Merge pull request #4092 from jtratner/refactor_string_special_methods

CLN: Refactor string special methods

Author: jreback

doc/source/release.rst

@@ -175,8 +175,18 @@ pandas 0.12
     ``bs4`` + ``html5lib`` when lxml fails to parse. a list of parsers to try
     until success is also valid
   - more consistency in the to_datetime return types (give string/array of string inputs) (:issue:`3888`)
+  - The internal ``pandas`` class hierarchy has changed (slightly). The
+    previous ``PandasObject`` now is called ``PandasContainer`` and a new
+    ``PandasObject`` has become the baseclass for ``PandasContainer`` as well
+    as ``Index``, ``Categorical``, ``GroupBy``, ``SparseList``, and
+    ``SparseArray`` (+ their base classes). Currently, ``PandasObject``
+    provides string methods (from ``StringMixin``). (:issue:`4090`, :issue:`4092`)
+  - New ``StringMixin`` that, given a ``__unicode__`` method, gets python 2 and
+    python 3 compatible string methods (``__str__``, ``__bytes__``, and
+    ``__repr__``). Plus string safety throughout. Now employed in many places
+    throughout the pandas library. (:issue:`4090`, :issue:`4092`)
 
-**Experimental Feautres**
+**Experimental Features**
 
   - Added experimental ``CustomBusinessDay`` class to support ``DateOffsets``
     with custom holiday calendars and custom weekmasks. (:issue:`2301`)

doc/source/v0.12.0.txt

@@ -8,13 +8,13 @@ enhancements along with a large number of bug fixes.
 
 Highlites include a consistent I/O API naming scheme, routines to read html,
 write multi-indexes to csv files, read & write STATA data files, read & write JSON format
-files, Python 3 support for ``HDFStore``, filtering of groupby expressions via ``filter``, and a 
+files, Python 3 support for ``HDFStore``, filtering of groupby expressions via ``filter``, and a
 revamped ``replace`` routine that accepts regular expressions.
 
 API changes
 ~~~~~~~~~~~
 
-  - The I/O API is now much more consistent with a set of top level ``reader`` functions 
+  - The I/O API is now much more consistent with a set of top level ``reader`` functions
     accessed like ``pd.read_csv()`` that generally return a ``pandas`` object.
 
     * ``read_csv``
@@ -38,7 +38,7 @@ API changes
     * ``to_clipboard``
 
 
-  - Fix modulo and integer division on Series,DataFrames to act similary to ``float`` dtypes to return 
+  - Fix modulo and integer division on Series,DataFrames to act similary to ``float`` dtypes to return
     ``np.nan`` or ``np.inf`` as appropriate (:issue:`3590`). This correct a numpy bug that treats ``integer``
     and ``float`` dtypes differently.
 
@@ -50,15 +50,15 @@ API changes
         p / p
         p / 0
 
-  - Add ``squeeze`` keyword to ``groupby`` to allow reduction from 
+  - Add ``squeeze`` keyword to ``groupby`` to allow reduction from
     DataFrame -> Series if groups are unique. This is a Regression from 0.10.1.
-    We are reverting back to the prior behavior. This means groupby will return the 
-    same shaped objects whether the groups are unique or not. Revert this issue (:issue:`2893`) 
+    We are reverting back to the prior behavior. This means groupby will return the
+    same shaped objects whether the groups are unique or not. Revert this issue (:issue:`2893`)
     with (:issue:`3596`).
 
     .. ipython:: python
 
-        df2 = DataFrame([{"val1": 1, "val2" : 20}, {"val1":1, "val2": 19}, 
+        df2 = DataFrame([{"val1": 1, "val2" : 20}, {"val1":1, "val2": 19},
                          {"val1":1, "val2": 27}, {"val1":1, "val2": 12}])
         def func(dataf):
             return dataf["val2"]  - dataf["val2"].mean()
@@ -96,9 +96,9 @@ API changes
     and thus you should cast to an appropriate numeric dtype if you need to
     plot something.
 
-  - Add ``colormap`` keyword to DataFrame plotting methods. Accepts either a 
-    matplotlib colormap object (ie, matplotlib.cm.jet) or a string name of such 
-    an object (ie, 'jet'). The colormap is sampled to select the color for each 
+  - Add ``colormap`` keyword to DataFrame plotting methods. Accepts either a
+    matplotlib colormap object (ie, matplotlib.cm.jet) or a string name of such
+    an object (ie, 'jet'). The colormap is sampled to select the color for each
     column. Please see :ref:`visualization.colormaps` for more information.
     (:issue:`3860`)
 
@@ -159,6 +159,18 @@ API changes
     ``bs4`` + ``html5lib`` when lxml fails to parse. a list of parsers to try
     until success is also valid
 
+  - The internal ``pandas`` class hierarchy has changed (slightly). The
+    previous ``PandasObject`` now is called ``PandasContainer`` and a new
+    ``PandasObject`` has become the baseclass for ``PandasContainer`` as well
+    as ``Index``, ``Categorical``, ``GroupBy``, ``SparseList``, and
+    ``SparseArray`` (+ their base classes). Currently, ``PandasObject``
+    provides string methods (from ``StringMixin``). (:issue:`4090`, :issue:`4092`)
+
+  - New ``StringMixin`` that, given a ``__unicode__`` method, gets python 2 and
+    python 3 compatible string methods (``__str__``, ``__bytes__``, and
+    ``__repr__``). Plus string safety throughout. Now employed in many places
+    throughout the pandas library. (:issue:`4090`, :issue:`4092`)
+
 I/O Enhancements
 ~~~~~~~~~~~~~~~~
 
