added f strings and typing to frame.py

pandas/core/frame.py

@@ -475,7 +475,7 @@ def __init__(
             except (ValueError, TypeError) as e:
                 exc = TypeError(
                     "DataFrame constructor called with "
-                    "incompatible data and dtype: {e}".format(e=e)
+                    f"incompatible data and dtype: {e}"
                 )
                 raise exc from e
 
@@ -1112,8 +1112,7 @@ def dot(self, other):
             rvals = np.asarray(other)
             if lvals.shape[1] != rvals.shape[0]:
                 raise ValueError(
-                    "Dot product shape mismatch, "
-                    "{s} vs {r}".format(s=lvals.shape, r=rvals.shape)
+                    f"Dot product shape mismatch, {lvals.shape} vs {rvals.shape}"
                 )
 
         if isinstance(other, DataFrame):
@@ -1129,7 +1128,7 @@ def dot(self, other):
             else:
                 return Series(result, index=left.index)
         else:  # pragma: no cover
-            raise TypeError("unsupported type: {oth}".format(oth=type(other)))
+            raise TypeError(f"unsupported type: {type(other)}")
 
     def __matmul__(self, other):
         """
@@ -1417,7 +1416,7 @@ def to_dict(self, orient="dict", into=dict):
                 for t in self.itertuples(name=None)
             )
         else:
-            raise ValueError("orient '{o}' not understood".format(o=orient))
+            raise ValueError(f"orient '{orient}' not understood")
 
     def to_gbq(
         self,
@@ -1836,9 +1835,7 @@ def to_records(self, index=True, column_dtypes=None, index_dtypes=None):
                 formats.append(dtype_mapping)
             else:
                 element = "row" if i < index_len else "column"
-                msg = ("Invalid dtype {dtype} specified for {element} {name}").format(
-                    dtype=dtype_mapping, element=element, name=name
-                )
+                msg = f"Invalid dtype {dtype_mapping} specified for {element} {name}"
                 raise ValueError(msg)
 
         return np.rec.fromarrays(arrays, dtype={"names": names, "formats": formats})
@@ -2307,7 +2304,7 @@ def info(
         lines.append(self.index._summary())
 
         if len(self.columns) == 0:
-            lines.append("Empty {name}".format(name=type(self).__name__))
+            lines.append(f"Empty {type(self).__name__}")
             fmt.buffer_put_lines(buf, lines)
             return
 
@@ -2335,10 +2332,7 @@ def _verbose_repr():
                 counts = self.count()
                 if len(cols) != len(counts):  # pragma: no cover
                     raise AssertionError(
-                        "Columns must equal counts "
-                        "({cols:d} != {counts:d})".format(
-                            cols=len(cols), counts=len(counts)
-                        )
+                        f"Columns must equal counts ({len(cols)} != {len(counts)})"
                     )
                 tmpl = "{count} non-null {dtype}"
 
@@ -2382,7 +2376,7 @@ def _sizeof_fmt(num, size_qualifier):
 
         counts = self._data.get_dtype_counts()
         dtypes = ["{k}({kk:d})".format(k=k[0], kk=k[1]) for k in sorted(counts.items())]
-        lines.append("dtypes: {types}".format(types=", ".join(dtypes)))
+        lines.append(f"dtypes: {', '.join(dtypes)}")
 
         if memory_usage is None:
             memory_usage = get_option("display.memory_usage")
@@ -2399,12 +2393,7 @@ def _sizeof_fmt(num, size_qualifier):
                 if "object" in counts or self.index._is_memory_usage_qualified():
                     size_qualifier = "+"
             mem_usage = self.memory_usage(index=True, deep=deep).sum()
-            lines.append(
-                "memory usage: {mem}\n".format(
-                    mem=_sizeof_fmt(mem_usage, size_qualifier)
-                )
-            )
-
+            lines.append(f"memory usage: {_sizeof_fmt(mem_usage, size_qualifier)}\n")
         fmt.buffer_put_lines(buf, lines)
 
