hi all, We've released pandas 0.11.0, a big release that span 3 months of continuous development, led primarily by the intrepid Jeff Reback and y-p. The release brings many new features, performance and API improvements, bug fixes, and other goodies.
Some highlights: - New precision indexing fields loc, iloc, at, and iat, to reduce occasional ambiguity in the catch-all hitherto ix method. - Expanded support for NumPy data types in DataFrame - NumExpr integration to accelerate various operator evaluation - New Cookbook and 10 minutes to pandas pages in the documentation by Jeff Reback - Improved DataFrame to CSV exporting performance - Experimental "rplot" branch with faceted plots with matplotlib merged and open for community hacking Source archives and Windows installers are on PyPI. Thanks to all who contributed to this release, especially Jeff and y-p. What's new: http://pandas.pydata.org/pandas-docs/stable/whatsnew.html Installers: http://pypi.python.org/pypi/pandas $ git log v0.10.1..v0.11.0 --pretty=format:%aN | sort | uniq -c | sort -rn 308 y-p 279 jreback 85 Vytautas Jancauskas 74 Wes McKinney 25 Stephen Lin 22 Andy Hayden 19 Chang She 13 Wouter Overmeire 8 Spencer Lyon 6 Phillip Cloud 6 Nicholaus E. Halecky 5 Thierry Moisan 5 Skipper Seabold 4 waitingkuo 4 Loïc Estève 4 Jeff Reback 4 Garrett Drapala 4 Alvaro Tejero-Cantero 3 lexual 3 Dražen Lučanin 3 dieterv77 3 dengemann 3 Dan Birken 3 Adam Greenhall 2 Will Furnass 2 Vytautas Jančauskas 2 Robert Gieseke 2 Peter Prettenhofer 2 Jonathan Chambers 2 Dieter Vandenbussche 2 Damien Garaud 2 Christopher Whelan 2 Chapman Siu 2 Brad Buran 1 vytas 1 Tim Akinbo 1 Thomas Kluyver 1 thauck 1 stephenwlin 1 K.-Michael Aye 1 Karmel Allison 1 Jeremy Wagner 1 James Casbon 1 Illia Polosukhin 1 Dražen Lučanin 1 davidjameshumphreys 1 Dan Davison 1 Chris Withers 1 Christian Geier 1 anomrake Happy data hacking! - Wes What is it ========== pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with relational, time series, or any other kind of labeled data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Links ===== Release Notes: http://github.com/pydata/pandas/blob/master/RELEASE.rst Documentation: http://pandas.pydata.org Installers: http://pypi.python.org/pypi/pandas Code Repository: http://github.com/pydata/pandas Mailing List: http://groups.google.com/group/pydata _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