@@ -184,7 +196,7 @@ I/O Enhancements
 
     .. warning::
 
-      You may have to install an older version of BeautifulSoup4, 
+      You may have to install an older version of BeautifulSoup4,
       :ref:`See the installation docs<install.optional_dependencies>`
 
   - Added module for reading and writing Stata files: ``pandas.io.stata`` (:issue:`1512`)
@@ -203,15 +215,15 @@ I/O Enhancements
     - The option, ``tupleize_cols`` can now be specified in both ``to_csv`` and
       ``read_csv``, to provide compatiblity for the pre 0.12 behavior of
       writing and reading multi-index columns via a list of tuples. The default in
-      0.12 is to write lists of tuples and *not* interpret list of tuples as a 
-      multi-index column.  
+      0.12 is to write lists of tuples and *not* interpret list of tuples as a
+      multi-index column.
 
       Note: The default behavior in 0.12 remains unchanged, but starting with 0.13,
-      the default *to* write and read multi-index columns will be in the new 
+      the default *to* write and read multi-index columns will be in the new
       format. (:issue:`3571`, :issue:`1651`, :issue:`3141`)
 
     - If an ``index_col`` is not specified (e.g. you don't have an index, or wrote it
-      with ``df.to_csv(..., index=False``), then any ``names`` on the columns index will 
+      with ``df.to_csv(..., index=False``), then any ``names`` on the columns index will
       be *lost*.
 
       .. ipython:: python
@@ -296,8 +308,8 @@ Other Enhancements
          pd.get_option('a.b')
          pd.get_option('b.c')
 
-  - The ``filter`` method for group objects returns a subset of the original 
-    object. Suppose we want to take only elements that belong to groups with a 
+  - The ``filter`` method for group objects returns a subset of the original
+    object. Suppose we want to take only elements that belong to groups with a
     group sum greater than 2.
 
     .. ipython:: python
@@ -317,7 +329,7 @@ Other Enhancements
        dff.groupby('B').filter(lambda x: len(x) > 2)
 
     Alternatively, instead of dropping the offending groups, we can return a
-    like-indexed objects where the groups that do not pass the filter are 
+    like-indexed objects where the groups that do not pass the filter are
     filled with NaNs.
 
     .. ipython:: python
@@ -333,9 +345,9 @@ Experimental Features
 
   - Added experimental ``CustomBusinessDay`` class to support ``DateOffsets``
     with custom holiday calendars and custom weekmasks. (:issue:`2301`)
-    
+
     .. note::
-        
+
        This uses the ``numpy.busdaycalendar`` API introduced in Numpy 1.7 and
        therefore requires Numpy 1.7.0 or newer.
 
@@ -416,7 +428,7 @@ Bug Fixes
     - Extend ``reindex`` to correctly deal with non-unique indices (:issue:`3679`)
     - ``DataFrame.itertuples()`` now works with frames with duplicate column
       names (:issue:`3873`)
-    - Bug in non-unique indexing via ``iloc`` (:issue:`4017`); added ``takeable`` argument to 
+    - Bug in non-unique indexing via ``iloc`` (:issue:`4017`); added ``takeable`` argument to
       ``reindex`` for location-based taking
 
   - ``DataFrame.from_records`` did not accept empty recarrays (:issue:`3682`)

pandas/core/base.py

@@ -0,0 +1,58 @@
+"""
+Base class(es) for all pandas objects.
+"""
+from pandas.util import py3compat
+
+class StringMixin(object):
+    """implements string methods so long as object defines a `__unicode__` method.
+    Handles Python2/3 compatibility transparently."""
+    # side note - this could be made into a metaclass if more than one object nees
+    def __str__(self):
+        """
+        Return a string representation for a particular object.
+
+        Invoked by str(obj) in both py2/py3.
+        Yields Bytestring in Py2, Unicode String in py3.
+        """
+
+        if py3compat.PY3:
+            return self.__unicode__()
+        return self.__bytes__()
+
+    def __bytes__(self):
+        """
+        Return a string representation for a particular object.
+
+        Invoked by bytes(obj) in py3 only.
+        Yields a bytestring in both py2/py3.
+        """
+        from pandas.core.config import get_option
+
+        encoding = get_option("display.encoding")
+        return self.__unicode__().encode(encoding, 'replace')
+
+    def __repr__(self):
+        """
+        Return a string representation for a particular object.
+
+        Yields Bytestring in Py2, Unicode String in py3.
+        """
+        return str(self)
+
+class PandasObject(StringMixin):
+    """baseclass for various pandas objects"""
+
+    @property
+    def _constructor(self):
+        """class constructor (for this class it's just `__class__`"""
+        return self.__class__
+
+    def __unicode__(self):
+        """
+        Return a string representation for a particular object.
+
+        Invoked by unicode(obj) in py2 only. Yields a Unicode String in both
+        py2/py3.
+        """
+        # Should be overwritten by base classes
+        return object.__repr__(self)

pandas/core/categorical.py

@@ -3,6 +3,7 @@
 import numpy as np
 
 from pandas.core.algorithms import factorize
+from pandas.core.base import PandasObject
 from pandas.core.index import Index
 import pandas.core.common as com
 from pandas.core.frame import DataFrame
@@ -25,8 +26,7 @@ def f(self, other):
 
     return f
 
-
-class Categorical(object):
+class Categorical(PandasObject):
     """
     Represents a categorical variable in classic R / S-plus fashion
 
