DOC: Correct grammar issues about a/an usage (#63042)

Author: star1327p

pandas/_libs/lib.pyx

@@ -2712,7 +2712,7 @@ def maybe_convert_objects(ndarray[object] objects,
                 break
         elif PyDateTime_Check(val) or cnp.is_datetime64_object(val):
 
-            # if we have an tz's attached then return the objects
+            # if we have a tz's attached then return the objects
             if convert_non_numeric:
                 if getattr(val, "tzinfo", None) is not None:
                     seen.datetimetz_ = True

pandas/_libs/tslibs/nattype.pyx

@@ -1168,7 +1168,7 @@ class NaTType(_NaT):
     fromordinal = _make_error_func(
         "fromordinal",
         """
-        Construct a timestamp from a a proleptic Gregorian ordinal.
+        Construct a timestamp from a proleptic Gregorian ordinal.
 
         This method creates a `Timestamp` object corresponding to the given
         proleptic Gregorian ordinal, which is a count of days from January 1,

pandas/_libs/tslibs/offsets.pyx

@@ -1803,7 +1803,7 @@ class DateOffset(RelativeDeltaOffset, metaclass=OffsetMeta):
     See Also
     --------
     dateutil.relativedelta.relativedelta : The relativedelta type is designed
-        to be applied to an existing datetime an can replace specific components of
+        to be applied to an existing datetime and can replace specific components of
         that datetime, or represents an interval of time.
 
     Examples

pandas/_libs/tslibs/timestamps.pyx

@@ -1869,7 +1869,7 @@ class Timestamp(_Timestamp):
     @classmethod
     def fromordinal(cls, ordinal, tz=None):
         """
-        Construct a timestamp from a a proleptic Gregorian ordinal.
+        Construct a timestamp from a proleptic Gregorian ordinal.
 
         This method creates a `Timestamp` object corresponding to the given
         proleptic Gregorian ordinal, which is a count of days from January 1,

pandas/core/algorithms.py

@@ -1703,7 +1703,7 @@ def map_array(
             ]
         else:
             # Dictionary does not have a default. Thus it's safe to
-            # convert to an Series for efficiency.
+            # convert to a Series for efficiency.
             # we specify the keys here to handle the
             # possibility that they are tuples
 

pandas/core/arrays/period.py

@@ -1335,7 +1335,7 @@ def dt64arr_to_periodarr(
     data, freq, tz=None
 ) -> tuple[npt.NDArray[np.int64], BaseOffset]:
     """
-    Convert an datetime-like array to values Period ordinals.
+    Convert a datetime-like array to values Period ordinals.
 
     Parameters
     ----------

pandas/core/arrays/string_arrow.py

@@ -107,10 +107,10 @@ class ArrowStringArray(ObjectStringArrayMixin, ArrowExtensionArray, BaseStringAr
     See Also
     --------
     :func:`array`
-        The recommended function for creating a ArrowStringArray.
+        The recommended function for creating an ArrowStringArray.
     Series.str
         The string methods are available on Series backed by
-        a ArrowStringArray.
+        an ArrowStringArray.
 
     Notes
     -----

pandas/core/dtypes/common.py

@@ -882,7 +882,7 @@ def is_unsigned_integer_dtype(arr_or_dtype) -> bool:
     See Also
     --------
     api.types.is_signed_integer_dtype : Check whether the provided array
-        or dtype is of an signed integer dtype.
+        or dtype is of a signed integer dtype.
     api.types.is_integer_dtype : Check whether the provided array or dtype
         is of an integer dtype.
     api.types.is_numeric_dtype : Check whether the provided array or dtype
@@ -1264,7 +1264,7 @@ def is_numeric_dtype(arr_or_dtype) -> bool:
     api.types.is_unsigned_integer_dtype: Check whether the provided array
         or dtype is of an unsigned integer dtype.
     api.types.is_signed_integer_dtype: Check whether the provided array
-        or dtype is of an signed integer dtype.
+        or dtype is of a signed integer dtype.
 
     Examples
     --------

pandas/core/groupby/generic.py

@@ -2913,7 +2913,7 @@ def take(
         Returns
         -------
         DataFrame
-            An DataFrame containing the elements taken from each group.
+            A DataFrame containing the elements taken from each group.
 
         See Also
         --------

pandas/core/indexers/utils.py

@@ -126,7 +126,7 @@ def check_setitem_lengths(indexer, value, values) -> bool:
     """
     Validate that value and indexer are the same length.
 
-    An special-case is allowed for when the indexer is a boolean array
+    A special-case is allowed for when the indexer is a boolean array
     and the number of true values equals the length of ``value``. In
     this case, no exception is raised.