NotImplementedError messages doc update #7872

pandas/core/base.py

@@ -19,7 +19,7 @@ class StringMixin(object):
     # Formatting
 
     def __unicode__(self):
-        raise NotImplementedError
+        raise NotImplementedError("StringMixin __unicode__ format")
 
     def __str__(self):
         """
@@ -382,7 +382,7 @@ def _box_func(self):
         """
         box function to get object from internal representation
         """
-        raise NotImplementedError
+        raise NotImplementedError("Box function to get object from internal representation")
 
     def _box_values(self, values):
         """

pandas/core/common.py

@@ -39,6 +39,11 @@ class SettingWithCopyWarning(Warning):
 class AmbiguousIndexError(PandasError, KeyError):
     pass
 
+class AbstractMethodError(NotImplementedError):
+    def __init__(self,m):
+        self.message = m
+    def __str__(self):
+        return "This method must be defined on the concrete class - "+self.message
 
 _POSSIBLY_CAST_DTYPES = set([np.dtype(t).name
                              for t in ['O', 'int8',
@@ -134,7 +139,7 @@ def _isnull_new(obj):
         return lib.checknull(obj)
     # hack (for now) because MI registers as ndarray
     elif isinstance(obj, pd.MultiIndex):
-        raise NotImplementedError("isnull is not defined for MultiIndex")
+        raise AbstractMethodError("isnull is not defined for MultiIndex")
     elif isinstance(obj, (ABCSeries, np.ndarray)):
         return _isnull_ndarraylike(obj)
     elif isinstance(obj, ABCGeneric):
@@ -160,7 +165,7 @@ def _isnull_old(obj):
         return lib.checknull_old(obj)
     # hack (for now) because MI registers as ndarray
     elif isinstance(obj, pd.MultiIndex):
-        raise NotImplementedError("isnull is not defined for MultiIndex")
+        raise AbstractMethodError("isnull is not defined for MultiIndex")
     elif isinstance(obj, (ABCSeries, np.ndarray)):
         return _isnull_ndarraylike_old(obj)
     elif isinstance(obj, ABCGeneric):

pandas/core/generic.py

@@ -135,7 +135,7 @@ def _init_mgr(self, mgr, axes=None, dtype=None, copy=False):
 
     @property
     def _constructor(self):
-        raise NotImplementedError
+        raise NotImplementedError("NDFrame _constructor")
 
     def __unicode__(self):
         # unicode representation based upon iterating over self
@@ -150,7 +150,7 @@ def _local_dir(self):
 
     @property
     def _constructor_sliced(self):
-        raise NotImplementedError
+        raise NotImplementedError("NDFrame _constructor_sliced")
 
     #----------------------------------------------------------------------
     # Axis
@@ -1073,7 +1073,7 @@ def _iget_item_cache(self, item):
         return lower
 
     def _box_item_values(self, key, values):
-        raise NotImplementedError
+        raise NotImplementedError("NDFrame _box_item_values")
 
     def _maybe_cache_changed(self, item, value):
         """
@@ -1653,7 +1653,7 @@ def _needs_reindex_multi(self, axes, method, level):
                 method is None and level is None and not self._is_mixed_type)
 
     def _reindex_multi(self, axes, copy, fill_value):
-        return NotImplemented
+        raise NotImplementedError("NDFrame _reindex_multi")
 
     _shared_docs['reindex_axis'] = (
         """Conform input object to new index with optional filling logic,
@@ -2179,7 +2179,7 @@ def fillna(self, value=None, method=None, axis=0, inplace=False,
                 raise ValueError('must specify a fill method or value')
             if self._is_mixed_type and axis == 1:
                 if inplace:
-                    raise NotImplementedError()
+                    raise NotImplementedError("fillna with inplace=True and _is_mixed_type=True and axis=1")
                 result = self.T.fillna(method=method, limit=limit).T
 