     def memory_usage(self, index=True, deep=False):
@@ -3069,8 +3058,8 @@ def query(self, expr, inplace=False, **kwargs):
         """
         inplace = validate_bool_kwarg(inplace, "inplace")
         if not isinstance(expr, str):
-            msg = "expr must be a string to be evaluated, {0} given"
-            raise ValueError(msg.format(type(expr)))
+            msg = f"expr must be a string to be evaluated, {type(expr)} given"
+            raise ValueError(msg)
         kwargs["level"] = kwargs.pop("level", 0) + 1
         kwargs["target"] = None
         res = self.eval(expr, **kwargs)
@@ -3287,11 +3276,7 @@ def select_dtypes(self, include=None, exclude=None):
 
         # can't both include AND exclude!
         if not include.isdisjoint(exclude):
-            raise ValueError(
-                "include and exclude overlap on {inc_ex}".format(
-                    inc_ex=(include & exclude)
-                )
-            )
+            raise ValueError(f"include and exclude overlap on {(include & exclude)}")
 
         # We raise when both include and exclude are empty
         # Hence, we can just shrink the columns we want to keep
@@ -4128,15 +4113,13 @@ def set_index(
                 try:
                     found = col in self.columns
                 except TypeError:
-                    raise TypeError(
-                        err_msg + " Received column of type {}".format(type(col))
-                    )
+                    raise TypeError(f"{err_msg}. Received column of type {type(col)}")
                 else:
                     if not found:
                         missing.append(col)
 
         if missing:
-            raise KeyError("None of {} are in the columns".format(missing))
+            raise KeyError(f"None of {missing} are in the columns")
 
         if inplace:
             frame = self
@@ -4180,17 +4163,15 @@ def set_index(
                 # check newest element against length of calling frame, since
                 # ensure_index_from_sequences would not raise for append=False.
                 raise ValueError(
-                    "Length mismatch: Expected {len_self} rows, "
-                    "received array of length {len_col}".format(
-                        len_self=len(self), len_col=len(arrays[-1])
-                    )
+                    f"Length mismatch: Expected {len(self)} rows, "
+                    f"received array of length {len(arrays[-1])}"
                 )
 
         index = ensure_index_from_sequences(arrays, names)
 
         if verify_integrity and not index.is_unique:
             duplicates = index[index.duplicated()].unique()
-            raise ValueError("Index has duplicate keys: {dup}".format(dup=duplicates))
+            raise ValueError(f"Index has duplicate keys: {duplicates}")
 
         # use set to handle duplicate column names gracefully in case of drop
         for c in set(to_remove):
@@ -4205,8 +4186,13 @@ def set_index(
             return frame
 
     def reset_index(
-        self, level=None, drop=False, inplace=False, col_level=0, col_fill=""
-    ):
+        self,
+        level: Optional[Union[Hashable, Sequence[Hashable]]] = None,
+        drop: bool = False,
+        inplace: bool = False,
+        col_level: Hashable = 0,
+        col_fill: Optional[Hashable] = "",
+    ) -> "DataFrame":
         """
         Reset the index, or a level of it.
 
@@ -4422,8 +4408,7 @@ def _maybe_casted_values(index, labels=None):
                         if len(col_name) not in (1, self.columns.nlevels):
                             raise ValueError(
                                 "col_fill=None is incompatible "
-                                "with incomplete column name "
-                                "{}".format(name)
+                                f"with incomplete column name {name}"
                             )
                         col_fill = col_name[0]
 
@@ -4589,7 +4574,7 @@ def dropna(self, axis=0, how="any", thresh=None, subset=None, inplace=False):
             mask = count > 0
         else:
             if how is not None:
-                raise ValueError("invalid how option: {h}".format(h=how))
+                raise ValueError(f"invalid how option: {how}")
             else:
                 raise TypeError("must specify how or thresh")
 
@@ -4600,7 +4585,12 @@ def dropna(self, axis=0, how="any", thresh=None, subset=None, inplace=False):
         else:
             return result
 
-    def drop_duplicates(self, subset=None, keep="first", inplace=False):
+    def drop_duplicates(
+        self,
+        subset: Optional[Union[Hashable, Sequence[Hashable]]] = None,
+        keep: Union[str, bool] = "first",
+        inplace: bool = False,
+    ) -> "DataFrame":
         """
         Return DataFrame with duplicate rows removed.
 
