def unconvert(values, dtype, compress=None):
as_is_ext = isinstance(values, ExtType) and values.code == 0
if as_is_ext:
values = values.data
if dtype == np.object_:
return np.array(values, dtype=object)
dtype = pandas_dtype(dtype).base
if not as_is_ext:
values = values.encode('latin1')
if compress == u'zlib':
import zlib
values = zlib.decompress(values)
return np.frombuffer(values, dtype=dtype)
elif compress == u'blosc':
import blosc
values = blosc.decompress(values)
return np.frombuffer(values, dtype=dtype)
# from a string
return np.fromstring(values, dtype=dtype)
def unconvert(values, dtype, compress=None):
as_is_ext = isinstance(values, ExtType) and values.code == 0
if as_is_ext:
values = values.data
if dtype == np.object_:
return np.array(values, dtype=object)
dtype = pandas_dtype(dtype).base
if not as_is_ext:
values = values.encode('latin1')
if compress == u'zlib':
import zlib
values = zlib.decompress(values)
return np.frombuffer(values, dtype=dtype)
elif compress == u'blosc':
import blosc
values = blosc.decompress(values)
return np.frombuffer(values, dtype=dtype)
# from a string
return np.fromstring(values, dtype=dtype)
def astype(self, dtype):
dtype = pandas_dtype(dtype)
if is_float_dtype(dtype) or is_integer_dtype(dtype):
values = self._values.astype(dtype)
elif is_object_dtype(dtype):
values = self._values
else:
raise TypeError('Setting %s dtype to anything other than '
'float64 or object is not supported' %
self.__class__)
return Index(values, name=self.name, dtype=dtype)
def astype(self, dtype):
dtype = pandas_dtype(dtype)
if is_float_dtype(dtype) or is_integer_dtype(dtype):
values = self._values.astype(dtype)
elif is_object_dtype(dtype):
values = self._values
else:
raise TypeError('Setting %s dtype to anything other than '
'float64 or object is not supported' %
self.__class__)
return Index(values, name=self.name, dtype=dtype)
def unconvert(values, dtype, compress=None):
as_is_ext = isinstance(values, ExtType) and values.code == 0
if as_is_ext:
values = values.data
if is_categorical_dtype(dtype):
return values
elif is_object_dtype(dtype):
return np.array(values, dtype=object)
dtype = pandas_dtype(dtype).base
if not as_is_ext:
values = values.encode('latin1')
if compress:
if compress == u'zlib':
_check_zlib()
decompress = zlib.decompress
elif compress == u'blosc':
_check_blosc()
decompress = blosc.decompress
else:
raise ValueError("compress must be one of 'zlib' or 'blosc'")
try:
return np.frombuffer(
_move_into_mutable_buffer(decompress(values)),
dtype=dtype,
)
except _BadMove as e:
# Pull the decompressed data off of the `_BadMove` exception.
# We don't just store this in the locals because we want to
# minimize the risk of giving users access to a `bytes` object
# whose data is also given to a mutable buffer.
values = e.args[0]
if len(values) > 1:
# The empty string and single characters are memoized in many
# string creating functions in the capi. This case should not
# warn even though we need to make a copy because we are only
# copying at most 1 byte.
warnings.warn(
'copying data after decompressing; this may mean that'
' decompress is caching its result',
PerformanceWarning,
)
# fall through to copying `np.fromstring`
# Copy the string into a numpy array.
return np.fromstring(values, dtype=dtype)
def astype(self, dtype, copy=True):
dtype = pandas_dtype(dtype)
if is_float_dtype(dtype):
values = self._values.astype(dtype, copy=copy)
elif is_integer_dtype(dtype):
if self.hasnans:
raise ValueError('cannot convert float NaN to integer')
values = self._values.astype(dtype, copy=copy)
elif is_object_dtype(dtype):
values = self._values.astype('object', copy=copy)
else:
raise TypeError('Setting %s dtype to anything other than '
'float64 or object is not supported' %
self.__class__)
return Index(values, name=self.name, dtype=dtype)
def decode(obj):
"""
Decoder for deserializing numpy data types.
