[SciPy-User] ANN: pandas 0.6.0 released

Wes McKinney wesmckinn@gmail....
Fri Nov 25 21:58:44 CST 2011


I'm pleased to announce the pandas 0.6.0 major release. It's been
about one month since the last major release. It includes 155 commits
and 16 pull requests closing 78 tickets on GitHub. Several new people
contributed code to the project for this release.

This upgrade is recommended for all users and should not cause any API
breakage for 0.5.0 users. There are a lot of miscellaneous new
functions and features, many performance enhancements, and a
significant number of bugs and corner cases encountered since the
0.5.0 release. See the full release notes below and on GitHub.

Some features to look forward to (or help with!) in the next couple releases:

- NumPy datetime64 type integration
- Enhanced GroupBy, especially for binning time series data
- Further performance enhancements to existing functionality

Many thanks to all the users who contributed code, bug reports, and
suggestions for new features.

best,
Wes

What is it
==========
pandas is a Python package providing fast, flexible, and expressive
data structures designed to make working with “relational” or
“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. Additionally, it has the broader goal of becoming
the most powerful and flexible open source data analysis /
manipulation tool available in any language.

Links
=====
Release Notes: https://github.com/wesm/pandas/blob/master/RELEASE.rst
Documentation: http://pandas.sourceforge.net
Installers: http://pypi.python.org/pypi/pandas
Code Repository: http://github.com/wesm/pandas
Mailing List: http://groups.google.com/group/pystatsmodels
Blog: http://blog.wesmckinney.com


pandas 0.6.0
============

**Release date:** 11/25/2011

**API Changes**

  - Arithmetic methods like `sum` will attempt to sum dtype=object values by
    default instead of excluding them (GH #382)

**New features / modules**

  - Add `melt` function to `pandas.core.reshape`
  - Add `level` parameter to group by level in Series and DataFrame
    descriptive statistics (PR #313)
  - Add `head` and `tail` methods to Series, analogous to to DataFrame (PR
    #296)
  - Add `Series.isin` function which checks if each value is contained in a
    passed sequence (GH #289)
  - Add `float_format` option to `Series.to_string`
  - Add `skip_footer` (GH #291) and `converters` (GH #343) options to
    `read_csv` and `read_table`
  - Add proper, tested weighted least squares to standard and panel OLS (GH
    #303)
  - Add `drop_duplicates` and `duplicated` functions for removing duplicate
    DataFrame rows and checking for duplicate rows, respectively (GH #319)
  - Implement logical (boolean) operators &, |, ^ on DataFrame (GH #347)
  - Add `Series.mad`, mean absolute deviation, matching DataFrame
  - Add `QuarterEnd` DateOffset (PR #321)
  - Add matrix multiplication function `dot` to DataFrame (GH #65)
  - Add `orient` option to `Panel.from_dict` to ease creation of mixed-type
    Panels (GH #359, #301)
  - Add `DataFrame.from_dict` with similar `orient` option
  - Can now pass list of tuples or list of lists to `DataFrame.from_records`
    for fast conversion to DataFrame (GH #357)
  - Can pass multiple levels to groupby, e.g. `df.groupby(level=[0, 1])` (GH
    #103)
  - Can sort by multiple columns in `DataFrame.sort_index` (GH #92, PR #362)
  - Add fast `get_value` and `put_value` methods to DataFrame and
    micro-performance tweaks (GH #360)
  - Add `cov` instance methods to Series and DataFrame (GH #194, PR #362)
  - Add bar plot option to `DataFrame.plot` (PR #348)
  - Add `idxmin` and `idxmax` functions to Series and DataFrame for computing
    index labels achieving maximum and minimum values (PR #286)
  - Add `read_clipboard` function for parsing DataFrame from OS clipboard,
    should work across platforms (GH #300)
  - Add `nunique` function to Series for counting unique elements (GH #297)
  - DataFrame constructor will use Series name if no columns passed (GH #373)
  - Support regular expressions and longer delimiters in read_table/read_csv,
    but does not handle quoted strings yet (GH #364)
  - Add `DataFrame.to_html` for formatting DataFrame to HTML (PR #387)
  - MaskedArray can be passed to DataFrame constructor and masked values will be
    converted to NaN (PR #396)
  - Add `DataFrame.boxplot` function (GH #368, others)
  - Can pass extra args, kwds to DataFrame.apply (GH #376)