                 # need to downcast here because of all of the transposes
@@ -2880,7 +2880,7 @@ def first(self, offset):
         """
         from pandas.tseries.frequencies import to_offset
         if not isinstance(self.index, DatetimeIndex):
-            raise NotImplementedError
+            raise NotImplementedError("Currently implemented for DatatimeIndex instance only")
 
         if len(self.index) == 0:
             return self
@@ -2914,7 +2914,7 @@ def last(self, offset):
         """
         from pandas.tseries.frequencies import to_offset
         if not isinstance(self.index, DatetimeIndex):
-            raise NotImplementedError
+            raise NotImplementedError("Currently implemented for DatatimeIndex instance only")
 
         if len(self.index) == 0:
             return self

pandas/core/groupby.py

@@ -283,7 +283,7 @@ def _set_grouper(self, obj, sort=False):
         return self.grouper
 
     def _get_binner_for_grouping(self, obj):
-        raise NotImplementedError
+        raise NotImplementedError("Binner for grouping")
 
     @property
     def groups(self):
@@ -644,7 +644,7 @@ def _python_apply_general(self, f):
                                          not_indexed_same=mutated)
 
     def aggregate(self, func, *args, **kwargs):
-        raise NotImplementedError
+        raise NotImplementedError("Groupby aggregrate")
 
     @Appender(_agg_doc)
     def agg(self, func, *args, **kwargs):
@@ -654,7 +654,7 @@ def _iterate_slices(self):
         yield self.name, self._selected_obj
 
     def transform(self, func, *args, **kwargs):
-        raise NotImplementedError
+        raise NotImplementedError("Groupby transform")
 
     def mean(self):
         """
@@ -1041,7 +1041,7 @@ def _python_agg_general(self, func, *args, **kwargs):
         return self._wrap_aggregated_output(output)
 
     def _wrap_applied_output(self, *args, **kwargs):
-        raise NotImplementedError
+        raise NotImplementedError("Groupby wrap applied output")
 
     def _concat_objects(self, keys, values, not_indexed_same=False):
         from pandas.tools.merge import concat
@@ -1404,7 +1404,7 @@ def aggregate(self, values, how, axis=0):
                 swapped = True
                 values = values.swapaxes(0, axis)
             if arity > 1:
-                raise NotImplementedError
+                raise NotImplementedError("BaseGrouper aggregate for arity > 1")
             out_shape = (self.ngroups,) + values.shape[1:]
 
         if is_numeric_dtype(values.dtype):
@@ -1459,7 +1459,7 @@ def _aggregate(self, result, counts, values, how, is_numeric):
         comp_ids, _, ngroups = self.group_info
         if values.ndim > 3:
             # punting for now
-            raise NotImplementedError
+            raise NotImplementedError("BaseGrouper aggregrate for > 3 dimensions")
         elif values.ndim > 2:
             for i, chunk in enumerate(values.transpose(2, 0, 1)):
 
@@ -1695,7 +1695,7 @@ def _aggregate(self, result, counts, values, how, is_numeric=True):
 
         if values.ndim > 3:
             # punting for now
-            raise NotImplementedError
+            raise NotImplementedError("BinGrouper aggregate for > 3 dimensions")
         elif values.ndim > 2:
             for i, chunk in enumerate(values.transpose(2, 0, 1)):
                 agg_func(result[:, :, i], counts, chunk, self.bins)
@@ -2399,7 +2399,7 @@ def aggregate(self, arg, *args, **kwargs):
             if self._selection is not None:
                 subset = obj
                 if isinstance(subset, DataFrame):
-                    raise NotImplementedError
+                    raise NotImplementedError("aggregate not implemented for subset being a Dataframe")
 
                 for fname, agg_how in compat.iteritems(arg):
                     colg = SeriesGroupBy(subset, selection=self._selection,
@@ -2459,7 +2459,7 @@ def _aggregate_multiple_funcs(self, arg):
         from pandas.tools.merge import concat
 
         if self.axis != 0:
-            raise NotImplementedError
+            raise NotImplementedError("Currently implemented for axis = 0")
 
         obj = self._obj_with_exclusions
 
@@ -2509,7 +2509,7 @@ def _aggregate_generic(self, func, *args, **kwargs):
         return self._wrap_generic_output(result, obj)
 
     def _wrap_aggregated_output(self, output, names=None):
-        raise NotImplementedError
+        raise NotImplementedError("NDFrameGroupBy wrap aggregated output")
 
