FIX: revert docs and fix for py3 compat

ci/script.sh

@@ -2,7 +2,7 @@
 
 echo "inside $0"
 
-if [ x"$LOCALE_OVERRIDE" != x"" ]; then
+if [ -n "$LOCALE_OVERRIDE" ]; then
     export LC_ALL="$LOCALE_OVERRIDE";
     echo "Setting LC_ALL to $LOCALE_OVERRIDE"
     (cd /; python -c 'import pandas; print("pandas detected console encoding: %s" % pandas.get_option("display.encoding"))')

doc/plots/stats/moment_plots.py

@@ -1,4 +1,3 @@
-from pandas.compat import range
 import numpy as np
 
 import matplotlib.pyplot as plt

doc/source/10min.rst

@@ -15,6 +15,7 @@
    randint = np.random.randint
    np.set_printoptions(precision=4, suppress=True)
    options.display.mpl_style='default'
+   from pandas.compat import lrange, lzip
 
    #### portions of this were borrowed from the
    #### Pandas cheatsheet  
@@ -64,7 +65,7 @@ Creating a ``DataFrame`` by passing a dict of objects that can be converted to s
 
    df2 = pd.DataFrame({ 'A' : 1., 
                         'B' : pd.Timestamp('20130102'), 
-                        'C' : pd.Series(1,index=range(4),dtype='float32'),
+                        'C' : pd.Series(1,index=lrange(4),dtype='float32'),
                         'D' : np.array([3] * 4,dtype='int32'), 
                         'E' : 'foo' })
    df2
@@ -510,7 +511,7 @@ Stack
 
 .. ipython:: python
 
-   tuples = zip(*[['bar', 'bar', 'baz', 'baz',
+   tuples = lzip(*[['bar', 'bar', 'baz', 'baz',
                    'foo', 'foo', 'qux', 'qux'],
                   ['one', 'two', 'one', 'two',
                    'one', 'two', 'one', 'two']])

doc/source/basics.rst

@@ -8,6 +8,7 @@
    from pandas import *
    randn = np.random.randn
    np.set_printoptions(precision=4, suppress=True)
+   from pandas.compat import lrange 
 
 ==============================
  Essential Basic Functionality
@@ -1090,16 +1091,16 @@ By default integer types are ``int64`` and float types are ``float64``,
 
 .. ipython:: python
 
-    DataFrame([1,2],columns=['a']).dtypes
-    DataFrame({'a' : [1,2] }).dtypes
-    DataFrame({'a' : 1 }, index=range(2)).dtypes
+   DataFrame([1, 2], columns=['a']).dtypes
+   DataFrame({'a': [1, 2]}).dtypes
+   DataFrame({'a': 1 }, index=lrange(2)).dtypes
 
 Numpy, however will choose *platform-dependent* types when creating arrays.
 The following **WILL** result in ``int32`` on 32-bit platform.
 
 .. ipython:: python
 
-    frame = DataFrame(np.array([1,2]))
+   frame = DataFrame(np.array([1, 2]))
 
 
 upcasting

doc/source/conf.py

@@ -12,7 +12,6 @@
 
 import sys
 import os
-from pandas.compat import u
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
@@ -64,8 +63,8 @@
 master_doc = 'index'
 
 # General information about the project.
-project = u('pandas')
-copyright = u('2008-2012, the pandas development team')
+project = u'pandas'
+copyright = u'2008-2012, the pandas development team'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
@@ -212,8 +211,8 @@
 # (source start file, target name, title, author, documentclass [howto/manual]).
 latex_documents = [
     ('index', 'pandas.tex',
-     u('pandas: powerful Python data analysis toolkit'),
-     u('Wes McKinney\n\& PyData Development Team'), 'manual'),
+     u'pandas: powerful Python data analysis toolkit',
+     u'Wes McKinney\n\& PyData Development Team', 'manual'),
 ]
 
 # The name of an image file (relative to this directory) to place at the top of

doc/source/cookbook.rst

@@ -56,8 +56,7 @@ Indexing using both row labels and conditionals, see
 <http://stackoverflow.com/questions/14725068/pandas-using-row-labels-in-boolean-indexing>`__
 
 Use loc for label-oriented slicing and iloc positional slicing, see
-`here
-<https://github.com/pydata/pandas/issues/2904>`__
+`here <https://github.com/pydata/pandas/issues/2904>`__
 
