def _slice(self, slicer):
""" Return a slice of myself. """
# only allow 1 dimensional slicing, but can
# in a 2-d case be passd (slice(None),....)
if isinstance(slicer, tuple) and len(slicer) == 2:
if not _is_null_slice(slicer[0]):
raise AssertionError("invalid slicing for a 1-ndim categorical")
slicer = slicer[1]
_codes = self._codes[slicer]
return Categorical(values=_codes,levels=self.levels, ordered=self.ordered,
name=self.name, fastpath=True)
def _slice(self, slicer):
""" Return a slice of myself. """
# only allow 1 dimensional slicing, but can
# in a 2-d case be passd (slice(None),....)
if isinstance(slicer, tuple) and len(slicer) == 2:
if not _is_null_slice(slicer[0]):
raise AssertionError("invalid slicing for a 1-ndim categorical")
slicer = slicer[1]
_codes = self._codes[slicer]
return Categorical(values=_codes,levels=self.levels, ordered=self.ordered,
name=self.name, fastpath=True)
def __setitem__(self, key, value):
""" Item assignment.
Raises
------
ValueError
If (one or more) Value is not in levels or if a assigned `Categorical` has not the
same levels
"""
# require identical level set
if isinstance(value, Categorical):
if not value.levels.equals(self.levels):
raise ValueError(
"cannot set a Categorical with another, without identical levels"
)
rvalue = value if com.is_list_like(value) else [value]
to_add = Index(rvalue) - self.levels
if len(to_add):
raise ValueError(
"cannot setitem on a Categorical with a new level,"
" set the levels first")
# set by position
if isinstance(key, (int, np.integer)):
pass
# tuple of indexers
elif isinstance(key, tuple):
# only allow 1 dimensional slicing, but can
# in a 2-d case be passd (slice(None),....)
if len(key) == 2:
if not _is_null_slice(key[0]):
raise AssertionError(
"invalid slicing for a 1-ndim categorical")
key = key[1]
elif len(key) == 1:
key = key[0]
else:
raise AssertionError(
"invalid slicing for a 1-ndim categorical")
else:
key = self._codes[key]
lindexer = self.levels.get_indexer(rvalue)
self._codes[key] = lindexer
def __setitem__(self, key, value):
""" Item assignment.
Raises
------
ValueError
If (one or more) Value is not in levels or if a assigned `Categorical` has not the
same levels
"""
# require identical level set
if isinstance(value, Categorical):
if not value.levels.equals(self.levels):
raise ValueError("cannot set a Categorical with another, without identical levels")
rvalue = value if com.is_list_like(value) else [value]
to_add = Index(rvalue)-self.levels
if len(to_add):
raise ValueError("cannot setitem on a Categorical with a new level,"
" set the levels first")
# set by position
if isinstance(key, (int, np.integer)):
pass
# tuple of indexers
elif isinstance(key, tuple):
# only allow 1 dimensional slicing, but can
# in a 2-d case be passd (slice(None),....)
if len(key) == 2:
if not _is_null_slice(key[0]):
raise AssertionError("invalid slicing for a 1-ndim categorical")
key = key[1]
elif len(key) == 1:
key = key[0]
else:
raise AssertionError("invalid slicing for a 1-ndim categorical")
else:
key = self._codes[key]
lindexer = self.levels.get_indexer(rvalue)
self._codes[key] = lindexer
def __setitem__(self, key, value):
""" Item assignment.
Raises
------
ValueError
If (one or more) Value is not in categories or if a assigned `Categorical` has not the
same categories
"""
# require identical categories set
if isinstance(value, Categorical):
if not value.categories.equals(self.categories):
raise ValueError("Cannot set a Categorical with another, without identical "
"categories")
rvalue = value if com.is_list_like(value) else [value]
to_add = Index(rvalue).difference(self.categories)
# no assignments of values not in categories, but it's always ok to set something to np.nan
if len(to_add) and not isnull(to_add).all():
raise ValueError("cannot setitem on a Categorical with a new category,"
" set the categories first")
# set by position
if isinstance(key, (int, np.integer)):
pass
# tuple of indexers (dataframe)
elif isinstance(key, tuple):
# only allow 1 dimensional slicing, but can
# in a 2-d case be passd (slice(None),....)
if len(key) == 2:
if not _is_null_slice(key[0]):
raise AssertionError("invalid slicing for a 1-ndim categorical")
key = key[1]
elif len(key) == 1:
key = key[0]
else:
raise AssertionError("invalid slicing for a 1-ndim categorical")
# slicing in Series or Categorical
elif isinstance(key, slice):
pass
# Array of True/False in Series or Categorical
else:
# There is a bug in numpy, which does not accept a Series as a indexer
# https://github.com/pydata/pandas/issues/6168
# https://github.com/numpy/numpy/issues/4240 -> fixed in numpy 1.9
# FIXME: remove when numpy 1.9 is the lowest numpy version pandas accepts...
key = np.asarray(key)
lindexer = self.categories.get_indexer(rvalue)
# FIXME: the following can be removed after https://github.com/pydata/pandas/issues/7820
# is fixed.
# float categories do currently return -1 for np.nan, even if np.nan is included in the
# index -> "repair" this here
if isnull(rvalue).any() and isnull(self.categories).any():
nan_pos = np.where(com.isnull(self.categories))[0]
lindexer[lindexer == -1] = nan_pos
self._codes[key] = lindexer
def __setitem__(self, key, value):
""" Item assignment.
Raises
------
ValueError
If (one or more) Value is not in levels or if a assigned `Categorical` has not the
same levels
"""
# require identical level set
if isinstance(value, Categorical):
if not value.levels.equals(self.levels):
raise ValueError(
"cannot set a Categorical with another, without identical levels"
)
rvalue = value if com.is_list_like(value) else [value]
to_add = Index(rvalue).difference(self.levels)
# no assignments of values not in levels, but it's always ok to set something to np.nan
if len(to_add) and not isnull(to_add).all():
raise ValueError(
"cannot setitem on a Categorical with a new level,"
" set the levels first")
# set by position
if isinstance(key, (int, np.integer)):
pass
# tuple of indexers (dataframe)
elif isinstance(key, tuple):
# only allow 1 dimensional slicing, but can
# in a 2-d case be passd (slice(None),....)
if len(key) == 2:
if not _is_null_slice(key[0]):
raise AssertionError(
"invalid slicing for a 1-ndim categorical")
key = key[1]
elif len(key) == 1:
key = key[0]
else:
raise AssertionError(
"invalid slicing for a 1-ndim categorical")
# slicing in Series or Categorical
elif isinstance(key, slice):
pass
# Array of True/False in Series or Categorical
else:
# There is a bug in numpy, which does not accept a Series as a indexer
# https://github.com/pydata/pandas/issues/6168
# https://github.com/numpy/numpy/issues/4240 -> fixed in numpy 1.9
# FIXME: remove when numpy 1.9 is the lowest numpy version pandas accepts...
key = np.asarray(key)
lindexer = self.levels.get_indexer(rvalue)
# FIXME: the following can be removed after https://github.com/pydata/pandas/issues/7820
# is fixed.
# float levels do currently return -1 for np.nan, even if np.nan is included in the index
# "repair" this here
if isnull(rvalue).any() and isnull(self.levels).any():
nan_pos = np.where(com.isnull(self.levels))[0]
lindexer[lindexer == -1] = nan_pos
self._codes[key] = lindexer
