Merge commit 'v0.17.0-8-gcac4ad2' into debian

* commit 'v0.17.0-8-gcac4ad2': (57 commits)
  BUG: to_excel duplicate columns
  BUG: HDFStore.append with encoded string itemsize, pandas-dev#11234
  BUG: remove midrule in latex output with header=False
  BUG: squeeze works on 0 length arrays, pandas-dev#11299, pandas-dev#8999
  DOC: add whatsnew 0.17.1 to index
  DOC: update resample docs
  timeseries: add tip about using groupby() rather than resample
  DOC: release_stats.sh script to report release stats
  DOC: edit release.rst
  CI: fix numpy to 1.9.3 in 2.7,3.5 builds for now, as packages for 1.10.0 not released ATM
  DOC: Included halflife as one 3 optional params that must be specified
  DOC: whatsnew 0.17.0 edits
  BUG/ERR: raise when trying to set a subset of values in a datetime64[ns, tz] column with another tz
  DOC: Add note about unicode layout
  DOC: hack to numpydoc to include attributes that are None (GH6100)
  DOC: add str accessor docstring pages to api.rst to avoid warning
  DOC: hack to numpydoc to avoid warnings for Categorical (not including members)
  skip some plotting tests if scipy is not installed
  add matplotlib to ci for 3.5
  COMPAT/PERF: lib.ismember_int64 on older numpies/cython not comparing correctly PERF: use np.in1d on larger isin sizes
  ...

Author: yarikoptic

README.md

@@ -4,6 +4,9 @@
 <tr>
   <td>Latest Release</td>
   <td><img src="https://img.shields.io/pypi/v/pandas.svg" alt="latest release" /></td>
+</tr>
+  <td></td>
+  <td><img src="https://anaconda.org/pandas/pandas/badges/version.svg" alt="latest release" /></td>
 </tr>
 <tr>
   <td>Package Status</td>

asv_bench/benchmarks/frame_methods.py

@@ -293,6 +293,14 @@ def setup(self):
     def time_frame_duplicated(self):
         self.df.duplicated()
 
+class frame_duplicated_wide(object):
+    goal_time = 0.2
+
+    def setup(self):
+        self.df = DataFrame(np.random.randn(1000, 100).astype(str))
+
+    def time_frame_duplicated_wide(self):
+        self.df.T.duplicated()
 
 class frame_fancy_lookup(object):
     goal_time = 0.2
@@ -929,4 +937,4 @@ def setup(self):
         self.s = Series((['abcdefg', np.nan] * 500000))
 
     def time_series_string_vector_slice(self):
-        self.s.str[:5]
+        self.s.str[:5]

asv_bench/benchmarks/series_methods.py

@@ -5,22 +5,21 @@ class series_isin_int64(object):
     goal_time = 0.2
 
     def setup(self):
-        self.s1 = Series(np.random.randn(10000))
-        self.s2 = Series(np.random.randint(1, 10, 10000))
         self.s3 = Series(np.random.randint(1, 10, 100000)).astype('int64')
+        self.s4 = Series(np.random.randint(1, 100, 10000000)).astype('int64')
         self.values = [1, 2]
-        self.s4 = self.s3.astype('object')
 
     def time_series_isin_int64(self):
         self.s3.isin(self.values)
 
+    def time_series_isin_int64_large(self):
+        self.s4.isin(self.values)
+
 
 class series_isin_object(object):
     goal_time = 0.2
 
     def setup(self):
-        self.s1 = Series(np.random.randn(10000))
-        self.s2 = Series(np.random.randint(1, 10, 10000))
         self.s3 = Series(np.random.randint(1, 10, 100000)).astype('int64')
         self.values = [1, 2]
         self.s4 = self.s3.astype('object')
@@ -71,4 +70,4 @@ def setup(self):
 
     def time_series_nsmallest2(self):
         self.s2.nsmallest(3, take_last=True)
-        self.s2.nsmallest(3, take_last=False)
+        self.s2.nsmallest(3, take_last=False)

ci/requirements-2.6.build

@@ -1,4 +1,4 @@
-numpy=1.7.0
+numpy=1.7.1
 cython=0.19.1
 dateutil=1.5
 pytz=2013b

ci/requirements-2.6.run

@@ -1,14 +1,16 @@
-numpy=1.7.0
+numpy=1.7.1
 dateutil=1.5
 pytz=2013b
 scipy=0.11.0
 xlwt=0.7.5
 xlrd=0.9.2
 statsmodels=0.4.3
+bottleneck=0.8.0
+numexpr=2.2.2
+pytables=3.0.0
 html5lib=1.0b2
 beautiful-soup=4.2.0
 psycopg2=2.5.1
-numexpr=1.4.2
 pymysql=0.6.0
 sqlalchemy=0.7.8
 xlsxwriter=0.4.6

ci/requirements-2.7.build

@@ -1,4 +1,4 @@
 dateutil=2.1
 pytz=2013b
-numpy=1.7.1
+numpy=1.9.3
 cython=0.19.1