 Extend a panel frame by transposing, adding a new dimension, and transposing back to the original dimensions, see
 `here
@@ -280,7 +279,7 @@ The :ref:`Plotting <visualization>` docs.
 <http://stackoverflow.com/questions/11067368/annotate-time-series-plot-in-matplotlib>`__
 
 `Annotate a time-series plot #2
-<http://stackoverflow.com/questions/17891493/annotating-points-from-a-pandas-dataframe-in-matplotlib-plot`__
+<http://stackoverflow.com/questions/17891493/annotating-points-from-a-pandas-dataframe-in-matplotlib-plot>`__
 
 Data In/Out
 -----------
@@ -295,8 +294,7 @@ CSV
 
 The :ref:`CSV <io.read_csv_table>` docs
 
-`read_csv in action
-<http://wesmckinney.com/blog/?p=635>`__
+`read_csv in action <http://wesmckinney.com/blog/?p=635>`__
 
 `appending to a csv
 <http://stackoverflow.com/questions/17134942/pandas-dataframe-output-end-of-csv>`__
@@ -317,7 +315,7 @@ using that handle to read.
 <http://stackoverflow.com/questions/15555005/get-inferred-dataframe-types-iteratively-using-chunksize>`__
 
 `Dealing with bad lines
-<https://github.com/pydata/pandas/issues/2886>`__
+<http://github.com/pydata/pandas/issues/2886>`__
 
 `Dealing with bad lines II
 <http://nipunbatra.wordpress.com/2013/06/06/reading-unclean-data-csv-using-pandas/>`__
@@ -359,7 +357,7 @@ The :ref:`HDFStores <io.hdf5>` docs
 <http://stackoverflow.com/questions/13926089/selecting-columns-from-pandas-hdfstore-table>`__
 
 `Managing heteregenous data using a linked multiple table hierarchy
-<https://github.com/pydata/pandas/issues/3032>`__
+<http://github.com/pydata/pandas/issues/3032>`__
 
 `Merging on-disk tables with millions of rows
 <http://stackoverflow.com/questions/14614512/merging-two-tables-with-millions-of-rows-in-python/14617925#14617925>`__
@@ -420,7 +418,7 @@ Miscellaneous
 The :ref:`Timedeltas <timeseries.timedeltas>` docs.
 
 `Operating with timedeltas
-<https://github.com/pydata/pandas/pull/2899>`__
+<http://github.com/pydata/pandas/pull/2899>`__
 
 `Create timedeltas with date differences
 <http://stackoverflow.com/questions/15683588/iterating-through-a-pandas-dataframe>`__

doc/source/enhancingperf.rst

@@ -28,14 +28,14 @@ Cython (Writing C extensions for pandas)
 
 For many use cases writing pandas in pure python and numpy is sufficient. In some 
 computationally heavy applications however, it can be possible to achieve sizeable
-speed-ups by offloading work to `cython <http://cython.org/>`_.
+speed-ups by offloading work to `cython <http://cython.org/>`__.
 
 This tutorial assumes you have refactored as much as possible in python, for example
 trying to remove for loops and making use of numpy vectorization, it's always worth
 optimising in python first.
 
 This tutorial walks through a "typical" process of cythonizing a slow computation.
-We use an `example from the cython documentation <http://docs.cython.org/src/quickstart/cythonize.html>`_ 
+We use an `example from the cython documentation <http://docs.cython.org/src/quickstart/cythonize.html>`__
 but in the context of pandas. Our final cythonized solution is around 100 times
 faster than the pure python.
 
@@ -73,7 +73,7 @@ We achieve our result by by using ``apply`` (row-wise):
 
 But clearly this isn't fast enough for us. Let's take a look and see where the
 time is spent during this operation (limited to the most time consuming
-four calls) using the `prun ipython magic function <http://ipython.org/ipython-doc/stable/api/generated/IPython.core.magics.execution.html#IPython.core.magics.execution.ExecutionMagics.prun>`_:
+four calls) using the `prun ipython magic function <http://ipython.org/ipython-doc/stable/api/generated/IPython.core.magics.execution.html#IPython.core.magics.execution.ExecutionMagics.prun>`__:
 
 .. ipython:: python
 
@@ -270,4 +270,4 @@ Further topics
 
 - Loading C modules into cython.
 
-Read more in the `cython docs <http://docs.cython.org/>`_.
+Read more in the `cython docs <http://docs.cython.org/>`__.

doc/source/faq.rst

@@ -21,6 +21,7 @@ Frequently Asked Questions (FAQ)
    import matplotlib.pyplot as plt
    plt.close('all')
    options.display.mpl_style='default'
+   from pandas.compat import lrange
 
 
 .. _ref-repr-control:
@@ -65,7 +66,7 @@ operations implemented, most of them are very fast as well.
 It's very possible however that certain functionality that would make your
 life easier is missing. In that case you have several options:
 
-1) Open an issue on `Github <https://github.com/pydata/pandas/issues/>`_ , explain your need and the sort of functionality you would like to see implemented.
+1) Open an issue on `Github <https://github.com/pydata/pandas/issues/>`__ , explain your need and the sort of functionality you would like to see implemented.
 2) Fork the repo, Implement the functionality yourself and open a PR
    on Github.
 3) Write a method that performs the operation you are interested in and
@@ -85,7 +86,7 @@ life easier is missing. In that case you have several options:
        return [x for x in self.columns if 'foo' in x]
 
    pd.DataFrame.just_foo_cols = just_foo_cols # monkey-patch the DataFrame class
-   df = pd.DataFrame([range(4)],columns= ["A","foo","foozball","bar"])
+   df = pd.DataFrame([lrange(4)],columns= ["A","foo","foozball","bar"])
    df.just_foo_cols()
    del pd.DataFrame.just_foo_cols # you can also remove the new method
 
@@ -258,7 +259,7 @@ using something similar to the following:
 
 .. ipython:: python
 
-   x = np.array(range(10), '>i4') # big endian
+   x = np.array(lrange(10), '>i4') # big endian
    newx = x.byteswap().newbyteorder() # force native byteorder
    s = Series(newx)
 

doc/source/gotchas.rst

@@ -9,6 +9,7 @@
    from pandas import *
    randn = np.random.randn
    np.set_printoptions(precision=4, suppress=True)
+   from pandas.compat import lrange
 
 *******************
 Caveats and Gotchas
@@ -437,8 +438,8 @@ parse HTML tables in the top-level pandas io function ``read_html``.
          # install the latest version of beautifulsoup4
          pip install 'bzr+lp:beautifulsoup'
 
-      Note that you need `bzr <http://bazaar.canonical.com/en>`_ and `git
-      <http://git-scm.com>`_ installed to perform the last two operations.
+      Note that you need `bzr <http://bazaar.canonical.com/en>`__ and `git
+      <http://git-scm.com>`__ installed to perform the last two operations.
 
 .. |svm| replace:: **strictly valid markup**
 .. _svm: http://validator.w3.org/docs/help.html#validation_basics
@@ -466,7 +467,7 @@ using something similar to the following:
 
 .. ipython:: python
 
-   x = np.array(range(10), '>i4') # big endian
+   x = np.array(lrange(10), '>i4') # big endian
    newx = x.byteswap().newbyteorder() # force native byteorder
    s = Series(newx)
 

doc/source/groupby.rst

@@ -12,6 +12,7 @@
    import matplotlib.pyplot as plt
    plt.close('all')
    options.display.mpl_style='default'
+   from pandas.compat import lzip
 
 *****************************
 Group By: split-apply-combine
@@ -198,9 +199,10 @@ natural to group by one of the levels of the hierarchy.
 .. ipython:: python
    :suppress:
 
+
    arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
              ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
-   tuples = zip(*arrays)
+   tuples = lzip(*arrays)
    tuples
    index = MultiIndex.from_tuples(tuples, names=['first', 'second'])
    s = Series(randn(8), index=index)
@@ -234,7 +236,7 @@ Also as of v0.6, grouping with multiple levels is supported.
    arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
              ['doo', 'doo', 'bee', 'bee', 'bop', 'bop', 'bop', 'bop'],
              ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
-   tuples = zip(*arrays)
+   tuples = lzip(*arrays)
    index = MultiIndex.from_tuples(tuples, names=['first', 'second', 'third'])
    s = Series(randn(8), index=index)
 

doc/source/indexing.rst

@@ -13,6 +13,7 @@
    randn = np.random.randn
    randint = np.random.randint
    np.set_printoptions(precision=4, suppress=True)
+   from pandas.compat import lrange, lzip
 
