DOC: Fix typos (#61580)

Author: omahs

doc/source/user_guide/reshaping.rst

@@ -395,7 +395,7 @@ variables and the values representing the presence of those variables per row.
    pd.get_dummies(df["key"])
    df["key"].str.get_dummies()
 
-``prefix`` adds a prefix to the the column names which is useful for merging the result
+``prefix`` adds a prefix to the column names which is useful for merging the result
 with the original :class:`DataFrame`:
 
 .. ipython:: python

doc/source/user_guide/user_defined_functions.rst

@@ -319,7 +319,7 @@ to the original data.
 
 In the example, the ``warm_up_all_days`` function computes the ``max`` like an aggregation, but instead
 of returning just the maximum value, it returns a ``DataFrame`` with the same shape as the original one
-with the values of each day replaced by the the maximum temperature of the city.
+with the values of each day replaced by the maximum temperature of the city.
 
 ``transform`` is also available for :meth:`SeriesGroupBy.transform`, :meth:`DataFrameGroupBy.transform` and
 :meth:`Resampler.transform`, where it's more common. You can read more about ``transform`` in groupby

doc/source/whatsnew/v1.1.0.rst

@@ -1039,7 +1039,7 @@ Missing
 ^^^^^^^
 - Calling :meth:`fillna` on an empty :class:`Series` now correctly returns a shallow copied object. The behaviour is now consistent with :class:`Index`, :class:`DataFrame` and a non-empty :class:`Series` (:issue:`32543`).
 - Bug in :meth:`Series.replace` when argument ``to_replace`` is of type dict/list and is used on a :class:`Series` containing ``<NA>`` was raising a ``TypeError``. The method now handles this by ignoring ``<NA>`` values when doing the comparison for the replacement (:issue:`32621`)
-- Bug in :meth:`~Series.any` and :meth:`~Series.all` incorrectly returning ``<NA>`` for all ``False`` or all ``True`` values using the nulllable Boolean dtype and with ``skipna=False`` (:issue:`33253`)
+- Bug in :meth:`~Series.any` and :meth:`~Series.all` incorrectly returning ``<NA>`` for all ``False`` or all ``True`` values using the nullable Boolean dtype and with ``skipna=False`` (:issue:`33253`)
 - Clarified documentation on interpolate with ``method=akima``. The ``der`` parameter must be scalar or ``None`` (:issue:`33426`)
 - :meth:`DataFrame.interpolate` uses the correct axis convention now. Previously interpolating along columns lead to interpolation along indices and vice versa. Furthermore interpolating with methods ``pad``, ``ffill``, ``bfill`` and ``backfill`` are identical to using these methods with :meth:`DataFrame.fillna` (:issue:`12918`, :issue:`29146`)
 - Bug in :meth:`DataFrame.interpolate` when called on a :class:`DataFrame` with column names of string type was throwing a ValueError. The method is now independent of the type of the column names (:issue:`33956`)

doc/source/whatsnew/v3.0.0.rst

@@ -833,7 +833,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrameGroupby.transform` and :meth:`SeriesGroupby.transform` with a reducer and ``observed=False`` that coerces dtype to float when there are unobserved categories. (:issue:`55326`)
 - Bug in :meth:`Rolling.apply` for ``method="table"`` where column order was not being respected due to the columns getting sorted by default. (:issue:`59666`)
 - Bug in :meth:`Rolling.apply` where the applied function could be called on fewer than ``min_period`` periods if ``method="table"``. (:issue:`58868`)
-- Bug in :meth:`Series.resample` could raise when the the date range ended shortly before a non-existent time. (:issue:`58380`)
+- Bug in :meth:`Series.resample` could raise when the date range ended shortly before a non-existent time. (:issue:`58380`)
 
 Reshaping
 ^^^^^^^^^

pandas/_libs/tslibs/conversion.pyx

@@ -797,7 +797,7 @@ cdef int64_t parse_pydatetime(
     dts : *npy_datetimestruct
         Needed to use in pydatetime_to_dt64, which writes to it.
     creso : NPY_DATETIMEUNIT
-        Resolution to store the the result.
+        Resolution to store the result.
 
     Raises
     ------

pandas/core/arrays/categorical.py

@@ -1666,7 +1666,7 @@ def __array__(
         Parameters
         ----------
         dtype : np.dtype or None
-            Specifies the the dtype for the array.
+            Specifies the dtype for the array.
 
         copy : bool or None, optional
             See :func:`numpy.asarray`.

pandas/tests/io/xml/test_to_xml.py

@@ -1345,7 +1345,7 @@ def test_ea_dtypes(any_numeric_ea_dtype, parser):
     assert equalize_decl(result).strip() == expected
 
 
-def test_unsuported_compression(parser, geom_df):
+def test_unsupported_compression(parser, geom_df):
     with pytest.raises(ValueError, match="Unrecognized compression type"):
         with tm.ensure_clean() as path:
             geom_df.to_xml(path, parser=parser, compression="7z")

pandas/tests/io/xml/test_xml.py

@@ -1961,7 +1961,7 @@ def test_wrong_compression(parser, compression, compression_only):
             read_xml(path, parser=parser, compression=attempted_compression)
 
 
-def test_unsuported_compression(parser):
+def test_unsupported_compression(parser):
     with pytest.raises(ValueError, match="Unrecognized compression type"):
         with tm.ensure_clean() as path:
             read_xml(path, parser=parser, compression="7z")

web/pandas/pdeps/0007-copy-on-write.md

@@ -525,7 +525,7 @@ following cases:
 * Selecting a single column (as a Series) out of a DataFrame is always a view
   (``df['a']``)
 * Slicing columns from a DataFrame creating a subset DataFrame (``df[['a':'b']]`` or
-  ``df.loc[:, 'a': 'b']``) is a view _if_ the the original DataFrame consists of a
+  ``df.loc[:, 'a': 'b']``) is a view _if_ the original DataFrame consists of a
   single block (single dtype, consolidated) and _if_ you are slicing (so not a list
   selection). In all other cases, getting a subset is always a copy.
 * Selecting rows _can_ return a view, when the row indexer is a `slice` object.

web/pandas/pdeps/0014-string-dtype.md

@@ -220,8 +220,8 @@ in pandas 2.3 and removed in pandas 3.0.
 
 The `storage` keyword of `StringDtype` is kept to disambiguate the underlying
 storage of the string data (using pyarrow or python objects), but an additional
-`na_value` is introduced to disambiguate the the variants using NA semantics
-and NaN semantics.
+`na_value` is introduced to disambiguate the variants using NA semantics and
+NaN semantics.
 
 Overview of the different ways to specify a dtype and the resulting concrete
 dtype of the data: