CLN: refactor core/index and tseries/index,period to have their formats and to_native_types be consistent

     in naming / operation
ENH: add support for Period formatting (as a native type)
TST: add period support to makeCustomDataFrame for testing purposes

Author: jreback

pandas/core/index.py

@@ -428,12 +428,10 @@ def take(self, indexer, axis=0):
         taken = self.view(np.ndarray).take(indexer)
         return self._constructor(taken, name=self.name)
 
-    def format(self, name=False, formatter=None, na_rep='NaN'):
+    def format(self, name=False, formatter=None, **kwargs):
         """
         Render a string representation of the Index
         """
-        from pandas.core.format import format_array
-
         header = []
         if name:
             header.append(com.pprint_thing(self.name,
@@ -443,11 +441,13 @@ def format(self, name=False, formatter=None, na_rep='NaN'):
         if formatter is not None:
             return header + list(self.map(formatter))
 
-        if self.is_all_dates:
-            return header + _date_formatter(self)
+        return self._format_with_header(header, **kwargs)
 
+    def _format_with_header(self, header, na_rep='NaN', **kwargs):
         values = self.values
 
+        from pandas.core.format import format_array
+
         if values.dtype == np.object_:
             values = lib.maybe_convert_objects(values, safe=1)
 
@@ -466,17 +466,18 @@ def format(self, name=False, formatter=None, na_rep='NaN'):
             result = _trim_front(format_array(values, None, justify='left'))
         return header + result
 
-    def to_native_types(self, slicer=None, na_rep='', float_format=None):
+    def to_native_types(self, slicer=None, **kwargs):
+        """ slice and dice then format """
         values = self
         if slicer is not None:
             values = values[slicer]
-        if self.is_all_dates:
-            return _date_formatter(values)
-        else:
-            mask = isnull(values)
-            values = np.array(values,dtype=object)
-            values[mask] = na_rep
+        return values._format_native_types(**kwargs)
 
+    def _format_native_types(self, na_rep='', **kwargs):
+        """ actually format my specific types """
+        mask = isnull(self)
+        values = np.array(self,dtype=object,copy=True)
+        values[mask] = na_rep
         return values.tolist()
 
     def equals(self, other):
@@ -1320,6 +1321,11 @@ def inferred_type(self):
     def _constructor(self):
         return Int64Index
 
+    @property
+    def asi8(self):
+        # do not cache or you'll create a memory leak
+        return self.values.view('i8')
+
     @property
     def is_all_dates(self):
         """
@@ -1489,11 +1495,8 @@ def __repr__(self):
     def __len__(self):
         return len(self.labels[0])
 
-    def to_native_types(self, slicer=None, na_rep='', float_format=None):
-        ix = self
-        if slicer:
-            ix = self[slicer]
-        return ix.tolist()
+    def _format_native_types(self, **kwargs):
+        return self.tolist()
 
     @property
     def _constructor(self):
@@ -2598,23 +2601,6 @@ def _wrap_joined_index(self, joined, other):
 
 # For utility purposes
 
-def _date_formatter(obj, na_rep=u'NaT'):
-    data = list(obj)
-
-    # tz formatter or time formatter
-    zero_time = time(0, 0)
-    for d in data:
-        if d.time() != zero_time or d.tzinfo is not None:
-            return [u'%s' % x for x in data ]
-
-    values = np.array(data,dtype=object)
-    mask = isnull(obj.values)
-    values[mask] = na_rep
-
-    imask = -mask
-    values[imask] = np.array([ u'%d-%.2d-%.2d' % (dt.year, dt.month, dt.day) for dt in values[imask] ])
-    return values.tolist()
-
 def _sparsify(label_list, start=0):
     pivoted = zip(*label_list)
     k = len(label_list)

pandas/tests/test_frame.py

@@ -21,7 +21,7 @@
 import pandas.core.format as fmt
 import pandas.core.datetools as datetools
 from pandas.core.api import (DataFrame, Index, Series, notnull, isnull,
-                             MultiIndex, DatetimeIndex, Timestamp)
+                             MultiIndex, DatetimeIndex, Timestamp, Period)
 from pandas.io.parsers import read_csv
 
 from pandas.util.testing import (assert_almost_equal,
@@ -4587,7 +4587,7 @@ def stuple_to_tuple(x):
                    cols=MultiIndex.from_tuples(map(stuple_to_tuple,recons.columns))
                    recons.columns = cols
 
