CLN: Fix some spelling

doc/source/development/policies.rst

@@ -16,7 +16,7 @@ deprecations, API compatibility, and version numbering.
 
 A pandas release number is made up of ``MAJOR.MINOR.PATCH``.
 
-API breaking changes should only occur in **major** releases. Theses changes
+API breaking changes should only occur in **major** releases. These changes
 will be documented, with clear guidance on what is changing, why it's changing,
 and how to migrate existing code to the new behavior.
 

doc/source/user_guide/timeseries.rst

@@ -282,20 +282,20 @@ You can pass only the columns that you need to assemble.
 Invalid data
 ~~~~~~~~~~~~
 
-The default behavior, ``errors='raise'``, is to raise when unparseable:
+The default behavior, ``errors='raise'``, is to raise when unparsable:
 
 .. code-block:: ipython
 
     In [2]: pd.to_datetime(['2009/07/31', 'asd'], errors='raise')
     ValueError: Unknown string format
 
-Pass ``errors='ignore'`` to return the original input when unparseable:
+Pass ``errors='ignore'`` to return the original input when unparsable:
 
 .. ipython:: python
 
    pd.to_datetime(['2009/07/31', 'asd'], errors='ignore')
 
-Pass ``errors='coerce'`` to convert unparseable data to ``NaT`` (not a time):
+Pass ``errors='coerce'`` to convert unparsable data to ``NaT`` (not a time):
 
 .. ipython:: python
 

doc/source/whatsnew/v0.17.0.rst

@@ -40,7 +40,7 @@ Highlights include:
 - Plotting methods are now available as attributes of the ``.plot`` accessor, see :ref:`here <whatsnew_0170.plot>`
 - The sorting API has been revamped to remove some long-time inconsistencies, see :ref:`here <whatsnew_0170.api_breaking.sorting>`
 - Support for a ``datetime64[ns]`` with timezones as a first-class dtype, see :ref:`here <whatsnew_0170.tz>`
-- The default for ``to_datetime`` will now be to ``raise`` when presented with unparseable formats,
+- The default for ``to_datetime`` will now be to ``raise`` when presented with unparsable formats,
   previously this would return the original input. Also, date parse
   functions now return consistent results. See :ref:`here <whatsnew_0170.api_breaking.to_datetime>`
 - The default for ``dropna`` in ``HDFStore`` has changed to ``False``, to store by default all rows even

doc/source/whatsnew/v0.20.0.rst

@@ -1201,7 +1201,7 @@ Modules privacy has changed
 
 Some formerly public python/c/c++/cython extension modules have been moved and/or renamed. These are all removed from the public API.
 Furthermore, the ``pandas.core``, ``pandas.compat``, and ``pandas.util`` top-level modules are now considered to be PRIVATE.
-If indicated, a deprecation warning will be issued if you reference theses modules. (:issue:`12588`)
+If indicated, a deprecation warning will be issued if you reference these modules. (:issue:`12588`)
 
 .. csv-table::
     :header: "Previous Location", "New Location", "Deprecated"

doc/source/whatsnew/v0.23.0.rst

@@ -998,7 +998,7 @@ Datetimelike API changes
 - Addition and subtraction of ``NaN`` from a :class:`Series` with ``dtype='timedelta64[ns]'`` will raise a ``TypeError`` instead of treating the ``NaN`` as ``NaT`` (:issue:`19274`)
 - ``NaT`` division with :class:`datetime.timedelta` will now return ``NaN`` instead of raising (:issue:`17876`)
 - Operations between a :class:`Series` with dtype ``dtype='datetime64[ns]'`` and a :class:`PeriodIndex` will correctly raises ``TypeError`` (:issue:`18850`)
-- Subtraction of :class:`Series` with timezone-aware ``dtype='datetime64[ns]'`` with mis-matched timezones will raise ``TypeError`` instead of ``ValueError`` (:issue:`18817`)
+- Subtraction of :class:`Series` with timezone-aware ``dtype='datetime64[ns]'`` with mismatched timezones will raise ``TypeError`` instead of ``ValueError`` (:issue:`18817`)
 - :class:`Timestamp` will no longer silently ignore unused or invalid ``tz`` or ``tzinfo`` keyword arguments (:issue:`17690`)
 - :class:`Timestamp` will no longer silently ignore invalid ``freq`` arguments (:issue:`5168`)
 - :class:`CacheableOffset` and :class:`WeekDay` are no longer available in the ``pandas.tseries.offsets`` module (:issue:`17830`)
@@ -1273,7 +1273,7 @@ Timedelta
 - Bug in :func:`Period.asfreq` where periods near ``datetime(1, 1, 1)`` could be converted incorrectly (:issue:`19643`, :issue:`19834`)
 - Bug in :func:`Timedelta.total_seconds()` causing precision errors, for example ``Timedelta('30S').total_seconds()==30.000000000000004`` (:issue:`19458`)
 - Bug in :func:`Timedelta.__rmod__` where operating with a ``numpy.timedelta64`` returned a ``timedelta64`` object instead of a ``Timedelta`` (:issue:`19820`)