ci/requirements-2.7.run

@@ -1,18 +1,18 @@
 dateutil=2.1
 pytz=2013b
-numpy=1.7.1
+numpy=1.9.3
 xlwt=0.7.5
-numexpr=2.2.2
-pytables=3.0.0
-matplotlib=1.3.1
+numexpr
+pytables
+matplotlib
 openpyxl=1.6.2
 xlrd=0.9.2
 sqlalchemy=0.9.6
 lxml=3.2.1
 scipy
 xlsxwriter=0.4.6
 boto=2.36.0
-bottleneck=0.8.0
+bottleneck
 psycopg2=2.5.2
 patsy
 pymysql=0.6.3

ci/requirements-2.7_SLOW.build

@@ -1,4 +1,4 @@
 python-dateutil
 pytz
-numpy
+numpy=1.8.2
 cython

ci/requirements-2.7_SLOW.run

@@ -1,7 +1,7 @@
 python-dateutil
 pytz
-numpy
-matplotlib
+numpy=1.8.2
+matplotlib=1.3.1
 scipy
 patsy
 statsmodels

ci/requirements-3.3.build

@@ -1,4 +1,4 @@
 python-dateutil
 pytz=2013b
-numpy=1.8.0
+numpy=1.7.1
 cython=0.19.1

ci/requirements-3.3.run

@@ -1,6 +1,6 @@
 python-dateutil
 pytz=2013b
-numpy=1.8.0
+numpy=1.7.1
 xlsxwriter=0.4.6
 xlrd=0.9.2
 xlwt

ci/requirements-3.4.build

@@ -1,4 +1,4 @@
 python-dateutil
 pytz
-numpy
+numpy=1.8.1
 cython

ci/requirements-3.4.run

@@ -1,6 +1,6 @@
 python-dateutil
 pytz
-numpy
+numpy=1.8.1
 openpyxl
 xlsxwriter
 xlrd

ci/requirements-3.4_SLOW.build

@@ -1,4 +1,4 @@
 python-dateutil
 pytz
-numpy
+numpy=1.9.3
 cython

ci/requirements-3.4_SLOW.run

@@ -1,6 +1,6 @@
 python-dateutil
 pytz
-numpy
+numpy=1.9.3
 openpyxl
 xlsxwriter
 xlrd

ci/requirements-3.5.build

@@ -1,4 +1,4 @@
 python-dateutil
 pytz
-numpy
+numpy=1.9.3
 cython

ci/requirements-3.5.run

@@ -1,6 +1,6 @@
 python-dateutil
 pytz
-numpy
+numpy=1.9.3
 openpyxl
 xlsxwriter
 xlrd
@@ -11,6 +11,7 @@ numexpr
 pytables
 html5lib
 lxml
+matplotlib
 
 # currently causing some warnings
 #sqlalchemy
@@ -20,4 +21,3 @@ lxml
 # not available from conda
 #beautiful-soup
 #bottleneck
-#matplotlib

doc/source/10min.rst

@@ -30,7 +30,7 @@
 This is a short introduction to pandas, geared mainly for new users.
 You can see more complex recipes in the :ref:`Cookbook<cookbook>`
 
-Customarily, we import as follows
+Customarily, we import as follows:
 
 .. ipython:: python
 

doc/source/_static/option_unicode01.png

@@ -89,7 +89,6 @@ SAS
    :toctree: generated/
 
    read_sas
-   XportReader
 
 SQL
 ~~~
@@ -110,10 +109,7 @@ Google BigQuery
 
    read_gbq
    to_gbq
-   generate_bq_schema
-   create_table
-   delete_table
-   table_exists
+
 
 .. currentmodule:: pandas
 
@@ -168,6 +164,14 @@ Top-level missing data
    isnull
    notnull
 
+Top-level conversions
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+   :toctree: generated/
+
+   to_numeric
+
 Top-level dealing with datetimelike
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -607,7 +611,7 @@ strings and apply several methods to it. These can be acccessed like
 
 ..
     The following is needed to ensure the generated pages are created with the
-    correct template (otherwise they would be created in the Series class page)
+    correct template (otherwise they would be created in the Series/Index class page)
 
 ..
     .. autosummary::
@@ -617,6 +621,10 @@ strings and apply several methods to it. These can be acccessed like
        Series.str
        Series.cat
        Series.dt
+       Index.str
+       CategoricalIndex.str
+       DatetimeIndex.str
+       TimedeltaIndex.str
 
 
 .. _api.categorical:

doc/source/_static/option_unicode02.png

@@ -1090,7 +1090,7 @@ decreasing.
 
 Note that the same result could have been achieved using
 :ref:`fillna <missing_data.fillna>` (except for ``method='nearest'``) or
-:ref:`interpolate <missing_data.interpolation>`:
+:ref:`interpolate <missing_data.interpolate>`:
 
 .. ipython:: python
 
@@ -1711,36 +1711,26 @@ then the more *general* one will be used as the result of the operation.
 object conversion
 ~~~~~~~~~~~~~~~~~
 