-               type_map = dict(i='i',f='f',s='O',u='O',dt='O')
+               type_map = dict(i='i',f='f',s='O',u='O',dt='O',p='O')
                if r_dtype:
                     if r_dtype == 'u': # unicode
                         r_dtype='O'
@@ -4599,6 +4599,11 @@ def stuple_to_tuple(x):
                         recons.index = np.array(map(Timestamp,recons.index),
                                                 dtype=r_dtype )
                         df.index = np.array(map(Timestamp,df.index),dtype=r_dtype )
+                    elif r_dtype == 'p':
+                        r_dtype='O'
+                        recons.index = np.array(map(Timestamp,recons.index.to_datetime()),
+                                                dtype=r_dtype )
+                        df.index = np.array(map(Timestamp,df.index.to_datetime()),dtype=r_dtype )
                     else:
                         r_dtype= type_map.get(r_dtype)
                         recons.index = np.array(recons.index,dtype=r_dtype )
@@ -4608,12 +4613,17 @@ def stuple_to_tuple(x):
                         c_dtype='O'
                         recons.columns = np.array(map(_to_uni,recons.columns),
                                                 dtype=c_dtype )
-                        df.Columns = np.array(map(_to_uni,df.columns),dtype=c_dtype )
+                        df.columns = np.array(map(_to_uni,df.columns),dtype=c_dtype )
                     elif c_dtype == 'dt':
                         c_dtype='O'
                         recons.columns = np.array(map(Timestamp,recons.columns),
                                                 dtype=c_dtype )
-                        df.Columns = np.array(map(Timestamp,df.columns),dtype=c_dtype )
+                        df.columns = np.array(map(Timestamp,df.columns),dtype=c_dtype )
+                    elif c_dtype == 'p':
+                        c_dtype='O'
+                        recons.columns = np.array(map(Timestamp,recons.columns.to_datetime()),
+                                                dtype=c_dtype )
+                        df.columns = np.array(map(Timestamp,df.columns.to_datetime()),dtype=c_dtype )
                     else:
                         c_dtype= type_map.get(c_dtype)
                         recons.columns = np.array(recons.columns,dtype=c_dtype )
@@ -4631,8 +4641,8 @@ def stuple_to_tuple(x):
                 _do_test(mkdf(nrows, ncols,r_idx_type='dt',
                               c_idx_type='s'),path, 'dt','s')
 
-        for r_idx_type in ['i', 'f','s','u']:
-            for c_idx_type in ['i', 'f','s','u','dt']:
+        for r_idx_type in ['i','s','u','p']:
+            for c_idx_type in ['i', 's','u','dt','p']:
                 for ncols in [1,2,128]:
                     base = int((chunksize// ncols or 1) or 1)
                     for nrows in [2,10,N-1,N,N+1,N+2,2*N-2,2*N-1,2*N,2*N+1,2*N+2,

pandas/tseries/index.py

@@ -568,6 +568,26 @@ def __contains__(self, key):
         except (KeyError, TypeError):
             return False
 
+    def _format_with_header(self, header, **kwargs):
+        return header + self._format_native_types(**kwargs)
+
+    def _format_native_types(self, na_rep=u'NaT', **kwargs):
+        data = list(self)
+
+        # tz formatter or time formatter
+        zero_time = time(0, 0)
+        for d in data:
+            if d.time() != zero_time or d.tzinfo is not None:
+                return [u'%s' % x for x in data ]
+            
+        values = np.array(data,dtype=object)
+        mask = isnull(self.values)
+        values[mask] = na_rep
+        
+        imask = -mask
+        values[imask] = np.array([ u'%d-%.2d-%.2d' % (dt.year, dt.month, dt.day) for dt in values[imask] ])
+        return values.tolist()
+
     def isin(self, values):
         """
         Compute boolean array of whether each index value is found in the
@@ -627,11 +647,6 @@ def astype(self, dtype):
         else:  # pragma: no cover
             raise ValueError('Cannot cast DatetimeIndex to dtype %s' % dtype)
 
-    @property
-    def asi8(self):
-        # do not cache or you'll create a memory leak
-        return self.values.view('i8')
-
     def _get_time_micros(self):
         utc = _utc()
         values = self.asi8

pandas/tseries/period.py

@@ -12,6 +12,7 @@
 import pandas.tseries.frequencies as _freq_mod
 
 import pandas.core.common as com
+from pandas.core.common import isnull
 
 from pandas.lib import Timestamp
 import pandas.lib as lib
@@ -792,6 +793,15 @@ def _mpl_repr(self):
         # how to represent ourselves to matplotlib
         return self._get_object_array()
 
