repr()

Author: MomIsBestFriend

pandas/core/missing.py

@@ -211,9 +211,10 @@ def interpolate_1d(
     valid_limit_directions = ["forward", "backward", "both"]
     limit_direction = limit_direction.lower()
     if limit_direction not in valid_limit_directions:
-        msg = "Invalid limit_direction: expecting one of {valid!r}, got {invalid!r}."
         raise ValueError(
-            msg.format(valid=valid_limit_directions, invalid=limit_direction)
+            f"Invalid limit_direction: expecting one of "
+            f"{repr(valid_limit_directions)}, "
+            f"got {repr(limit_direction)}."
         )
 
     if limit_area is not None:

pandas/core/nanops.py

@@ -60,8 +60,10 @@ def __call__(self, f):
         def _f(*args, **kwargs):
             obj_iter = itertools.chain(args, kwargs.values())
             if any(self.check(obj) for obj in obj_iter):
-                msg = "reduction operation {name!r} not allowed for this dtype"
-                raise TypeError(msg.format(name=f.__name__.replace("nan", "")))
+                f_name = f.__name__.replace("nan", "")
+                raise TypeError(
+                    f"reduction operation {repr(f_name)} not allowed for this dtype"
+                )
             try:
                 with np.errstate(invalid="ignore"):
                     return f(*args, **kwargs)

pandas/core/reshape/concat.py

@@ -542,8 +542,8 @@ def _get_concat_axis(self) -> Index:
                 for i, x in enumerate(self.objs):
                     if not isinstance(x, Series):
                         raise TypeError(
-                            "Cannot concatenate type 'Series' "
-                            "with object of type {type!r}".format(type=type(x).__name__)
+                            f"Cannot concatenate type 'Series' with object "
+                            f"of type {repr(type(x).__name__)}"
                         )
                     if x.name is not None:
                         names[i] = x.name

pandas/core/reshape/merge.py

@@ -1194,9 +1194,7 @@ def _validate_specification(self):
                         )
                     )
                 if not common_cols.is_unique:
-                    raise MergeError(
-                        "Data columns not unique: {common!r}".format(common=common_cols)
-                    )
+                    raise MergeError(f"Data columns not unique: {repr(common_cols)}")
                 self.left_on = self.right_on = common_cols
         elif self.on is not None:
             if self.left_on is not None or self.right_on is not None:

pandas/core/reshape/tile.py

@@ -376,9 +376,8 @@ def _bins_to_cuts(
     if len(unique_bins) < len(bins) and len(bins) != 2:
         if duplicates == "raise":
             raise ValueError(
-                "Bin edges must be unique: {bins!r}.\nYou "
-                "can drop duplicate edges by setting "
-                "the 'duplicates' kwarg".format(bins=bins)
+                f"Bin edges must be unique: {repr(bins)}.\n "
+                f"You can drop duplicate edges by setting the 'duplicates' kwarg"
             )
         else:
             bins = unique_bins

pandas/core/series.py

@@ -2944,7 +2944,7 @@ def _try_kind_sort(arr):
             sortedIdx[n:] = idx[good][argsorted]
             sortedIdx[:n] = idx[bad]
         else:
-            raise ValueError("invalid na_position: {!r}".format(na_position))
+            raise ValueError(f"invalid na_position: {repr(na_position)}")
 
         result = self._constructor(arr[sortedIdx], index=self.index[sortedIdx])
 

pandas/core/sorting.py

@@ -208,7 +208,7 @@ def lexsort_indexer(keys, orders=None, na_position="last"):
             cat = Categorical(key, ordered=True)
 
         if na_position not in ["last", "first"]:
-            raise ValueError("invalid na_position: {!r}".format(na_position))
+            raise ValueError(f"invalid na_position: {repr(na_position)}")
 
         n = len(cat.categories)
         codes = cat.codes.copy()
@@ -264,7 +264,7 @@ def nargsort(items, kind="quicksort", ascending: bool = True, na_position="last"
     elif na_position == "first":
         indexer = np.concatenate([nan_idx, indexer])
     else:
-        raise ValueError("invalid na_position: {!r}".format(na_position))
+        raise ValueError(f"invalid na_position: {repr(na_position)}")
     return indexer
 
 

pandas/core/strings.py

@@ -1932,13 +1932,10 @@ def _forbid_nonstring_types(func):
         @wraps(func)
         def wrapper(self, *args, **kwargs):
             if self._inferred_dtype not in allowed_types:
-                msg = (
-                    "Cannot use .str.{name} with values of inferred dtype "
-                    "{inf_type!r}.".format(
-                        name=func_name, inf_type=self._inferred_dtype
-                    )
+                raise TypeError(
+                    f"Cannot use .str.{func_name} with values of "
+                    f"inferred dtype {repr(self.inferred_dtype)}."
                 )
-                raise TypeError(msg)
             return func(self, *args, **kwargs)
 
         wrapper.__name__ = func_name

pandas/io/formats/css.py

@@ -173,7 +173,7 @@ def __call__(self, declarations_str, inherited=None):
 
     def size_to_pt(self, in_val, em_pt=None, conversions=UNIT_RATIOS):
         def _error():
-            warnings.warn("Unhandled size: {val!r}".format(val=in_val), CSSWarning)
+            warnings.warn(f"Unhandled size: {repr(in_val)}", CSSWarning)
             return self.size_to_pt("1!!default", conversions=conversions)
 
         try:
@@ -252,7 +252,6 @@ def parse(self, declarations_str):
                 yield prop, val
             else:
                 warnings.warn(
-                    "Ill-formatted attribute: expected a colon "
-                    "in {decl!r}".format(decl=decl),
+                    f"Ill-formatted attribute: expected a colon in {repr(decl)}",
                     CSSWarning,
                 )

pandas/io/formats/excel.py

@@ -321,7 +321,7 @@ def color_to_excel(self, val):
         try:
             return self.NAMED_COLORS[val]
         except KeyError:
-            warnings.warn("Unhandled color format: {val!r}".format(val=val), CSSWarning)
+            warnings.warn(f"Unhandled color format: {repr(val)}", CSSWarning)
 
     def build_number_format(self, props):
         return {"format_code": props.get("number-format")}

pandas/io/formats/style.py

@@ -627,29 +627,25 @@ def _apply(self, func, axis=0, subset=None, **kwargs):
             result = func(data, **kwargs)
             if not isinstance(result, pd.DataFrame):
                 raise TypeError(
-                    "Function {func!r} must return a DataFrame when "
-                    "passed to `Styler.apply` with axis=None".format(func=func)
+                    f"Function {repr(func)} must return a DataFrame when "
+                    f"passed to `Styler.apply` with axis=None"
                 )
             if not (
                 result.index.equals(data.index) and result.columns.equals(data.columns)
             ):
-                msg = (
-                    "Result of {func!r} must have identical index and "
-                    "columns as the input".format(func=func)
+                raise ValueError(
+                    f"Result of {repr(func)} must have identical "
+                    f"index and columns as the input"
                 )
-                raise ValueError(msg)
 
         result_shape = result.shape
         expected_shape = self.data.loc[subset].shape
         if result_shape != expected_shape:
-            msg = (
-                "Function {func!r} returned the wrong shape.\n"
-                "Result has shape: {res}\n"
-                "Expected shape:   {expect}".format(
-                    func=func, res=result.shape, expect=expected_shape
-                )
+            raise ValueError(
+                f"Function {repr(func)} returned the wrong shape.\n"
+                f"Result has shape: {result.shape}\n"
+                f"Expected shape:   {expected_shape}"
             )
-            raise ValueError(msg)
         self._update_ctx(result)
         return self