BUG: pd.cut with duplicate index Series lowest included

azure-pipelines.yml

@@ -21,12 +21,12 @@ jobs:
 - template: ci/azure/posix.yml
   parameters:
     name: macOS
-    vmImage: macOS-10.14
+    vmImage: macOS-10.15
 
 - template: ci/azure/windows.yml
   parameters:
     name: Windows
-    vmImage: vs2017-win2016
+    vmImage: windows-2019
 
 - job: py38_32bit
   pool:

doc/source/development/contributing_codebase.rst

@@ -812,7 +812,21 @@ Changes should be reflected in the release notes located in ``doc/source/whatsne
 This file contains an ongoing change log for each release.  Add an entry to this file to
 document your fix, enhancement or (unavoidable) breaking change.  Make sure to include the
 GitHub issue number when adding your entry (using ``:issue:`1234``` where ``1234`` is the
-issue/pull request number).
+issue/pull request number). Your entry should be written using full sentences and proper
+grammar.
+
+When mentioning parts of the API, use a Sphinx ``:func:``, ``:meth:``, or ``:class:``
+directive as appropriate. Not all public API functions and methods have a
+documentation page; ideally links would only be added if they resolve. You can
+usually find similar examples by checking the release notes for one of the previous
+versions.
+
+If your code is a bugfix, add your entry to the relevant bugfix section. Avoid
+adding to the ``Other`` section; only in rare cases should entries go there.
+Being as concise as possible, the description of the bug should include how the
+user may encounter it and an indication of the bug itself, e.g.
+"produces incorrect results" or "incorrectly raises". It may be necessary to also
+indicate the new behavior.
 
 If your code is an enhancement, it is most likely necessary to add usage
 examples to the existing documentation.  This can be done following the section

doc/source/whatsnew/v1.0.0.rst

@@ -338,19 +338,20 @@ maps labels to their new names along the default axis, is allowed to be passed b
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> df = pd.DataFrame([[1]])
-   >>> df.rename({0: 1}, {0: 2})
+   In [1]: df = pd.DataFrame([[1]])
+   In [2]: df.rename({0: 1}, {0: 2})
+   Out[2]:
    FutureWarning: ...Use named arguments to resolve ambiguity...
       2
    1  1
 
 *pandas 1.0.0*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> df.rename({0: 1}, {0: 2})
+   In [3]: df.rename({0: 1}, {0: 2})
    Traceback (most recent call last):
    ...
    TypeError: rename() takes from 1 to 2 positional arguments but 3 were given
@@ -359,26 +360,28 @@ Note that errors will now be raised when conflicting or potentially ambiguous ar
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> df.rename({0: 1}, index={0: 2})
+   In [4]: df.rename({0: 1}, index={0: 2})
+   Out[4]:
       0
    1  1
 
-   >>> df.rename(mapper={0: 1}, index={0: 2})
+   In [5]: df.rename(mapper={0: 1}, index={0: 2})
+   Out[5]:
       0
    2  1
 
 *pandas 1.0.0*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> df.rename({0: 1}, index={0: 2})
+   In [6]: df.rename({0: 1}, index={0: 2})
    Traceback (most recent call last):
    ...
    TypeError: Cannot specify both 'mapper' and any of 'index' or 'columns'
 
-   >>> df.rename(mapper={0: 1}, index={0: 2})
+   In [7]: df.rename(mapper={0: 1}, index={0: 2})
    Traceback (most recent call last):
    ...
    TypeError: Cannot specify both 'mapper' and any of 'index' or 'columns'
@@ -405,12 +408,12 @@ Extended verbose info output for :class:`~pandas.DataFrame`
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> df = pd.DataFrame({"int_col": [1, 2, 3],
+   In [1]: df = pd.DataFrame({"int_col": [1, 2, 3],
    ...                    "text_col": ["a", "b", "c"],
    ...                    "float_col": [0.0, 0.1, 0.2]})
-   >>> df.info(verbose=True)
+   In [2]: df.info(verbose=True)
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 3 entries, 0 to 2
    Data columns (total 3 columns):
@@ -440,14 +443,16 @@ Extended verbose info output for :class:`~pandas.DataFrame`
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> pd.array(["a", None])
+   In [1]: pd.array(["a", None])
+   Out[1]:
    <PandasArray>
    ['a', None]
    Length: 2, dtype: object
 
