STY: "{foo!r}" -> "{repr(foo)}" #batch-1

pandas/_libs/interval.pyx

@@ -179,8 +179,8 @@ cdef class IntervalMixin:
             When `other` is not closed exactly the same as self.
         """
         if self.closed != other.closed:
-            msg = f"'{name}.closed' is '{other.closed}', expected '{self.closed}'."
-            raise ValueError(msg)
+            raise ValueError(f"'{name}.closed' is {repr(other.closed)}, "
+                             f"expected {repr(self.closed)}.")
 
 
 cdef _interval_like(other):
@@ -316,7 +316,7 @@ cdef class Interval(IntervalMixin):
                 not tz_compare(left.tzinfo, right.tzinfo)):
             # GH 18538
             msg = (f"left and right must have the same time zone, got "
-                   f"'{left.tzinfo}' and '{right.tzinfo}'")
+                   f"{repr(left.tzinfo)}' and {repr(right.tzinfo)}")
             raise ValueError(msg)
         self.left = left
         self.right = right
@@ -379,7 +379,7 @@ cdef class Interval(IntervalMixin):
 
         left, right = self._repr_base()
         name = type(self).__name__
-        repr_str = f'{name}({left!r}, {right!r}, closed={self.closed!r})'
+        repr_str = f'{name}({repr(left)}, {repr(right)}, closed={repr(self.closed)})'
         return repr_str
 
     def __str__(self) -> str:

pandas/conftest.py

@@ -88,7 +88,7 @@ def spmatrix(request):
     return getattr(sparse, request.param + "_matrix")
 
 
-@pytest.fixture(params=[0, 1, "index", "columns"], ids=lambda x: "axis {!r}".format(x))
+@pytest.fixture(params=[0, 1, "index", "columns"], ids=lambda x: f"axis {repr(x)}")
 def axis(request):
     """
      Fixture for returning the axis numbers of a DataFrame.
@@ -99,7 +99,7 @@ def axis(request):
 axis_frame = axis
 
 
-@pytest.fixture(params=[0, "index"], ids=lambda x: "axis {!r}".format(x))
+@pytest.fixture(params=[0, "index"], ids=lambda x: f"axis {repr(x)}")
 def axis_series(request):
     """
      Fixture for returning the axis numbers of a Series.

pandas/core/accessor.py

@@ -183,9 +183,9 @@ def _register_accessor(name, cls):
     def decorator(accessor):
         if hasattr(cls, name):
             warnings.warn(
-                "registration of accessor {!r} under name {!r} for type "
-                "{!r} is overriding a preexisting attribute with the same "
-                "name.".format(accessor, name, cls),
+                f"registration of accessor {repr(accessor)} under name "
+                f"{repr(name)} for type {repr(cls)} is overriding a preexisting"
+                f"attribute with the same name.",
                 UserWarning,
                 stacklevel=2,
             )

pandas/core/algorithms.py

@@ -1194,10 +1194,8 @@ def compute(self, method):
             dtype = frame[column].dtype
             if not self.is_valid_dtype_n_method(dtype):
                 raise TypeError(
-                    (
-                        "Column {column!r} has dtype {dtype}, cannot use method "
-                        "{method!r} with this dtype"
-                    ).format(column=column, dtype=dtype, method=method)
+                    f"Column {repr(column)} has dtype {dtype}, "
+                    f"cannot use method {repr(method)} with this dtype"
                 )
 
         def get_indexer(current_indexer, other_indexer):

pandas/core/arrays/categorical.py

@@ -1632,7 +1632,7 @@ def sort_values(self, inplace=False, ascending=True, na_position="last"):
         """
         inplace = validate_bool_kwarg(inplace, "inplace")
         if na_position not in ["last", "first"]:
-            raise ValueError(f"invalid na_position: {na_position!r}")
+            raise ValueError(f"invalid na_position: {repr(na_position)}")
 
         sorted_idx = nargsort(self, ascending=ascending, na_position=na_position)
 
@@ -1769,8 +1769,8 @@ def fillna(self, value=None, method=None, limit=None):
 
             else:
                 raise TypeError(
-                    '"value" parameter must be a scalar, dict '
-                    f'or Series, but you passed a {type(value).__name__!r}"'
+                    f"'value' parameter must be a scalar, dict "
+                    f"or Series, but you passed a {type(value).__name__}"
                 )
 
         return self._constructor(codes, dtype=self.dtype, fastpath=True)

pandas/core/arrays/numpy_.py

@@ -45,7 +45,7 @@ def __init__(self, dtype):
         self._type = dtype.type
 
     def __repr__(self) -> str:
-        return "PandasDtype({!r})".format(self.name)
+        return f"PandasDtype({repr(self.name)})"
 
