Version 0.6.0 (November 25, 2011)¶
New features¶
Added
meltfunction topandas.core.reshapeAdded
levelparameter to group by level in Series and DataFrame descriptive statistics (GH313)Added
headandtailmethods to Series, analogous to DataFrame (GH296)Added
Series.isinfunction which checks if each value is contained in a passed sequence (GH289)Added
float_formatoption toSeries.to_stringAdded
skip_footer(GH291) andconverters(GH343) options toread_csvandread_tableAdded
drop_duplicatesandduplicatedfunctions for removing duplicate DataFrame rows and checking for duplicate rows, respectively (GH319)Implemented operators ‘&’, ‘|’, ‘^’, ‘-‘ on DataFrame (GH347)
Added
Series.mad, mean absolute deviationAdded
orientoption toDataFrame.from_dictAdded passing list of tuples or list of lists to
DataFrame.from_records(GH357)Allow multiple columns in
byargument ofDataFrame.sort_index(GH92, GH362)Added fast
get_valueandput_valuemethods to DataFrame (GH360)Added
covinstance methods to Series and DataFrame (GH194, GH362)Added
read_clipboardfunction to parse DataFrame from clipboard (GH300)Added
nuniquefunction to Series for counting unique elements (GH297)Made DataFrame constructor use Series name if no columns passed (GH373)
Added
DataFrame.to_htmlfor writing DataFrame to HTML (GH387)Added support for MaskedArray data in DataFrame, masked values converted to NaN (GH396)
Added
rawoption toDataFrame.applyfor performance if only need ndarray (GH309)Added proper, tested weighted least squares to standard and panel OLS (GH303)
Performance enhancements¶
VBENCH Cythonized
cache_readonly, resulting in substantial micro-performance enhancements throughout the code base (GH361)VBENCH Special Cython matrix iterator for applying arbitrary reduction operations with 3-5x better performance than
np.apply_along_axis(GH309)VBENCH Improved performance of
MultiIndex.from_tuplesVBENCH Special Cython matrix iterator for applying arbitrary reduction operations
VBENCH + DOCUMENT Add
rawoption toDataFrame.applyfor getting better performance whenVBENCH Faster cythonized count by level in Series and DataFrame (GH341)
VBENCH? Significant GroupBy performance enhancement with multiple keys with many “empty” combinations
VBENCH New Cython vectorized function
map_inferspeeds upSeries.applyandSeries.mapsignificantly when passed elementwise Python function, motivated by (GH355)VBENCH Significantly improved performance of
Series.order, which also makes np.unique called on a Series faster (GH327)VBENCH Vastly improved performance of GroupBy on axes with a MultiIndex (GH299)
Contributors¶
A total of 8 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.
Adam Klein +
Chang She +
Dieter Vandenbussche
Jeff Hammerbacher +
Nathan Pinger +
Thomas Kluyver
Wes McKinney
Wouter Overmeire +