DOCS: remove outdated PeriodDtype.from_date_offset comment, use DateOffset instead of BaseOffset in docstrings

pandas/_libs/tslibs/period.pyx

@@ -1752,9 +1752,6 @@ cdef class _Period(PeriodMixin):
     def __cinit__(self, int64_t ordinal, BaseOffset freq):
         self.ordinal = ordinal
         self.freq = freq
-        # Note: this is more performant than PeriodDtype.from_date_offset(freq)
-        #  because from_date_offset cannot be made a cdef method (until cython
-        #  supported cdef classmethods)
         self._dtype = PeriodDtypeBase(freq._period_dtype_code, freq.n)
 
     @classmethod

pandas/core/arrays/categorical.py

@@ -2974,8 +2974,7 @@ def _delegate_method(self, name: str, *args, **kwargs):
         from pandas import Series
 
         method = getattr(self._parent, name)
-        res = method(*args, **kwargs)
-        if res is not None:
+        if (res := method(*args, **kwargs)) is not None:
             return Series(res, index=self._index, name=self._name)
 
 

pandas/core/base.py

@@ -126,8 +126,7 @@ def __sizeof__(self) -> int:
         Generates the total memory usage for an object that returns
         either a value or Series of values
         """
-        memory_usage = getattr(self, "memory_usage", None)
-        if memory_usage:
+        if (memory_usage := getattr(self, "memory_usage", None)):
             mem = memory_usage(deep=True)
             return int(mem if is_scalar(mem) else mem.sum())
 

pandas/core/computation/engines.py

@@ -36,9 +36,8 @@ def _check_ne_builtin_clash(expr: Expr) -> None:
         Terms can contain
     """
     names = expr.names
-    overlap = names & _ne_builtins
 
-    if overlap:
+    if (overlap := names & _ne_builtins):
         s = ", ".join([repr(x) for x in overlap])
         raise NumExprClobberingError(
             f'Variables in expression "{expr}" overlap with builtins: ({s})'

pandas/core/dtypes/common.py

@@ -1819,8 +1819,7 @@ def pandas_dtype(dtype) -> DtypeObj:
         return StringDtype(na_value=np.nan)
 
     # registered extension types
-    result = registry.find(dtype)
-    if result is not None:
+    if (result := registry.find(dtype)) is not None:
         if isinstance(result, type):
             # GH 31356, GH 54592
             warnings.warn(

pandas/core/dtypes/dtypes.py

@@ -889,8 +889,7 @@ def construct_from_string(cls, string: str_type) -> DatetimeTZDtype:
             )
 
         msg = f"Cannot construct a 'DatetimeTZDtype' from '{string}'"
-        match = cls._match.match(string)
-        if match:
+        if (match := cls._match.match(string)):
             d = match.groupdict()
             try:
                 return cls(unit=d["unit"], tz=d["tz"])
@@ -1999,9 +1998,8 @@ def _parse_subtype(dtype: str) -> tuple[str, bool]:
             When the subtype cannot be extracted.
         """
         xpr = re.compile(r"Sparse\[(?P<subtype>[^,]*)(, )?(?P<fill_value>.*?)?\]$")
-        m = xpr.match(dtype)
         has_fill_value = False
-        if m:
+        if (m := xpr.match(dtype)):
             subtype = m.groupdict()["subtype"]
             has_fill_value = bool(m.groupdict()["fill_value"])
         elif dtype == "Sparse":

pandas/core/groupby/grouper.py

@@ -460,8 +460,7 @@ def __init__(
         # we have a single grouper which may be a myriad of things,
         # some of which are dependent on the passing in level
 
-        ilevel = self._ilevel
-        if ilevel is not None:
+        if (ilevel := self._ilevel) is not None:
             # In extant tests, the new self.grouping_vector matches
             #  `index.get_level_values(ilevel)` whenever
             #  mapper is None and isinstance(index, MultiIndex)
@@ -547,8 +546,7 @@ def _passed_categorical(self) -> bool:
 
     @cache_readonly
     def name(self) -> Hashable:
-        ilevel = self._ilevel
-        if ilevel is not None:
+        if (ilevel := self._ilevel) is not None:
             return self._index.names[ilevel]
 
         if isinstance(self._orig_grouper, (Index, Series)):

pandas/core/groupby/ops.py

@@ -695,9 +695,8 @@ def size(self) -> Series:
         Compute group sizes.
         """
         ids = self.ids
-        ngroups = self.ngroups
         out: np.ndarray | list
-        if ngroups:
+        if (ngroups := self.ngroups):
             out = np.bincount(ids[ids != -1], minlength=ngroups)
         else:
             out = []

pandas/core/indexes/base.py

@@ -6108,9 +6108,8 @@ def _raise_if_missing(self, key, indexer, axis_name: str_t) -> None:
 
         # Count missing values
         missing_mask = indexer < 0
-        nmissing = missing_mask.sum()
 
-        if nmissing:
+        if (nmissing := missing_mask.sum()):
             if nmissing == len(indexer):
                 raise KeyError(f"None of [{key}] are in the [{axis_name}]")
 

pandas/core/interchange/from_dataframe.py

@@ -144,8 +144,7 @@ def _from_dataframe(df: DataFrameXchg, allow_copy: bool = True) -> pd.DataFrame:
     else:
         pandas_df = pd.concat(pandas_dfs, axis=0, ignore_index=True, copy=False)
 
-    index_obj = df.metadata.get("pandas.index", None)
-    if index_obj is not None:
+    if (index_obj := df.metadata.get("pandas.index", None)) is not None:
         pandas_df.index = index_obj
 
     return pandas_df
@@ -372,8 +371,7 @@ def string_column_to_ndarray(col: Column) -> tuple[np.ndarray, Any]:
 def parse_datetime_format_str(format_str, data) -> pd.Series | np.ndarray:
     """Parse datetime `format_str` to interpret the `data`."""
     # timestamp 'ts{unit}:tz'
-    timestamp_meta = re.match(r"ts([smun]):(.*)", format_str)
-    if timestamp_meta:
+    if (timestamp_meta := re.match(r"ts([smun]):(.*)", format_str)):
         unit, tz = timestamp_meta.group(1), timestamp_meta.group(2)
         if unit != "s":
             # the format string describes only a first letter of the unit, so
@@ -386,8 +384,7 @@ def parse_datetime_format_str(format_str, data) -> pd.Series | np.ndarray:
         return data
 
     # date 'td{Days/Ms}'
-    date_meta = re.match(r"td([Dm])", format_str)
-    if date_meta:
+    if (date_meta := re.match(r"td([Dm])", format_str)):
         unit = date_meta.group(1)
         if unit == "D":
             # NumPy doesn't support DAY unit, so converting days to seconds

pandas/core/strings/object_array.py

@@ -530,8 +530,7 @@ def g(x):
         def f(x):
             if not isinstance(x, str):
                 return empty_row
-            m = regex.search(x)
-            if m:
+            if (m := regex.search(x)):
                 return [na_value if item is None else item for item in m.groups()]
             else:
                 return empty_row

pandas/core/window/rolling.py

@@ -741,8 +741,7 @@ def _apply(
         codes = self._grouper.codes
         levels = copy.copy(self._grouper.levels)
 
-        group_indices = self._grouper.indices.values()
-        if group_indices:
+        if (group_indices := self._grouper.indices.values()):
             indexer = np.concatenate(list(group_indices))
         else:
             indexer = np.array([], dtype=np.intp)