     def _aggregate_item_by_item(self, func, *args, **kwargs):
         # only for axis==0
@@ -3050,7 +3050,7 @@ def _iterate_slices(self):
                 slice_axis = self._selection_list
             slicer = lambda x: self._selected_obj[x]
         else:
-            raise NotImplementedError
+            raise NotImplementedError("Currently implemented for axis = 0")
 
         for val in slice_axis:
             if val in self.exclusions:
@@ -3115,10 +3115,10 @@ def _aggregate_item_by_item(self, func, *args, **kwargs):
             new_axes[self.axis] = self.grouper.result_index
             return Panel._from_axes(result, new_axes)
         else:
-            raise NotImplementedError
+            raise NotImplementedError("Currently implemented for axis>0")
 
     def _wrap_aggregated_output(self, output, names=None):
-        raise NotImplementedError
+        raise NotImplementedError("PanelGroupBy _wrap_aggregated_output")
 
 
 class NDArrayGroupBy(GroupBy):
@@ -3172,7 +3172,7 @@ def _chop(self, sdata, slice_obj):
         return sdata.iloc[slice_obj]
 
     def apply(self, f):
-        raise NotImplementedError
+        raise NotImplementedError("DataSplitter apply function")
 
 
 class ArraySplitter(DataSplitter):

pandas/core/indexing.py

@@ -7,7 +7,7 @@
 import pandas.core.common as com
 from pandas.core.common import (_is_bool_indexer, is_integer_dtype,
                                 _asarray_tuplesafe, is_list_like, isnull,
-                                ABCSeries, ABCDataFrame, ABCPanel, is_float)
+                                ABCSeries, ABCDataFrame, ABCPanel, is_float, AbstractMethodError)
 import pandas.lib as lib
 
 import numpy as np
@@ -55,7 +55,7 @@ def __call__(self, *args, **kwargs):
         return self
 
     def __iter__(self):
-        raise NotImplementedError('ix is not iterable')
+        raise AbstractMethodError('ix is not iterable')
 
     def __getitem__(self, key):
         if type(key) is tuple:
@@ -120,7 +120,7 @@ def __setitem__(self, key, value):
         self._setitem_with_indexer(indexer, value)
 
     def _has_valid_type(self, k, axis):
-        raise NotImplementedError()
+        raise AbstractMethodError("Valid type checking for _NDFrameIndexer is not implemented")
 
     def _has_valid_tuple(self, key):
         """ check the key for valid keys across my indexer """
@@ -644,7 +644,7 @@ def _align_frame(self, indexer, df):
     def _align_panel(self, indexer, df):
         is_frame = self.obj.ndim == 2
         is_panel = self.obj.ndim >= 3
-        raise NotImplementedError("cannot set using an indexer with a Panel "
+        raise AbstractMethodError("cannot set using an indexer with a Panel "
                                   "yet!")
 
     def _getitem_tuple(self, tup):
@@ -1141,7 +1141,7 @@ def __getitem__(self, key):
             return self._getitem_axis(key, axis=0)
 
     def _getitem_axis(self, key, axis=0, validate_iterable=False):
-        raise NotImplementedError()
+        raise AbstractMethodError("Get item along given axis in _LocationIndexer is not implemented")
 
     def _getbool_axis(self, key, axis=0):
         labels = self.obj._get_axis(axis)
@@ -1299,7 +1299,7 @@ def _has_valid_type(self, key, axis):
         if com._is_bool_indexer(key):
             if hasattr(key, 'index') and isinstance(key.index, Index):
                 if key.index.inferred_type == 'integer':
-                    raise NotImplementedError(
+                    raise AbstractMethodError(
                         "iLocation based boolean indexing on an integer type "
                         "is not available"
                     )

pandas/core/internals.py

@@ -243,7 +243,7 @@ def fillna(self, value, limit=None, inplace=False, downcast=None):
         mask = isnull(self.values)
         if limit is not None:
             if self.ndim > 2:
-                raise NotImplementedError
+                raise NotImplementedError("fillna function not implemented for more than 2 dimensions")
             mask[mask.cumsum(self.ndim-1)>limit]=False
 
         value = self._try_fill(value)
@@ -363,10 +363,10 @@ def convert(self, copy=True, **kwargs):
         return [self.copy()] if copy else [self]
 
