BUG/DOC: Categorical fixes (GH7918)

doc/source/categorical.rst

@@ -509,35 +509,7 @@ The same applies to ``df.append(df)``.
 Getting Data In/Out
 -------------------
 
-Writing data (`Series`, `Frames`) to a HDF store and reading it in entirety works. Querying the HDF
-store does not yet work.
-
-.. ipython:: python
-   :suppress:
-
-    hdf_file = "test.h5"
-
-.. ipython:: python
-
-    hdf_file = "test.h5"
-    s = pd.Series(pd.Categorical(['a', 'b', 'b', 'a', 'a', 'c'], levels=['a','b','c','d']))
-    df = pd.DataFrame({"s":s, "vals":[1,2,3,4,5,6]})
-    df.to_hdf(hdf_file, "frame")
-    df2 = pd.read_hdf(hdf_file, "frame")
-    df2
-    try:
-        pd.read_hdf(hdf_file, "frame", where = ['index>2'])
-    except TypeError as e:
-        print("TypeError: " + str(e))
-
-.. ipython:: python
-   :suppress:
-
-    try:
-        os.remove(hdf_file)
-    except:
-        pass
-
+Writing data (`Series`, `Frames`) to a HDF store that contains a ``category`` dtype will currently raise ``NotImplementedError``.
 
 Writing to a CSV file will convert the data, effectively removing any information about the
 `Categorical` (levels and ordering). So if you read back the CSV file you have to convert the

doc/source/v0.15.0.txt

@@ -137,7 +137,7 @@ Categoricals in Series/DataFrame
 
 :class:`~pandas.Categorical` can now be included in `Series` and `DataFrames` and gained new
 methods to manipulate. Thanks to Jan Schultz for much of this API/implementation. (:issue:`3943`, :issue:`5313`, :issue:`5314`,
-:issue:`7444`, :issue:`7839`, :issue:`7848`, :issue:`7864`).
+:issue:`7444`, :issue:`7839`, :issue:`7848`, :issue:`7864`, :issue:`7914`).
 
 For full docs, see the :ref:`Categorical introduction <categorical>` and the :ref:`API documentation <api.categorical>`.
 

pandas/core/internals.py

@@ -82,6 +82,11 @@ def _consolidate_key(self):
     def _is_single_block(self):
         return self.ndim == 1
 
+    @property
+    def is_view(self):
+        """ return a boolean if I am possibly a view """
+        return self.values.base is not None
+
     @property
     def is_datelike(self):
         """ return True if I am a non-datelike """
@@ -1558,6 +1563,11 @@ def __init__(self, values, placement,
                                                fastpath=True, placement=placement,
                                                **kwargs)
 
+    @property
+    def is_view(self):
+        """ I am never a view """
+        return False
+
     def to_dense(self):
         return self.values.to_dense().view()
 
@@ -2522,7 +2532,7 @@ def is_datelike_mixed_type(self):
     def is_view(self):
         """ return a boolean if we are a single block and are a view """
         if len(self.blocks) == 1:
-            return self.blocks[0].values.base is not None
+            return self.blocks[0].is_view
 
         # It is technically possible to figure out which blocks are views
         # e.g. [ b.values.base is not None for b in self.blocks ]

pandas/io/pytables.py

@@ -1782,7 +1782,7 @@ def set_atom(self, block, block_items, existing_col, min_itemsize,
                 "[unicode] is not implemented as a table column")
 
         elif dtype == 'category':
-            raise NotImplementedError
+            raise NotImplementedError("cannot store a category dtype")
 
         # this is basically a catchall; if say a datetime64 has nans then will
         # end up here ###
@@ -2420,6 +2420,9 @@ def write_array(self, key, value, items=None):
         empty_array = self._is_empty_array(value.shape)
         transposed = False
 