 ***************************
 Indexing and Selecting Data
@@ -293,7 +294,7 @@ The ``.iloc`` attribute is the primary access method. The following are valid in
 
 .. ipython:: python
 
-   s1 = Series(np.random.randn(5),index=range(0,10,2))
+   s1 = Series(np.random.randn(5),index=lrange(0,10,2))
    s1
    s1.iloc[:3]
    s1.iloc[3]
@@ -310,8 +311,8 @@ With a DataFrame
 .. ipython:: python
 
    df1 = DataFrame(np.random.randn(6,4),
-                   index=range(0,12,2),
-                   columns=range(0,8,2))
+                   index=lrange(0,12,2),
+                   columns=lrange(0,8,2))
    df1
 
 Select via integer slicing
@@ -786,7 +787,7 @@ numpy array.  For instance,
 .. ipython:: python
 
   dflookup = DataFrame(np.random.rand(20,4), columns = ['A','B','C','D'])
-  dflookup.lookup(xrange(0,10,2), ['B','C','A','B','D'])
+  dflookup.lookup(lrange(0,10,2), ['B','C','A','B','D'])
 
 Setting values in mixed-type DataFrame
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -896,7 +897,7 @@ display:
 
 .. ipython:: python
 
-   index = Index(range(5), name='rows')
+   index = Index(lrange(5), name='rows')
    columns = Index(['A', 'B', 'C'], name='cols')
    df = DataFrame(np.random.randn(5, 3), index=index, columns=columns)
    df
@@ -971,7 +972,7 @@ can think of ``MultiIndex`` an array of tuples where each tuple is unique. A
 
    arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
              ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
-   tuples = zip(*arrays)
+   tuples = lzip(*arrays)
    tuples
    index = MultiIndex.from_tuples(tuples, names=['first', 'second'])
    s = Series(randn(8), index=index)

doc/source/install.rst

@@ -47,11 +47,11 @@ ___________
 
     Windows, all, stable, :ref:`all-platforms`,  ``pip install pandas``
     Mac, all, stable, :ref:`all-platforms`,  ``pip install pandas``
-    Linux, Debian, stable, `official Debian repository <http://packages.debian.org/search?keywords=pandas&searchon=names&suite=all&section=all>`_ , ``sudo apt-get install python-pandas``
-    Linux, Debian & Ubuntu, unstable (latest packages), `NeuroDebian <http://neuro.debian.net/index.html#how-to-use-this-repository>`_ , ``sudo apt-get install python-pandas``
-    Linux, Ubuntu, stable, `official Ubuntu repository <http://packages.ubuntu.com/search?keywords=pandas&searchon=names&suite=all&section=all>`_ , ``sudo apt-get install python-pandas``
-    Linux, Ubuntu, unstable (daily builds), `PythonXY PPA  <https://code.launchpad.net/~pythonxy/+archive/pythonxy-devel>`_; activate by: ``sudo add-apt-repository ppa:pythonxy/pythonxy-devel && sudo apt-get update``, ``sudo apt-get install python-pandas``
-	Linux, OpenSuse & Fedora, stable, `OpenSuse Repository  <http://software.opensuse.org/package/python-pandas?search_term=pandas>`_ , ``zypper in  python-pandas``
+    Linux, Debian, stable, `official Debian repository <http://packages.debian.org/search?keywords=pandas&searchon=names&suite=all&section=all>`__ , ``sudo apt-get install python-pandas``
+    Linux, Debian & Ubuntu, unstable (latest packages), `NeuroDebian <http://neuro.debian.net/index.html#how-to-use-this-repository>`__ , ``sudo apt-get install python-pandas``
+    Linux, Ubuntu, stable, `official Ubuntu repository <http://packages.ubuntu.com/search?keywords=pandas&searchon=names&suite=all&section=all>`__ , ``sudo apt-get install python-pandas``
+    Linux, Ubuntu, unstable (daily builds), `PythonXY PPA  <https://code.launchpad.net/~pythonxy/+archive/pythonxy-devel>`__; activate by: ``sudo add-apt-repository ppa:pythonxy/pythonxy-devel && sudo apt-get update``, ``sudo apt-get install python-pandas``
+	Linux, OpenSuse & Fedora, stable, `OpenSuse Repository  <http://software.opensuse.org/package/python-pandas?search_term=pandas>`__ , ``zypper in  python-pandas``