@@ -134,9 +134,9 @@ def __array__(self, dtype=None):
     def __len__(self):
         return len(self.labels)
 
-    def __repr__(self):
+    def __unicode__(self):
         temp = 'Categorical: %s\n%s\n%s'
-        values = np.asarray(self)
+        values = com.pprint_thing(np.asarray(self))
         levheader = 'Levels (%d): ' % len(self.levels)
         levstring = np.array_repr(self.levels,
                                   max_line_width=60)
@@ -145,9 +145,9 @@ def __repr__(self):
         lines = levstring.split('\n')
         levstring = '\n'.join([lines[0]] +
                               [indent + x.lstrip() for x in lines[1:]])
+        name = '' if self.name is None else self.name
+        return temp % (name, values, levheader + levstring)
 
-        return temp % ('' if self.name is None else self.name,
-                       repr(values), levheader + levstring)
 
     def __getitem__(self, key):
         if isinstance(key, (int, np.integer)):

pandas/core/common.py

@@ -64,10 +64,10 @@ def _isnull_new(obj):
     if lib.isscalar(obj):
         return lib.checknull(obj)
 
-    from pandas.core.generic import PandasObject
+    from pandas.core.generic import PandasContainer
     if isinstance(obj, np.ndarray):
         return _isnull_ndarraylike(obj)
-    elif isinstance(obj, PandasObject):
+    elif isinstance(obj, PandasContainer):
         # TODO: optimize for DataFrame, etc.
         return obj.apply(isnull)
     elif isinstance(obj, list) or hasattr(obj, '__array__'):
@@ -91,10 +91,10 @@ def _isnull_old(obj):
     if lib.isscalar(obj):
         return lib.checknull_old(obj)
 
-    from pandas.core.generic import PandasObject
+    from pandas.core.generic import PandasContainer
     if isinstance(obj, np.ndarray):
         return _isnull_ndarraylike_old(obj)
-    elif isinstance(obj, PandasObject):
+    elif isinstance(obj, PandasContainer):
         # TODO: optimize for DataFrame, etc.
         return obj.apply(_isnull_old)
     elif isinstance(obj, list) or hasattr(obj, '__array__'):

pandas/core/frame.py

@@ -584,10 +584,6 @@ def _verbose_info(self, value):
     def axes(self):
         return [self.index, self.columns]
 
-    @property
-    def _constructor(self):
-        return self.__class__
-
     @property
     def shape(self):
         return (len(self.index), len(self.columns))
@@ -653,28 +649,6 @@ def _repr_fits_horizontal_(self,ignore_width=False):
 
         return repr_width < width
 
-    def __str__(self):
-        """
-        Return a string representation for a particular DataFrame
-
-        Invoked by str(df) in both py2/py3.
-        Yields Bytestring in Py2, Unicode String in py3.
-        """
-
-        if py3compat.PY3:
-            return self.__unicode__()
-        return self.__bytes__()
-
-    def __bytes__(self):
-        """
-        Return a string representation for a particular DataFrame
-
-        Invoked by bytes(df) in py3 only.
-        Yields a bytestring in both py2/py3.
-        """
-        encoding = com.get_option("display.encoding")
-        return self.__unicode__().encode(encoding, 'replace')
-
     def __unicode__(self):
         """
         Return a string representation for a particular DataFrame
@@ -714,14 +688,6 @@ def __unicode__(self):
 
         return value
 
-    def __repr__(self):
-        """
-        Return a string representation for a particular DataFrame
-
-        Yields Bytestring in Py2, Unicode String in py3.
-        """
-        return str(self)
-
     def _repr_html_(self):
         """
         Return a html representation for a particular DataFrame.

pandas/core/generic.py

@@ -1,20 +1,21 @@
 # pylint: disable=W0231,E1101
 
 import numpy as np
+import pandas.lib as lib
+from pandas.core.base import PandasObject
 
 from pandas.core.index import MultiIndex
 import pandas.core.indexing as indexing
 from pandas.core.indexing import _maybe_convert_indices
 from pandas.tseries.index import DatetimeIndex
 import pandas.core.common as com
-import pandas.lib as lib
 
 
 class PandasError(Exception):
     pass
 
 
-class PandasObject(object):
+class PandasContainer(PandasObject):
 
     _AXIS_NUMBERS = {
         'index': 0,
@@ -52,6 +53,12 @@ def __hash__(self):
         raise TypeError('{0!r} objects are mutable, thus they cannot be'
                               ' hashed'.format(self.__class__.__name__))
 
+    def __unicode__(self):
+        # unicode representation based upon iterating over self
+        # (since, by definition, `PandasContainers` are iterable)
+        prepr = '[%s]' % ','.join(map(com.pprint_thing, self))
+        return '%s(%s)' % (self.__class__.__name__, prepr)
+
 
