v0.21.1 (December 12, 2017)

This is a minor bug-fix release in the 0.21.x series and includes some small regression fixes, bug fixes and performance improvements. We recommend that all users upgrade to this version.

Highlights include:

  • Temporarily restore matplotlib datetime plotting functionality. This should resolve issues for users who implicitly relied on pandas to plot datetimes with matplotlib. See here.
  • Improvements to the Parquet IO functions introduced in 0.21.0. See here.

Restore Matplotlib datetime Converter Registration

Pandas implements some matplotlib converters for nicely formatting the axis labels on plots with datetime or Period values. Prior to pandas 0.21.0, these were implicitly registered with matplotlib, as a side effect of import pandas.

In pandas 0.21.0, we required users to explicitly register the converter. This caused problems for some users who relied on those converters being present for regular matplotlib.pyplot plotting methods, so we’re temporarily reverting that change; pandas 0.21.1 again registers the converters on import, just like before 0.21.0.

We’ve added a new option to control the converters: pd.options.plotting.matplotlib.register_converters. By default, they are registered. Toggling this to False removes pandas’ formatters and restore any converters we overwrote when registering them (GH18301).

We’re working with the matplotlib developers to make this easier. We’re trying to balance user convenience (automatically registering the converters) with import performance and best practices (importing pandas shouldn’t have the side effect of overwriting any custom converters you’ve already set). In the future we hope to have most of the datetime formatting functionality in matplotlib, with just the pandas-specific converters in pandas. We’ll then gracefully deprecate the automatic registration of converters in favor of users explicitly registering them when they want them.

New features

Improvements to the Parquet IO functionality

Other Enhancements

Deprecations

Performance Improvements

  • Improved performance of plotting large series/dataframes (GH18236).

Bug Fixes

Conversion

  • Bug in TimedeltaIndex subtraction could incorrectly overflow when NaT is present (GH17791)
  • Bug in DatetimeIndex subtracting datetimelike from DatetimeIndex could fail to overflow (GH18020)
  • Bug in IntervalIndex.copy() when copying and IntervalIndex with non-default closed (GH18339)
  • Bug in DataFrame.to_dict() where columns of datetime that are tz-aware were not converted to required arrays when used with orient='records', raising TypeError (GH18372)
  • Bug in DateTimeIndex and date_range() where mismatching tz-aware start and end timezones would not raise an err if end.tzinfo is None (GH18431)
  • Bug in Series.fillna() which raised when passed a long integer on Python 2 (GH18159).

Indexing

  • Bug in a boolean comparison of a datetime.datetime and a datetime64[ns] dtype Series (GH17965)
  • Bug where a MultiIndex with more than a million records was not raising AttributeError when trying to access a missing attribute (GH18165)
  • Bug in IntervalIndex constructor when a list of intervals is passed with non-default closed (GH18334)
  • Bug in Index.putmask when an invalid mask passed (GH18368)
  • Bug in masked assignment of a timedelta64[ns] dtype Series, incorrectly coerced to float (GH18493)

I/O

  • Bug in class:~pandas.io.stata.StataReader not converting date/time columns with display formatting addressed (GH17990). Previously columns with display formatting were normally left as ordinal numbers and not converted to datetime objects.
  • Bug in read_csv() when reading a compressed UTF-16 encoded file (GH18071)
  • Bug in read_csv() for handling null values in index columns when specifying na_filter=False (GH5239)
  • Bug in read_csv() when reading numeric category fields with high cardinality (GH18186)
  • Bug in DataFrame.to_csv() when the table had MultiIndex columns, and a list of strings was passed in for header (GH5539)
  • Bug in parsing integer datetime-like columns with specified format in read_sql (GH17855).
  • Bug in DataFrame.to_msgpack() when serializing data of the numpy.bool_ datatype (GH18390)
  • Bug in read_json() not decoding when reading line delimited JSON from S3 (GH17200)
  • Bug in pandas.io.json.json_normalize() to avoid modification of meta (GH18610)
  • Bug in to_latex() where repeated MultiIndex values were not printed even though a higher level index differed from the previous row (GH14484)
  • Bug when reading NaN-only categorical columns in HDFStore (GH18413)
  • Bug in DataFrame.to_latex() with longtable=True where a latex multicolumn always spanned over three columns (GH17959)

Plotting

  • Bug in DataFrame.plot() and Series.plot() with DatetimeIndex where a figure generated by them is not pickleable in Python 3 (GH18439)

Groupby/Resample/Rolling

  • Bug in DataFrame.resample(...).apply(...) when there is a callable that returns different columns (GH15169)
  • Bug in DataFrame.resample(...) when there is a time change (DST) and resampling frequency is 12h or higher (GH15549)
  • Bug in pd.DataFrameGroupBy.count() when counting over a datetimelike column (GH13393)
  • Bug in rolling.var where calculation is inaccurate with a zero-valued array (GH18430)

Reshaping

  • Error message in pd.merge_asof() for key datatype mismatch now includes datatype of left and right key (GH18068)
  • Bug in pd.concat when empty and non-empty DataFrames or Series are concatenated (GH18178 GH18187)
  • Bug in DataFrame.filter(...) when unicode is passed as a condition in Python 2 (GH13101)
  • Bug when merging empty DataFrames when np.seterr(divide='raise') is set (GH17776)

Numeric

  • Bug in pd.Series.rolling.skew() and rolling.kurt() with all equal values has floating issue (GH18044)

Categorical

  • Bug in DataFrame.astype() where casting to ‘category’ on an empty DataFrame causes a segmentation fault (GH18004)
  • Error messages in the testing module have been improved when items have different CategoricalDtype (GH18069)
  • CategoricalIndex can now correctly take a pd.api.types.CategoricalDtype as its dtype (GH18116)
  • Bug in Categorical.unique() returning read-only codes array when all categories were NaN (GH18051)
  • Bug in DataFrame.groupby(axis=1) with a CategoricalIndex (GH18432)

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