**Improvements to existing features**

  - Raise more helpful exception if date parsing fails in DateRange (GH #298)
  - Vastly improved performance of GroupBy on axes with a MultiIndex (GH #299)
  - Print level names in hierarchical index in Series repr (GH #305)
  - Return DataFrame when performing GroupBy on selected column and
    as_index=False (GH #308)
  - Can pass vector to `on` argument in `DataFrame.join` (GH #312)
  - Don't show Series name if it's None in the repr, also omit length for short
    Series (GH #317)
  - Show legend by default in `DataFrame.plot`, add `legend` boolean flag (GH
    #324)
  - Significantly improved performance of `Series.order`, which also makes
    np.unique called on a Series faster (GH #327)
  - Faster cythonized count by level in Series and DataFrame (GH #341)
  - Raise exception if dateutil 2.0 installed on Python 2.x runtime (GH #346)
  - Significant GroupBy performance enhancement with multiple keys with many
    "empty" combinations
  - New Cython vectorized function `map_infer` speeds up `Series.apply` and
    `Series.map` significantly when passed elementwise Python function,
    motivated by PR #355
  - Cythonized `cache_readonly`, resulting in substantial micro-performance
    enhancements throughout the codebase (GH #361)
  - Special Cython matrix iterator for applying arbitrary reduction operations
    with 3-5x better performance than `np.apply_along_axis` (GH #309)
  - Add `raw` option to `DataFrame.apply` for getting better performance when
    the passed function only requires an ndarray (GH #309)
  - Improve performance of `MultiIndex.from_tuples`
  - Can pass multiple levels to `stack` and `unstack` (GH #370)
  - Can pass multiple values columns to `pivot_table` (GH #381)
  - Can call `DataFrame.delevel` with standard Index with name set (GH #393)
  - Use Series name in GroupBy for result index (GH #363)
  - Refactor Series/DataFrame stat methods to use common set of NaN-friendly
    function
  - Handle NumPy scalar integers at C level in Cython conversion routines

**Bug fixes**

  - Fix bug in `DataFrame.to_csv` when writing a DataFrame with an index
    name (GH #290)
  - DataFrame should clear its Series caches on consolidation, was causing
    "stale" Series to be returned in some corner cases (GH #304)
  - DataFrame constructor failed if a column had a list of tuples (GH #293)
  - Ensure that `Series.apply` always returns a Series and implement
    `Series.round` (GH #314)
  - Support boolean columns in Cythonized groupby functions (GH #315)
  - `DataFrame.describe` should not fail if there are no numeric columns,
    instead return categorical describe (GH #323)
  - Fixed bug which could cause columns to be printed in wrong order in
    `DataFrame.to_string` if specific list of columns passed (GH #325)
  - Fix legend plotting failure if DataFrame columns are integers (GH #326)
  - Shift start date back by one month for Yahoo! Finance API in pandas.io.data
    (GH #329)
  - Fix `DataFrame.join` failure on unconsolidated inputs (GH #331)
  - DataFrame.min/max will no longer fail on mixed-type DataFrame (GH #337)
  - Fix `read_csv` / `read_table` failure when passing list to index_col that is
    not in ascending order (GH #349)
  - Fix failure passing Int64Index to Index.union when both are monotonic
  - Fix error when passing SparseSeries to (dense) DataFrame constructor
  - Added missing bang at top of setup.py (GH #352)
  - Change `is_monotonic` on MultiIndex so it properly compares the tuples
  - Fix MultiIndex outer join logic (GH #351)
  - Set index name attribute with single-key groupby (GH #358)
  - Bug fix in reflexive binary addition in Series and DataFrame for
    non-commutative operations (like string concatenation) (GH #353)
  - setupegg.py will invoke Cython (GH #192)
  - Fix block consolidation bug after inserting column into MultiIndex (GH #366)
  - Fix bug in join operations between Index and Int64Index (GH #367)
  - Handle min_periods=0 case in moving window functions (GH #365)
  - Fixed corner cases in DataFrame.apply/pivot with empty DataFrame (GH #378)
  - Fixed repr exception when Series name is a tuple
  - Always return DateRange from `asfreq` (GH #390)
  - Pass level names to `swaplavel` (GH #379)
  - Don't lose index names in `MultiIndex.droplevel` (GH #394)
  - Infer more proper return type in `DataFrame.apply` when no columns or rows
    depending on whether the passed function is a reduction (GH #389)
  - Always return NA/NaN from Series.min/max and DataFrame.min/max when all of a
    row/column/values are NA (GH #384)
  - Enable partial setting with .ix / advanced indexing (GH #397)
  - Handle mixed-type DataFrames correctly in unstack, do not lose type
    information (GH #403)
  - Fix integer name formatting bug in Index.format and in Series.__repr__
  - Handle label types other than string passed to groupby (GH #405)
  - Fix bug in .ix-based indexing with partial retrieval when a label is not
    contained in a level
  - Index name was not being pickled (GH #408)
  - Level name should be passed to result index in GroupBy.apply (GH #416)

Thanks
------

- Craig Austin
- Marius Cobzarenco
- Joel Cross
- Jeff Hammerbacher
- Adam Klein
- Thomas Kluyver
- Jev Kuznetsov
- Kieran O'Mahony
- Wouter Overmeire
- Nathan Pinger
- Christian Prinoth
- Skipper Seabold
- Chang She
- Ted Square
- Aman Thakral
- Chris Uga
- Dieter Vandenbussche
- carljv
- rsamson


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