     #----------------------------------------------------------------------
     # Axis name business
@@ -578,9 +585,10 @@ def to_json(self, path_or_buf=None, orient=None, date_format='epoch',
 
 # install the indexerse
 for _name, _indexer in indexing.get_indexers_list():
-    PandasObject._create_indexer(_name,_indexer)
+    PandasContainer._create_indexer(_name,_indexer)
+
 
-class NDFrame(PandasObject):
+class NDFrame(PandasContainer):
     """
     N-dimensional analogue of DataFrame. Store multi-dimensional in a
     size-mutable, labeled data structure
@@ -625,17 +633,10 @@ def astype(self, dtype, copy = True, raise_on_error = True):
         mgr = self._data.astype(dtype, copy = copy, raise_on_error = raise_on_error)
         return self._constructor(mgr)
 
-    @property
-    def _constructor(self):
-        return NDFrame
-
     @property
     def axes(self):
         return self._data.axes
 
-    def __repr__(self):
-        return 'NDFrame'
-
     @property
     def values(self):
         return self._data.as_matrix()

pandas/core/groupby.py

@@ -2,6 +2,7 @@
 import types
 import numpy as np
 
+from pandas.core.base import PandasObject
 from pandas.core.categorical import Categorical
 from pandas.core.frame import DataFrame
 from pandas.core.generic import NDFrame
@@ -100,7 +101,7 @@ def _last(x):
         return _last(x)
 
 
-class GroupBy(object):
+class GroupBy(PandasObject):
     """
     Class for grouping and aggregating relational data. See aggregate,
     transform, and apply functions on this object.
@@ -201,6 +202,10 @@ def __init__(self, obj, keys=None, axis=0, level=None,
     def __len__(self):
         return len(self.indices)
 
+    def __unicode__(self):
+        # TODO: Better unicode/repr for GroupBy object
+        return object.__repr__(self)
+
     @property
     def groups(self):
         return self.grouper.groups

pandas/core/index.py

@@ -9,6 +9,7 @@
 import pandas.algos as _algos
 import pandas.index as _index
 from pandas.lib import Timestamp
+from pandas.core.base import PandasObject
 
 from pandas.util.decorators import cache_readonly
 from pandas.core.common import isnull
@@ -47,7 +48,7 @@ def _shouldbe_timestamp(obj):
             or tslib.is_timestamp_array(obj))
 
 
-class Index(np.ndarray):
+class Index(PandasObject, np.ndarray):
     """
     Immutable ndarray implementing an ordered, sliceable set. The basic object
     storing axis labels for all pandas objects
@@ -142,28 +143,6 @@ def __array_finalize__(self, obj):
     def _shallow_copy(self):
         return self.view()
 
-    def __str__(self):
-        """
-        Return a string representation for a particular Index
-
-        Invoked by str(df) in both py2/py3.
-        Yields Bytestring in Py2, Unicode String in py3.
-        """
-
-        if py3compat.PY3:
-            return self.__unicode__()
-        return self.__bytes__()
-
-    def __bytes__(self):
-        """
-        Return a string representation for a particular Index
-
-        Invoked by bytes(df) in py3 only.
-        Yields a bytestring in both py2/py3.
-        """
-        encoding = com.get_option("display.encoding")
-        return self.__unicode__().encode(encoding, 'replace')
-
     def __unicode__(self):
         """
         Return a string representation for a particular Index
@@ -173,14 +152,6 @@ def __unicode__(self):
         prepr = com.pprint_thing(self, escape_chars=('\t', '\r', '\n'),quote_strings=True)
         return '%s(%s, dtype=%s)' % (type(self).__name__, prepr, self.dtype)
 
-    def __repr__(self):
-        """
-        Return a string representation for a particular Index
-
-        Yields Bytestring in Py2, Unicode String in py3.
-        """
-        return str(self)
-
     def to_series(self):
         """
         return a series with both index and values equal to the index keys
@@ -237,10 +208,6 @@ def _set_names(self, values):
 
     names = property(fset=_set_names, fget=_get_names)
 
-    @property
-    def _constructor(self):
-        return Index
-
     @property
     def _has_complex_internals(self):
         # to disable groupby tricks in MultiIndex
@@ -1408,10 +1375,6 @@ def __new__(cls, data, dtype=None, copy=False, name=None):
     def inferred_type(self):
         return 'integer'
 
-    @property
-    def _constructor(self):
-        return Int64Index
-
     @property
     def asi8(self):
         # do not cache or you'll create a memory leak
@@ -1531,28 +1494,6 @@ def _array_values(self):
     def dtype(self):
         return np.dtype('O')
 