"""
typ = obj.get(u'typ')
if typ is None:
return obj
elif typ == u'timestamp':
return Timestamp(obj[u'value'], tz=obj[u'tz'], offset=obj[u'offset'])
elif typ == u'nat':
return NaT
elif typ == u'period':
return Period(ordinal=obj[u'ordinal'], freq=obj[u'freq'])
elif typ == u'index':
dtype = dtype_for(obj[u'dtype'])
data = unconvert(obj[u'data'], dtype,
obj.get(u'compress'))
return globals()[obj[u'klass']](data, dtype=dtype, name=obj[u'name'])
elif typ == u'range_index':
return globals()[obj[u'klass']](obj[u'start'],
obj[u'stop'],
obj[u'step'],
name=obj[u'name'])
elif typ == u'multi_index':
dtype = dtype_for(obj[u'dtype'])
data = unconvert(obj[u'data'], dtype,
obj.get(u'compress'))
data = [tuple(x) for x in data]
return globals()[obj[u'klass']].from_tuples(data, names=obj[u'names'])
elif typ == u'period_index':
data = unconvert(obj[u'data'], np.int64, obj.get(u'compress'))
d = dict(name=obj[u'name'], freq=obj[u'freq'])
return globals()[obj[u'klass']](data, **d)
elif typ == u'datetime_index':
data = unconvert(obj[u'data'], np.int64, obj.get(u'compress'))
d = dict(name=obj[u'name'], freq=obj[u'freq'], verify_integrity=False)
result = globals()[obj[u'klass']](data, **d)
tz = obj[u'tz']
# reverse tz conversion
if tz is not None:
result = result.tz_localize('UTC').tz_convert(tz)
return result
elif typ == u'series':
dtype = dtype_for(obj[u'dtype'])
pd_dtype = pandas_dtype(dtype)
np_dtype = pandas_dtype(dtype).base
index = obj[u'index']
result = globals()[obj[u'klass']](unconvert(obj[u'data'], dtype,
obj[u'compress']),
index=index,
dtype=np_dtype,
name=obj[u'name'])
tz = getattr(pd_dtype, 'tz', None)
if tz:
result = result.dt.tz_localize('UTC').dt.tz_convert(tz)
return result
elif typ == u'block_manager':
axes = obj[u'axes']
def create_block(b):
values = unconvert(b[u'values'], dtype_for(b[u'dtype']),
b[u'compress']).reshape(b[u'shape'])
# locs handles duplicate column names, and should be used instead
# of items; see GH 9618
if u'locs' in b:
placement = b[u'locs']
else:
placement = axes[0].get_indexer(b[u'items'])
return make_block(values=values,
klass=getattr(internals, b[u'klass']),
placement=placement,
dtype=b[u'dtype'])
blocks = [create_block(b) for b in obj[u'blocks']]
return globals()[obj[u'klass']](BlockManager(blocks, axes))
elif typ == u'datetime':
return parse(obj[u'data'])
elif typ == u'datetime64':
return np.datetime64(parse(obj[u'data']))
elif typ == u'date':
return parse(obj[u'data']).date()
elif typ == u'timedelta':
return timedelta(*obj[u'data'])
elif typ == u'timedelta64':
return np.timedelta64(int(obj[u'data']))
# elif typ == 'sparse_series':
# dtype = dtype_for(obj['dtype'])
# return globals()[obj['klass']](
# unconvert(obj['sp_values'], dtype, obj['compress']),
# sparse_index=obj['sp_index'], index=obj['index'],
# fill_value=obj['fill_value'], kind=obj['kind'], name=obj['name'])
# elif typ == 'sparse_dataframe':
# return globals()[obj['klass']](
# obj['data'], columns=obj['columns'],
# default_fill_value=obj['default_fill_value'],
# default_kind=obj['default_kind']
# )
# elif typ == 'sparse_panel':
# return globals()[obj['klass']](
# obj['data'], items=obj['items'],
# default_fill_value=obj['default_fill_value'],
# default_kind=obj['default_kind'])
elif typ == u'block_index':
return globals()[obj[u'klass']](obj[u'length'], obj[u'blocs'],
obj[u'blengths'])
elif typ == u'int_index':
return globals()[obj[u'klass']](obj[u'length'], obj[u'indices'])
elif typ == u'ndarray':
return unconvert(obj[u'data'], np.typeDict[obj[u'dtype']],
obj.get(u'compress')).reshape(obj[u'shape'])
elif typ == u'np_scalar':
if obj.get(u'sub_typ') == u'np_complex':
return c2f(obj[u'real'], obj[u'imag'], obj[u'dtype'])
else:
dtype = dtype_for(obj[u'dtype'])
try:
return dtype(obj[u'data'])
except:
return dtype.type(obj[u'data'])
elif typ == u'np_complex':
return complex(obj[u'real'] + u'+' + obj[u'imag'] + u'j')
elif isinstance(obj, (dict, list, set)):
return obj
else:
return obj
def decode(obj):
"""
Decoder for deserializing numpy data types.
