CLN: annotations in core.dtypes

pandas/core/dtypes/base.py

@@ -87,7 +87,8 @@ def __str__(self) -> str:
         return self.name
 
     def __eq__(self, other):
-        """Check whether 'other' is equal to self.
+        """
+        Check whether 'other' is equal to self.
 
         By default, 'other' is considered equal if either
 
@@ -115,7 +116,7 @@ def __eq__(self, other):
             )
         return False
 
-    def __hash__(self):
+    def __hash__(self) -> int:
         return hash(tuple(getattr(self, attr) for attr in self._metadata))
 
     def __ne__(self, other):
@@ -171,7 +172,8 @@ def name(self) -> str:
 
     @property
     def names(self) -> Optional[List[str]]:
-        """Ordered list of field names, or None if there are no fields.
+        """
+        Ordered list of field names, or None if there are no fields.
 
         This is for compatibility with NumPy arrays, and may be removed in the
         future.
@@ -233,16 +235,19 @@ def construct_from_string(cls, string: str):
         ...                         "'{}'".format(cls.__name__, string))
         """
         if not isinstance(string, str):
-            raise TypeError("Expects a string, got {}".format(type(string)))
+            raise TypeError("Expects a string, got {typ}".format(typ=type(string)))
         if string != cls.name:
             raise TypeError(
-                "Cannot construct a '{}' from '{}'".format(cls.__name__, string)
+                "Cannot construct a '{cls}' from '{string}'".format(
+                    cls=cls.__name__, string=string
+                )
             )
         return cls()
 
     @classmethod
     def is_dtype(cls, dtype) -> bool:
-        """Check if we match 'dtype'.
+        """
+        Check if we match 'dtype'.
 
         Parameters
         ----------

pandas/core/dtypes/cast.py

@@ -72,7 +72,7 @@ def maybe_convert_platform(values):
     return values
 
 
-def is_nested_object(obj):
+def is_nested_object(obj) -> bool:
     """
     return a boolean if we have a nested object, e.g. a Series with 1 or
     more Series elements
@@ -500,11 +500,11 @@ def _ensure_dtype_type(value, dtype):
 
 def infer_dtype_from(val, pandas_dtype: bool = False):
     """
-    interpret the dtype from a scalar or array. This is a convenience
-    routines to infer dtype from a scalar or an array
+    Interpret the dtype from a scalar or array.
 
     Parameters
     ----------
+    val : object
     pandas_dtype : bool, default False
         whether to infer dtype including pandas extension types.
         If False, scalar/array belongs to pandas extension types is inferred as
@@ -517,7 +517,7 @@ def infer_dtype_from(val, pandas_dtype: bool = False):
 
 def infer_dtype_from_scalar(val, pandas_dtype: bool = False):
     """
-    interpret the dtype from a scalar
+    Interpret the dtype from a scalar.
 
     Parameters
     ----------
@@ -592,7 +592,7 @@ def infer_dtype_from_scalar(val, pandas_dtype: bool = False):
 
 def infer_dtype_from_array(arr, pandas_dtype: bool = False):
     """
-    infer the dtype from a scalar or array
+    Infer the dtype from a scalar or array.
 
     Parameters
     ----------
@@ -647,7 +647,8 @@ def infer_dtype_from_array(arr, pandas_dtype: bool = False):
 
 
 def maybe_infer_dtype_type(element):
-    """Try to infer an object's dtype, for use in arithmetic ops
+    """
+    Try to infer an object's dtype, for use in arithmetic ops.
 
     Uses `element.dtype` if that's available.
     Objects implementing the iterator protocol are cast to a NumPy array,
@@ -679,8 +680,9 @@ def maybe_infer_dtype_type(element):
     return tipo
 
 
-def maybe_upcast(values, fill_value=np.nan, dtype=None, copy=False):
-    """ provide explicit type promotion and coercion
+def maybe_upcast(values, fill_value=np.nan, dtype=None, copy: bool = False):
+    """
+    Provide explicit type promotion and coercion.
 
     Parameters
     ----------
@@ -759,7 +761,7 @@ def conv(r, dtype):
     return [conv(r, dtype) for r, dtype in zip(result, dtypes)]
 
 
-def astype_nansafe(arr, dtype, copy=True, skipna=False):
+def astype_nansafe(arr, dtype, copy: bool = True, skipna: bool = False):
     """
     Cast the elements of an array to a given dtype a nan-safe manner.
 
@@ -982,7 +984,7 @@ def soft_convert_objects(
     return values
 
 
-def maybe_castable(arr):
+def maybe_castable(arr) -> bool:
     # return False to force a non-fastpath
 
     # check datetime64[ns]/timedelta64[ns] are valid
@@ -996,7 +998,7 @@ def maybe_castable(arr):
     return arr.dtype.name not in _POSSIBLY_CAST_DTYPES
 
 
-def maybe_infer_to_datetimelike(value, convert_dates=False):
+def maybe_infer_to_datetimelike(value, convert_dates: bool = False):
     """
     we might have a array (or single object) that is datetime like,
     and no dtype is passed don't change the value unless we find a
@@ -1103,7 +1105,7 @@ def try_timedelta(v):
     return value
 
 
-def maybe_cast_to_datetime(value, dtype, errors="raise"):
+def maybe_cast_to_datetime(value, dtype, errors: str = "raise"):
     """ try to cast the array/value to a datetimelike dtype, converting float
     nan to iNaT
     """
@@ -1292,7 +1294,7 @@ def find_common_type(types):
 
 def cast_scalar_to_array(shape, value, dtype=None):
     """
-    create np.ndarray of specified shape and dtype, filled with values
+    Create np.ndarray of specified shape and dtype, filled with values.
 
     Parameters
     ----------
@@ -1318,7 +1320,7 @@ def cast_scalar_to_array(shape, value, dtype=None):
     return values
 
 
-def construct_1d_arraylike_from_scalar(value, length, dtype):
+def construct_1d_arraylike_from_scalar(value, length: int, dtype):
     """
     create a np.ndarray / pandas type of specified shape and dtype
     filled with values
@@ -1383,7 +1385,7 @@ def construct_1d_object_array_from_listlike(values):
     return result
 
 
-def construct_1d_ndarray_preserving_na(values, dtype=None, copy=False):
+def construct_1d_ndarray_preserving_na(values, dtype=None, copy: bool = False):
     """
     Construct a new ndarray, coercing `values` to `dtype`, preserving NA.
 
@@ -1424,7 +1426,7 @@ def construct_1d_ndarray_preserving_na(values, dtype=None, copy=False):
     return subarr
 
 
-def maybe_cast_to_integer_array(arr, dtype, copy=False):
+def maybe_cast_to_integer_array(arr, dtype, copy: bool = False):
     """
     Takes any dtype and returns the casted version, raising for when data is
     incompatible with integer/unsigned integer dtypes.