-    def __str__(self):
-        """
-        Return a string representation for a particular Index
-
-        Invoked by str(df) in both py2/py3.
-        Yields Bytestring in Py2, Unicode String in py3.
-        """
-
-        if py3compat.PY3:
-            return self.__unicode__()
-        return self.__bytes__()
-
-    def __bytes__(self):
-        """
-        Return a string representation for a particular Index
-
-        Invoked by bytes(df) in py3 only.
-        Yields a bytestring in both py2/py3.
-        """
-        encoding = com.get_option("display.encoding")
-        return self.__unicode__().encode(encoding, 'replace')
-
     def __unicode__(self):
         """
         Return a string representation for a particular Index
@@ -1566,14 +1507,6 @@ def __unicode__(self):
 
         return output % summary
 
-    def __repr__(self):
-        """
-        Return a string representation for a particular Index
-
-        Yields Bytestring in Py2, Unicode String in py3.
-        """
-        return str(self)
-
     def __len__(self):
         return len(self.labels[0])
 

pandas/core/internals.py

@@ -4,6 +4,7 @@
 
 from numpy import nan
 import numpy as np
+from pandas.core.base import PandasObject
 
 from pandas.core.common import (_possibly_downcast_to_dtype, isnull, _NS_DTYPE,
                                 _TD_DTYPE)
@@ -19,7 +20,7 @@
 from pandas.util import py3compat
 
 
-class Block(object):
+class Block(PandasObject):
     """
     Canonical n-dimensional unit of homogeneous dtype contained in a pandas
     data structure
@@ -91,14 +92,12 @@ def set_ref_items(self, ref_items, maybe_rename=True):
             self.items = ref_items.take(self.ref_locs)
         self.ref_items = ref_items
 
-    def __repr__(self):
+    def __unicode__(self):
         shape = ' x '.join([com.pprint_thing(s) for s in self.shape])
         name = type(self).__name__
         result = '%s: %s, %s, dtype %s' % (
             name, com.pprint_thing(self.items), shape, self.dtype)
-        if py3compat.PY3:
-            return unicode(result)
-        return com.console_encode(result)
+        return result
 
     def __contains__(self, item):
         return item in self.items
@@ -969,7 +968,7 @@ def make_block(values, items, ref_items, klass=None, fastpath=False, placement=N
 # TODO: flexible with index=None and/or items=None
 
 
-class BlockManager(object):
+class BlockManager(PandasObject):
     """
     Core internal data structure to implement DataFrame
 
@@ -1213,7 +1212,7 @@ def __setstate__(self, state):
     def __len__(self):
         return len(self.items)
 
-    def __repr__(self):
+    def __unicode__(self):
         output = 'BlockManager'
         for i, ax in enumerate(self.axes):
             if i == 0:
@@ -1222,7 +1221,7 @@ def __repr__(self):
                 output += '\nAxis %d: %s' % (i, ax)
 
         for block in self.blocks:
-            output += '\n%s' % repr(block)
+            output += '\n%s' % com.pprint_thing(block)
         return output
 
     @property

pandas/core/panel.py

@@ -186,10 +186,6 @@ class Panel(NDFrame):
     major_axis = lib.AxisProperty(1)
     minor_axis = lib.AxisProperty(2)
 
-    @property
-    def _constructor(self):
-        return type(self)
-
     # return the type of the slice constructor
     _constructor_sliced = DataFrame
 
@@ -466,28 +462,6 @@ def __invert__(self):
     #----------------------------------------------------------------------
     # Magic methods
 
-    def __str__(self):
-        """
-        Return a string representation for a particular Panel
-
-        Invoked by str(df) in both py2/py3.
-        Yields Bytestring in Py2, Unicode String in py3.
-        """
-
-        if py3compat.PY3:
-            return self.__unicode__()
-        return self.__bytes__()
-
-    def __bytes__(self):
-        """
-        Return a string representation for a particular Panel
-
-        Invoked by bytes(df) in py3 only.
-        Yields a bytestring in both py2/py3.
-        """
-        encoding = com.get_option("display.encoding")
-        return self.__unicode__().encode(encoding, 'replace')
-
     def __unicode__(self):
         """
         Return a string representation for a particular Panel
@@ -515,14 +489,6 @@ def axis_pretty(a):
             [class_name, dims] + [axis_pretty(a) for a in self._AXIS_ORDERS])
         return output
 
-    def __repr__(self):
-        """
-        Return a string representation for a particular Panel
-
-        Yields Bytestring in Py2, Unicode String in py3.
-        """
-        return str(self)
-
     def __iter__(self):
         return iter(getattr(self, self._info_axis))
 

pandas/core/series.py

@@ -394,8 +394,7 @@ def f(self, axis=0, dtype=None, out=None, skipna=True, level=None):
 #----------------------------------------------------------------------
 # Series class
 
-
-class Series(pa.Array, generic.PandasObject):
+class Series(generic.PandasContainer, pa.Array):
     """
     One-dimensional ndarray with axis labels (including time series).
     Labels need not be unique but must be any hashable type. The object
@@ -520,10 +519,6 @@ def __init__(self, data=None, index=None, dtype=None, name=None,
                  copy=False):
         pass
 
-    @property
-    def _constructor(self):
-        return Series
-
     @property
     def _can_hold_na(self):
         return not is_integer_dtype(self.dtype)
@@ -1096,28 +1091,6 @@ def reset_index(self, level=None, drop=False, name=None, inplace=False):
 
             return df.reset_index(level=level, drop=drop)
 
