DOC: Correct many grammatical issues (#63217)

Author: star1327p

pandas/_libs/hashtable_class_helper.pxi.in

@@ -1070,7 +1070,7 @@ cdef class StringHashTable(HashTable):
             val = values[i]
 
             if isinstance(val, str):
-                # GH#31499 if we have a np.str_ PyUnicode_AsUTF8 won't recognize
+                # GH#31499 if we have an np.str_ PyUnicode_AsUTF8 won't recognize
                 #  it as a str, even though isinstance does.
                 v = PyUnicode_AsUTF8(<str>val)
             else:
@@ -1108,7 +1108,7 @@ cdef class StringHashTable(HashTable):
             val = values[i]
 
             if isinstance(val, str):
-                # GH#31499 if we have a np.str_ PyUnicode_AsUTF8 won't recognize
+                # GH#31499 if we have an np.str_ PyUnicode_AsUTF8 won't recognize
                 #  it as a str, even though isinstance does.
                 v = PyUnicode_AsUTF8(<str>val)
             else:

pandas/_libs/index.pyx

@@ -58,7 +58,7 @@ cdef bint is_definitely_invalid_key(object val):
 
 cdef ndarray _get_bool_indexer(ndarray values, object val, ndarray mask = None):
     """
-    Return a ndarray[bool] of locations where val matches self.values.
+    Return an ndarray[bool] of locations where val matches self.values.
 
     If val is not NA, this is equivalent to `self.values == val`
     """

pandas/_libs/tslib.pyx

@@ -120,7 +120,7 @@ def format_array_from_datetime(
     NPY_DATETIMEUNIT reso=NPY_FR_ns,
 ) -> np.ndarray:
     """
-    return a np object array of the string formatted values
+    return an np object array of the string formatted values
 
     Parameters
     ----------

pandas/_libs/tslibs/conversion.pyx

@@ -276,7 +276,7 @@ cdef (int64_t, int) precision_from_unit(
 
 cdef int64_t get_datetime64_nanos(object val, NPY_DATETIMEUNIT reso) except? -1:
     """
-    Extract the value and unit from a np.datetime64 object, then convert the
+    Extract the value and unit from an np.datetime64 object, then convert the
     value to nanoseconds if necessary.
     """
     cdef:

pandas/_libs/tslibs/nattype.pyx

@@ -1885,7 +1885,7 @@ cdef bint checknull_with_nat(object val):
 
 cdef bint is_dt64nat(object val):
     """
-    Is this a np.datetime64 object np.datetime64("NaT").
+    Is this an np.datetime64 object np.datetime64("NaT").
     """
     if cnp.is_datetime64_object(val):
         return cnp.get_datetime64_value(val) == NPY_NAT
@@ -1894,7 +1894,7 @@ cdef bint is_dt64nat(object val):
 
 cdef bint is_td64nat(object val):
     """
-    Is this a np.timedelta64 object np.timedelta64("NaT").
+    Is this an np.timedelta64 object np.timedelta64("NaT").
     """
     if cnp.is_timedelta64_object(val):
         return cnp.get_timedelta64_value(val) == NPY_NAT

pandas/_libs/tslibs/offsets.pyx

@@ -818,7 +818,7 @@ cdef class BaseOffset:
         state["normalize"] = self.normalize
 
         # we don't want to actually pickle the calendar object
-        # as its a np.busyday; we recreate on deserialization
+        # as its an np.busyday; we recreate on deserialization
         state.pop("calendar", None)
         if "kwds" in state:
             state["kwds"].pop("calendar", None)

pandas/_libs/tslibs/timedeltas.pyx

@@ -1489,7 +1489,7 @@ cdef class _Timedelta(timedelta):
         """
         cdef:
             str abbrev = npy_unit_to_abbrev(self._creso)
-        # TODO: way to create a np.timedelta64 obj with the reso directly
+        # TODO: way to create an np.timedelta64 obj with the reso directly
         #  instead of having to get the abbrev?
         return np.timedelta64(self._value, abbrev)
 

pandas/_libs/tslibs/timestamps.pyx

@@ -412,7 +412,7 @@ cdef class _Timestamp(ABCTimestamp):
 
     @classmethod
     def _from_dt64(cls, dt64: np.datetime64):
-        # construct a Timestamp from a np.datetime64 object, keeping the
+        # construct a Timestamp from an np.datetime64 object, keeping the
         #  resolution of the input.
         # This is here mainly so we can incrementally implement non-nano
         #  (e.g. only tznaive at first)

pandas/_testing/__init__.py

@@ -402,7 +402,7 @@ def get_cython_table_params(ndframe, func_names_and_expected):
     ----------
     ndframe : DataFrame or Series
     func_names_and_expected : Sequence of two items
-        The first item is a name of a NDFrame method ('sum', 'prod') etc.
+        The first item is a name of an NDFrame method ('sum', 'prod') etc.
         The second item is the expected return value.
 
     Returns

pandas/_testing/asserters.py

@@ -803,7 +803,7 @@ def assert_extension_array_equal(
             ):
                 return
         # Avoid slow object-dtype comparisons
-        # np.asarray for case where we have a np.MaskedArray
+        # np.asarray for case where we have an np.MaskedArray
         assert_numpy_array_equal(
             np.asarray(left.asi8),
             np.asarray(right.asi8),