-   >>> pd.array([1, None])
+   In [2]: pd.array([1, None])
+   Out[2]:
    <PandasArray>
    [1, None]
    Length: 2, dtype: object
@@ -470,15 +475,17 @@ As a reminder, you can specify the ``dtype`` to disable all inference.
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> a = pd.array([1, 2, None], dtype="Int64")
-   >>> a
+   In [1]: a = pd.array([1, 2, None], dtype="Int64")
+   In [2]: a
+   Out[2]:
    <IntegerArray>
    [1, 2, NaN]
    Length: 3, dtype: Int64
 
-   >>> a[2]
+   In [3]: a[2]
+   Out[3]:
    nan
 
 *pandas 1.0.0*
@@ -499,9 +506,10 @@ will now raise.
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-    >>> np.asarray(a, dtype="float")
+    In [1]: np.asarray(a, dtype="float")
+    Out[1]:
     array([ 1.,  2., nan])
 
 *pandas 1.0.0*
@@ -525,9 +533,10 @@ will now be ``pd.NA`` instead of ``np.nan`` in presence of missing values
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-    >>> pd.Series(a).sum(skipna=False)
+    In [1]: pd.Series(a).sum(skipna=False)
+    Out[1]:
     nan
 
 *pandas 1.0.0*
@@ -543,9 +552,10 @@ integer dtype for the values.
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> pd.Series([2, 1, 1, None], dtype="Int64").value_counts().dtype
+   In [1]: pd.Series([2, 1, 1, None], dtype="Int64").value_counts().dtype
+   Out[1]:
    dtype('int64')
 
 *pandas 1.0.0*
@@ -565,15 +575,17 @@ Comparison operations on a :class:`arrays.IntegerArray` now returns a
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> a = pd.array([1, 2, None], dtype="Int64")
-   >>> a
+   In [1]: a = pd.array([1, 2, None], dtype="Int64")
+   In [2]: a
+   Out[2]:
    <IntegerArray>
    [1, 2, NaN]
    Length: 3, dtype: Int64
 
-   >>> a > 1
+   In [3]: a > 1
+   Out[3]:
    array([False,  True, False])
 
 *pandas 1.0.0*
@@ -640,9 +652,10 @@ scalar values in the result are instances of the extension dtype's scalar type.
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> df.resample("2D").agg(lambda x: 'a').A.dtype
+   In [1]> df.resample("2D").agg(lambda x: 'a').A.dtype
+   Out[1]:
    CategoricalDtype(categories=['a', 'b'], ordered=False)
 
 *pandas 1.0.0*
@@ -657,9 +670,10 @@ depending on how the results are cast back to the original dtype.
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> df.resample("2D").agg(lambda x: 'c')
+   In [1] df.resample("2D").agg(lambda x: 'c')
+   Out[1]:
 
         A
    0  NaN
@@ -871,10 +885,10 @@ matplotlib directly rather than :meth:`~DataFrame.plot`.
 
 To use pandas formatters with a matplotlib plot, specify
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> import pandas as pd
-   >>> pd.options.plotting.matplotlib.register_converters = True
+   In [1]: import pandas as pd
+   In [2]: pd.options.plotting.matplotlib.register_converters = True
 
 Note that plots created by :meth:`DataFrame.plot` and :meth:`Series.plot` *do* register the converters
 automatically. The only behavior change is when plotting a date-like object via ``matplotlib.pyplot.plot``

doc/source/whatsnew/v1.2.1.rst

@@ -52,30 +52,34 @@ DataFrame / Series combination) would ignore the indices, only match
 the inputs by shape, and use the index/columns of the first DataFrame for
 the result:
 
-.. code-block:: python
+.. code-block:: ipython
 
-    >>> df1 = pd.DataFrame({"a": [1, 2], "b": [3, 4]}, index=[0, 1])
-    ... df2 = pd.DataFrame({"a": [1, 2], "b": [3, 4]}, index=[1, 2])
-    >>> df1
+    In [1]: df1 = pd.DataFrame({"a": [1, 2], "b": [3, 4]}, index=[0, 1])
+    In [2]: df2 = pd.DataFrame({"a": [1, 2], "b": [3, 4]}, index=[1, 2])
+    In [3]: df1
+    Out[3]:
        a  b
     0  1  3
     1  2  4
-    >>> df2
+    In [4]: df2
+    Out[4]:
        a  b
     1  1  3
     2  2  4
 
-    >>> np.add(df1, df2)
+    In [5]: np.add(df1, df2)
+    Out[5]:
        a  b
     0  2  6
     1  4  8
 
