What’s new in 0.23.0 (May 15, 2018)¶
This is a major release from 0.22.0 and includes a number of API changes, deprecations, new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version.
Highlights include:
- Round-trippable JSON format with ‘table’ orient.
- Instantiation from dicts respects order for Python 3.6+.
- Dependent column arguments for assign.
- Merging / sorting on a combination of columns and index levels.
- Extending Pandas with custom types.
- Excluding unobserved categories from groupby.
- Changes to make output shape of DataFrame.apply consistent.
Check the API Changes and deprecations before updating.
Warning
Starting January 1, 2019, pandas feature releases will support Python 3 only. See Plan for dropping Python 2.7 for more.
What’s new in v0.23.0
- New features
- JSON read/write round-trippable with
orient='table'
.assign()
accepts dependent arguments- Merging on a combination of columns and index levels
- Sorting by a combination of columns and index levels
- Extending Pandas with Custom Types (Experimental)
- New
observed
keyword for excluding unobserved categories ingroupby
- Rolling/Expanding.apply() accepts
raw=False
to pass aSeries
to the function DataFrame.interpolate
has gained thelimit_area
kwargget_dummies
now supportsdtype
argument- Timedelta mod method
.rank()
handlesinf
values whenNaN
are presentSeries.str.cat
has gained thejoin
kwargDataFrame.astype
performs column-wise conversion toCategorical
- Other Enhancements
- JSON read/write round-trippable with
- Backwards incompatible API changes
- Dependencies have increased minimum versions
- Instantiation from dicts preserves dict insertion order for python 3.6+
- Deprecate Panel
- pandas.core.common removals
- Changes to make output of
DataFrame.apply
consistent - Concatenation will no longer sort
- Build Changes
- Index Division By Zero Fills Correctly
- Extraction of matching patterns from strings
- Default value for the
ordered
parameter ofCategoricalDtype
- Better pretty-printing of DataFrames in a terminal
- Datetimelike API Changes
- Other API Changes
- Deprecations
- Removal of prior version deprecations/changes
- Performance Improvements
- Documentation Changes
- Bug Fixes
- Contributors
New features¶
JSON read/write round-trippable with orient='table'
¶
A DataFrame
can now be written to and subsequently read back via JSON while preserving metadata through usage of the orient='table'
argument (see GH18912 and GH9146). Previously, none of the available orient
values guaranteed the preservation of dtypes and index names, amongst other metadata.
In [1]: df = pd.DataFrame({'foo': [1, 2, 3, 4],
...: 'bar': ['a', 'b', 'c', 'd'],
...: 'baz': pd.date_range('2018-01-01', freq='d', periods=4),
...: 'qux': pd.Categorical(['a', 'b', 'c', 'c'])},
...: index=pd.Index(range(4), name='idx'))
...:
In [2]: df
Out[2]:
foo bar baz qux
idx
0 1 a 2018-01-01 a
1 2 b 2018-01-02 b
2 3 c 2018-01-03 c
3 4 d 2018-01-04 c
[4 rows x 4 columns]
In [3]: df.dtypes