@@ -4637,7 +4627,11 @@ def drop_duplicates(self, subset=None, keep="first", inplace=False):
         else:
             return self[-duplicated]
 
-    def duplicated(self, subset=None, keep="first"):
+    def duplicated(
+        self,
+        subset: Optional[Union[Hashable, Sequence[Hashable]]] = None,
+        keep: Union[str, bool] = "first",
+    ) -> "Series":
         """
         Return boolean Series denoting duplicate rows.
 
@@ -7208,7 +7202,7 @@ def corr(self, method="pearson", min_periods=1):
             raise ValueError(
                 "method must be either 'pearson', "
                 "'spearman', 'kendall', or a callable, "
-                "'{method}' was supplied".format(method=method)
+                f"'{method}' was supplied"
             )
 
         return self._constructor(correl, index=idx, columns=cols)
@@ -7399,9 +7393,9 @@ def c(x):
 
         else:
             raise ValueError(
-                "Invalid method {method} was passed, "
+                f"Invalid method {method} was passed, "
                 "valid methods are: 'pearson', 'kendall', "
-                "'spearman', or callable".format(method=method)
+                "'spearman', or callable"
             )
 
         if not drop:
@@ -7531,8 +7525,7 @@ def _count_level(self, level, axis=0, numeric_only=False):
 
         if not isinstance(count_axis, ABCMultiIndex):
             raise TypeError(
-                "Can only count levels on hierarchical "
-                "{ax}.".format(ax=self._get_axis_name(axis))
+                f"Can only count levels on hierarchical {self._get_axis_name(axis)}."
             )
 
         if frame._is_mixed_type:
@@ -7590,8 +7583,8 @@ def _get_data(axis_matters):
                     data = self._get_bool_data()
             else:  # pragma: no cover
                 msg = (
-                    "Generating numeric_only data with filter_type {f}"
-                    "not supported.".format(f=filter_type)
+                    f"Generating numeric_only data with filter_type {filter_type} "
+                    "not supported."
                 )
                 raise NotImplementedError(msg)
             return data
@@ -8000,7 +7993,7 @@ def to_timestamp(self, freq=None, how="start", axis=0, copy=True):
         elif axis == 1:
             new_data.set_axis(0, self.columns.to_timestamp(freq=freq, how=how))
         else:  # pragma: no cover
-            raise AssertionError("Axis must be 0 or 1. Got {ax!s}".format(ax=axis))
+            raise AssertionError(f"Axis must be 0 or 1. Got {axis}")
 
         return self._constructor(new_data)
 
@@ -8034,7 +8027,7 @@ def to_period(self, freq=None, axis=0, copy=True):
         elif axis == 1:
             new_data.set_axis(0, self.columns.to_period(freq=freq))
         else:  # pragma: no cover
-            raise AssertionError("Axis must be 0 or 1. Got {ax!s}".format(ax=axis))
+            raise AssertionError(f"Axis must be 0 or 1. Got {axis}")
 
         return self._constructor(new_data)
 
@@ -8123,8 +8116,8 @@ def isin(self, values):
         else:
             if not is_list_like(values):
                 raise TypeError(
-                    f"only list-like or dict-like objects are allowed "
-                    f"to be passed to DataFrame.isin(), "
+                    "only list-like or dict-like objects are allowed "
+                    "to be passed to DataFrame.isin(), "
                     f"you passed a {repr(type(values).__name__)}"
                 )
             return DataFrame(
@@ -8170,4 +8163,4 @@ def _from_nested_dict(data):
 
 
 def _put_str(s, space):
-    return "{s}".format(s=s)[:space].ljust(space)
+    return str(s)[:space].ljust(space)