Version 0.6.0 (November 25, 2011)#
New features#
Added
melt
function topandas.core.reshape
Added
level
parameter to group by level in Series and DataFrame descriptive statistics (GH 313)Added
head
andtail
methods to Series, analogous to DataFrame (GH 296)Added
Series.isin
function which checks if each value is contained in a passed sequence (GH 289)Added
float_format
option toSeries.to_string
Added
skip_footer
(GH 291) andconverters
(GH 343) options toread_csv
andread_table
Added
drop_duplicates
andduplicated
functions for removing duplicate DataFrame rows and checking for duplicate rows, respectively (GH 319)Implemented operators ‘&’, ‘|’, ‘^’, ‘-’ on DataFrame (GH 347)
Added
Series.mad
, mean absolute deviationAdded
orient
option toDataFrame.from_dict
Added passing list of tuples or list of lists to
DataFrame.from_records
(GH 357)Allow multiple columns in
by
argument ofDataFrame.sort_index
(GH 92, GH 362)Added fast
get_value
andput_value
methods to DataFrame (GH 360)Added
cov
instance methods to Series and DataFrame (GH 194, GH 362)Added
read_clipboard
function to parse DataFrame from clipboard (GH 300)Added
nunique
function to Series for counting unique elements (GH 297)Made DataFrame constructor use Series name if no columns passed (GH 373)
Added
DataFrame.to_html
for writing DataFrame to HTML (GH 387)Added support for MaskedArray data in DataFrame, masked values converted to NaN (GH 396)
Added
raw
option toDataFrame.apply
for performance if only need ndarray (GH 309)Added proper, tested weighted least squares to standard and panel OLS (GH 303)
Performance enhancements#
VBENCH Cythonized
cache_readonly
, resulting in substantial micro-performance enhancements throughout the code base (GH 361)VBENCH Special Cython matrix iterator for applying arbitrary reduction operations with 3-5x better performance than
np.apply_along_axis
(GH 309)VBENCH Improved performance of
MultiIndex.from_tuples
VBENCH Special Cython matrix iterator for applying arbitrary reduction operations
VBENCH + DOCUMENT Add
raw
option toDataFrame.apply
for getting better performance whenVBENCH Faster cythonized count by level in Series and DataFrame (GH 341)
VBENCH? Significant GroupBy performance enhancement with multiple keys with many “empty” combinations
VBENCH New Cython vectorized function
map_infer
speeds upSeries.apply
andSeries.map
significantly when passed elementwise Python function, motivated by (GH 355)VBENCH Significantly improved performance of
Series.order
, which also makes np.unique called on a Series faster (GH 327)VBENCH Vastly improved performance of GroupBy on axes with a MultiIndex (GH 299)
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 +