DOC: Correct more typos in code comments

doc/source/user_guide/io.rst

@@ -343,7 +343,7 @@ on_bad_lines : {{'error', 'warn', 'skip'}}, default 'error'
     Specifies what to do upon encountering a bad line (a line with too many fields).
     Allowed values are :
 
-    - 'error', raise an ParserError when a bad line is encountered.
+    - 'error', raise a ParserError when a bad line is encountered.
     - 'warn', print a warning when a bad line is encountered and skip that line.
     - 'skip', skip bad lines without raising or warning when they are encountered.
 

pandas/_libs/lib.pyx

@@ -2593,7 +2593,7 @@ def maybe_convert_objects(ndarray[object] objects,
         Whether to convert numeric entries.
     convert_to_nullable_dtype : bool, default False
         If an array-like object contains only integer or boolean values (and NaN) is
-        encountered, whether to convert and return an Boolean/IntegerArray.
+        encountered, whether to convert and return a Boolean/IntegerArray.
     convert_non_numeric : bool, default False
         Whether to convert datetime, timedelta, period, interval types.
     dtype_if_all_nat : np.dtype, ExtensionDtype, or None, default None

pandas/_libs/tslibs/fields.pyx

@@ -146,7 +146,7 @@ def get_date_name_field(
     NPY_DATETIMEUNIT reso=NPY_FR_ns,
 ):
     """
-    Given a int64-based datetime index, return array of strings of date
+    Given an int64-based datetime index, return array of strings of date
     name based on requested field (e.g. day_name)
     """
     cdef:
@@ -335,7 +335,7 @@ def get_date_field(
     NPY_DATETIMEUNIT reso=NPY_FR_ns,
 ):
     """
-    Given a int64-based datetime index, extract the year, month, etc.,
+    Given an int64-based datetime index, extract the year, month, etc.,
     field and return an array of these values.
     """
     cdef:
@@ -502,7 +502,7 @@ def get_timedelta_field(
     NPY_DATETIMEUNIT reso=NPY_FR_ns,
 ):
     """
-    Given a int64-based timedelta index, extract the days, hrs, sec.,
+    Given an int64-based timedelta index, extract the days, hrs, sec.,
     field and return an array of these values.
     """
     cdef:
@@ -555,7 +555,7 @@ def get_timedelta_days(
     NPY_DATETIMEUNIT reso=NPY_FR_ns,
 ):
     """
-    Given a int64-based timedelta index, extract the days,
+    Given an int64-based timedelta index, extract the days,
     field and return an array of these values.
     """
     cdef:
@@ -592,7 +592,7 @@ cpdef isleapyear_arr(ndarray years):
 @cython.boundscheck(False)
 def build_isocalendar_sarray(const int64_t[:] dtindex, NPY_DATETIMEUNIT reso):
     """
-    Given a int64-based datetime array, return the ISO 8601 year, week, and day
+    Given an int64-based datetime array, return the ISO 8601 year, week, and day
     as a structured array.
     """
     cdef:

pandas/_libs/tslibs/offsets.pyx

@@ -827,7 +827,7 @@ cdef class BaseOffset:
     @property
     def nanos(self):
         """
-        Returns a integer of the total number of nanoseconds for fixed frequencies.
+        Returns an integer of the total number of nanoseconds for fixed frequencies.
 
         Raises
         ------

pandas/_libs/tslibs/timedeltas.pyx

@@ -334,7 +334,7 @@ cdef convert_to_timedelta64(object ts, str unit):
     Handle these types of objects:
         - timedelta/Timedelta
 
-    Return an timedelta64[ns] object
+    Return a timedelta64[ns] object
     """
     # Caller is responsible for checking unit not in ["Y", "y", "M"]
     if isinstance(ts, _Timedelta):

pandas/_libs/tslibs/timestamps.pyx

@@ -1717,7 +1717,7 @@ cdef class _Timestamp(ABCTimestamp):
 
     def to_period(self, freq=None):
         """
-        Return an period of which this timestamp is an observation.
+        Return a period of which this timestamp is an observation.
 
         This method converts the given Timestamp to a Period object,
         which represents a span of time,such as a year, month, etc.,

pandas/core/arrays/boolean.py

@@ -273,7 +273,7 @@ class BooleanArray(BaseMaskedArray):
     BooleanArray implements Kleene logic (sometimes called three-value
     logic) for logical operations. See :ref:`boolean.kleene` for more.
 
