, c_meta))
cdef class RecordBatch(_PandasConvertible):
"""
Batch of rows of columns of equal length
Warning
-------
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()
def __reduce__(self):
return _reconstruct_record_batch, (self.columns, self.schema)
def __len__(self):
return self.batch.num_rows()
def replace_schema_metadata(self, metadata=None):
"""
EXPERIMENTAL: 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[CKeyValueMetadata] c_meta
shared_ptr[CRecordBatch] c_batch
if metadata is not None:
if not isinstance(metadata, dict):
raise TypeError('Metadata must be an instance of dict')
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
@property
def columns(self):
"""
List of all columns in numerical order
Returns
-------
list of pa.Column
"""
return [self.column(i) for i in range(self.num_columns)]
def column(self, i):
"""
Select single column from record batch
Returns
-------
column : pyarrow.Array
"""
if not -self.num_columns <= i < self.num_columns:
raise IndexError(
'Record batch column index {:d} is out of range'.format(i)
)
return pyarrow_wrap_array(self.batch.column(i))
def __getitem__(self, key):
if isinstance(key, slice):
return _normalize_slice(self, key)
else:
return self.column(_normalize_index(key, self.num_columns))
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
CMemoryPool* pool = maybe_unbox_memory_pool(memory_pool)
with nogil:
check_status(SerializeRecordBatch(deref(self.batch),
pool, &buffer))
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 array 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')
if length is None:
result = self.batch.Slice(offset)
else:
result = self.batch.Slice(offset, length)
return pyarrow_wrap_batch(result)
def equals(self, RecordBatch other):
cdef:
CRecordBatch* this_batch = self.batch
CRecordBatch* other_batch = other.batch
c_bool result
with nogil:
result = this_batch.Equals(deref(other_batch))
return result
def to_pydict(self):
"""
Converted the arrow::RecordBatch to an OrderedDict
Returns
-------
OrderedDict
"""
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 OrderedDict(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, bint preserve_index=True,
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.
preserve_index : bool, optional
Whether to store the index as an additional column in the resulting
``RecordBatch``.
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
"""
names, arrays, metadata = pdcompat.dataframe_to_arrays(
df, schema, preserve_index, nthreads=nthreads, columns=columns
)
return cls.from_arrays(arrays, names, metadata)
@staticmethod
def from_arrays(list arrays, names, metadata=None):
"""
Construct a RecordBatch from multiple pyarrow.Arrays
Parameters
----------
arrays: list of pyarrow.Array
column-wise data vectors
names: pyarrow.Schema or list of str
schema or list of labels for the columns
Returns
-------
pyarrow.RecordBatch
"""
cdef:
Array arr
c_string c_name
shared_ptr[CSchema] c_schema
vector[shared_ptr[CArray]] c_arrays
int64_t num_rows
int64_t i
int64_t number_of_arrays = len(arrays)
if len(arrays) > 0:
num_rows = len(arrays[0])
else:
num_rows = 0
if isinstance(names, Schema):
c_schema = (<Schema> names).sp_schema
else:
_schema_from_arrays(arrays, names, metadata, &c_schema)
c_arrays.reserve(len(arrays))
for arr in 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)
return pyarrow_wrap_batch(
CRecordBatch.Make(c_schema, num_rows, c_arrays))
def _reconstruct_record_batch(columns, schema):
"""
Internal: reconstruct RecordBatch from pickled components.
"""
return RecordBatch.from_arrays(columns, schema)
def table_to_blocks(PandasOptions options, Table table,
MemoryPool memory_pool, categories):
cdef:
PyObject* result_obj
shared_ptr[CTable] c_table = table.sp_table
CMemoryPool* pool
unordered_set[c_string] categorical_columns
if categories is not None:
categorical_columns = {tobytes(cat) for cat in categories}
pool = maybe_unbox_memory_pool(memory_pool)
with nogil:
check_status(
libarrow.ConvertTableToPandas(
options, categorical_columns, c_table, pool, &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 __repr__(self):
return 'pyarrow.{}\n{}'.format(type(self).__name__, str(self.schema))
cdef void init(self, const shared_ptr[CTable]& table):
self.sp_table = table
self.table = table.get()
def _validate(self):
"""
Validate table consistency.
"""
with nogil:
check_status(self.table.Validate())
def __reduce__(self):
# Reduce the columns as ChunkedArrays to avoid serializing schema
# data twice
columns = [col.data for col in self.columns]
return _reconstruct_table, (columns, self.schema)
def replace_schema_metadata(self, metadata=None):
"""
EXPERIMENTAL: 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
-------
shallow_copy : Table
"""
cdef:
shared_ptr[CKeyValueMetadata] c_meta
shared_ptr[CTable] c_table
if metadata is not None:
if not isinstance(metadata, dict):
raise TypeError('Metadata must be an instance of dict')
c_meta = pyarrow_unwrap_metadata(metadata)