-    def __str__(self):
-        """
-        Return a string representation for a particular DataFrame
-
-        Invoked by str(df) in both py2/py3.
-        Yields Bytestring in Py2, Unicode String in py3.
-        """
-
-        if py3compat.PY3:
-            return self.__unicode__()
-        return self.__bytes__()
-
-    def __bytes__(self):
-        """
-        Return a string representation for a particular DataFrame
-
-        Invoked by bytes(df) in py3 only.
-        Yields a bytestring in both py2/py3.
-        """
-        encoding = com.get_option("display.encoding")
-        return self.__unicode__().encode(encoding, 'replace')
-
     def __unicode__(self):
         """
         Return a string representation for a particular DataFrame
@@ -1142,14 +1115,6 @@ def __unicode__(self):
             raise AssertionError()
         return result
 
-    def __repr__(self):
-        """
-        Return a string representation for a particular Series
-
-        Yields Bytestring in Py2, Unicode String in py3.
-        """
-        return str(self)
-
     def _tidy_repr(self, max_vals=20):
         """
 

pandas/io/excel.py

@@ -73,9 +73,6 @@ def __init__(self, path_or_buf, kind=None, **kwds):
             data = path_or_buf.read()
             self.book = xlrd.open_workbook(file_contents=data)
 
-    def __repr__(self):
-        return object.__repr__(self)
-
     def parse(self, sheetname, header=0, skiprows=None, skip_footer=0,
               index_col=None, parse_cols=None, parse_dates=False,
               date_parser=None, na_values=None, thousands=None, chunksize=None,

pandas/io/pytables.py

@@ -17,7 +17,8 @@
 from pandas.sparse.api import SparseSeries, SparseDataFrame, SparsePanel
 from pandas.sparse.array import BlockIndex, IntIndex
 from pandas.tseries.api import PeriodIndex, DatetimeIndex
-from pandas.core.common import adjoin, is_list_like
+from pandas.core.base import StringMixin
+from pandas.core.common import adjoin, is_list_like, pprint_thing
 from pandas.core.algorithms import match, unique
 from pandas.core.categorical import Categorical
 from pandas.core.common import _asarray_tuplesafe
@@ -218,7 +219,7 @@ def read_hdf(path_or_buf, key, **kwargs):
     # a passed store; user controls open/close
     f(path_or_buf, False)
 
-class HDFStore(object):
+class HDFStore(StringMixin):
     """
     dict-like IO interface for storing pandas objects in PyTables
     format.
@@ -315,8 +316,8 @@ def __contains__(self, key):
     def __len__(self):
         return len(self.groups())
 
-    def __repr__(self):
-        output = '%s\nFile path: %s\n' % (type(self), self._path)
+    def __unicode__(self):
+        output = '%s\nFile path: %s\n' % (type(self), pprint_thing(self._path))
 
         if len(self.keys()):
             keys   = []
@@ -326,11 +327,11 @@ def __repr__(self):
                 try:
                     s = self.get_storer(k)
                     if s is not None:
-                        keys.append(str(s.pathname or k))
-                        values.append(str(s or 'invalid_HDFStore node'))
-                except (Exception), detail:
+                        keys.append(pprint_thing(s.pathname or k))
+                        values.append(pprint_thing(s or 'invalid_HDFStore node'))
+                except Exception as detail:
                     keys.append(k)
-                    values.append("[invalid_HDFStore node: %s]" % str(detail))
+                    values.append("[invalid_HDFStore node: %s]" % pprint_thing(detail))
 
             output += adjoin(12, keys, values)
         else:
@@ -984,7 +985,7 @@ def get_values(self):
         self.close()
         return results
 
-class IndexCol(object):
+class IndexCol(StringMixin):
     """ an index column description class
 
         Parameters
@@ -1050,10 +1051,9 @@ def set_table(self, table):
         self.table = table
         return self
 
-    def __repr__(self):
-        return "name->%s,cname->%s,axis->%s,pos->%s,kind->%s" % (self.name, self.cname, self.axis, self.pos, self.kind)
-
-    __str__ = __repr__
+    def __unicode__(self):
+        temp = tuple(map(pprint_thing, (self.name, self.cname, self.axis, self.pos, self.kind)))
+        return "name->%s,cname->%s,axis->%s,pos->%s,kind->%s" % temp
 
     def __eq__(self, other):
         """ compare 2 col items """
@@ -1570,7 +1570,7 @@ class GenericDataIndexableCol(DataIndexableCol):
     def get_attr(self):
         pass
 
-class Storer(object):
+class Storer(StringMixin):
     """ represent an object in my store
           facilitate read/write of various types of objects
           this is an abstract base class
@@ -1610,19 +1610,16 @@ def set_version(self):
     def pandas_type(self):
         return _ensure_decoded(getattr(self.group._v_attrs, 'pandas_type', None))
 
-    def __repr__(self):
-        """ return a pretty representatgion of myself """
+    def __unicode__(self):
+        """ return a pretty representation of myself """
         self.infer_axes()
         s = self.shape
         if s is not None:
             if isinstance(s, (list,tuple)):
-                s = "[%s]" % ','.join([ str(x) for x in s ])
+                s = "[%s]" % ','.join([pprint_thing(x) for x in s])
             return "%-12.12s (shape->%s)" % (self.pandas_type,s)
         return self.pandas_type
 
-    def __str__(self):
-        return self.__repr__()
-
     def set_object_info(self):
         """ set my pandas type & version """
         self.attrs.pandas_type = self.pandas_kind
@@ -3435,7 +3432,7 @@ def _need_convert(kind):
         return True
     return False
 
-class Term(object):
+class Term(StringMixin):
     """create a term object that holds a field, op, and value
 
     Parameters
@@ -3540,10 +3537,9 @@ def __init__(self, field, op=None, value=None, queryables=None, encoding=None):
         if len(self.q):
             self.eval()
 
-    def __str__(self):
-        return "field->%s,op->%s,value->%s" % (self.field, self.op, self.value)
-
-    __repr__ = __str__
+    def __unicode__(self):
+        attrs = map(pprint_thing, (self.field, self.op, self.value))
+        return "field->%s,op->%s,value->%s" % tuple(attrs)
 
     @property
     def is_valid(self):

pandas/io/stata.py

@@ -15,6 +15,7 @@
 
 import sys
 import struct
+from pandas.core.base import StringMixin
 from pandas.core.frame import DataFrame
 from pandas.core.series import Series
 from pandas.core.categorical import Categorical
@@ -163,7 +164,7 @@ def _datetime_to_stata_elapsed(date, fmt):
         raise ValueError("fmt %s not understood" % fmt)
 
 
-class StataMissingValue(object):
+class StataMissingValue(StringMixin):
     """
     An observation's missing value.
 
@@ -192,10 +193,12 @@ def __init__(self, offset, value):
     string = property(lambda self: self._str, doc="The Stata representation of the missing value: '.', '.a'..'.z'")
     value = property(lambda self: self._value, doc='The binary representation of the missing value.')
 
-    def __str__(self):
-        return self._str
+    def __unicode__(self):
+        return self.string
 
-    __str__.__doc__ = string.__doc__
+    def __repr__(self):
+        # not perfect :-/
+        return "%s(%s)" % (self.__class__, self)
 
 
 class StataParser(object):

pandas/io/tests/test_json/test_pandas.py

@@ -33,7 +33,7 @@
 
 _mixed_frame = _frame.copy()
 
-class TestPandasObjects(unittest.TestCase):
+class TestPandasContainer(unittest.TestCase):
 
     def setUp(self):
         self.ts = tm.makeTimeSeries()
@@ -68,7 +68,7 @@ def _check_orient(df, orient, dtype=None, numpy=False, convert_axes=True, check_
                     if type(detail) == raise_ok:
                         return
                     raise
-                    
+
             unser = unser.sort()
 
             if dtype is False:
@@ -104,7 +104,7 @@ def _check_all_orients(df, dtype=None, convert_axes=True, raise_ok=None):
                 _check_orient(df, "split", dtype=dtype)
                 _check_orient(df, "index", dtype=dtype)
                 _check_orient(df, "values", dtype=dtype)
-            
+
             _check_orient(df, "columns", dtype=dtype, convert_axes=False)
             _check_orient(df, "records", dtype=dtype, convert_axes=False)
             _check_orient(df, "split", dtype=dtype, convert_axes=False)
@@ -347,7 +347,7 @@ def test_convert_dates(self):
         assert_series_equal(result,ts)
 
     def test_date_format(self):
-        
+
         df = self.tsframe.copy()
         df['date'] = Timestamp('20130101')
         df_orig = df.copy()
@@ -412,7 +412,7 @@ def test_misc_example(self):
     @network
     @slow
     def test_round_trip_exception_(self):
-		# GH 3867
+        # GH 3867
 
         df = pd.read_csv('https://raw.github.com/hayd/lahman2012/master/csvs/Teams.csv')
         s = df.to_json()
@@ -429,9 +429,9 @@ def test_url(self):
             result = read_json(url,convert_dates=True)
             for c in ['created_at','closed_at','updated_at']:
                 self.assert_(result[c].dtype == 'datetime64[ns]')
-            
+
             url = 'http://search.twitter.com/search.json?q=pandas%20python'
             result = read_json(url)
-            
+
         except urllib2.URLError:
             raise nose.SkipTest

pandas/sparse/array.py

@@ -8,6 +8,7 @@
 import numpy as np
 
 import operator
+from pandas.core.base import PandasObject
 import pandas.core.common as com
 
 from pandas.util import py3compat
@@ -86,8 +87,7 @@ def _sparse_fillop(this, other, name):
 
     return result, result_index
 
-
-class SparseArray(np.ndarray):
+class SparseArray(PandasObject, np.ndarray):
     """Data structure for labeled, sparse floating point data
 
 Parameters
@@ -184,9 +184,9 @@ def __setstate__(self, state):
     def __len__(self):
         return self.sp_index.length
 
-    def __repr__(self):
-        return '%s\n%s' % (np.ndarray.__repr__(self),
-                           repr(self.sp_index))
+    def __unicode__(self):
+        return '%s\n%s' % (com.pprint_thing(self),
+                           com.pprint_thing(self.sp_index))
 
     # Arithmetic operators
 

pandas/sparse/list.py

@@ -1,10 +1,12 @@
 import numpy as np
+from pandas.core.base import PandasObject
+from pandas.core.common import pprint_thing
 
 from pandas.sparse.array import SparseArray
 import pandas._sparse as splib
 
 
-class SparseList(object):
+class SparseList(PandasObject):
     """
     Data structure for accumulating data to be converted into a
     SparseArray. Has similar API to the standard Python list
@@ -21,9 +23,9 @@ def __init__(self, data=None, fill_value=np.nan):
         if data is not None:
             self.append(data)
 
