Merge branch 'remove-doc-decorators2' of github.com:Chiwendaiyue/pandas into remove-doc-decorators2

Author: cloudboat

doc/source/whatsnew/v3.0.0.rst

@@ -748,10 +748,16 @@ Other API changes
   the dtype of the resulting Index (:issue:`60797`)
 - :class:`IncompatibleFrequency` now subclasses ``TypeError`` instead of ``ValueError``. As a result, joins with mismatched frequencies now cast to object like other non-comparable joins, and arithmetic with indexes with mismatched frequencies align (:issue:`55782`)
 - :class:`Series` "flex" methods like :meth:`Series.add` no longer allow passing a :class:`DataFrame` for ``other``; use the DataFrame reversed method instead (:issue:`46179`)
+- :func:`date_range` and :func:`timedelta_range` no longer default to ``unit="ns"``, instead will infer a unit from the ``start``, ``end``, and ``freq`` parameters. Explicitly specify a desired ``unit`` to override these (:issue:`59031`)
 - :meth:`CategoricalIndex.append` no longer attempts to cast different-dtype indexes to the caller's dtype (:issue:`41626`)
 - :meth:`ExtensionDtype.construct_array_type` is now a regular method instead of a ``classmethod`` (:issue:`58663`)
+- Arithmetic operations between a :class:`Series`, :class:`Index`, or :class:`ExtensionArray` with a ``list`` now consistently wrap that list with an array equivalent to ``Series(my_list).array``. To do any other kind of type inference or casting, do so explicitly before operating (:issue:`62552`)
 - Comparison operations between :class:`Index` and :class:`Series` now consistently return :class:`Series` regardless of which object is on the left or right (:issue:`36759`)
 - Numpy functions like ``np.isinf`` that return a bool dtype when called on a :class:`Index` object now return a bool-dtype :class:`Index` instead of ``np.ndarray`` (:issue:`52676`)
+- Methods that can operate in-place (:meth:`~DataFrame.replace`, :meth:`~DataFrame.fillna`,
+  :meth:`~DataFrame.ffill`, :meth:`~DataFrame.bfill`, :meth:`~DataFrame.interpolate`,
+  :meth:`~DataFrame.where`, :meth:`~DataFrame.mask`, :meth:`~DataFrame.clip`) now return
+  the modified DataFrame or Series (``self``) instead of ``None`` when ``inplace=True`` (:issue:`63207`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_300.deprecations:
@@ -1184,6 +1190,7 @@ I/O
 - Fix bug in ``on_bad_lines`` callable when returning too many fields: now emits
   ``ParserWarning`` and truncates extra fields regardless of ``index_col`` (:issue:`61837`)
 - Bug in :func:`pandas.json_normalize` inconsistently handling non-dict items in ``data`` when ``max_level`` was set. The function will now raise a ``TypeError`` if ``data`` is a list containing non-dict items (:issue:`62829`)
+- Bug in :func:`pandas.json_normalize` raising ``TypeError`` when ``meta`` contained a non-string key (e.g., ``int``) and ``record_path`` was specified, which was inconsistent with the behavior when ``record_path`` was ``None`` (:issue:`63019`)
 - Bug in :meth:`.DataFrame.to_json` when ``"index"`` was a value in the :attr:`DataFrame.column` and :attr:`Index.name` was ``None``. Now, this will fail with a ``ValueError`` (:issue:`58925`)
 - Bug in :meth:`.io.common.is_fsspec_url` not recognizing chained fsspec URLs (:issue:`48978`)
 - Bug in :meth:`DataFrame._repr_html_` which ignored the ``"display.float_format"`` option (:issue:`59876`)

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/np_datetime.pyx

@@ -208,7 +208,7 @@ class OutOfBoundsTimedelta(ValueError):
 
     Examples
     --------
-    >>> pd.date_range(start="1/1/1700", freq="B", periods=100000)
+    >>> pd.date_range(start="1/1/1700", freq="B", periods=100000, unit="ns")
     Traceback (most recent call last):
     OutOfBoundsTimedelta: Cannot cast 139999 days 00:00:00
     to unit='ns' without overflow.

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

@@ -83,6 +83,7 @@ from pandas._libs.tslibs.offsets import Day
 
 from pandas._libs.tslibs.util cimport (
     is_array,
+    is_bool_object,
     is_float_object,
     is_integer_object,
 )
@@ -1488,7 +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)
 
@@ -2311,6 +2312,13 @@ class Timedelta(_Timedelta):
                 return self.__mul__(item)
             return other * self.to_timedelta64()
 
+        elif is_bool_object(other):
+            # GH#62316
+            raise TypeError(
+                "Cannot multiply Timedelta by bool. "
+                "Explicitly cast to integer instead."
+            )
+
         return NotImplemented
 
     __rmul__ = __mul__

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)