Python Recommender System Library¶

0.1.2 released (November 02, 2013)¶

The major 0.1.2 release is out. Since 2012 we decided to migrate all the source code for this new repository python-recsys. The old organization muricoca is now terminated, since the name was not adequate for the project.

New important features and architecture for crab. Some highlights among new features include:

• Integrated JSON reading and writing with the read_json functions and methods like DataFrame.to_json.
• New HTML table reading function read_html which will use either lxml or BeautifulSoup under the hood.
• Support for reading and writing STATA format files.

Please check out the What’s New page for much more detail.

Find binaries and source archives for v0.1.2.0 are available now

Note we are posting stable daily development builds of the pandas windows binaries here. You can download them via the Development Version section of the “get pandas” page.

What problem does crab solve?¶

Python has long been great for data munging and preparation, but less so for data analysis and modeling. pandas helps fill this gap, enabling you to carry out your entire data analysis workflow in Python without having to switch to a more domain specific language like R.

Combined with the excellent IPython toolkit and other libraries, the environment for doing data analysis in Python excels in performance, productivity, and the ability to collaborate.

pandas does not implement significant modeling functionality outside of linear and panel regression; for this, look to statsmodels and scikit-learn. More work is still needed to make Python a first class statistical modeling environment, but we are well on our way toward that goal.

Library Highlights¶

• A fast and efficient DataFrame object for data manipulation with integrated indexing;
• Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;
• Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form;
• Flexible reshaping and pivoting of data sets;
• Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;
• Columns can be inserted and deleted from data structures for size mutability;
• Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;
• High performance merging and joining of data sets;
• Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;
• Time series-functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data;
• Highly optimized for performance, with critical code paths written in Cython or C.
• Python with pandas is in use in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more.