1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
|
.. _whatsnew_121:
What's new in 1.2.1 (January 20, 2021)
--------------------------------------
These are the changes in pandas 1.2.1. See :ref:`release` for a full changelog
including other versions of pandas.
{{ header }}
.. ---------------------------------------------------------------------------
.. _whatsnew_121.regressions:
Fixed regressions
~~~~~~~~~~~~~~~~~
- Fixed regression in :meth:`~DataFrame.to_csv` that created corrupted zip files when there were more rows than ``chunksize`` (:issue:`38714`)
- Fixed regression in :meth:`~DataFrame.to_csv` opening ``codecs.StreamReaderWriter`` in binary mode instead of in text mode (:issue:`39247`)
- Fixed regression in :meth:`read_csv` and other read functions were the encoding error policy (``errors``) did not default to ``"replace"`` when no encoding was specified (:issue:`38989`)
- Fixed regression in :func:`read_excel` with non-rawbyte file handles (:issue:`38788`)
- Fixed regression in :meth:`DataFrame.to_stata` not removing the created file when an error occurred (:issue:`39202`)
- Fixed regression in ``DataFrame.__setitem__`` raising ``ValueError`` when expanding :class:`DataFrame` and new column is from type ``"0 - name"`` (:issue:`39010`)
- Fixed regression in setting with :meth:`DataFrame.loc` raising ``ValueError`` when :class:`DataFrame` has unsorted :class:`MultiIndex` columns and indexer is a scalar (:issue:`38601`)
- Fixed regression in setting with :meth:`DataFrame.loc` raising ``KeyError`` with :class:`MultiIndex` and list-like columns indexer enlarging :class:`DataFrame` (:issue:`39147`)
- Fixed regression in :meth:`~DataFrame.groupby()` with :class:`Categorical` grouping column not showing unused categories for ``grouped.indices`` (:issue:`38642`)
- Fixed regression in :meth:`.DataFrameGroupBy.sem` and :meth:`.SeriesGroupBy.sem` where the presence of non-numeric columns would cause an error instead of being dropped (:issue:`38774`)
- Fixed regression in :meth:`.DataFrameGroupBy.diff` raising for ``int8`` and ``int16`` columns (:issue:`39050`)
- Fixed regression in :meth:`DataFrame.groupby` when aggregating an ``ExtensionDType`` that could fail for non-numeric values (:issue:`38980`)
- Fixed regression in :meth:`.Rolling.skew` and :meth:`.Rolling.kurt` modifying the object inplace (:issue:`38908`)
- Fixed regression in :meth:`DataFrame.any` and :meth:`DataFrame.all` not returning a result for tz-aware ``datetime64`` columns (:issue:`38723`)
- Fixed regression in :meth:`DataFrame.apply` with ``axis=1`` using str accessor in apply function (:issue:`38979`)
- Fixed regression in :meth:`DataFrame.replace` raising ``ValueError`` when :class:`DataFrame` has dtype ``bytes`` (:issue:`38900`)
- Fixed regression in :meth:`Series.fillna` that raised ``RecursionError`` with ``datetime64[ns, UTC]`` dtype (:issue:`38851`)
- Fixed regression in comparisons between ``NaT`` and ``datetime.date`` objects incorrectly returning ``True`` (:issue:`39151`)
- Fixed regression in calling NumPy :func:`~numpy.ufunc.accumulate` ufuncs on DataFrames, e.g. ``np.maximum.accumulate(df)`` (:issue:`39259`)
- Fixed regression in repr of float-like strings of an ``object`` dtype having trailing 0's truncated after the decimal (:issue:`38708`)
- Fixed regression that raised ``AttributeError`` with PyArrow versions [0.16.0, 1.0.0) (:issue:`38801`)
- Fixed regression in :func:`pandas.testing.assert_frame_equal` raising ``TypeError`` with ``check_like=True`` when :class:`Index` or columns have mixed dtype (:issue:`39168`)
We have reverted a commit that resulted in several plotting related regressions in pandas 1.2.0 (:issue:`38969`, :issue:`38736`, :issue:`38865`, :issue:`38947` and :issue:`39126`).
As a result, bugs reported as fixed in pandas 1.2.0 related to inconsistent tick labeling in bar plots are again present (:issue:`26186` and :issue:`11465`)
.. ---------------------------------------------------------------------------
.. _whatsnew_121.ufunc_deprecation:
Calling NumPy ufuncs on non-aligned DataFrames
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Before pandas 1.2.0, calling a NumPy ufunc on non-aligned DataFrames (or
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:: ipython
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
In [4]: df2
Out[4]:
a b
1 1 3
2 2 4
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:: ipython
In [6]: df1 + df2
Out[6]:
a b
0 NaN NaN
1 3.0 7.0
2 NaN NaN
In pandas 1.2.0, we refactored how NumPy ufuncs are called on DataFrames, and
this started to align the inputs first (:issue:`39184`), as happens in other
pandas operations and as it happens for ufuncs called on Series objects.
For pandas 1.2.1, we restored the previous behaviour to avoid a breaking
change, but the above example of ``np.add(df1, df2)`` with non-aligned inputs
will now to raise a warning, and a future pandas 2.0 release will start
aligning the inputs first (:issue:`39184`). Calling a NumPy ufunc on Series
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:: ipython
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:: ipython
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
2 NaN NaN
.. ---------------------------------------------------------------------------
.. _whatsnew_121.bug_fixes:
Bug fixes
~~~~~~~~~
- Bug in :meth:`read_csv` with ``float_precision="high"`` caused segfault or wrong parsing of long exponent strings. This resulted in a regression in some cases as the default for ``float_precision`` was changed in pandas 1.2.0 (:issue:`38753`)
- Bug in :func:`read_csv` not closing an opened file handle when a ``csv.Error`` or ``UnicodeDecodeError`` occurred while initializing (:issue:`39024`)
- Bug in :func:`pandas.testing.assert_index_equal` raising ``TypeError`` with ``check_order=False`` when :class:`Index` has mixed dtype (:issue:`39168`)
.. ---------------------------------------------------------------------------
.. _whatsnew_121.other:
Other
~~~~~
- The deprecated attributes ``_AXIS_NAMES`` and ``_AXIS_NUMBERS`` of :class:`DataFrame` and :class:`Series` will no longer show up in ``dir`` or ``inspect.getmembers`` calls (:issue:`38740`)
- Bumped minimum fastparquet version to 0.4.0 to avoid ``AttributeError`` from numba (:issue:`38344`)
- Bumped minimum pymysql version to 0.8.1 to avoid test failures (:issue:`38344`)
- Fixed build failure on MacOS 11 in Python 3.9.1 (:issue:`38766`)
- Added reference to backwards incompatible ``check_freq`` arg of :func:`testing.assert_frame_equal` and :func:`testing.assert_series_equal` in :ref:`pandas 1.1.0 what's new <whatsnew_110.api_breaking.testing.check_freq>` (:issue:`34050`)
.. ---------------------------------------------------------------------------
.. _whatsnew_121.contributors:
Contributors
~~~~~~~~~~~~
.. contributors:: v1.2.0..v1.2.1
|