-    To construct an BooleanArray from generic array-like input, use
+    To construct a BooleanArray from generic array-like input, use
     :func:`pandas.array` specifying ``dtype="boolean"`` (see examples
     below).
 
@@ -313,7 +313,7 @@ class BooleanArray(BaseMaskedArray):
 
     Examples
     --------
-    Create an BooleanArray with :func:`pandas.array`:
+    Create a BooleanArray with :func:`pandas.array`:
 
     >>> pd.array([True, False, None], dtype="boolean")
     <BooleanArray>

pandas/core/arrays/datetimes.py

@@ -1205,7 +1205,7 @@ def to_period(self, freq=None) -> PeriodArray:
         ----------
         freq : str or Period, optional
             One of pandas' :ref:`period aliases <timeseries.period_aliases>`
-            or an Period object. Will be inferred by default.
+            or a Period object. Will be inferred by default.
 
         Returns
         -------

pandas/core/arrays/floating.py

@@ -78,7 +78,7 @@ class FloatingArray(NumericArray):
     - data: contains a numpy float array of the appropriate dtype
     - mask: a boolean array holding a mask on the data, True is missing
 
-    To construct an FloatingArray from generic array-like input, use
+    To construct a FloatingArray from generic array-like input, use
     :func:`pandas.array` with one of the float dtypes (see examples).
 
     See :ref:`integer_na` for more.
@@ -114,7 +114,7 @@ class FloatingArray(NumericArray):
 
     Examples
     --------
-    Create an FloatingArray with :func:`pandas.array`:
+    Create a FloatingArray with :func:`pandas.array`:
 
     >>> pd.array([0.1, None, 0.3], dtype=pd.Float32Dtype())
     <FloatingArray>

pandas/core/base.py

@@ -1120,7 +1120,7 @@ def nunique(self, dropna: bool = True) -> int:
         Returns
         -------
         int
-            A integer indicating the number of unique elements in the object.
+            An integer indicating the number of unique elements in the object.
 
         See Also
         --------

pandas/core/dtypes/astype.py

@@ -140,7 +140,7 @@ def _astype_float_to_int_nansafe(
     values: np.ndarray, dtype: np.dtype, copy: bool
 ) -> np.ndarray:
     """
-    astype with a check preventing converting NaN to an meaningless integer value.
+    astype with a check preventing converting NaN to a meaningless integer value.
     """
     if not np.isfinite(values).all():
         raise IntCastingNaNError(

pandas/core/dtypes/dtypes.py

@@ -1387,7 +1387,7 @@ def construct_from_string(cls, string: str_type) -> IntervalDtype:
             return cls(string)
 
         msg = (
-            f"Cannot construct a 'IntervalDtype' from '{string}'.\n\n"
+            f"Cannot construct an 'IntervalDtype' from '{string}'.\n\n"
             "Incorrectly formatted string passed to constructor. "
             "Valid formats include Interval or Interval[dtype] "
             "where dtype is numeric, datetime, or timedelta"

pandas/core/indexes/base.py

@@ -2868,7 +2868,7 @@ def drop_duplicates(self, *, keep: DropKeep = "first") -> Self:
 
         Examples
         --------
-        Generate an pandas.Index with duplicate values.
+        Generate a pandas.Index with duplicate values.
 
         >>> idx = pd.Index(["llama", "cow", "llama", "beetle", "llama", "hippo"])
 

pandas/core/indexes/extension.py

@@ -115,7 +115,7 @@ def inherit_names(
     names: list[str], delegate: type, cache: bool = False, wrap: bool = False
 ) -> Callable[[type[_ExtensionIndexT]], type[_ExtensionIndexT]]:
     """
-    Class decorator to pin attributes from an ExtensionArray to a Index subclass.
+    Class decorator to pin attributes from an ExtensionArray to an Index subclass.
 
     Parameters
     ----------

pandas/core/util/hashing.py

@@ -188,7 +188,7 @@ def hash_tuples(
     hash_key: str = _default_hash_key,
 ) -> npt.NDArray[np.uint64]:
     """
-    Hash an MultiIndex / listlike-of-tuples efficiently.
+    Hash a MultiIndex / listlike-of-tuples efficiently.
 
     Parameters
     ----------
@@ -261,7 +261,7 @@ def hash_array(
     See Also
     --------
     util.hash_pandas_object : Return a data hash of the Index/Series/DataFrame.
-    util.hash_tuples : Hash an MultiIndex / listlike-of-tuples efficiently.
+    util.hash_tuples : Hash a MultiIndex / listlike-of-tuples efficiently.
 
     Examples
     --------

pandas/io/excel/_xlsxwriter.py

@@ -102,7 +102,7 @@ def convert(cls, style_dict, num_format_str=None) -> dict[str, Any]:
         style_dict : style dictionary to convert
         num_format_str : optional number format string
         """
-        # Create a XlsxWriter format object.
+        # Create an XlsxWriter format object.
         props = {}
 
         if num_format_str is not None:

pandas/io/parsers/python_parser.py

@@ -669,8 +669,8 @@ def _infer_columns(
                         this_columns[i] = col
                         counts[col] = cur_count + 1
                 elif have_mi_columns:
-                    # if we have grabbed an extra line, but its not in our
-                    # format so save in the buffer, and create an blank extra
+                    # if we have grabbed an extra line, but it's not in our
+                    # format so save in the buffer, and create a blank extra
                     # line for the rest of the parsing code
                     if hr == header[-1]:
                         lc = len(this_columns)

pandas/io/pytables.py

@@ -4999,7 +4999,7 @@ def indexables(self):
 
     # error: Signature of "write" incompatible with supertype "AppendableTable"
     def write(self, **kwargs) -> None:  # type: ignore[override]
-        raise NotImplementedError("cannot write on an generic table")
+        raise NotImplementedError("cannot write on a generic table")
 
 
 class AppendableMultiFrameTable(AppendableFrameTable):
@@ -5371,7 +5371,7 @@ def _need_convert(kind: str) -> bool:
 
 def _maybe_adjust_name(name: str, version: Sequence[int]) -> str:
     """
-    Prior to 0.10.1, we named values blocks like: values_block_0 an the
+    Prior to 0.10.1, we named values blocks like: values_block_0 and the
     name values_0, adjust the given name if necessary.
 
     Parameters

pandas/tests/generic/test_label_or_level_utils.py

@@ -84,10 +84,10 @@ def test_is_level_reference_df_ambig(df_ambig, axis):
     assert_label_reference(df_ambig, ["L1"], axis=axis)
 
     # df has an on-axis level named L2 and it is not ambiguous
-    # Therefore L2 is an level reference
+    # Therefore L2 is a level reference
     assert_level_reference(df_ambig, ["L2"], axis=axis)
 
-    # df has a column named L3 and it not an level reference
+    # df has a column named L3 and it is not a level reference
     assert_label_reference(df_ambig, ["L3"], axis=axis)
 
 

pandas/tests/indexes/period/test_constructors.py

@@ -139,7 +139,7 @@ def test_base_constructor_with_period_dtype(self):
         "values_constructor", [list, np.array, PeriodIndex, PeriodArray._from_sequence]
     )
     def test_index_object_dtype(self, values_constructor):
-        # Index(periods, dtype=object) is an Index (not an PeriodIndex)
+        # Index(periods, dtype=object) is an Index (not a PeriodIndex)
         periods = [
             Period("2011-01", freq="M"),
             NaT,

pandas/tests/io/excel/test_odf.py

@@ -24,7 +24,7 @@ def test_read_invalid_types_raises():
 
 
 def test_read_writer_table():
-    # Also test reading tables from an text OpenDocument file
+    # Also test reading tables from a text OpenDocument file
     # (.odt)
     index = pd.Index(["Row 1", "Row 2", "Row 3"], name="Header")
     expected = pd.DataFrame(

pandas/tests/strings/test_extract.py

@@ -682,7 +682,7 @@ def test_extractall_same_as_extract(any_string_dtype):
 
 
 def test_extractall_same_as_extract_subject_index(any_string_dtype):
-    # same as above tests, but s has an MultiIndex.
+    # same as above tests, but s has a MultiIndex.
     mi = MultiIndex.from_tuples(
         [("A", "first"), ("B", "second"), ("C", "third")],
         names=("capital", "ordinal"),