STYLE: fix pylint: no-else-raise

ci/fix_wheels.py

@@ -4,7 +4,12 @@
 import zipfile
 
 try:
-    _, wheel_path, dest_dir = sys.argv
+    if len(sys.argv) != 3:
+        raise ValueError(
+            "User must pass the path to the wheel and the destination directory."
+        )
+    wheel_path = sys.argv[1]
+    dest_dir = sys.argv[2]
     # Figure out whether we are building on 32 or 64 bit python
     is_32 = sys.maxsize <= 2**32
     PYTHON_ARCH = "x86" if is_32 else "x64"
@@ -50,5 +55,4 @@
 if not success:
     os.remove(repaired_wheel_path)
     raise exception
-else:
-    print(f"Successfully repaired wheel was written to {repaired_wheel_path}")
+print(f"Successfully repaired wheel was written to {repaired_wheel_path}")

doc/make.py

@@ -259,8 +259,7 @@ def latex(self, force=False):
                     "You should check the file "
                     '"build/latex/pandas.pdf" for problems.'
                 )
-            else:
-                self._run_os("make")
+            self._run_os("make")
             return ret_code
 
     def latex_forced(self):

pandas/_testing/_io.py

@@ -243,10 +243,7 @@ def wrapper(*args, **kwargs):
 
             if not isinstance(err, error_classes) or raise_on_error:
                 raise
-            else:
-                pytest.skip(
-                    f"Skipping test due to lack of connectivity and error {err}"
-                )
+            pytest.skip(f"Skipping test due to lack of connectivity and error {err}")
 
     return wrapper
 

pandas/compat/_optional.py

@@ -141,8 +141,7 @@ def import_optional_dependency(
     except ImportError:
         if errors == "raise":
             raise ImportError(msg)
-        else:
-            return None
+        return None
 
     # Handle submodules: if we have submodule, grab parent module from sys.modules
     parent = name.split(".")[0]

pandas/compat/numpy/function.py

@@ -412,8 +412,7 @@ def validate_resampler_func(method: str, args, kwargs) -> None:
                 "numpy operations are not valid with resample. "
                 f"Use .resample(...).{method}() instead"
             )
-        else:
-            raise TypeError("too many arguments passed in")
+        raise TypeError("too many arguments passed in")
 
 
 def validate_minmax_axis(axis: AxisInt | None, ndim: int = 1) -> None:

pandas/conftest.py

@@ -1163,8 +1163,7 @@ def deco(*args):
                 raise ValueError(
                     f"Could not find file {path} and --strict-data-files is set."
                 )
-            else:
-                pytest.skip(f"Could not find {path}.")
+            pytest.skip(f"Could not find {path}.")
         return path
 
     return deco

pandas/core/apply.py

@@ -464,7 +464,7 @@ def apply_str(self) -> DataFrame | Series:
                 "axis" not in arg_names or f in ("corrwith", "skew")
             ):
                 raise ValueError(f"Operation {f} does not support axis=1")
-            elif "axis" in arg_names:
+            if "axis" in arg_names:
                 self.kwargs["axis"] = self.axis
         return self._try_aggregate_string_function(obj, f, *self.args, **self.kwargs)
 
@@ -762,7 +762,7 @@ def apply_broadcast(self, target: DataFrame) -> DataFrame:
             # must be a scalar or 1d
             if ares > 1:
                 raise ValueError("too many dims to broadcast")
-            elif ares == 1:
+            if ares == 1:
 
                 # must match return dim
                 if result_compare != len(res):
@@ -1179,7 +1179,7 @@ def reconstruct_func(
                 "Function names must be unique if there is no new column names "
                 "assigned"
             )
-        elif func is None:
+        if func is None:
             # nicer error message
             raise TypeError("Must provide 'func' or tuples of '(column, aggfunc).")
 

pandas/core/arrays/datetimelike.py

@@ -2264,7 +2264,7 @@ def validate_inferred_freq(
                 "values does not conform to passed frequency "
                 f"{freq.freqstr}"
             )
-        elif freq is None:
+        if freq is None:
             freq = inferred_freq
         freq_infer = False
 

pandas/core/arrays/datetimes.py

@@ -2368,7 +2368,7 @@ def validate_tz_from_dtype(
         if dtz is not None:
             if tz is not None and not timezones.tz_compare(tz, dtz):
                 raise ValueError("cannot supply both a tz and a dtype with a tz")
-            elif explicit_tz_none:
+            if explicit_tz_none:
                 raise ValueError("Cannot pass both a timezone-aware dtype and tz=None")
             tz = dtz
 