-.. note::
-
-    The syntax of :meth:`~DataFrame.convert_objects`  changed in 0.17.0. See
-    :ref:`API changes <whatsnew_0170.api_breaking.convert_objects>`
-    for more details.
-
-:meth:`~DataFrame.convert_objects` is a method that converts columns from
-the ``object`` dtype to datetimes, timedeltas or floats. For example, to
-attempt conversion of object data that are *number like*, e.g. could be a
-string that represents a number, pass ``numeric=True``. By default, this will
-attempt a soft conversion and so will only succeed if the entire column is
-convertible. To force the conversion, add the keyword argument ``coerce=True``.
-This will force strings and number-like objects to be numbers if
-possible, and other values will be set to ``np.nan``.
+:meth:`~DataFrame.convert_objects` is a method to try to force conversion of types from the ``object`` dtype to other types.
+To force conversion of specific types that are *number like*, e.g. could be a string that represents a number,
+pass ``convert_numeric=True``. This will force strings and numbers alike to be numbers if possible, otherwise
+they will be set to ``np.nan``.
 
 .. ipython:: python
 
    df3['D'] = '1.'
    df3['E'] = '1'
-   df3.convert_objects(numeric=True).dtypes
+   df3.convert_objects(convert_numeric=True).dtypes
 
    # same, but specific dtype conversion
    df3['D'] = df3['D'].astype('float16')
    df3['E'] = df3['E'].astype('int32')
    df3.dtypes
 
-To force conversion to ``datetime64[ns]``, pass ``datetime=True`` and ``coerce=True``.
+To force conversion to ``datetime64[ns]``, pass ``convert_dates='coerce'``.
 This will convert any datetime-like object to dates, forcing other values to ``NaT``.
 This might be useful if you are reading in data which is mostly dates,
-but occasionally contains non-dates that you wish to represent as missing.
+but occasionally has non-dates intermixed and you want to represent as missing.
 
 .. ipython:: python
 
@@ -1749,15 +1739,10 @@ but occasionally contains non-dates that you wish to represent as missing.
                  'foo', 1.0, 1, pd.Timestamp('20010104'),
                  '20010105'], dtype='O')
    s
-   s.convert_objects(datetime=True, coerce=True)
+   s.convert_objects(convert_dates='coerce')
 
-Without passing ``coerce=True``, :meth:`~DataFrame.convert_objects` will attempt
-*soft* conversion of any *object* dtypes, meaning that if all
+In addition, :meth:`~DataFrame.convert_objects` will attempt the *soft* conversion of any *object* dtypes, meaning that if all
 the objects in a Series are of the same type, the Series will have that dtype.
-Note that setting ``coerce=True`` does not *convert* arbitrary types to either
-``datetime64[ns]`` or ``timedelta64[ns]``. For example, a series containing string
-dates will not be converted to a series of datetimes. To convert between types,
-see :ref:`converting to timestamps <timeseries.converting>`.
 
 gotchas
 ~~~~~~~

doc/source/_static/option_unicode03.png

@@ -34,7 +34,7 @@ Data Structures
 ---------------
 
 General Terminology Translation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. csv-table::
     :header: "pandas", "SAS"
@@ -142,10 +142,10 @@ and did not have column names, the pandas command would be:
 
 .. code-block:: python
 
-   tips = pd.read_csv('tips.csv', sep='\t', header=False)
+   tips = pd.read_csv('tips.csv', sep='\t', header=None)
 
    # alternatively, read_table is an alias to read_csv with tab delimiter
-   tips = pd.read_table('tips.csv', header=False)
+   tips = pd.read_table('tips.csv', header=None)
 
 In addition to text/csv, pandas supports a variety of other data formats
 such as Excel, HDF5, and SQL databases.  These are all read via a ``pd.read_*``

doc/source/_static/option_unicode04.png

@@ -154,8 +154,8 @@ Creating a Development Environment
 
 An easy way to create a *pandas* development environment is as follows.
 
-- Install either :ref:`Install Anaconda <install-anaconda>` or :ref:`Install miniconda <install-miniconda>`
-- Make sure that you have :ref:`cloned the repository <contributing-forking>`
+- Install either :ref:`Install Anaconda <install.anaconda>` or :ref:`Install miniconda <install.miniconda>`
+- Make sure that you have :ref:`cloned the repository <contributing.forking>`
 - ``cd`` to the pandas source directory
 
 Tell ``conda`` to create a new environment, named ``pandas_dev``, or any name you would like for this environment by running:
@@ -339,7 +339,7 @@ follow the Numpy Docstring Standard (see above), but you don't need to install
 this because a local copy of ``numpydoc`` is included in the *pandas* source
 code.
 
-It is easiest to :ref:`create a development environment <contributing-dev_env>`, then install:
+It is easiest to :ref:`create a development environment <contributing.dev_env>`, then install:
 
 ::
 
@@ -567,7 +567,7 @@ It is also useful to run tests in your current environment. You can simply do it
 
 which would be equivalent to ``asv run --quick --show-stderr --python=same``. This
 will launch every test only once, display stderr from the benchmarks and use your
-local ``python'' that comes from your $PATH.
+local ``python`` that comes from your $PATH.
 
 Information on how to write a benchmark can be found in
 `*asv*'s documentation http://asv.readthedocs.org/en/latest/writing_benchmarks.html`.