-- Multiplication of :class:`TimedeltaIndex` by ``TimedeltaIndex`` will now raise ``TypeError`` instead of raising ``ValueError`` in cases of length mis-match (:issue:`19333`)
+- Multiplication of :class:`TimedeltaIndex` by ``TimedeltaIndex`` will now raise ``TypeError`` instead of raising ``ValueError`` in cases of length mismatch (:issue:`19333`)
 - Bug in indexing a :class:`TimedeltaIndex` with a ``np.timedelta64`` object which was raising a ``TypeError`` (:issue:`20393`)
 
 

doc/source/whatsnew/v0.24.0.rst

@@ -419,7 +419,7 @@ Other enhancements
 - :meth:`Index.difference`, :meth:`Index.intersection`, :meth:`Index.union`, and :meth:`Index.symmetric_difference` now have an optional ``sort`` parameter to control whether the results should be sorted if possible (:issue:`17839`, :issue:`24471`)
 - :meth:`read_excel()` now accepts ``usecols`` as a list of column names or callable (:issue:`18273`)
 - :meth:`MultiIndex.to_flat_index` has been added to flatten multiple levels into a single-level :class:`Index` object.
-- :meth:`DataFrame.to_stata` and :class:`pandas.io.stata.StataWriter117` can write mixed sting columns to Stata strl format (:issue:`23633`)
+- :meth:`DataFrame.to_stata` and :class:`pandas.io.stata.StataWriter117` can write mixed string columns to Stata strl format (:issue:`23633`)
 - :meth:`DataFrame.between_time` and :meth:`DataFrame.at_time` have gained the ``axis`` parameter (:issue:`8839`)
 - :meth:`DataFrame.to_records` now accepts ``index_dtypes`` and ``column_dtypes`` parameters to allow different data types in stored column and index records (:issue:`18146`)
 - :class:`IntervalIndex` has gained the :attr:`~IntervalIndex.is_overlapping` attribute to indicate if the ``IntervalIndex`` contains any overlapping intervals (:issue:`23309`)
@@ -510,7 +510,7 @@ even when ``'\n'`` was passed in ``line_terminator``.
 
 *New behavior* on Windows:
 
-Passing ``line_terminator`` explicitly, set thes ``line terminator`` to that character.
+Passing ``line_terminator`` explicitly, set the ``line terminator`` to that character.
 
 .. code-block:: ipython
 
@@ -1885,7 +1885,7 @@ Reshaping
 - :meth:`DataFrame.nlargest` and :meth:`DataFrame.nsmallest` now returns the correct n values when keep != 'all' also when tied on the first columns (:issue:`22752`)
 - Constructing a DataFrame with an index argument that wasn't already an instance of :class:`~pandas.core.Index` was broken (:issue:`22227`).
 - Bug in :class:`DataFrame` prevented list subclasses to be used to construction (:issue:`21226`)
-- Bug in :func:`DataFrame.unstack` and :func:`DataFrame.pivot_table` returning a missleading error message when the resulting DataFrame has more elements than int32 can handle. Now, the error message is improved, pointing towards the actual problem (:issue:`20601`)
+- Bug in :func:`DataFrame.unstack` and :func:`DataFrame.pivot_table` returning a misleading error message when the resulting DataFrame has more elements than int32 can handle. Now, the error message is improved, pointing towards the actual problem (:issue:`20601`)
 - Bug in :func:`DataFrame.unstack` where a ``ValueError`` was raised when unstacking timezone aware values (:issue:`18338`)
 - Bug in :func:`DataFrame.stack` where timezone aware values were converted to timezone naive values (:issue:`19420`)
 - Bug in :func:`merge_asof` where a ``TypeError`` was raised when ``by_col`` were timezone aware values (:issue:`21184`)

doc/source/whatsnew/v1.2.0.rst

@@ -325,7 +325,7 @@ I/O
 - :meth:`to_csv` did not support zip compression for binary file object not having a filename (:issue:`35058`)
 - :meth:`to_csv` and :meth:`read_csv` did not honor ``compression`` and ``encoding`` for path-like objects that are internally converted to file-like objects (:issue:`35677`, :issue:`26124`, and :issue:`32392`)
 - :meth:`to_picke` and :meth:`read_pickle` did not support compression for file-objects (:issue:`26237`, :issue:`29054`, and :issue:`29570`)
-- Bug in :func:`LongTableBuilder.middle_separator` was duplicating LaTeX longtable entires in the List of Tables of a LaTeX document (:issue:`34360`)
+- Bug in :func:`LongTableBuilder.middle_separator` was duplicating LaTeX longtable entries in the List of Tables of a LaTeX document (:issue:`34360`)
 - Bug in :meth:`read_csv` with ``engine='python'`` truncating data if multiple items present in first row and first element started with BOM (:issue:`36343`)
 - Removed ``private_key`` and ``verbose`` from :func:`read_gbq` as they are no longer supported in ``pandas-gbq`` (:issue:`34654`, :issue:`30200`)
 

pandas/core/aggregation.py

@@ -377,7 +377,7 @@ def validate_func_kwargs(
     (['one', 'two'], ['min', 'max'])
     """
     no_arg_message = "Must provide 'func' or named aggregation **kwargs."