+        if com.is_categorical_dtype(value):
+            raise NotImplementedError("cannot store a category dtype")
+
         if not empty_array:
             value = value.T
             transposed = True
@@ -3451,10 +3454,10 @@ def read_column(self, column, where=None, start=None, stop=None, **kwargs):
                 # column must be an indexable or a data column
                 c = getattr(self.table.cols, column)
                 a.set_info(self.info)
-                return Series(_set_tz(a.convert(c[start:stop], 
+                return Series(_set_tz(a.convert(c[start:stop],
                                                 nan_rep=self.nan_rep,
                                                 encoding=self.encoding
-                                                ).take_data(), 
+                                                ).take_data(),
                                       a.tz, True))
 
         raise KeyError("column [%s] not found in the table" % column)

pandas/io/tests/test_pytables.py

@@ -4318,7 +4318,7 @@ def test_tseries_select_index_column(self):
         # check that no tz still works
         rng = date_range('1/1/2000', '1/30/2000')
         frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
-        
+
         with ensure_clean_store(self.path) as store:
             store.append('frame', frame)
             result = store.select_column('frame', 'index')
@@ -4327,7 +4327,7 @@ def test_tseries_select_index_column(self):
         # check utc
         rng = date_range('1/1/2000', '1/30/2000', tz='UTC')
         frame = DataFrame(np.random.randn(len(rng), 4), index=rng)
-        
+
         with ensure_clean_store(self.path) as store:
             store.append('frame', frame)
             result = store.select_column('frame', 'index')
@@ -4398,13 +4398,15 @@ def test_categorical(self):
 
             s = Series(Categorical(['a', 'b', 'b', 'a', 'a', 'c'], levels=['a','b','c','d']))
 
-            self.assertRaises(NotImplementedError, store.append, 's', s, format='table')
+            self.assertRaises(NotImplementedError, store.put, 's_fixed', s, format='fixed')
+            self.assertRaises(NotImplementedError, store.append, 's_table', s, format='table')
             #store.append('s', s, format='table')
             #result = store.select('s')
             #tm.assert_series_equal(s, result)
 
             df = DataFrame({"s":s, "vals":[1,2,3,4,5,6]})
-            self.assertRaises(NotImplementedError, store.append, 'df', df, format='table')
+            self.assertRaises(NotImplementedError, store.put, 'df_fixed', df, format='fixed')
+            self.assertRaises(NotImplementedError, store.append, 'df_table', df, format='table')
             #store.append('df', df, format='table')
             #result = store.select('df')
             #tm.assert_frame_equal(df, df2)
@@ -4413,17 +4415,17 @@ def test_categorical(self):
             # FIXME: TypeError: cannot pass a where specification when reading from a Fixed format store. this store must be selected in its entirety
             #result = store.select('df', where = ['index>2'])
             #tm.assert_frame_equal(df[df.index>2],result)
-    
+
     def test_duplicate_column_name(self):
         df = DataFrame(columns=["a", "a"], data=[[0, 0]])
-        
+
         with ensure_clean_path(self.path) as path:
             self.assertRaises(ValueError, df.to_hdf, path, 'df', format='fixed')
-            
+
             df.to_hdf(path, 'df', format='table')
             other = read_hdf(path, 'df')
             tm.assert_frame_equal(df, other)
-        
+
 
 def _test_sort(obj):
     if isinstance(obj, DataFrame):

pandas/tests/test_categorical.py

@@ -1185,6 +1185,30 @@ def test_slicing_and_getting_ops(self):
         tm.assert_frame_equal(res_df, df)
         self.assertTrue(com.is_categorical_dtype(res_df["cats"]))
 
+    def test_slicing_doc_examples(self):
+
+        #GH 7918
+        cats = Categorical(["a","b","b","b","c","c","c"], levels=["a","b","c"])
+        idx = Index(["h","i","j","k","l","m","n",])
+        values= [1,2,2,2,3,4,5]
+        df = DataFrame({"cats":cats,"values":values}, index=idx)
+
+        result = df.iloc[2:4,:]
+        expected = DataFrame({"cats":Categorical(['b','b'],levels=['a','b','c']),"values":[2,2]}, index=['j','k'])
+        tm.assert_frame_equal(result, expected)
+
+        result = df.iloc[2:4,:].dtypes
+        expected = Series(['category','int64'],['cats','values'])
+        tm.assert_series_equal(result, expected)
+
+        result = df.loc["h":"j","cats"]
+        expected = Series(Categorical(['a','b','b'],levels=['a','b','c']),index=['h','i','j'])
+        tm.assert_series_equal(result, expected)
+
+        result = df.ix["h":"j",0:1]
+        expected = DataFrame({'cats' : Series(Categorical(['a','b','b'],levels=['a','b','c']),index=['h','i','j']) })
+        tm.assert_frame_equal(result, expected)
+
     def test_assigning_ops(self):
 
         # systematically test the assigning operations: