Merge commit 'v0.7.0rc1-183-gcc2a8a7' into debian

* commit 'v0.7.0rc1-183-gcc2a8a7': (86 commits)
  DOC: release notes
  ENH: can pass file handle or StringIO to Series.to_csv and DataFrame.to_csv, GH pandas-dev#765
  BUG: fix subtle bug in maybe_convert_objects causing indexes to be mutated, test coverage, fix pandas-dev#766
  TST: merge test coverage and trim floating point zeros even if there are NAs
  TST: skip excel tests if libraries not installed
  TST: more merge test coverage, refactoring
  ENH: more intelligent inference about index_col for Excel files, test coverage for PR pandas-dev#735
  BUG: fix docstring
  TST: test coverage
  Fix up docstrings
  Combine xlsx tests with xls to avoid code duplication
  Add some additional excel reading/writing tests
  Document sheet_name arg in docstring, give it a default value
  Add to_excel in Panel and corresponding test
  Test to_excel with MultiIndex
  Document writing multiple DataFrames to different sheets
  Add support for reading/writing .xlsx using openpyxl
  Special case np.int64 in ExcelWriter and add unittest
  Missed one variable rename
  Add unittest for to_excel
  ...

Author: yarikoptic

RELEASE.rst

@@ -85,6 +85,12 @@ pandas 0.7.0
   - Add new ``value_range`` function to return min/max of a dataframe (GH #288)
   - Add ``drop`` parameter to ``reset_index`` method of ``DataFrame`` and added
     method to ``Series`` as well (GH #699)
+  - Add ``isin`` method to Index objects, works just like ``Series.isin`` (GH
+    #657)
+  - Implement array interface on Panel so that ufuncs work (re: #740)
+  - Add ``sort`` option to ``DataFrame.join`` (GH #731)
+  - Improved handling of NAs (propagation) in binary operations with
+    dtype=object arrays (GH #737)
 
 **API Changes**
 
@@ -169,6 +175,9 @@ pandas 0.7.0
   - Add option to Series.to_csv to omit the index (PR #684)
   - Add ``delimiter`` as an alternative to ``sep`` in ``read_csv`` and other
     parsing functions
+  - Substantially improved performance of groupby on DataFrames with many
+    columns by aggregating blocks of columns all at once (GH #745)
+  - Can pass a file handle or StringIO to Series/DataFrame.to_csv (GH #765)
 
 **Bug fixes**
 
@@ -249,6 +258,11 @@ pandas 0.7.0
   - Raise Exception in DateRange when offset with n=0 is passed (GH #683)
   - Fix get/set inconsistency with .ix property and integer location but
     non-integer index (GH #707)
+  - Use right dropna function for SparseSeries. Return dense Series for NA fill
+    value (GH #730)
+  - Fix Index.format bug causing incorrectly string-formatted Series with
+    datetime indexes (# 758)
+  - Fix errors caused by object dtype arrays passed to ols (GH #759)
 
 Thanks
 ------

doc/make.py

@@ -26,9 +26,20 @@
 SPHINX_BUILD = 'sphinxbuild'
 
 def sf():
-    'push a copy to the sf site'
+    'push a copy to the sf'
     os.system('cd build/html; rsync -avz . wesmckinn,[email protected]'
               ':/home/groups/p/pa/pandas/htdocs/ -essh --cvs-exclude')
+
+def upload_dev():
+    'push a copy to the pydata dev directory'
+    os.system('cd build/html; rsync -avz . [email protected]'
+              ':/usr/share/nginx/pandas/pandas-docs/dev/ -essh')
+
+def upload_stable():
+    'push a copy to the pydata dev directory'
+    os.system('cd build/html; rsync -avz . [email protected]'
+              ':/usr/share/nginx/pandas/pandas-docs/stable/ -essh')
+
 def sfpdf():
     'push a copy to the sf site'
     os.system('cd build/latex; scp pandas.pdf wesmckinn,[email protected]'
@@ -83,6 +94,8 @@ def all():
 
 funcd = {
     'html'     : html,
+    'upload_dev' : upload_dev,
+    'upload_stable' : upload_stable,
     'latex'    : latex,
     'clean'    : clean,
     'sf'       : sf,

doc/source/index.rst

@@ -125,4 +125,4 @@ See the package overview for more detail about what's in the library.
     related
     comparison_with_r
     api
-    vbench
+

doc/source/indexing.rst

@@ -654,7 +654,7 @@ instance:
 
 .. ipython:: python
 
-   midx = MultiIndex(levels=[['one', 'two'], ['x','y']],
+   midx = MultiIndex(levels=[['zero', 'one'], ['x','y']],
                      labels=[[1,1,0,0],[1,0,1,0]])
    df = DataFrame(randn(4,2), index=midx)
    print df
@@ -670,13 +670,15 @@ The need for sortedness
 ~~~~~~~~~~~~~~~~~~~~~~~
 
 **Caveat emptor**: the present implementation of ``MultiIndex`` requires that
-the labels be lexicographically sorted into groups for some of the slicing /
-indexing routines to work correctly. You can think about this as meaning that
-the axis is broken up into a tree structure, where every leaf in a particular
-branch shares the same labels at that level of the hierarchy. However, the
-``MultiIndex`` does not enforce this: **you are responsible for ensuring that
-things are properly sorted**. There is an important new method ``sortlevel``
-which will lexicographically sort an axis with a ``MultiIndex``:
+the labels be sorted for some of the slicing / indexing routines to work
+correctly. You can think about breaking the axis into unique groups, where at
+the hierarchical level of interest, each distinct group shares a label, but no
+two have the same label. However, the ``MultiIndex`` does not enforce this:
+**you are responsible for ensuring that things are properly sorted**. There is
+an important new method ``sortlevel`` to sort an axis within a ``MultiIndex``
+so that its labels are grouped and sorted by the original ordering of the
+associated factor at that level. Note that this does not necessarily mean the
+labels will be sorted lexicographically!
 
 .. ipython:: python
 

pandas/__init__.py

@@ -23,8 +23,8 @@
 from pandas.sparse.api import *
 from pandas.stats.api import *
 
-from pandas.core.common import set_printoptions, reset_printoptions
-from pandas.core.common import set_eng_float_format
+from pandas.core.format import (set_printoptions, reset_printoptions,
+                                set_eng_float_format)
 from pandas.io.parsers import read_csv, read_table, read_clipboard, ExcelFile
 from pandas.io.pytables import HDFStore
 from pandas.util.testing import debug

pandas/core/algorithms.py

@@ -0,0 +1,111 @@
+"""
+Generic data algorithms. This module is experimental at the moment and not
+intended for public consumption
+"""
+
+import numpy as np
+
+from pandas.core.series import Series
+import pandas.core.common as com
+import pandas._tseries as lib
+
+def match(values, index):
+    """
+
+
+    Parameters
+    ----------
+
+    Returns
+    -------
+    match : ndarray
+    """
+    if com.is_float_dtype(index):
+        return _match_generic(values, index, lib.Float64HashTable,
+                              com._ensure_float64)
+    elif com.is_integer_dtype(index):
+        return _match_generic(values, index, lib.Int64HashTable,
+                              com._ensure_int64)
+    else:
+        return _match_generic(values, index, lib.PyObjectHashTable,
+                              com._ensure_object)
+
+def _get_hash_table_and_cast(values):
+    if com.is_float_dtype(values):
+        klass = lib.Float64HashTable
+        values = com._ensure_float64(values)
+    elif com.is_integer_dtype(values):
+        klass = lib.Int64HashTable
+        values = com._ensure_int64(values)
+    else:
+        klass = lib.PyObjectHashTable
+        values = com._ensure_object(values)
+    return klass, values
+
+def count(values, uniques=None):
+    if uniques is not None:
+        raise NotImplementedError
+    else:
+        if com.is_float_dtype(values):
+            return _count_generic(values, lib.Float64HashTable,
+                                  com._ensure_float64)
+        elif com.is_integer_dtype(values):
+            return _count_generic(values, lib.Int64HashTable,
+                                  com._ensure_int64)
+        else:
+            return _count_generic(values, lib.PyObjectHashTable,
+                                  com._ensure_object)
+
+def _count_generic(values, table_type, type_caster):
+    values = type_caster(values)
+    table = table_type(len(values))
+    uniques, labels, counts = table.factorize(values)
+
+    return Series(counts, index=uniques)
+
+def _match_generic(values, index, table_type, type_caster):
+    values = type_caster(values)
+    index = type_caster(index)
+    table = table_type(len(index))
+    table.map_locations(index)
+    return table.lookup(values)
+
+def factorize(values, sort=False, order=None, na_sentinel=-1):
+    """
+    Encode input values as an enumerated type or categorical variable
+
+    Parameters
+    ----------
+    values : sequence
+    sort :
+    order :
+
+    Returns
+    -------
+    """
+    hash_klass, values = _get_hash_table_and_cast(values)
+
+    uniques = []
+    table = hash_klass(len(values))
+    labels, counts = table.get_labels(values, uniques, 0, na_sentinel)
+
+    uniques = com._asarray_tuplesafe(uniques)
+    if sort and len(counts) > 0:
+        sorter = uniques.argsort()
+        reverse_indexer = np.empty(len(sorter), dtype=np.int32)
+        reverse_indexer.put(sorter, np.arange(len(sorter)))
+
+        mask = labels < 0
+        labels = reverse_indexer.take(labels)
+        np.putmask(labels, mask, -1)
+
+        uniques = uniques.take(sorter)
+        counts = counts.take(sorter)
+
+    return labels, uniques, counts
+
+def unique(values):
+    """
+
+    """
+    pass

pandas/core/api.py

@@ -5,7 +5,8 @@
 from pandas.core.datetools import DateOffset
 import pandas.core.datetools as datetools
 
-from pandas.core.common import isnull, notnull, set_printoptions, save, load
+from pandas.core.common import isnull, notnull, save, load
+from pandas.core.format import set_printoptions
 from pandas.core.index import Index, Int64Index, Factor, MultiIndex
 from pandas.core.daterange import DateRange
 from pandas.core.series import Series, TimeSeries