-    tuple_given_message = "func is expected but recieved {} in **kwargs."
+    tuple_given_message = "func is expected but received {} in **kwargs."
     columns = list(kwargs)
     func = []
     for col_func in kwargs.values():

pandas/core/arrays/datetimelike.py

@@ -168,7 +168,7 @@ def _unbox_scalar(self, value: DTScalarOrNaT, setitem: bool = False) -> int:
         value : Period, Timestamp, Timedelta, or NaT
             Depending on subclass.
         setitem : bool, default False
-            Whether to check compatiblity with setitem strictness.
+            Whether to check compatibility with setitem strictness.
 
         Returns
         -------
@@ -1123,7 +1123,7 @@ def _sub_period(self, other):
         raise TypeError(f"cannot subtract Period from a {type(self).__name__}")
 
     def _add_period(self, other: Period):
-        # Overriden by TimedeltaArray
+        # Overridden by TimedeltaArray
         raise TypeError(f"cannot add Period to a {type(self).__name__}")
 
     def _add_offset(self, offset):

pandas/core/arrays/sparse/array.py

@@ -986,7 +986,7 @@ def _concat_same_type(cls, to_concat):
             # get an identical index as concating the values and then
             # creating a new index. We don't want to spend the time trying
             # to merge blocks across arrays in `to_concat`, so the resulting
-            # BlockIndex may have more blocs.
+            # BlockIndex may have more blocks.
             blengths = []
             blocs = []
 

pandas/core/frame.py

@@ -5133,7 +5133,7 @@ def drop_duplicates(
         0  Yum Yum   cup     4.0
         2  Indomie   cup     3.5
 
-        To remove duplicates and keep last occurences, use ``keep``.
+        To remove duplicates and keep last occurrences, use ``keep``.
 
         >>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
             brand style  rating

pandas/core/generic.py

@@ -3406,7 +3406,7 @@ def _maybe_update_cacher(
         if cacher is not None:
             ref = cacher[1]()
 
-            # we are trying to reference a dead referant, hence
+            # we are trying to reference a dead referent, hence
             # a copy
             if ref is None:
                 del self._cacher
@@ -3420,7 +3420,7 @@ def _maybe_update_cacher(
                     ref._item_cache.pop(cacher[0], None)
 
         if verify_is_copy:
-            self._check_setitem_copy(stacklevel=5, t="referant")
+            self._check_setitem_copy(stacklevel=5, t="referent")
 
         if clear:
             self._clear_item_cache()
@@ -3781,10 +3781,10 @@ def _check_is_chained_assignment_possible(self) -> bool_t:
         if self._is_view and self._is_cached:
             ref = self._get_cacher()
             if ref is not None and ref._is_mixed_type:
-                self._check_setitem_copy(stacklevel=4, t="referant", force=True)
+                self._check_setitem_copy(stacklevel=4, t="referent", force=True)
             return True
         elif self._is_copy:
-            self._check_setitem_copy(stacklevel=4, t="referant")
+            self._check_setitem_copy(stacklevel=4, t="referent")
         return False
 
     def _check_setitem_copy(self, stacklevel=4, t="setting", force=False):
@@ -3837,7 +3837,7 @@ def _check_setitem_copy(self, stacklevel=4, t="setting", force=False):
         if isinstance(self._is_copy, str):
             t = self._is_copy
 
-        elif t == "referant":
+        elif t == "referent":
             t = (
                 "\n"
                 "A value is trying to be set on a copy of a slice from a "

pandas/core/groupby/generic.py

@@ -1430,7 +1430,7 @@ def _choose_path(self, fast_path: Callable, slow_path: Callable, group: DataFram
         except AssertionError:
             raise
         except Exception:
-            # GH#29631 For user-defined function, we cant predict what may be
+            # GH#29631 For user-defined function, we can't predict what may be
             #  raised; see test_transform.test_transform_fastpath_raises
             return path, res
 

pandas/core/indexers.py

@@ -144,7 +144,7 @@ def check_setitem_lengths(indexer, value, values) -> bool:
     no_op = False
 
     if isinstance(indexer, (np.ndarray, list)):
-        # We can ignore other listlikes becasue they are either
+        # We can ignore other listlikes because they are either
         #  a) not necessarily 1-D indexers, e.g. tuple
         #  b) boolean indexers e.g. BoolArray
         if is_list_like(value):

pandas/core/indexes/base.py

@@ -4503,7 +4503,7 @@ def sort_values(
         idx = ensure_key_mapped(self, key)
 
         # GH 35584. Sort missing values according to na_position kwarg
-        # ignore na_position for MutiIndex
+        # ignore na_position for MultiIndex
         if not isinstance(
             self, (ABCMultiIndex, ABCDatetimeIndex, ABCTimedeltaIndex, ABCPeriodIndex)
         ):

pandas/core/indexes/interval.py

@@ -1023,7 +1023,7 @@ def intersection(
     def _intersection_unique(self, other: "IntervalIndex") -> "IntervalIndex":
         """
         Used when the IntervalIndex does not have any common endpoint,
-        no mater left or right.
+        no matter left or right.
         Return the intersection with another IntervalIndex.
 
         Parameters

pandas/core/internals/managers.py

@@ -225,7 +225,7 @@ def set_axis(self, axis: int, new_labels: Index) -> None:
 
     @property
     def _is_single_block(self) -> bool:
-        # Assumes we are 2D; overriden by SingleBlockManager
+        # Assumes we are 2D; overridden by SingleBlockManager
         return len(self.blocks) == 1
 
     def _rebuild_blknos_and_blklocs(self) -> None:

pandas/core/strings.py

@@ -1470,7 +1470,7 @@ def str_pad(arr, width, side="left", fillchar=" "):
         character. Equivalent to ``Series.str.pad(side='left')``.
     Series.str.ljust : Fills the right side of strings with an arbitrary
         character. Equivalent to ``Series.str.pad(side='right')``.
-    Series.str.center : Fills boths sides of strings with an arbitrary
+    Series.str.center : Fills both sides of strings with an arbitrary
         character. Equivalent to ``Series.str.pad(side='both')``.
     Series.str.zfill : Pad strings in the Series/Index by prepending '0'
         character. Equivalent to ``Series.str.pad(side='left', fillchar='0')``.
@@ -2918,7 +2918,7 @@ def zfill(self, width):
             character.
         Series.str.pad : Fills the specified sides of strings with an arbitrary
             character.
-        Series.str.center : Fills boths sides of strings with an arbitrary
+        Series.str.center : Fills both sides of strings with an arbitrary
             character.
 
         Notes

pandas/errors/__init__.py

@@ -225,7 +225,7 @@ class DuplicateLabelError(ValueError):
 
 class InvalidIndexError(Exception):
     """
-    Exception raised when attemping to use an invalid index key.
+    Exception raised when attempting to use an invalid index key.
 
     .. versionadded:: 1.1.0
     """

pandas/io/parsers.py

@@ -227,7 +227,7 @@
       result 'foo'
 
     If a column or index cannot be represented as an array of datetimes,
-    say because of an unparseable value or a mixture of timezones, the column
+    say because of an unparsable value or a mixture of timezones, the column
     or index will be returned unaltered as an object data type. For
     non-standard datetime parsing, use ``pd.to_datetime`` after
     ``pd.read_csv``. To parse an index or column with a mixture of timezones,

pandas/io/stata.py

@@ -499,7 +499,7 @@ class CategoricalConversionWarning(Warning):
 dataset with an iterator results in categorical variable with different
 categories. This occurs since it is not possible to know all possible values
 until the entire dataset has been read. To avoid this warning, you can either
-read dataset without an interator, or manually convert categorical data by
+read dataset without an iterator, or manually convert categorical data by
 ``convert_categoricals`` to False and then accessing the variable labels
 through the value_labels method of the reader.
 """

pandas/plotting/_misc.py

@@ -318,7 +318,7 @@ def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
 
     Examples
     --------
-    This example draws a basic bootstap plot for a Series.
+    This example draws a basic bootstrap plot for a Series.
 
     .. plot::
         :context: close-figs

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -564,7 +564,7 @@ def test_mangled(self):
     def test_named_agg_nametuple(self, inp):
         # GH34422
         s = pd.Series([1, 1, 2, 2, 3, 3, 4, 5])
-        msg = f"func is expected but recieved {type(inp).__name__}"
+        msg = f"func is expected but received {type(inp).__name__}"
         with pytest.raises(TypeError, match=msg):
             s.groupby(s.values).agg(a=inp)