 This contrasts with how other pandas operations work, which first align
 the inputs:
 
-.. code-block:: python
+.. code-block:: ipython
 
-    >>> df1 + df2
+    In [6]: df1 + df2
+    Out[6]:
          a    b
     0  NaN  NaN
     1  3.0  7.0
@@ -94,20 +98,22 @@ objects (eg ``np.add(s1, s2)``) already aligns and continues to do so.
 To avoid the warning and keep the current behaviour of ignoring the indices,
 convert one of the arguments to a NumPy array:
 
-.. code-block:: python
+.. code-block:: ipython
 
-    >>> np.add(df1, np.asarray(df2))
+    In [7]: np.add(df1, np.asarray(df2))
+    Out[7]:
        a  b
     0  2  6
     1  4  8
 
 To obtain the future behaviour and silence the warning, you can align manually
 before passing the arguments to the ufunc:
 
-.. code-block:: python
+.. code-block:: ipython
 
-    >>> df1, df2 = df1.align(df2)
-    >>> np.add(df1, df2)
+    In [8]: df1, df2 = df1.align(df2)
+    In [9]: np.add(df1, df2)
+    Out[9]:
          a    b
     0  NaN  NaN
     1  3.0  7.0

doc/source/whatsnew/v1.3.1.rst

@@ -16,6 +16,7 @@ Fixed regressions
 ~~~~~~~~~~~~~~~~~
 - Pandas could not be built on PyPy (:issue:`42355`)
 - :class:`DataFrame` constructed with with an older version of pandas could not be unpickled (:issue:`42345`)
+- Performance regression in constructing a :class:`DataFrame` from a dictionary of dictionaries (:issue:`42338`)
 -
 
 .. ---------------------------------------------------------------------------
@@ -24,7 +25,8 @@ Fixed regressions
 
 Bug fixes
 ~~~~~~~~~
--
+
+-Bug in :meth:`pandas.cut` on :class:`Series` with duplicate indices (:issue:`42185`) and non-exact :meth:`pandas.CategoricalIndex` (:issue:`42425`)
 -
 
 .. ---------------------------------------------------------------------------

doc/source/whatsnew/v1.4.0.rst

@@ -150,6 +150,7 @@ Deprecations
 - Deprecated :meth:`Index.is_type_compatible` (:issue:`42113`)
 - Deprecated ``method`` argument in :meth:`Index.get_loc`, use ``index.get_indexer([label], method=...)`` instead (:issue:`42269`)
 - Deprecated treating integer keys in :meth:`Series.__setitem__` as positional when the index is a :class:`Float64Index` not containing the key, a :class:`IntervalIndex` with no entries containing the key, or a :class:`MultiIndex` with leading :class:`Float64Index` level not containing the key (:issue:`33469`)
+- Deprecated treating ``numpy.datetime64`` objects as UTC times when passed to the :class:`Timestamp` constructor along with a timezone. In a future version, these will be treated as wall-times. To retain the old behavior, use ``Timestamp(dt64).tz_localize("UTC").tz_convert(tz)`` (:issue:`24559`)
 -
 
 .. ---------------------------------------------------------------------------
@@ -212,7 +213,7 @@ Interval
 Indexing
 ^^^^^^^^
 - Bug in indexing on a :class:`Series` or :class:`DataFrame` with a :class:`DatetimeIndex` when passing a string, the return type depended on whether the index was monotonic (:issue:`24892`)
--
+- Bug in :meth:`DataFrame.truncate` and :meth:`Series.truncate` when the object's Index has a length greater than one but only one unique value (:issue:`42365`)
 
 Missing
 ^^^^^^^

pandas/_libs/lib.pyi

@@ -51,6 +51,7 @@ def is_string_array(values: np.ndarray, skipna: bool = False): ...
 def is_float_array(values: np.ndarray, skipna: bool = False): ...
 def is_integer_array(values: np.ndarray, skipna: bool = False): ...
 def is_bool_array(values: np.ndarray, skipna: bool = False): ...
+def fast_multiget(mapping: dict, keys: np.ndarray, default=np.nan) -> np.ndarray: ...
 def fast_unique_multiple_list_gen(gen: Generator, sort: bool = True) -> list: ...
 def fast_unique_multiple_list(lists: list, sort: bool = True) -> list: ...
 def fast_unique_multiple(arrays: list, sort: bool = True) -> list: ...