⚡️ Speed up method DataCol.get_atom_data by 7%

pandas/io/pytables.py

@@ -128,6 +128,8 @@
 
     from pandas.core.internals import Block
 
+_VALUES_BLOCK_RE = re.compile(r"values_block_(\d+)")
+
 # versioning attribute
 _version = "0.15.2"
 
@@ -1750,7 +1752,7 @@ def info(self) -> str:
 
         if self.is_open:
             lkeys = sorted(self.keys())
-            if len(lkeys):
+            if lkeys:
                 keys = []
                 values = []
 
@@ -2561,6 +2563,14 @@ def get_atom_string(cls, shape, itemsize):
     @classmethod
     def get_atom_coltype(cls, kind: str) -> type[Col]:
         """return the PyTables column class for this column"""
+        cache = getattr(cls, "_coltype_cache", None)
+        if cache is None:
+            cache = {}
+            setattr(cls, "_coltype_cache", cache)
+
+        if kind in cache:
+            return cache[kind]
+
         if kind.startswith("uint"):
             k4 = kind[4:]
             col_name = f"UInt{k4}Col"
@@ -2571,7 +2581,9 @@ def get_atom_coltype(cls, kind: str) -> type[Col]:
             kcap = kind.capitalize()
             col_name = f"{kcap}Col"
 
-        return getattr(_tables(), col_name)
+        coltype = getattr(_tables(), col_name)
+        cache[kind] = coltype
+        return coltype
 
     @classmethod
     def get_atom_data(cls, shape, kind: str) -> Col:
@@ -4505,7 +4517,7 @@ 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
@@ -4625,7 +4637,7 @@ def delete(
             groups = list(diff[diff > 1].index)
 
             # 1 group
-            if not len(groups):
+            if not groups:
                 groups = [0]
 
             # final element
@@ -5091,7 +5103,7 @@ 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)
@@ -5265,7 +5277,7 @@ def _maybe_adjust_name(name: str, version: Sequence[int]) -> str:
         raise ValueError("Version is incorrect, expected sequence of 3 integers.")
 
     if version[0] == 0 and version[1] <= 10 and version[2] == 0:
-        m = re.search(r"values_block_(\d+)", name)
+        m = _VALUES_BLOCK_RE.search(name)
         if m:
             grp = m.groups()[0]
             name = f"values_{grp}"