def _reconstruct_block(item):
# Construct the individual blocks converting dictionary types to pandas
# categorical types and Timestamps-with-timezones types to the proper
# pandas Blocks
block_arr = item['block']
placement = item['placement']
if 'dictionary' in item:
cat = pd.Categorical.from_codes(block_arr,
categories=item['dictionary'],
ordered=item['ordered'])
block = _int.make_block(cat, placement=placement,
klass=_int.CategoricalBlock)
elif 'timezone' in item:
dtype = _make_datetimetz(item['timezone'])
block = _int.make_block(block_arr, placement=placement,
klass=_int.DatetimeTZBlock,
dtype=dtype)
elif 'object' in item:
block = _int.make_block(builtin_pickle.loads(block_arr),
placement=placement, klass=_int.ObjectBlock)
else:
block = _int.make_block(block_arr, placement=placement)
return block
def _reconstruct_block(item):
import pandas.core.internals as _int
# Construct the individual blocks converting dictionary types to pandas
# categorical types and Timestamps-with-timezones types to the proper
# pandas Blocks
block_arr = item['block']
placement = item['placement']
if 'dictionary' in item:
cat = _pandas_api.categorical_type.from_codes(
block_arr, categories=item['dictionary'], ordered=item['ordered'])
block = _int.make_block(cat,
placement=placement,
klass=_int.CategoricalBlock)
elif 'timezone' in item:
dtype = make_datetimetz(item['timezone'])
block = _int.make_block(block_arr,
placement=placement,
klass=_int.DatetimeTZBlock,
dtype=dtype)
elif 'object' in item:
block = _int.make_block(builtin_pickle.loads(block_arr),
placement=placement,
klass=_int.ObjectBlock)
else:
block = _int.make_block(block_arr, placement=placement)
return block
def _reconstruct_block(item):
import pandas.core.internals as _int
# Construct the individual blocks converting dictionary types to pandas
# categorical types and Timestamps-with-timezones types to the proper
# pandas Blocks
block_arr = item.get('block', None)
placement = item['placement']
if 'dictionary' in item:
cat = _pandas_api.categorical_type.from_codes(
block_arr, categories=item['dictionary'], ordered=item['ordered'])
block = _int.make_block(cat,
placement=placement,
klass=_int.CategoricalBlock)
elif 'timezone' in item:
dtype = make_datetimetz(item['timezone'])
block = _int.make_block(block_arr,
placement=placement,
klass=_int.DatetimeTZBlock,
dtype=dtype)
elif 'object' in item:
block = _int.make_block(builtin_pickle.loads(block_arr),
placement=placement,
klass=_int.ObjectBlock)
elif 'py_array' in item:
arr = item['py_array']
# TODO have mechanism to know a method to create a
# pandas ExtensionArray given the pyarrow type
# Now hardcode here to create a pandas IntegerArray for the example
arr = arr.chunk(0)
buflist = arr.buffers()
data = np.frombuffer(
buflist[-1],
dtype=arr.type.to_pandas_dtype())[arr.offset:arr.offset + len(arr)]
bitmask = buflist[0]
if bitmask is not None:
mask = pa.BooleanArray.from_buffers(pa.bool_(), len(arr),
[None, bitmask])
mask = np.asarray(mask)
else:
mask = np.ones(len(arr), dtype=bool)
block_arr = _pandas_api.pd.arrays.IntegerArray(data.copy(),
~mask,
copy=False)
# create ExtensionBlock
block = _int.make_block(block_arr,
placement=placement,
klass=_int.ExtensionBlock)
else:
block = _int.make_block(block_arr, placement=placement)
return block
def _load_pickle_from_buffer(data):
as_memoryview = memoryview(data)
return builtin_pickle.loads(as_memoryview)
def _load_pickle_from_buffer(data):
as_memoryview = memoryview(data)
if six.PY2:
return builtin_pickle.loads(as_memoryview.tobytes())
else:
return builtin_pickle.loads(as_memoryview)
def _reconstruct_block(item, columns=None, extension_columns=None):
"""
Construct a pandas Block from the `item` dictionary coming from pyarrow's
serialization or returned by arrow::python::ConvertTableToPandas.
This function takes care of converting dictionary types to pandas
categorical, Timestamp-with-timezones to the proper pandas Block, and
conversion to pandas ExtensionBlock
Parameters
----------
item : dict
For basic types, this is a dictionary in the form of
{'block': np.ndarray of values, 'placement': pandas block placement}.
Additional keys are present for other types (dictionary, timezone,
object).
columns :
Column names of the table being constructed, used for extension types
extension_columns : dict
Dictionary of {column_name: pandas_dtype} that includes all columns
and corresponding dtypes that will be converted to a pandas
ExtensionBlock.
Returns
-------
pandas Block
"""
import pandas.core.internals as _int
block_arr = item.get('block', None)
placement = item['placement']
if 'dictionary' in item:
cat = _pandas_api.categorical_type.from_codes(
block_arr, categories=item['dictionary'],
ordered=item['ordered'])
block = _int.make_block(cat, placement=placement,
klass=_int.CategoricalBlock)
elif 'timezone' in item:
dtype = make_datetimetz(item['timezone'])
block = _int.make_block(block_arr, placement=placement,
klass=_int.DatetimeTZBlock,
dtype=dtype)
elif 'object' in item:
block = _int.make_block(builtin_pickle.loads(block_arr),
placement=placement, klass=_int.ObjectBlock)
elif 'py_array' in item:
# create ExtensionBlock
arr = item['py_array']
assert len(placement) == 1
name = columns[placement[0]]
pandas_dtype = extension_columns[name]
if not hasattr(pandas_dtype, '__from_arrow__'):
raise ValueError("This column does not support to be converted "
"to a pandas ExtensionArray")
pd_ext_arr = pandas_dtype.__from_arrow__(arr)
block = _int.make_block(pd_ext_arr, placement=placement,
klass=_int.ExtensionBlock)
else:
block = _int.make_block(block_arr, placement=placement)
return block
def _load_pickle_from_buffer(data):
as_memoryview = memoryview(data)
if six.PY2:
return builtin_pickle.loads(as_memoryview.tobytes())
else:
return builtin_pickle.loads(as_memoryview)