"""
typ = obj.get(u'typ')
if typ is None:
return obj
elif typ == u'timestamp':
return Timestamp(obj[u'value'], tz=obj[u'tz'], offset=obj[u'offset'])
elif typ == u'nat':
return NaT
elif typ == u'period':
return Period(ordinal=obj[u'ordinal'], freq=obj[u'freq'])
elif typ == u'index':
dtype = dtype_for(obj[u'dtype'])
data = unconvert(obj[u'data'], dtype, obj.get(u'compress'))
return globals()[obj[u'klass']](data, dtype=dtype, name=obj[u'name'])
elif typ == u'range_index':
return globals()[obj[u'klass']](obj[u'start'],
obj[u'stop'],
obj[u'step'],
name=obj[u'name'])
elif typ == u'multi_index':
dtype = dtype_for(obj[u'dtype'])
data = unconvert(obj[u'data'], dtype, obj.get(u'compress'))
data = [tuple(x) for x in data]
return globals()[obj[u'klass']].from_tuples(data, names=obj[u'names'])
elif typ == u'period_index':
data = unconvert(obj[u'data'], np.int64, obj.get(u'compress'))
d = dict(name=obj[u'name'], freq=obj[u'freq'])
return globals()[obj[u'klass']](data, **d)
elif typ == u'datetime_index':
data = unconvert(obj[u'data'], np.int64, obj.get(u'compress'))
d = dict(name=obj[u'name'], freq=obj[u'freq'], verify_integrity=False)
result = globals()[obj[u'klass']](data, **d)
tz = obj[u'tz']
# reverse tz conversion
if tz is not None:
result = result.tz_localize('UTC').tz_convert(tz)
return result
elif typ == u'series':
dtype = dtype_for(obj[u'dtype'])
pd_dtype = pandas_dtype(dtype)
np_dtype = pandas_dtype(dtype).base
index = obj[u'index']
result = globals()[obj[u'klass']](unconvert(obj[u'data'], dtype,
obj[u'compress']),
index=index,
dtype=np_dtype,
name=obj[u'name'])
tz = getattr(pd_dtype, 'tz', None)
if tz:
result = result.dt.tz_localize('UTC').dt.tz_convert(tz)
return result
elif typ == u'block_manager':
axes = obj[u'axes']
def create_block(b):
values = unconvert(b[u'values'], dtype_for(b[u'dtype']),
b[u'compress']).reshape(b[u'shape'])
# locs handles duplicate column names, and should be used instead
# of items; see GH 9618
if u'locs' in b:
placement = b[u'locs']
else:
placement = axes[0].get_indexer(b[u'items'])
return make_block(values=values,
klass=getattr(internals, b[u'klass']),
placement=placement,
dtype=b[u'dtype'])
blocks = [create_block(b) for b in obj[u'blocks']]
return globals()[obj[u'klass']](BlockManager(blocks, axes))
elif typ == u'datetime':
return parse(obj[u'data'])
elif typ == u'datetime64':
return np.datetime64(parse(obj[u'data']))
elif typ == u'date':
return parse(obj[u'data']).date()
elif typ == u'timedelta':
return timedelta(*obj[u'data'])
elif typ == u'timedelta64':
return np.timedelta64(int(obj[u'data']))
# elif typ == 'sparse_series':
# dtype = dtype_for(obj['dtype'])
# return globals()[obj['klass']](
# unconvert(obj['sp_values'], dtype, obj['compress']),
# sparse_index=obj['sp_index'], index=obj['index'],
# fill_value=obj['fill_value'], kind=obj['kind'], name=obj['name'])
# elif typ == 'sparse_dataframe':
# return globals()[obj['klass']](
# obj['data'], columns=obj['columns'],
# default_fill_value=obj['default_fill_value'],
# default_kind=obj['default_kind']
# )
# elif typ == 'sparse_panel':
# return globals()[obj['klass']](
# obj['data'], items=obj['items'],
# default_fill_value=obj['default_fill_value'],
# default_kind=obj['default_kind'])
elif typ == u'block_index':
return globals()[obj[u'klass']](obj[u'length'], obj[u'blocs'],
obj[u'blengths'])
elif typ == u'int_index':
return globals()[obj[u'klass']](obj[u'length'], obj[u'indices'])
elif typ == u'ndarray':
return unconvert(obj[u'data'], np.typeDict[obj[u'dtype']],
obj.get(u'compress')).reshape(obj[u'shape'])
elif typ == u'np_scalar':
if obj.get(u'sub_typ') == u'np_complex':
return c2f(obj[u'real'], obj[u'imag'], obj[u'dtype'])
else:
dtype = dtype_for(obj[u'dtype'])
try:
return dtype(obj[u'data'])
except:
return dtype.type(obj[u'data'])
elif typ == u'np_complex':
return complex(obj[u'real'] + u'+' + obj[u'imag'] + u'j')
elif isinstance(obj, (dict, list, set)):
return obj
else:
return obj
