# What’s new in 1.2.1 (January 20, 2021)#

These are the changes in pandas 1.2.1. See Release notes for a full changelog including other versions of pandas.

## Fixed regressions#

Fixed regression in

`to_csv()`

that created corrupted zip files when there were more rows than`chunksize`

(GH38714)Fixed regression in

`to_csv()`

opening`codecs.StreamReaderWriter`

in binary mode instead of in text mode (GH39247)Fixed regression in

`read_csv()`

and other read functions were the encoding error policy (`errors`

) did not default to`"replace"`

when no encoding was specified (GH38989)Fixed regression in

`read_excel()`

with non-rawbyte file handles (GH38788)Fixed regression in

`DataFrame.to_stata()`

not removing the created file when an error occurred (GH39202)Fixed regression in

`DataFrame.__setitem__`

raising`ValueError`

when expanding`DataFrame`

and new column is from type`"0 - name"`

(GH39010)Fixed regression in setting with

`DataFrame.loc()`

raising`ValueError`

when`DataFrame`

has unsorted`MultiIndex`

columns and indexer is a scalar (GH38601)Fixed regression in setting with

`DataFrame.loc()`

raising`KeyError`

with`MultiIndex`

and list-like columns indexer enlarging`DataFrame`

(GH39147)Fixed regression in

`groupby()`

with`Categorical`

grouping column not showing unused categories for`grouped.indices`

(GH38642)Fixed regression in

`DataFrameGroupBy.sem()`

and`SeriesGroupBy.sem()`

where the presence of non-numeric columns would cause an error instead of being dropped (GH38774)Fixed regression in

`DataFrameGroupBy.diff()`

raising for`int8`

and`int16`

columns (GH39050)Fixed regression in

`DataFrame.groupby()`

when aggregating an`ExtensionDType`

that could fail for non-numeric values (GH38980)Fixed regression in

`Rolling.skew()`

and`Rolling.kurt()`

modifying the object inplace (GH38908)Fixed regression in

`DataFrame.any()`

and`DataFrame.all()`

not returning a result for tz-aware`datetime64`

columns (GH38723)Fixed regression in

`DataFrame.apply()`

with`axis=1`

using str accessor in apply function (GH38979)Fixed regression in

`DataFrame.replace()`

raising`ValueError`

when`DataFrame`

has dtype`bytes`

(GH38900)Fixed regression in

`Series.fillna()`

that raised`RecursionError`

with`datetime64[ns, UTC]`

dtype (GH38851)Fixed regression in comparisons between

`NaT`

and`datetime.date`

objects incorrectly returning`True`

(GH39151)Fixed regression in calling NumPy

`accumulate()`

ufuncs on DataFrames, e.g.`np.maximum.accumulate(df)`

(GH39259)Fixed regression in repr of float-like strings of an

`object`

dtype having trailing 0’s truncated after the decimal (GH38708)Fixed regression that raised

`AttributeError`

with PyArrow versions [0.16.0, 1.0.0) (GH38801)Fixed regression in

`pandas.testing.assert_frame_equal()`

raising`TypeError`

with`check_like=True`

when`Index`

or columns have mixed dtype (GH39168)

We have reverted a commit that resulted in several plotting related regressions in pandas 1.2.0 (GH38969, GH38736, GH38865, GH38947 and GH39126). As a result, bugs reported as fixed in pandas 1.2.0 related to inconsistent tick labeling in bar plots are again present (GH26186 and GH11465)

## 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:

```
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:

```
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 (GH39184), 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 (GH39184). 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:

```
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:

```
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
```

## Bug fixes#

Bug in

`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 (GH38753)Bug in

`read_csv()`

not closing an opened file handle when a`csv.Error`

or`UnicodeDecodeError`

occurred while initializing (GH39024)Bug in

`pandas.testing.assert_index_equal()`

raising`TypeError`

with`check_order=False`

when`Index`

has mixed dtype (GH39168)

## Other#

The deprecated attributes

`_AXIS_NAMES`

and`_AXIS_NUMBERS`

of`DataFrame`

and`Series`

will no longer show up in`dir`

or`inspect.getmembers`

calls (GH38740)Bumped minimum fastparquet version to 0.4.0 to avoid

`AttributeError`

from numba (GH38344)Bumped minimum pymysql version to 0.8.1 to avoid test failures (GH38344)

Fixed build failure on MacOS 11 in Python 3.9.1 (GH38766)

Added reference to backwards incompatible

`check_freq`

arg of`testing.assert_frame_equal()`

and`testing.assert_series_equal()`

in pandas 1.1.0 what’s new (GH34050)

## Contributors#

A total of 20 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

Ada Draginda +

Andrew Wieteska

Bryan Cutler

Fangchen Li

Joris Van den Bossche

Matthew Roeschke

Matthew Zeitlin +

MeeseeksMachine

Micael Jarniac

Omar Afifi +

Pandas Development Team

Richard Shadrach

Simon Hawkins

Terji Petersen

Torsten Wörtwein

WANG Aiyong

jbrockmendel

kylekeppler

mzeitlin11

patrick