DOC: PR06 docstring fixes

pandas/core/arrays/categorical.py

@@ -636,7 +636,7 @@ def from_codes(cls, codes, categories=None, ordered=None, dtype=None):
 
         Parameters
         ----------
-        codes : array-like, integers
+        codes : array-like of int
             An integer array, where each integer points to a category in
             categories or dtype.categories, or else is -1 for NaN.
         categories : index-like, optional
@@ -647,8 +647,8 @@ def from_codes(cls, codes, categories=None, ordered=None, dtype=None):
             Whether or not this categorical is treated as an ordered
             categorical. If not given here or in `dtype`, the resulting
             categorical will be unordered.
-        dtype : CategoricalDtype or the string "category", optional
-            If :class:`CategoricalDtype`, cannot be used together with
+        dtype : CategoricalDtype or "category", optional
+            If :class:`Categoarray-like, intricalDtype`, cannot be used together with
             `categories` or `ordered`.
 
             .. versionadded:: 0.24.0

pandas/core/arrays/datetimelike.py

@@ -670,7 +670,7 @@ def value_counts(self, dropna=False):
 
         Parameters
         ----------
-        dropna : boolean, default True
+        dropna : bool, default True
             Don't include counts of NaT values.
 
         Returns
@@ -728,7 +728,7 @@ def _maybe_mask_results(self, result, fill_value=iNaT, convert=None):
         ----------
         result : a ndarray
         fill_value : object, default iNaT
-        convert : string/dtype or None
+        convert : str, dtype or None
 
         Returns
         -------
@@ -1168,7 +1168,7 @@ def _time_shift(self, periods, freq=None):
         ----------
         periods : int
             Number of periods to shift by.
-        freq : pandas.DateOffset, pandas.Timedelta, or string
+        freq : pandas.DateOffset, pandas.Timedelta, or str
             Frequency increment to shift by.
         """
         if freq is not None and freq != self.freq:

pandas/core/arrays/integer.py

@@ -95,7 +95,7 @@ def integer_array(values, dtype=None, copy=False):
     values : 1D list-like
     dtype : dtype, optional
         dtype to coerce
-    copy : boolean, default False
+    copy : bool, default False
 
     Returns
     -------
@@ -140,8 +140,8 @@ def coerce_to_array(values, dtype, mask=None, copy=False):
     ----------
     values : 1D list-like
     dtype : integer dtype
-    mask : boolean 1D array, optional
-    copy : boolean, default False
+    mask : bool 1D array, optional
+    copy : bool, default False
         if True, copy the input
 
     Returns
@@ -542,7 +542,7 @@ def value_counts(self, dropna=True):
 
         Parameters
         ----------
-        dropna : boolean, default True
+        dropna : bool, default True
             Don't include counts of NaN.
 
         Returns

pandas/core/arrays/period.py

@@ -444,7 +444,7 @@ def to_timestamp(self, freq=None, how="start"):
 
         Parameters
         ----------
-        freq : string or DateOffset, optional
+        freq : str or DateOffset, optional
             Target frequency. The default is 'D' for week or longer,
             'S' otherwise
         how : {'s', 'e', 'start', 'end'}
@@ -515,7 +515,7 @@ def _time_shift(self, periods, freq=None):
         ----------
         periods : int
             Number of periods to shift by.
-        freq : pandas.DateOffset, pandas.Timedelta, or string
+        freq : pandas.DateOffset, pandas.Timedelta, or str
             Frequency increment to shift by.
         """
         if freq is not None:

pandas/core/dtypes/cast.py

@@ -1006,7 +1006,7 @@ def maybe_infer_to_datetimelike(value, convert_dates=False):
     Parameters
     ----------
     value : np.array / Series / Index / list-like
-    convert_dates : boolean, default False
+    convert_dates : bool, default False
        if True try really hard to convert dates (such as datetime.date), other
        leave inferred dtype 'date' alone
 
@@ -1439,7 +1439,7 @@ def maybe_cast_to_integer_array(arr, dtype, copy=False):
         The array to cast.
     dtype : str, np.dtype
         The integer dtype to cast the array to.
-    copy: boolean, default False
+    copy: bool, default False
         Whether to make a copy of the array before returning.
 
     Returns

pandas/core/dtypes/common.py

@@ -152,7 +152,7 @@ def ensure_int_or_float(arr: ArrayLike, copy: bool = False) -> np.array:
     ----------
     arr : array-like
           The array whose data type we want to enforce.
-    copy: boolean
+    copy: bool
           Whether to copy the original array or reuse
           it in place, if possible.
 

pandas/core/dtypes/concat.py

@@ -192,10 +192,10 @@ def union_categoricals(to_union, sort_categories=False, ignore_order=False):
     ----------
     to_union : list-like of Categorical, CategoricalIndex,
                or Series with dtype='category'
-    sort_categories : boolean, default False
+    sort_categories : bool, default False
         If true, resulting categories will be lexsorted, otherwise
         they will be ordered as they appear in the data.
-    ignore_order : boolean, default False
+    ignore_order : bool, default False
         If true, the ordered attribute of the Categoricals will be ignored.
         Results in an unordered categorical.
 

pandas/core/dtypes/dtypes.py

@@ -85,7 +85,7 @@ def find(
         """
         Parameters
         ----------
-        dtype : Type[ExtensionDtype] or string
+        dtype : Type[ExtensionDtype] or str
 
         Returns
         -------

pandas/core/dtypes/missing.py

@@ -521,7 +521,7 @@ def na_value_for_dtype(dtype, compat=True):
     Parameters
     ----------
     dtype : string / dtype
-    compat : boolean, default True
+    compat : bool, default True
 
     Returns
     -------