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# What’s new in 0.23.1 (June 12, 2018)

This is a minor bug-fix release in the 0.23.x series and includes some small regression fixes and bug fixes. We recommend that all users upgrade to this version.

Warning

Starting January 1, 2019, pandas feature releases will support Python 3 only. See Dropping Python 2.7 for more.

## Fixed regressions

Comparing Series with datetime.date

We’ve reverted a 0.23.0 change to comparing a `Series` holding datetimes and a `datetime.date` object (GH 21152). In pandas 0.22 and earlier, comparing a Series holding datetimes and `datetime.date` objects would coerce the `datetime.date` to a datetime before comparing. This was inconsistent with Python, NumPy, and `DatetimeIndex`, which never consider a datetime and `datetime.date` equal.

In 0.23.0, we unified operations between DatetimeIndex and Series, and in the process changed comparisons between a Series of datetimes and `datetime.date` without warning.

We’ve temporarily restored the 0.22.0 behavior, so datetimes and dates may again compare equal, but restore the 0.23.0 behavior in a future release.

To summarize, here’s the behavior in 0.22.0, 0.23.0, 0.23.1:

    # 0.22.0... Silently coerce the datetime.date
    >>> import datetime
    >>> pd.Series(pd.date_range('2017', periods=2)) == datetime.date(2017, 1, 1)
    0     True
    1    False
    dtype: bool

    # 0.23.0... Do not coerce the datetime.date
    >>> pd.Series(pd.date_range('2017', periods=2)) == datetime.date(2017, 1, 1)
    0    False
    1    False
    dtype: bool

    # 0.23.1... Coerce the datetime.date with a warning
    >>> pd.Series(pd.date_range('2017', periods=2)) == datetime.date(2017, 1, 1)
    /bin/python:1: FutureWarning: Comparing Series of datetimes with 'datetime.date'.  Currently, the
    'datetime.date' is coerced to a datetime. In the future pandas will
    not coerce, and the values not compare equal to the 'datetime.date'.
    To retain the current behavior, convert the 'datetime.date' to a
    datetime with 'pd.Timestamp'.
      #!/bin/python3
    0     True
    1    False
    dtype: bool

In addition, ordering comparisons will raise a `TypeError` in the future.

Other fixes

- Reverted the ability of `to_sql()` to perform multivalue inserts as this caused regression in certain cases (GH 21103). In the future this will be made configurable.

- Fixed regression in the `DatetimeIndex.date` and `DatetimeIndex.time` attributes in case of timezone-aware data: `DatetimeIndex.time` returned a tz-aware time instead of tz-naive (GH 21267) and `DatetimeIndex.date` returned incorrect date when the input date has a non-UTC timezone (GH 21230).

- Fixed regression in `pandas.io.json.json_normalize()` when called with `None` values in nested levels in JSON, and to not drop keys with value as `None` (GH 21158, GH 21356).

- Bug in `to_csv()` causes encoding error when compression and encoding are specified (GH 21241, GH 21118)

- Bug preventing pandas from being importable with -OO optimization (GH 21071)

- Bug in `Categorical.fillna()` incorrectly raising a `TypeError` when `value` the individual categories are iterable and `value` is an iterable (GH 21097, GH 19788)

- Fixed regression in constructors coercing NA values like `None` to strings when passing `dtype=str` (GH 21083)

- Regression in `pivot_table()` where an ordered `Categorical` with missing values for the pivot’s `index` would give a mis-aligned result (GH 21133)

- Fixed regression in merging on boolean index/columns (GH 21119).

## Performance improvements

- Improved performance of `CategoricalIndex.is_monotonic_increasing()`, `CategoricalIndex.is_monotonic_decreasing()` and `CategoricalIndex.is_monotonic()` (GH 21025)

- Improved performance of `CategoricalIndex.is_unique()` (GH 21107)

## Bug fixes

Groupby/resample/rolling

- Bug in `DataFrame.agg()` where applying multiple aggregation functions to a `DataFrame` with duplicated column names would cause a stack overflow (GH 21063)

- Bug in `GroupBy.ffill()` and `GroupBy.bfill()` where the fill within a grouping would not always be applied as intended due to the implementations’ use of a non-stable sort (GH 21207)

- Bug in `GroupBy.rank()` where results did not scale to 100% when specifying `method='dense'` and `pct=True`

- Bug in `pandas.DataFrame.rolling()` and `pandas.Series.rolling()` which incorrectly accepted a 0 window size rather than raising (GH 21286)

Data-type specific

- Bug in `Series.str.replace()` where the method throws `TypeError` on Python 3.5.2 (GH 21078)

- Bug in `Timedelta` where passing a float with a unit would prematurely round the float precision (GH 14156)

- Bug in `pandas.testing.assert_index_equal()` which raised `AssertionError` incorrectly, when comparing two `CategoricalIndex` objects with param `check_categorical=False` (GH 19776)

Sparse

- Bug in `SparseArray.shape` which previously only returned the shape `SparseArray.sp_values` (GH 21126)

Indexing

- Bug in `Series.reset_index()` where appropriate error was not raised with an invalid level name (GH 20925)

- Bug in `interval_range()` when `start`/`periods` or `end`/`periods` are specified with float `start` or `end` (GH 21161)

- Bug in `MultiIndex.set_names()` where error raised for a `MultiIndex` with `nlevels == 1` (GH 21149)

- Bug in `IntervalIndex` constructors where creating an `IntervalIndex` from categorical data was not fully supported (GH 21243, GH 21253)

- Bug in `MultiIndex.sort_index()` which was not guaranteed to sort correctly with `level=1`; this was also causing data misalignment in particular `DataFrame.stack()` operations (GH 20994, GH 20945, GH 21052)

Plotting

- New keywords (sharex, sharey) to turn on/off sharing of x/y-axis by subplots generated with pandas.DataFrame().groupby().boxplot() (GH 20968)

I/O

- Bug in IO methods specifying `compression='zip'` which produced uncompressed zip archives (GH 17778, GH 21144)

- Bug in `DataFrame.to_stata()` which prevented exporting DataFrames to buffers and most file-like objects (GH 21041)

- Bug in `read_stata()` and `StataReader` which did not correctly decode utf-8 strings on Python 3 from Stata 14 files (dta version 118) (GH 21244)

- Bug in IO JSON `read_json()` reading empty JSON schema with `orient='table'` back to `DataFrame` caused an error (GH 21287)

Reshaping

- Bug in `concat()` where error was raised in concatenating `Series` with numpy scalar and tuple names (GH 21015)

- Bug in `concat()` warning message providing the wrong guidance for future behavior (GH 21101)

Other

- Tab completion on `Index` in IPython no longer outputs deprecation warnings (GH 21125)

- Bug preventing pandas being used on Windows without C++ redistributable installed (GH 21106)

## Contributors

For contributors, please see /usr/share/doc/contributors_list.txt or https://github.com/pandas-dev/pandas/graphs/contributors