CLN Upgrade pandas/core syntax (pandas-dev#36453)

Author: MarcoGorelli

pandas/core/arrays/datetimes.py

@@ -570,8 +570,7 @@ def __iter__(self):
             converted = ints_to_pydatetime(
                 data[start_i:end_i], tz=self.tz, freq=self.freq, box="timestamp"
             )
-            for v in converted:
-                yield v
+            yield from converted
 
     def astype(self, dtype, copy=True):
         # We handle

pandas/core/arrays/sparse/array.py

@@ -1427,7 +1427,7 @@ def sparse_arithmetic_method(self, other):
                     # TODO: look into _wrap_result
                     if len(self) != len(other):
                         raise AssertionError(
-                            (f"length mismatch: {len(self)} vs. {len(other)}")
+                            f"length mismatch: {len(self)} vs. {len(other)}"
                         )
                     if not isinstance(other, SparseArray):
                         dtype = getattr(other, "dtype", None)

pandas/core/common.py

@@ -62,8 +62,7 @@ def flatten(l):
     """
     for el in l:
         if iterable_not_string(el):
-            for s in flatten(el):
-                yield s
+            yield from flatten(el)
         else:
             yield el
 
@@ -434,10 +433,8 @@ def random_state(state=None):
         return np.random
     else:
         raise ValueError(
-            (
-                "random_state must be an integer, array-like, a BitGenerator, "
-                "a numpy RandomState, or None"
-            )
+            "random_state must be an integer, array-like, a BitGenerator, "
+            "a numpy RandomState, or None"
         )
 
 

pandas/core/computation/expr.py

@@ -153,7 +153,7 @@ def _preparse(
     the ``tokenize`` module and ``tokval`` is a string.
     """
     assert callable(f), "f must be callable"
-    return tokenize.untokenize((f(x) for x in tokenize_string(source)))
+    return tokenize.untokenize(f(x) for x in tokenize_string(source))
 
 
 def _is_type(t):

pandas/core/computation/pytables.py

@@ -554,7 +554,7 @@ def __init__(
                 else:
                     w = _validate_where(w)
                     where[idx] = w
-            _where = " & ".join((f"({w})" for w in com.flatten(where)))
+            _where = " & ".join(f"({w})" for w in com.flatten(where))
         else:
             _where = where
 

pandas/core/generic.py

@@ -1681,10 +1681,7 @@ def _get_label_or_level_values(self, key: str, axis: int = 0) -> np.ndarray:
 
             label_axis_name = "column" if axis == 0 else "index"
             raise ValueError(
-                (
-                    f"The {label_axis_name} label '{key}' "
-                    f"is not unique.{multi_message}"
-                )
+                f"The {label_axis_name} label '{key}' is not unique.{multi_message}"
             )
 
         return values
@@ -1725,10 +1722,8 @@ def _drop_labels_or_levels(self, keys, axis: int = 0):
 
         if invalid_keys:
             raise ValueError(
-                (
-                    "The following keys are not valid labels or "
-                    f"levels for axis {axis}: {invalid_keys}"
-                )
+                "The following keys are not valid labels or "
+                f"levels for axis {axis}: {invalid_keys}"
             )
 
         # Compute levels and labels to drop

pandas/core/groupby/base.py

@@ -93,15 +93,8 @@ def _gotitem(self, key, ndim, subset=None):
 )
 
 series_apply_allowlist = (
-    (
-        common_apply_allowlist
-        | {
-            "nlargest",
-            "nsmallest",
-            "is_monotonic_increasing",
-            "is_monotonic_decreasing",
-        }
-    )
+    common_apply_allowlist
+    | {"nlargest", "nsmallest", "is_monotonic_increasing", "is_monotonic_decreasing"}
 ) | frozenset(["dtype", "unique"])
 
 dataframe_apply_allowlist = common_apply_allowlist | frozenset(["dtypes", "corrwith"])

pandas/core/groupby/generic.py

@@ -1212,7 +1212,7 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False):
                 return result
 
         else:
-            all_indexed_same = all_indexes_same((x.index for x in values))
+            all_indexed_same = all_indexes_same(x.index for x in values)
 
             # GH3596
             # provide a reduction (Frame -> Series) if groups are

pandas/core/indexes/base.py

@@ -504,7 +504,7 @@ def _maybe_check_unique(self):
         if not self.is_unique:
             msg = """Index has duplicates."""
             duplicates = self._format_duplicate_message()
-            msg += "\n{}".format(duplicates)
+            msg += f"\n{duplicates}"
 
             raise DuplicateLabelError(msg)
 
@@ -4315,10 +4315,8 @@ def identical(self, other) -> bool:
         return (
             self.equals(other)
             and all(
-                (
-                    getattr(self, c, None) == getattr(other, c, None)
-                    for c in self._comparables
-                )
+                getattr(self, c, None) == getattr(other, c, None)
+                for c in self._comparables
             )
             and type(self) == type(other)
         )

pandas/core/indexes/datetimelike.py

@@ -398,7 +398,7 @@ def _partial_date_slice(
 
             if len(self) and (
                 (use_lhs and t1 < self[0] and t2 < self[0])
-                or ((use_rhs and t1 > self[-1] and t2 > self[-1]))
+                or (use_rhs and t1 > self[-1] and t2 > self[-1])
             ):
                 # we are out of range
                 raise KeyError

pandas/core/reshape/merge.py

@@ -1837,7 +1837,7 @@ def _get_single_indexer(join_key, index, sort: bool = False):
 def _left_join_on_index(left_ax: Index, right_ax: Index, join_keys, sort: bool = False):
     if len(join_keys) > 1:
         if not (
-            (isinstance(right_ax, MultiIndex) and len(join_keys) == right_ax.nlevels)
+            isinstance(right_ax, MultiIndex) and len(join_keys) == right_ax.nlevels
         ):
             raise AssertionError(
                 "If more than one join key is given then "