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⚡️ Speed up method DataIndexableCol.get_atom_data by 12% #108

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⚡️ Speed up method DataIndexableCol.get_atom_data by 12% #108

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10 changes: 6 additions & 4 deletions pandas/io/pytables.py
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Original file line number Diff line number Diff line change
Expand Up @@ -99,6 +99,7 @@
adjoin,
pprint_thing,
)
from functools import lru_cache

if TYPE_CHECKING:
from collections.abc import (
Expand Down Expand Up @@ -1750,7 +1751,7 @@ def info(self) -> str:

if self.is_open:
lkeys = sorted(self.keys())
if len(lkeys):
if lkeys:
keys = []
values = []

Expand Down Expand Up @@ -2559,6 +2560,7 @@ def get_atom_string(cls, shape, itemsize):
return _tables().StringCol(itemsize=itemsize, shape=shape[0])

@classmethod
@lru_cache(maxsize=64)
def get_atom_coltype(cls, kind: str) -> type[Col]:
"""return the PyTables column class for this column"""
if kind.startswith("uint"):
Expand Down Expand Up @@ -4505,7 +4507,7 @@ def write_data(self, chunksize: int | None, dropna: bool = False) -> None:
masks.append(mask.astype("u1", copy=False))

# consolidate masks
if len(masks):
if masks:
mask = masks[0]
for m in masks[1:]:
mask = mask & m
Expand Down Expand Up @@ -4625,7 +4627,7 @@ def delete(
groups = list(diff[diff > 1].index)

# 1 group
if not len(groups):
if not groups:
groups = [0]

# final element
Expand Down Expand Up @@ -5091,7 +5093,7 @@ def _maybe_convert_for_string_atom(
if bvalues.dtype != object:
return bvalues

bvalues = cast(np.ndarray, bvalues)
bvalues = cast("np.ndarray", bvalues)

dtype_name = bvalues.dtype.name
inferred_type = lib.infer_dtype(bvalues, skipna=False)
Expand Down