     @property
     def numpy_dtype(self):

pandas/core/computation/align.py

@@ -100,10 +100,10 @@ def _align_core(terms):
                 ordm = np.log10(max(1, abs(reindexer_size - term_axis_size)))
                 if ordm >= 1 and reindexer_size >= 10000:
                     w = (
-                        "Alignment difference on axis {axis} is larger "
-                        "than an order of magnitude on term {term!r}, by "
-                        "more than {ordm:.4g}; performance may suffer"
-                    ).format(axis=axis, term=terms[i].name, ordm=ordm)
+                        f"Alignment difference on axis {axis} is larger "
+                        f"than an order of magnitude on term {repr(terms[i].name)}, "
+                        f"by more than {ordm:.4g}; performance may suffer"
+                    )
                     warnings.warn(w, category=PerformanceWarning, stacklevel=6)
 
                 f = partial(ti.reindex, reindexer, axis=axis, copy=False)

pandas/core/computation/eval.py

@@ -47,8 +47,7 @@ def _check_engine(engine):
     if engine not in _engines:
         valid = list(_engines.keys())
         raise KeyError(
-            "Invalid engine {engine!r} passed, valid engines are"
-            " {valid}".format(engine=engine, valid=valid)
+            f"Invalid engine {repr(engine)} passed, valid engines are {valid}"
         )
 
     # TODO: validate this in a more general way (thinking of future engines
@@ -82,8 +81,8 @@ def _check_parser(parser: str):
 
     if parser not in _parsers:
         raise KeyError(
-            "Invalid parser {parser!r} passed, valid parsers are"
-            " {valid}".format(parser=parser, valid=_parsers.keys())
+            f"Invalid parser {repr(parser)} passed, "
+            f"valid parsers are {_parsers.keys()}"
         )
 
 
@@ -93,8 +92,8 @@ def _check_resolvers(resolvers):
             if not hasattr(resolver, "__getitem__"):
                 name = type(resolver).__name__
                 raise TypeError(
-                    "Resolver of type {name!r} does not implement "
-                    "the __getitem__ method".format(name=name)
+                    f"Resolver of type {repr(name)} does not "
+                    f"implement the __getitem__ method"
                 )
 
 

pandas/core/computation/expr.py

@@ -294,9 +294,7 @@ def _node_not_implemented(node_name, cls):
     """
 
     def f(self, *args, **kwargs):
-        raise NotImplementedError(
-            "{name!r} nodes are not implemented".format(name=node_name)
-        )
+        raise NotImplementedError(f"{repr(node_name)} nodes are not implemented")
 
     return f
 

pandas/core/computation/expressions.py

@@ -175,17 +175,15 @@ def _bool_arith_check(
     if _has_bool_dtype(a) and _has_bool_dtype(b):
         if op_str in unsupported:
             warnings.warn(
-                "evaluating in Python space because the {op!r} "
-                "operator is not supported by numexpr for "
-                "the bool dtype, use {alt_op!r} instead".format(
-                    op=op_str, alt_op=unsupported[op_str]
-                )
+                f"evaluating in Python space because the {repr(op_str)} "
+                f"operator is not supported by numexpr for "
+                f"the bool dtype, use {repr(unsupported[op_str])} instead"
             )
             return False
 
         if op_str in not_allowed:
             raise NotImplementedError(
-                "operator {op!r} not implemented for bool dtypes".format(op=op_str)
+                f"operator {repr(op_str)} not implemented for bool dtypes"
             )
     return True
 

pandas/core/computation/ops.py

@@ -56,11 +56,12 @@ class UndefinedVariableError(NameError):
     """
 
     def __init__(self, name, is_local: bool):
+        base_msg = f"{repr(name)} is not defined"
         if is_local:
-            msg = "local variable {0!r} is not defined"
+            msg = f"local variable {base_msg}"
         else:
-            msg = "name {0!r} is not defined"
-        super().__init__(msg.format(name))
+            msg = f"name {base_msg}"
+        super().__init__(msg)
 
 
 class Term:
@@ -143,10 +144,7 @@ def type(self):
 
     @property
     def raw(self) -> str:
-        return pprint_thing(
-            "{0}(name={1!r}, type={2})"
-            "".format(type(self).__name__, self.name, self.type)
-        )
+        return f"{type(self).__name__}(name={repr(self.name)}, type={self.type})"
 
     @property
     def is_datetime(self) -> bool:
@@ -374,8 +372,7 @@ def __init__(self, op: str, lhs, rhs, **kwargs):
             # has to be made a list for python3
             keys = list(_binary_ops_dict.keys())
             raise ValueError(
-                "Invalid binary operator {0!r}, valid"
-                " operators are {1}".format(op, keys)
+                f"Invalid binary operator {repr(op)}, valid operators are {keys}"
             )
 
     def __call__(self, env):
@@ -548,8 +545,8 @@ def __init__(self, op: str, operand):
             self.func = _unary_ops_dict[op]
         except KeyError:
             raise ValueError(
-                "Invalid unary operator {0!r}, valid operators "
-                "are {1}".format(op, _unary_ops_syms)
+                f"Invalid unary operator {repr(op)}, "
+                f"valid operators are {_unary_ops_syms}"
             )
 
     def __call__(self, env):