Merge branch 'bug-agg-nonunique-col' of https://github.com/luke396/pandas into bug-agg-nonunique-col

Author: luke

doc/source/development/contributing.rst

@@ -349,7 +349,7 @@ If using :ref:`mamba <contributing.mamba>`, do::
 If using :ref:`pip <contributing.pip>` , do::
 
     # activate the virtual environment based on your platform
-    pythom -m pip install --upgrade -r requirements-dev.txt
+    python -m pip install --upgrade -r requirements-dev.txt
 
 Tips for a successful pull request
 ==================================

doc/source/user_guide/basics.rst

@@ -329,16 +329,6 @@ You can test if a pandas object is empty, via the :attr:`~DataFrame.empty` prope
    df.empty
    pd.DataFrame(columns=list("ABC")).empty
 
-To evaluate single-element pandas objects in a boolean context, use the method
-:meth:`~DataFrame.bool`:
-
-.. ipython:: python
-
-   pd.Series([True]).bool()
-   pd.Series([False]).bool()
-   pd.DataFrame([[True]]).bool()
-   pd.DataFrame([[False]]).bool()
-
 .. warning::
 
    You might be tempted to do the following:

doc/source/user_guide/gotchas.rst

@@ -121,16 +121,6 @@ Below is how to check if any of the values are ``True``:
     if pd.Series([False, True, False]).any():
         print("I am any")
 