pandas/core/arrays/interval.py

@@ -314,17 +314,17 @@ def _simple_new(
                 f"right [{type(right).__name__}] types"
             )
             raise ValueError(msg)
-        elif is_categorical_dtype(left.dtype) or is_string_dtype(left.dtype):
+        if is_categorical_dtype(left.dtype) or is_string_dtype(left.dtype):
             # GH 19016
             msg = (
                 "category, object, and string subtypes are not supported "
                 "for IntervalArray"
             )
             raise TypeError(msg)
-        elif isinstance(left, ABCPeriodIndex):
+        if isinstance(left, ABCPeriodIndex):
             msg = "Period dtypes are not supported, use a PeriodIndex instead"
             raise ValueError(msg)
-        elif isinstance(left, ABCDatetimeIndex) and str(left.tz) != str(right.tz):
+        if isinstance(left, ABCDatetimeIndex) and str(left.tz) != str(right.tz):
             msg = (
                 "left and right must have the same time zone, got "
                 f"'{left.tz}' and '{right.tz}'"
@@ -1321,7 +1321,7 @@ def mid(self) -> Index:
     def overlaps(self, other):
         if isinstance(other, (IntervalArray, ABCIntervalIndex)):
             raise NotImplementedError
-        elif not isinstance(other, Interval):
+        if not isinstance(other, Interval):
             msg = f"`other` must be Interval-like, got {type(other).__name__}"
             raise TypeError(msg)
 

pandas/core/arrays/masked.py

@@ -655,7 +655,7 @@ def _arith_method(self, other, op):
                     raise NotImplementedError(
                         f"operator '{op_name}' not implemented for bool dtypes"
                     )
-                elif op_name in {"mod", "rmod"}:
+                if op_name in {"mod", "rmod"}:
                     dtype = "int8"
                 else:
                     dtype = "bool"
@@ -1034,7 +1034,7 @@ def _quantile(
                 # I think this should be out_mask=self.isna().all(axis=1)
                 #  but am holding off until we have tests
                 raise NotImplementedError
-            elif self.isna().all():
+            if self.isna().all():
                 out_mask = np.ones(res.shape, dtype=bool)
             else:
                 out_mask = np.zeros(res.shape, dtype=bool)

pandas/core/arrays/period.py

@@ -1167,7 +1167,7 @@ def _make_field_arrays(*fields) -> list[np.ndarray]:
         if isinstance(x, (list, np.ndarray, ABCSeries)):
             if length is not None and len(x) != length:
                 raise ValueError("Mismatched Period array lengths")
-            elif length is None:
+            if length is None:
                 length = len(x)
 
     # error: Argument 2 to "repeat" has incompatible type "Optional[int]"; expected

pandas/core/arrays/sparse/array.py

@@ -781,7 +781,7 @@ def fillna(
         ):
             raise ValueError("Must specify one of 'method' or 'value'.")
 
-        elif method is not None:
+        if method is not None:
             msg = "fillna with 'method' requires high memory usage."
             warnings.warn(
                 msg,
@@ -1172,8 +1172,7 @@ def _take_without_fill(self: SparseArrayT, indices) -> SparseArrayT:
         if (indices.max() >= n) or (indices.min() < -n):
             if n == 0:
                 raise IndexError("cannot do a non-empty take from an empty axes.")
-            else:
-                raise IndexError("out of bounds value in 'indices'.")
+            raise IndexError("out of bounds value in 'indices'.")
 
         if to_shift.any():
             indices = indices.copy()

pandas/core/arrays/timedeltas.py

@@ -490,7 +490,7 @@ def __truediv__(self, other):
         if len(other) != len(self):
             raise ValueError("Cannot divide vectors with unequal lengths")
 
-        elif is_timedelta64_dtype(other.dtype):
+        if is_timedelta64_dtype(other.dtype):
             # let numpy handle it
             return self._ndarray / other
 
@@ -554,7 +554,7 @@ def __rtruediv__(self, other):
         if len(other) != len(self):
             raise ValueError("Cannot divide vectors with unequal lengths")
 
-        elif is_timedelta64_dtype(other.dtype):
+        if is_timedelta64_dtype(other.dtype):
             # let numpy handle it
             return other / self._ndarray
 
@@ -606,7 +606,7 @@ def __floordiv__(self, other):
         if len(other) != len(self):
             raise ValueError("Cannot divide with unequal lengths")
 
-        elif is_timedelta64_dtype(other.dtype):
+        if is_timedelta64_dtype(other.dtype):
             other = type(self)(other)
 
             # numpy timedelta64 does not natively support floordiv, so operate
@@ -675,7 +675,7 @@ def __rfloordiv__(self, other):
         if len(other) != len(self):
             raise ValueError("Cannot divide with unequal lengths")
 
-        elif is_timedelta64_dtype(other.dtype):
+        if is_timedelta64_dtype(other.dtype):
             other = type(self)(other)
             # numpy timedelta64 does not natively support floordiv, so operate
             #  on the i8 values

pandas/core/common.py

@@ -388,7 +388,7 @@ def standardize_mapping(into):
         into = type(into)
     if not issubclass(into, abc.Mapping):
         raise TypeError(f"unsupported type: {into}")
-    elif into == defaultdict:
+    if into == defaultdict:
         raise TypeError("to_dict() only accepts initialized defaultdicts")
     return into
 

pandas/core/computation/eval.py

@@ -344,7 +344,7 @@ def eval(
                     "Multi-line expressions are only valid "
                     "if all expressions contain an assignment"
                 )
-            elif inplace:
+            if inplace:
                 raise ValueError("Cannot operate inplace if there is no assignment")
 
         # assign if needed

pandas/core/construction.py

@@ -698,7 +698,7 @@ def _sanitize_ndim(
     if getattr(result, "ndim", 0) == 0:
         raise ValueError("result should be arraylike with ndim > 0")
 
-    elif result.ndim == 1:
+    if result.ndim == 1:
         # the result that we want
         result = _maybe_repeat(result, index)
 

pandas/core/dtypes/cast.py

@@ -1866,7 +1866,7 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
                 return element
             raise LossySetitemError
 
-        elif is_integer(element) or (is_float(element) and element.is_integer()):
+        if is_integer(element) or (is_float(element) and element.is_integer()):
             # e.g. test_setitem_series_int8 if we have a python int 1
             #  tipo may be np.int32, despite the fact that it will fit
             #  in smaller int dtypes.
@@ -1893,7 +1893,7 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
 
                 # Anything other than integer we cannot hold
                 raise LossySetitemError
-            elif (
+            if (
                 dtype.kind == "u"
                 and isinstance(element, np.ndarray)
                 and element.dtype.kind == "i"
@@ -1905,9 +1905,9 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
                     #  itemsize issues there?
                     return casted
                 raise LossySetitemError
-            elif dtype.itemsize < tipo.itemsize:
+            if dtype.itemsize < tipo.itemsize:
                 raise LossySetitemError
-            elif not isinstance(tipo, np.dtype):
+            if not isinstance(tipo, np.dtype):
                 # i.e. nullable IntegerDtype; we can put this into an ndarray
                 #  losslessly iff it has no NAs
                 if element._hasna:
@@ -1918,7 +1918,7 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
 
         raise LossySetitemError
 
-    elif dtype.kind == "f":
+    if dtype.kind == "f":
         if lib.is_integer(element) or lib.is_float(element):
             casted = dtype.type(element)
             if np.isnan(casted) or casted == element:
@@ -1931,7 +1931,7 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
             if tipo.kind not in ["f", "i", "u"]:
                 # Anything other than float/integer we cannot hold
                 raise LossySetitemError
-            elif not isinstance(tipo, np.dtype):
+            if not isinstance(tipo, np.dtype):
                 # i.e. nullable IntegerDtype or FloatingDtype;
                 #  we can put this into an ndarray losslessly iff it has no NAs
                 if element._hasna:
@@ -1950,7 +1950,7 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
 
         raise LossySetitemError
 
-    elif dtype.kind == "c":
+    if dtype.kind == "c":
         if lib.is_integer(element) or lib.is_complex(element) or lib.is_float(element):
             if np.isnan(element):
                 # see test_where_complex GH#6345
@@ -1968,7 +1968,7 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
             raise LossySetitemError
         raise LossySetitemError
 
-    elif dtype.kind == "b":
+    if dtype.kind == "b":
         if tipo is not None:
             if tipo.kind == "b":
                 if not isinstance(tipo, np.dtype):
@@ -1982,7 +1982,7 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
             return element
         raise LossySetitemError
 
-    elif dtype.kind == "S":
+    if dtype.kind == "S":
         # TODO: test tests.frame.methods.test_replace tests get here,
         #  need more targeted tests.  xref phofl has a PR about this
         if tipo is not None:

pandas/core/dtypes/common.py

@@ -1471,7 +1471,7 @@ def get_dtype(arr_or_dtype) -> DtypeObj:
         raise TypeError("Cannot deduce dtype from null object")
 
     # fastpath
-    elif isinstance(arr_or_dtype, np.dtype):
+    if isinstance(arr_or_dtype, np.dtype):
         return arr_or_dtype
     elif isinstance(arr_or_dtype, type):
         return np.dtype(arr_or_dtype)
@@ -1639,8 +1639,7 @@ def validate_all_hashable(*args, error_name: str | None = None) -> None:
     if not all(is_hashable(arg) for arg in args):
         if error_name:
             raise TypeError(f"{error_name} must be a hashable type")
-        else:
-            raise TypeError("All elements must be hashable")
+        raise TypeError("All elements must be hashable")
 
 
 def pandas_dtype(dtype) -> DtypeObj:

pandas/core/dtypes/dtypes.py

@@ -523,7 +523,7 @@ def validate_categories(categories, fastpath: bool = False) -> Index:
             raise TypeError(
                 f"Parameter 'categories' must be list-like, was {repr(categories)}"
             )
-        elif not isinstance(categories, ABCIndex):
+        if not isinstance(categories, ABCIndex):
             categories = Index._with_infer(categories, tupleize_cols=False)
 
         if not fastpath: