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.


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'.
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#


  • 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 SparseArray.shape which previously only returned the shape SparseArray.sp_values (GH 21126)



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


  • 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)


  • 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)


  • 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)


A total of 30 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

  • Adam J. Stewart

  • Adam Kim +

  • Aly Sivji

  • Chalmer Lowe +

  • Damini Satya +

  • Dr. Irv

  • Gabe Fernando +

  • Giftlin Rajaiah

  • Jeff Reback

  • Jeremy Schendel +

  • Joris Van den Bossche

  • Kalyan Gokhale +

  • Kevin Sheppard

  • Matthew Roeschke

  • Max Kanter +

  • Ming Li

  • Pyry Kovanen +

  • Stefano Cianciulli

  • Tom Augspurger

  • Uddeshya Singh +

  • Wenhuan

  • William Ayd

  • chris-b1

  • gfyoung

  • h-vetinari

  • nprad +

  • ssikdar1 +

  • tmnhat2001

  • topper-123

  • zertrin +