-To evaluate single-element pandas objects in a boolean context, use the method
-:meth:`~DataFrame.bool`:
-
-.. ipython:: python
-
-   pd.Series([True]).bool()
-   pd.Series([False]).bool()
-   pd.DataFrame([[True]]).bool()
-   pd.DataFrame([[False]]).bool()
-
 Bitwise boolean
 ~~~~~~~~~~~~~~~
 

doc/source/whatsnew/v0.13.0.rst

@@ -153,12 +153,16 @@ API changes
 
   Added the ``.bool()`` method to ``NDFrame`` objects to facilitate evaluating of single-element boolean Series:
 
-  .. ipython:: python
+  .. code-block:: python
 
-     pd.Series([True]).bool()
-     pd.Series([False]).bool()
-     pd.DataFrame([[True]]).bool()
-     pd.DataFrame([[False]]).bool()
+     >>> pd.Series([True]).bool()
+      True
+     >>> pd.Series([False]).bool()
+      False
+     >>> pd.DataFrame([[True]]).bool()
+      True
+     >>> pd.DataFrame([[False]]).bool()
+      False
 
 - All non-Index NDFrames (``Series``, ``DataFrame``, ``Panel``, ``Panel4D``,
   ``SparsePanel``, etc.), now support the entire set of arithmetic operators

doc/source/whatsnew/v2.1.0.rst

@@ -125,6 +125,7 @@ Deprecations
 - Deprecated the 'axis' keyword in :meth:`.GroupBy.idxmax`, :meth:`.GroupBy.idxmin`, :meth:`.GroupBy.fillna`, :meth:`.GroupBy.take`, :meth:`.GroupBy.skew`, :meth:`.GroupBy.rank`, :meth:`.GroupBy.cumprod`, :meth:`.GroupBy.cumsum`, :meth:`.GroupBy.cummax`, :meth:`.GroupBy.cummin`, :meth:`.GroupBy.pct_change`, :meth:`GroupBy.diff`, :meth:`.GroupBy.shift`, and :meth:`DataFrameGroupBy.corrwith`; for ``axis=1`` operate on the underlying :class:`DataFrame` instead (:issue:`50405`, :issue:`51046`)
 - Deprecated passing a dictionary to :meth:`.SeriesGroupBy.agg`; pass a list of aggregations instead (:issue:`50684`)
 - Deprecated logical operations (``|``, ``&``, ``^``) between pandas objects and dtype-less sequences (e.g. ``list``, ``tuple``), wrap a sequence in a :class:`Series` or numpy array before operating instead (:issue:`51521`)
+- Deprecated the methods :meth:`Series.bool` and :meth:`DataFrame.bool` (:issue:`51749`)
 - Deprecated :meth:`DataFrame.swapaxes` and :meth:`Series.swapaxes`, use :meth:`DataFrame.transpose` or :meth:`Series.transpose` instead (:issue:`51946`)
 - Deprecated parameter ``convert_type`` in :meth:`Series.apply` (:issue:`52140`)
 -

pandas/_libs/tslibs/offsets.pyx

@@ -1350,18 +1350,18 @@ class DateOffset(RelativeDeltaOffset, metaclass=OffsetMeta):
     valid dates.  For example, Bday(2) can be added to a date to move
     it two business days forward.  If the date does not start on a
     valid date, first it is moved to a valid date.  Thus pseudo code
-    is:
+    is::
 
-    def __add__(date):
-      date = rollback(date) # does nothing if date is valid
-      return date + <n number of periods>
+        def __add__(date):
+          date = rollback(date) # does nothing if date is valid
+          return date + <n number of periods>
 
     When a date offset is created for a negative number of periods,
-    the date is first rolled forward.  The pseudo code is:
+    the date is first rolled forward.  The pseudo code is::
 
-    def __add__(date):
-      date = rollforward(date) # does nothing is date is valid
-      return date + <n number of periods>
+        def __add__(date):
+          date = rollforward(date) # does nothing if date is valid
+          return date + <n number of periods>
 
     Zero presents a problem.  Should it roll forward or back?  We
     arbitrarily have it rollforward:

pandas/_libs/tslibs/timestamps.pyx

@@ -1293,6 +1293,9 @@ class Timestamp(_Timestamp):
         Unit used for conversion if ts_input is of type int or float. The
         valid values are 'D', 'h', 'm', 's', 'ms', 'us', and 'ns'. For
         example, 's' means seconds and 'ms' means milliseconds.
+
+        For float inputs, the result will be stored in nanoseconds, and
+        the unit attribute will be set as ``'ns'``.
     fold : {0, 1}, default None, keyword-only
         Due to daylight saving time, one wall clock time can occur twice
         when shifting from summer to winter time; fold describes whether the

pandas/conftest.py

@@ -137,6 +137,11 @@ def pytest_collection_modifyitems(items, config) -> None:
     ignored_doctest_warnings = [
         # Docstring divides by zero to show behavior difference
         ("missing.mask_zero_div_zero", "divide by zero encountered"),
+        (
+            "pandas.core.generic.NDFrame.bool",
+            "(Series|DataFrame).bool is now deprecated and will be removed "
+            "in future version of pandas",
+        ),
     ]
 
     for item in items:

pandas/core/algorithms.py

@@ -838,7 +838,7 @@ def value_counts(
     if bins is not None:
         from pandas.core.reshape.tile import cut
 
-        values = Series(values)
+        values = Series(values, copy=False)
         try:
             ii = cut(values, bins, include_lowest=True)
         except TypeError as err:
@@ -861,7 +861,7 @@ def value_counts(
     else:
         if is_extension_array_dtype(values):
             # handle Categorical and sparse,
-            result = Series(values)._values.value_counts(dropna=dropna)
+            result = Series(values, copy=False)._values.value_counts(dropna=dropna)
             result.name = name
             result.index.name = index_name
             counts = result._values
@@ -893,7 +893,7 @@ def value_counts(
                 idx = idx.astype(object)
             idx.name = index_name
 
-            result = Series(counts, index=idx, name=name)
+            result = Series(counts, index=idx, name=name, copy=False)
 
     if sort:
         result = result.sort_values(ascending=ascending)

pandas/core/arrays/_mixins.py

@@ -445,7 +445,7 @@ def value_counts(self, dropna: bool = True) -> Series:
 
         index_arr = self._from_backing_data(np.asarray(result.index._data))
         index = Index(index_arr, name=result.index.name)
-        return Series(result._values, index=index, name=result.name)
+        return Series(result._values, index=index, name=result.name, copy=False)
 
     def _quantile(
         self,