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CLN: annotate and de-nest write_array #30012

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Merged
merged 2 commits into from
Dec 4, 2019
Merged

CLN: annotate and de-nest write_array #30012

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CLN: annotate and de-nest write_array
jbrockmendel Dec 3, 2019
8c95bf3
Merge branch 'master' of https://github.com/pandas-dev/pandas into cl…
jbrockmendel Dec 4, 2019
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90 changes: 44 additions & 46 deletions pandas/io/pytables.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,6 +39,7 @@
is_list_like,
is_timedelta64_dtype,
)
from pandas.core.dtypes.generic import ABCExtensionArray
from pandas.core.dtypes.missing import array_equivalent

from pandas import (
Expand All @@ -53,7 +54,7 @@
concat,
isna,
)
from pandas._typing import FrameOrSeries
from pandas._typing import ArrayLike, FrameOrSeries
from pandas.core.arrays.categorical import Categorical
import pandas.core.common as com
from pandas.core.computation.pytables import PyTablesExpr, maybe_expression
Expand Down Expand Up @@ -2959,7 +2960,7 @@ def read_index_node(
data = node[start:stop]
# If the index was an empty array write_array_empty() will
# have written a sentinel. Here we relace it with the original.
if "shape" in node._v_attrs and self._is_empty_array(node._v_attrs.shape):
if "shape" in node._v_attrs and np.prod(node._v_attrs.shape) == 0:
data = np.empty(node._v_attrs.shape, dtype=node._v_attrs.value_type,)
kind = _ensure_decoded(node._v_attrs.kind)
name = None
Expand Down Expand Up @@ -3005,25 +3006,27 @@ def read_index_node(

return index

def write_array_empty(self, key: str, value):
def write_array_empty(self, key: str, value: ArrayLike):
""" write a 0-len array """

# ugly hack for length 0 axes
arr = np.empty((1,) * value.ndim)
self._handle.create_array(self.group, key, arr)
getattr(self.group, key)._v_attrs.value_type = str(value.dtype)
getattr(self.group, key)._v_attrs.shape = value.shape
node = getattr(self.group, key)
node._v_attrs.value_type = str(value.dtype)
node._v_attrs.shape = value.shape

def _is_empty_array(self, shape) -> bool:
"""Returns true if any axis is zero length."""
return any(x == 0 for x in shape)
def write_array(self, key: str, value: ArrayLike, items: Optional[Index] = None):
# TODO: we only have one test that gets here, the only EA
# that gets passed is DatetimeArray, and we never have
# both self._filters and EA
assert isinstance(value, (np.ndarray, ABCExtensionArray)), type(value)

def write_array(self, key: str, value, items=None):
if key in self.group:
self._handle.remove_node(self.group, key)

# Transform needed to interface with pytables row/col notation
empty_array = self._is_empty_array(value.shape)
empty_array = value.size == 0
transposed = False

if is_categorical_dtype(value):
Expand All @@ -3038,29 +3041,29 @@ def write_array(self, key: str, value, items=None):
value = value.T
transposed = True

atom = None
if self._filters is not None:
atom = None
try:
# get the atom for this datatype
atom = _tables().Atom.from_dtype(value.dtype)
except ValueError:
pass

if atom is not None:
# create an empty chunked array and fill it from value
if not empty_array:
ca = self._handle.create_carray(
self.group, key, atom, value.shape, filters=self._filters
)
ca[:] = value
getattr(self.group, key)._v_attrs.transposed = transposed
if atom is not None:
# We only get here if self._filters is non-None and
# the Atom.from_dtype call succeeded

else:
self.write_array_empty(key, value)
# create an empty chunked array and fill it from value
if not empty_array:
ca = self._handle.create_carray(
self.group, key, atom, value.shape, filters=self._filters
)
ca[:] = value

return
else:
self.write_array_empty(key, value)

if value.dtype.type == np.object_:
elif value.dtype.type == np.object_:

# infer the type, warn if we have a non-string type here (for
# performance)
Expand All @@ -3070,35 +3073,30 @@ def write_array(self, key: str, value, items=None):
elif inferred_type == "string":
pass
else:
try:
items = list(items)
except TypeError:
pass
ws = performance_doc % (inferred_type, key, items)
warnings.warn(ws, PerformanceWarning, stacklevel=7)

vlarr = self._handle.create_vlarray(self.group, key, _tables().ObjectAtom())
vlarr.append(value)

elif empty_array:
self.write_array_empty(key, value)
elif is_datetime64_dtype(value.dtype):
self._handle.create_array(self.group, key, value.view("i8"))
getattr(self.group, key)._v_attrs.value_type = "datetime64"
elif is_datetime64tz_dtype(value.dtype):
# store as UTC
# with a zone
self._handle.create_array(self.group, key, value.asi8)

node = getattr(self.group, key)
node._v_attrs.tz = _get_tz(value.tz)
node._v_attrs.value_type = "datetime64"
elif is_timedelta64_dtype(value.dtype):
self._handle.create_array(self.group, key, value.view("i8"))
getattr(self.group, key)._v_attrs.value_type = "timedelta64"
else:
if empty_array:
self.write_array_empty(key, value)
else:
if is_datetime64_dtype(value.dtype):
self._handle.create_array(self.group, key, value.view("i8"))
getattr(self.group, key)._v_attrs.value_type = "datetime64"
elif is_datetime64tz_dtype(value.dtype):
# store as UTC
# with a zone
self._handle.create_array(self.group, key, value.asi8)

node = getattr(self.group, key)
node._v_attrs.tz = _get_tz(value.tz)
node._v_attrs.value_type = "datetime64"
elif is_timedelta64_dtype(value.dtype):
self._handle.create_array(self.group, key, value.view("i8"))
getattr(self.group, key)._v_attrs.value_type = "timedelta64"
else:
self._handle.create_array(self.group, key, value)
self._handle.create_array(self.group, key, value)

getattr(self.group, key)._v_attrs.transposed = transposed

Expand Down