Hello all, (resending, as this didn't make it through to the ML the first time) I'm very happy to announce the release of a new data analysis library that many of you will hopefully find useful. This release is the product of a long period of development and use; hence, despite the low version number, it is quite suitable for general use. The documentation is still a bit sparse but will become much more complete in the coming weeks and months. Info / Documentation: http://pandas.sourceforge.net/ Overview slides: http://pandas.googlecode.com/files/nyfpug.pdf What it is ========== pandas is a library for pan-el da-ta analysis, i.e. multidimensional time series and cross-sectional data sets commonly found in statistics, econometrics, or finance. It provides convenient and easy-to-understand NumPy-based data structures for generic labeled data, with focus on automatically aligning data based on its label(s) and handling missing observations. One major goal of the library is to simplify the implementation of statistical models on unreliable data. Main Features ============= * Data structures: for 1, 2, and 3 dimensional labeled data sets. Some of their main features include: * Automatically aligning data * Handling missing observations in calculations * Convenient slicing and reshaping ("reindexing") functions * Provide 'group by' aggregation or transformation functionality * Tools for merging / joining together data sets * Simple matplotlib integration for plotting * Date tools: objects for expressing date offsets or generating date ranges; some functionality similar to scikits.timeseries * Statistical models: convenient ordinary least squares and panel OLS implementations for in-sample or rolling time series / cross-sectional regressions. These will hopefully be the starting point for implementing other models pandas is not necessarily intended as a standalone library but rather as something which can be used in tandem with other NumPy-based packages like scikits.statsmodels. Where possible wheel-reinvention has largely been avoided. Also, its time series manipulation capability is not as extensive as scikits.timeseries; pandas does have its own time series object which fits into the unified data model. Some other useful tools for time series data (moving average, standard deviation, etc.) are available in the codebase but do not yet have a convenient interface. These will be highlighted in a future release. Where to get it =============== The source code is currently hosted on googlecode at: http://pandas.googlecode.com Releases can be downloaded currently on the Python package index or using easy_install PyPi: http://pypi.python.org/pypi/pandas/ License ======= BSD Documentation ============= The official documentation is hosted on SourceForge. http://pandas.sourceforge.net/ The sphinx documentation is still in an incomplete state, but it should provide a good starting point for learning how to use the library. Expect the docs to continue to expand as time goes on. Background ========== Work on pandas started at AQR (a quantitative hedge fund) in 2008 and has been under active development since then. Discussion and Development ========================== Since pandas development is related to a number of other scientific Python projects, questions are welcome on the scipy-user mailing list. Specialized discussions or design issues should take place on the pystatsmodels mailing list / google group, where scikits.statsmodels and other libraries will also be discussed: http://groups.google.com/group/pystatsmodels Best regards, Wes McKinney