     def _can_hold_element(self, value):
-        raise NotImplementedError()
+        raise NotImplementedError("Block _can_hold_element function")
 
     def _try_cast(self, value):
-        raise NotImplementedError()
+        raise NotImplementedError("Block _try_cast")
 
     def _try_cast_result(self, result, dtype=None):
         """ try to cast the result to our original type,
@@ -1519,7 +1519,7 @@ def fillna(self, value, limit=None,
         value = self._try_fill(value)
         if limit is not None:
             if self.ndim > 2:
-                raise NotImplementedError
+                raise NotImplementedError("fillna function not implemented for more than 2 dimensions")
             mask[mask.cumsum(self.ndim-1)>limit]=False
 
         np.putmask(values, mask, value)
@@ -1741,7 +1741,7 @@ def interpolate(self, method='pad', axis=0, inplace=False,
     def fillna(self, value, limit=None, inplace=False, downcast=None):
         # we may need to upcast our fill to match our dtype
         if limit is not None:
-            raise NotImplementedError
+            raise NotImplementedError("fillna currently implemented only for limit=None")
         if issubclass(self.dtype.type, np.floating):
             value = float(value)
         values = self.values if inplace else self.values.copy()

pandas/core/panel.py

@@ -595,10 +595,10 @@ def conform(self, frame, axis='items'):
         return frame.reindex(**self._extract_axes_for_slice(self, axes))
 
     def head(self, n=5):
-        raise NotImplementedError
+        raise NotImplementedError("Returns the head content of a Panel")
 
     def tail(self, n=5):
-        raise NotImplementedError
+        raise NotImplementedError("Returns the tail content of a Panel")
 
     def _needs_reindex_multi(self, axes, method, level):
         """ don't allow a multi reindex on Panel or above ndim """

pandas/core/panelnd.py

@@ -99,7 +99,7 @@ def _combine_with_constructor(self, other, func):
     for f in ['to_frame', 'to_excel', 'to_sparse', 'groupby', 'join', 'filter',
               'dropna', 'shift']:
         def func(self, *args, **kwargs):
-            raise NotImplementedError
+            raise NotImplementedError("%s is not implemented" % f)
         setattr(klass, f, func)
 
     # add the aggregate operations

pandas/core/series.py

@@ -156,7 +156,7 @@ def __init__(self, data=None, index=None, dtype=None, name=None,
                 dtype = self._validate_dtype(dtype)
 
             if isinstance(data, MultiIndex):
-                raise NotImplementedError
+                raise NotImplementedError("Series initialization currently is not implemented for MultiIndex")
             elif isinstance(data, Index):
                 # need to copy to avoid aliasing issues
                 if name is None:

pandas/core/strings.py

@@ -680,7 +680,7 @@ def str_slice_replace(arr, start=None, stop=None, repl=None):
     -------
     replaced : array
     """
-    raise NotImplementedError
+    raise NotImplementedError("String slice replace not implemented")
 
 
 def str_strip(arr, to_strip=None):
@@ -985,7 +985,7 @@ def slice(self, start=None, stop=None, step=1):
 
     @copy(str_slice)
     def slice_replace(self, i=None, j=None):
-        raise NotImplementedError
+        raise NotImplementedError("String slice replace is not implemented.")
 
     @copy(str_decode)
     def decode(self, encoding, errors="strict"):

pandas/index.pyx

@@ -234,10 +234,10 @@ cdef class IndexEngine:
         self._ensure_mapping_populated()
 
     def _call_monotonic(self, values):
-        raise NotImplementedError
+        raise NotImplementedError("IndexEngine _call_monotonic is not implemented")
 
     cdef _make_hash_table(self, n):
-        raise NotImplementedError
+        raise NotImplementedError("IndexEngine _make_hash_table is not implemented")
 
     cdef _check_type(self, object val):
         hash(val)

pandas/io/ga.py

@@ -240,7 +240,7 @@ def get_profile(self, account_id=None, web_property_id=None, name=None,
         return _get_match(profiles, name, id, **kwargs)
 
     def create_query(self, *args, **kwargs):
-        raise NotImplementedError()
+        raise NotImplementedError("Create query in GDataReader is not implemented")
 
     @Substitution(extras='')
     @Appender(_GA_READER_DOC)