-    def __repr__(self):
+    def __unicode__(self):
         contents = '\n'.join(repr(c) for c in self._chunks)
-        return '%s\n%s' % (object.__repr__(self), contents)
+        return '%s\n%s' % (object.__repr__(self), pprint_thing(contents))
 
     def __len__(self):
         return sum(len(c) for c in self._chunks)

pandas/sparse/series.py

@@ -241,8 +241,9 @@ def __setstate__(self, state):
     def __len__(self):
         return self.sp_index.length
 
-    def __repr__(self):
-        series_rep = Series.__repr__(self)
+    def __unicode__(self):
+        # currently, unicode is same as repr...fixes infinite loop
+        series_rep = Series.__unicode__(self)
         rep = '%s\n%s' % (series_rep, repr(self.sp_index))
         return rep
 

pandas/stats/fama_macbeth.py

@@ -1,3 +1,4 @@
+from pandas.core.base import StringMixin
 from pandas.util.py3compat import StringIO
 
 import numpy as np
@@ -26,7 +27,7 @@ def fama_macbeth(**kwargs):
     return klass(**kwargs)
 
 
-class FamaMacBeth(object):
+class FamaMacBeth(StringMixin):
     def __init__(self, y, x, intercept=True, nw_lags=None,
                  nw_lags_beta=None,
                  entity_effects=False, time_effects=False, x_effects=None,
@@ -114,7 +115,7 @@ def _coef_table(self):
 
         return buffer.getvalue()
 
-    def __repr__(self):
+    def __unicode__(self):
         return self.summary
 
     @cache_readonly

pandas/stats/ols.py

@@ -10,6 +10,7 @@
 import numpy as np
 
 from pandas.core.api import DataFrame, Series, isnull
+from pandas.core.base import StringMixin
 from pandas.core.common import _ensure_float64
 from pandas.core.index import MultiIndex
 from pandas.core.panel import Panel
@@ -22,7 +23,7 @@
 _FP_ERR = 1e-8
 
 
-class OLS(object):
+class OLS(StringMixin):
     """
     Runs a full sample ordinary least squares regression.
 
@@ -581,7 +582,7 @@ def summary(self):
 
         return template % params
 
-    def __repr__(self):
+    def __unicode__(self):
         return self.summary
 
     @cache_readonly

pandas/stats/var.py

@@ -1,7 +1,7 @@
 from __future__ import division
 
 import numpy as np
-
+from pandas.core.base import StringMixin
 from pandas.util.decorators import cache_readonly
 from pandas.core.frame import DataFrame
 from pandas.core.panel import Panel
@@ -11,7 +11,7 @@
 from pandas.stats.ols import _combine_rhs
 
 
-class VAR(object):
+class VAR(StringMixin):
     """
     Estimates VAR(p) regression on multivariate time series data
     presented in pandas data structures.
@@ -477,7 +477,7 @@ def _sigma(self):
 
         return np.dot(resid, resid.T) / (n - k)
 
-    def __repr__(self):
+    def __unicode__(self):
         return self.summary
 
 

pandas/tseries/index.py

@@ -488,7 +488,7 @@ def _mpl_repr(self):
         # how to represent ourselves to matplotlib
         return tslib.ints_to_pydatetime(self.asi8, self.tz)
 
-    def __repr__(self):
+    def __unicode__(self):
         from pandas.core.format import _format_datetime64
         values = self.values
 
@@ -514,8 +514,6 @@ def __repr__(self):
 
         return summary
 
-    __str__ = __repr__
-
     def __reduce__(self):
         """Necessary for making this object picklable"""
         object_state = list(np.ndarray.__reduce__(self))

pandas/tseries/period.py

@@ -3,6 +3,7 @@
 
 from datetime import datetime, date
 import numpy as np
+from pandas.core.base import PandasObject
 
 import pandas.tseries.offsets as offsets
 from pandas.tseries.frequencies import (get_freq_code as _gfc,
@@ -40,7 +41,7 @@ def f(self):
     return property(f)
 
 
-class Period(object):
+class Period(PandasObject):
     """
     Represents an period of time
 
@@ -272,28 +273,6 @@ def __repr__(self):
 
         return "Period('%s', '%s')" % (formatted, freqstr)
 
-    def __str__(self):
-        """
-        Return a string representation for a particular DataFrame
-
-        Invoked by str(df) in both py2/py3.
-        Yields Bytestring in Py2, Unicode String in py3.
-        """
-
-        if py3compat.PY3:
-            return self.__unicode__()
-        return self.__bytes__()
-
-    def __bytes__(self):
-        """
-        Return a string representation for a particular DataFrame
-
-        Invoked by bytes(df) in py3 only.
-        Yields a bytestring in both py2/py3.
-        """
-        encoding = com.get_option("display.encoding")
-        return self.__unicode__().encode(encoding, 'replace')
-
     def __unicode__(self):
         """
         Return a string representation for a particular DataFrame
@@ -303,9 +282,7 @@ def __unicode__(self):
         """
         base, mult = _gfc(self.freq)
         formatted = tslib.period_format(self.ordinal, base)
-        value = (u"%s" % formatted)
-        assert type(value) == unicode
-
+        value = ("%s" % formatted)
         return value