+    def equals(self, other):
+        """
+        Determines if two Index objects contain the same elements.
+        """
+        if self is other:
+            return True
+
+        return np.array_equal(self.asi8, other.asi8)
+
     def tolist(self):
         """
         Return a list of Period objects
@@ -1029,16 +1039,18 @@ def __getitem__(self, key):
 
             return PeriodIndex(result, name=self.name, freq=self.freq)
 
-    def format(self, name=False, formatter=None):
-        """
-        Render a string representation of the Index
-        """
-        header = []
+    def _format_with_header(self, header, **kwargs):
+        return header + self._format_native_types(**kwargs)
 
-        if name:
-            header.append(str(self.name) if self.name is not None else '')
+    def _format_native_types(self, na_rep=u'NaT', **kwargs):
 
-        return header + ['%s' % Period(x, freq=self.freq) for x in self]
+        values = np.array(list(self),dtype=object)
+        mask = isnull(self.values)
+        values[mask] = na_rep
+        
+        imask = -mask
+        values[imask] = np.array([ u'%s' % dt for dt in values[imask] ])
+        return values.tolist()
 
     def __array_finalize__(self, obj):
         if self.ndim == 0:  # pragma: no cover

pandas/util/testing.py

@@ -406,7 +406,7 @@ def makeCustomIndex(nentries, nlevels, prefix='#', names=False, ndupe_l=None,
        label will repeated at the corresponding level, you can specify just
        the first few, the rest will use the default ndupe_l of 1.
        len(ndupe_l) <= nlevels.
-    idx_type - "i"/"f"/"s"/"u"/"dt".
+    idx_type - "i"/"f"/"s"/"u"/"dt/"p".
        If idx_type is not None, `idx_nlevels` must be 1.
        "i"/"f" creates an integer/float index,
        "s"/"u" creates a string/unicode index
@@ -422,7 +422,7 @@ def makeCustomIndex(nentries, nlevels, prefix='#', names=False, ndupe_l=None,
     assert (names is None or names is False
             or names is True or len(names) is nlevels)
     assert idx_type is None or \
-        (idx_type in ('i', 'f', 's', 'u', 'dt') and nlevels == 1)
+        (idx_type in ('i', 'f', 's', 'u', 'dt', 'p') and nlevels == 1)
 
     if names is True:
         # build default names
@@ -437,7 +437,7 @@ def makeCustomIndex(nentries, nlevels, prefix='#', names=False, ndupe_l=None,
 
     # specific 1D index type requested?
     idx_func = dict(i=makeIntIndex, f=makeFloatIndex, s=makeStringIndex,
-                    u=makeUnicodeIndex, dt=makeDateIndex).get(idx_type)
+                    u=makeUnicodeIndex, dt=makeDateIndex, p=makePeriodIndex).get(idx_type)
     if idx_func:
         idx = idx_func(nentries)
         # but we need to fill in the name
@@ -446,7 +446,7 @@ def makeCustomIndex(nentries, nlevels, prefix='#', names=False, ndupe_l=None,
         return idx
     elif idx_type is not None:
         raise ValueError('"%s" is not a legal value for `idx_type`, use  '
-                         '"i"/"f"/"s"/"u"/"dt".' % idx_type)
+                         '"i"/"f"/"s"/"u"/"dt/"p".' % idx_type)
 
     if len(ndupe_l) < nlevels:
         ndupe_l.extend([1] * (nlevels - len(ndupe_l)))
@@ -540,9 +540,9 @@ def makeCustomDataframe(nrows, ncols, c_idx_names=True, r_idx_names=True,
     assert c_idx_nlevels > 0
     assert r_idx_nlevels > 0
     assert r_idx_type is None or \
-        (r_idx_type in ('i', 'f', 's', 'u', 'dt') and r_idx_nlevels == 1)
+        (r_idx_type in ('i', 'f', 's', 'u', 'dt', 'p') and r_idx_nlevels == 1)
     assert c_idx_type is None or \
-        (c_idx_type in ('i', 'f', 's', 'u', 'dt') and c_idx_nlevels == 1)
+        (c_idx_type in ('i', 'f', 's', 'u', 'dt', 'p') and c_idx_nlevels == 1)
 
     columns = makeCustomIndex(ncols, nlevels=c_idx_nlevels, prefix='C',
                               names=c_idx_names, ndupe_l=c_ndupe_l,