-
-
Notifications
You must be signed in to change notification settings - Fork 33
/
pandas.py
227 lines (180 loc) · 7.49 KB
/
pandas.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
from functools import partial
import pickle
import pandas as pd
from packaging.version import Version
PANDAS_GE_210 = Version(pd.__version__).release >= (2, 1, 0)
PANDAS_GE_300 = Version(pd.__version__).major >= 3
if PANDAS_GE_300:
from pandas.api.internals import create_dataframe_from_blocks
create_block_manager_from_blocks = None
make_block = None
else:
create_dataframe_from_blocks = None
try:
from pandas.core.internals.managers import create_block_manager_from_blocks
except ImportError:
from pandas.core.internals import create_block_manager_from_blocks
from pandas.core.internals import make_block
from . import numpy as pnp
from .core import Interface
from .encode import Encode
from .utils import extend, framesplit, frame
from pandas.api.types import is_extension_array_dtype
from pandas.api.extensions import ExtensionArray
def is_extension_array(x):
return isinstance(x, ExtensionArray)
dumps = partial(pickle.dumps, protocol=pickle.HIGHEST_PROTOCOL)
class PandasColumns(Interface):
def __init__(self, partd=None):
self.partd = pnp.Numpy(partd)
Interface.__init__(self)
def append(self, data, **kwargs):
for k, df in data.items():
self.iset(extend(k, '.columns'), dumps(list(df.columns)))
self.iset(extend(k, '.index-name'), dumps(df.index.name))
# TODO: don't use values, it does some work. Look at _blocks instead
# pframe/cframe do this well
arrays = {extend(k, col): df[col].values
for k, df in data.items()
for col in df.columns}
arrays.update({extend(k, '.index'): df.index.values
for k, df in data.items()})
# TODO: handle categoricals
self.partd.append(arrays, **kwargs)
def _get(self, keys, columns=None, **kwargs):
if columns is None:
columns = self.partd.partd.get([extend(k, '.columns') for k in keys],
**kwargs)
columns = list(map(pickle.loads, columns))
else:
columns = [columns] * len(keys)
index_names = self.partd.partd.get([extend(k, '.index-name')
for k in keys], **kwargs)
index_names = map(pickle.loads, index_names)
keys = [[extend(k, '.index'), [extend(k, col) for col in cols]]
for k, cols in zip(keys, columns)]
arrays = self.partd.get(keys, **kwargs)
return [pd.DataFrame(dict(zip(cols, arrs)), columns=cols,
index=pd.Index(index, name=iname))
for iname, (index, arrs), cols in zip(index_names, arrays, columns)]
def __getstate__(self):
return {'partd': self.partd}
def _iset(self, key, value):
return self.partd._iset(key, value)
def drop(self):
return self.partd.drop()
@property
def lock(self):
return self.partd.partd.lock
def __exit__(self, *args):
self.drop()
self.partd.__exit__(self, *args)
def __del__(self):
self.partd.__del__()
def index_to_header_bytes(ind):
# These have special `__reduce__` methods, just use pickle
if isinstance(ind, (pd.DatetimeIndex,
pd.MultiIndex,
pd.RangeIndex)):
return None, dumps(ind)
if isinstance(ind, pd.CategoricalIndex):
cat = (ind.ordered, ind.categories)
values = ind.codes
else:
cat = None
values = ind.values
if is_extension_array_dtype(ind):
return None, dumps(ind)
header = (type(ind), {k: getattr(ind, k, None) for k in ind._attributes}, values.dtype, cat)
bytes = pnp.compress(pnp.serialize(values), values.dtype)
return header, bytes
def index_from_header_bytes(header, bytes):
if header is None:
return pickle.loads(bytes)
typ, attr, dtype, cat = header
data = pnp.deserialize(pnp.decompress(bytes, dtype), dtype, copy=True)
if cat:
data = pd.Categorical.from_codes(data, cat[1], ordered=cat[0])
return typ.__new__(typ, data=data, **attr)
def block_to_header_bytes(block):
values = block.values
if isinstance(values, pd.Categorical):
extension = ('categorical_type', (values.ordered, values.categories))
values = values.codes
elif isinstance(block, pd.DatetimeTZDtype):
extension = ('datetime64_tz_type', (block.values.tzinfo,))
values = values.view('i8')
elif is_extension_array_dtype(block.dtype) or is_extension_array(values):
extension = ("other", ())
else:
extension = ('numpy_type', ())
header = (block.mgr_locs.as_array, values.dtype, values.shape, extension)
if extension == ("other", ()):
bytes = pickle.dumps(values)
else:
bytes = pnp.compress(pnp.serialize(values), values.dtype)
return header, bytes
def block_from_header_bytes(header, bytes, create_block: bool):
placement, dtype, shape, (extension_type, extension_values) = header
if extension_type == "other":
values = pickle.loads(bytes)
else:
values = pnp.deserialize(pnp.decompress(bytes, dtype), dtype,
copy=True).reshape(shape)
if extension_type == 'categorical_type':
values = pd.Categorical.from_codes(values,
extension_values[1],
ordered=extension_values[0])
elif extension_type == 'datetime64_tz_type':
tz_info = extension_values[0]
values = pd.DatetimeIndex(values).tz_localize('utc').tz_convert(
tz_info)
if create_block:
return make_block(values, placement=placement)
return values, placement
def serialize(df):
""" Serialize and compress a Pandas DataFrame
Uses Pandas blocks, snappy, and blosc to deconstruct an array into bytes
"""
col_header, col_bytes = index_to_header_bytes(df.columns)
ind_header, ind_bytes = index_to_header_bytes(df.index)
headers = [col_header, ind_header]
bytes = [col_bytes, ind_bytes]
for block in df._mgr.blocks:
h, b = block_to_header_bytes(block)
headers.append(h)
bytes.append(b)
frames = [dumps(headers)] + bytes
return b''.join(map(frame, frames))
def deserialize(bytes):
""" Deserialize and decompress bytes back to a pandas DataFrame """
frames = list(framesplit(bytes))
headers = pickle.loads(frames[0])
bytes = frames[1:]
axes = [index_from_header_bytes(headers[0], bytes[0]),
index_from_header_bytes(headers[1], bytes[1])]
blocks = [block_from_header_bytes(h, b, create_block=not PANDAS_GE_300)
for (h, b) in zip(headers[2:], bytes[2:])]
if PANDAS_GE_300:
return pd.api.internals.create_dataframe_from_blocks(blocks, axes[1], axes[0])
elif PANDAS_GE_210:
return pd.DataFrame._from_mgr(create_block_manager_from_blocks(blocks, axes), axes=axes)
else:
return pd.DataFrame(create_block_manager_from_blocks(blocks, axes))
def join(dfs):
if not dfs:
return pd.DataFrame()
else:
result = pd.concat(dfs)
dtypes = {
col: "category"
for col in result.columns
if (
isinstance(dfs[0][col].dtype, pd.CategoricalDtype)
and not isinstance(result[col].dtype, pd.CategoricalDtype)
)
}
if dtypes:
result = result.astype(dtypes)
return result
PandasBlocks = partial(Encode, serialize, deserialize, join)