def test_1d_bool(self):
arr = np.array([0, 1, 0], dtype=bool)
result = algos.take_1d(arr, [0, 2, 2, 1])
expected = arr.take([0, 2, 2, 1])
tm.assert_numpy_array_equal(result, expected)
result = algos.take_1d(arr, [0, 2, -1])
assert result.dtype == np.object_
def wrapper(left, right, name=name, na_op=na_op):
if isinstance(right, pd.DataFrame):
return NotImplemented
time_converted = _TimeOp.maybe_convert_for_time_op(left, right, name,
na_op)
if time_converted is None:
lvalues, rvalues = left, right
dtype = None
wrap_results = lambda x: x
elif time_converted is NotImplemented:
return NotImplemented
else:
left, right = time_converted.left, time_converted.right
lvalues, rvalues = time_converted.lvalues, time_converted.rvalues
dtype = time_converted.dtype
wrap_results = time_converted.wrap_results
na_op = time_converted.na_op
if isinstance(rvalues, ABCSeries):
rindex = getattr(rvalues, 'index', rvalues)
name = _maybe_match_name(left, rvalues)
lvalues = getattr(lvalues, 'values', lvalues)
rvalues = getattr(rvalues, 'values', rvalues)
if left.index.equals(rindex):
index = left.index
else:
index, lidx, ridx = left.index.join(rindex, how='outer',
return_indexers=True)
if lidx is not None:
lvalues = algos.take_1d(lvalues, lidx)
if ridx is not None:
rvalues = algos.take_1d(rvalues, ridx)
arr = na_op(lvalues, rvalues)
return left._constructor(wrap_results(arr), index=index,
name=name, dtype=dtype)
else:
# scalars
if (hasattr(lvalues, 'values') and
not isinstance(lvalues, pd.DatetimeIndex)):
lvalues = lvalues.values
return left._constructor(wrap_results(na_op(lvalues, rvalues)),
index=left.index, name=left.name,
dtype=dtype)
def _maybe_add_join_keys(self, result, left_indexer, right_indexer):
# insert group keys
keys = zip(self.join_names, self.left_on, self.right_on)
for i, (name, lname, rname) in enumerate(keys):
if not _should_fill(lname, rname):
continue
if name in result:
key_indexer = result.columns.get_loc(name)
if left_indexer is not None and right_indexer is not None:
if name in self.left:
if len(self.left) == 0:
continue
na_indexer = (left_indexer == -1).nonzero()[0]
if len(na_indexer) == 0:
continue
right_na_indexer = right_indexer.take(na_indexer)
result.iloc[na_indexer, key_indexer] = (
algos.take_1d(self.right_join_keys[i],
right_na_indexer))
elif name in self.right:
if len(self.right) == 0:
continue
na_indexer = (right_indexer == -1).nonzero()[0]
if len(na_indexer) == 0:
continue
left_na_indexer = left_indexer.take(na_indexer)
result.iloc[na_indexer, key_indexer] = (
algos.take_1d(self.left_join_keys[i],
left_na_indexer))
elif left_indexer is not None \
and isinstance(self.left_join_keys[i], np.ndarray):
if name is None:
name = 'key_%d' % i
# a faster way?
key_col = algos.take_1d(self.left_join_keys[i], left_indexer)
na_indexer = (left_indexer == -1).nonzero()[0]
right_na_indexer = right_indexer.take(na_indexer)
key_col.put(na_indexer, algos.take_1d(self.right_join_keys[i],
right_na_indexer))
result.insert(i, name, key_col)
def test_1d_fill_nonna(self, dtype_fill_out_dtype):
dtype, fill_value, out_dtype = dtype_fill_out_dtype
data = np.random.randint(0, 2, 4).astype(dtype)
indexer = [2, 1, 0, -1]
result = algos.take_1d(data, indexer, fill_value=fill_value)
assert ((result[[0, 1, 2]] == data[[2, 1, 0]]).all())
assert (result[3] == fill_value)
assert (result.dtype == out_dtype)
indexer = [2, 1, 0, 1]
result = algos.take_1d(data, indexer, fill_value=fill_value)
assert ((result[[0, 1, 2, 3]] == data[indexer]).all())
assert (result.dtype == dtype)
def _delegate_property_get(self, name):
from pandas import Series
result = getattr(self.values, name)
# maybe need to upcast (ints)
if isinstance(result, np.ndarray):
if is_integer_dtype(result):
result = result.astype('int64')
elif not is_list_like(result):
return result
result = np.asarray(result)
# blow up if we operate on categories
if self.orig is not None:
result = take_1d(result, self.orig.cat.codes)
# return the result as a Series, which is by definition a copy
result = Series(result, index=self.index, name=self.name)
# setting this object will show a SettingWithCopyWarning/Error
result.is_copy = ("modifications to a property of a datetimelike "
"object are not supported and are discarded. "
"Change values on the original.")
return result
def get_indexer(self, target, method=None, limit=None, tolerance=None):
from pandas.core.arrays.categorical import _recode_for_categories
method = missing.clean_reindex_fill_method(method)
target = ibase._ensure_index(target)
if self.is_unique and self.equals(target):
return np.arange(len(self), dtype='intp')
if method == 'pad' or method == 'backfill':
raise NotImplementedError("method='pad' and method='backfill' not "
"implemented yet for CategoricalIndex")
elif method == 'nearest':
raise NotImplementedError("method='nearest' not implemented yet "
'for CategoricalIndex')
if (isinstance(target, CategoricalIndex) and
self.values.is_dtype_equal(target)):
if self.values.equals(target.values):
# we have the same codes
codes = target.codes
else:
codes = _recode_for_categories(target.codes,
target.categories,
self.values.categories)
else:
if isinstance(target, CategoricalIndex):
code_indexer = self.categories.get_indexer(target.categories)
codes = take_1d(code_indexer, target.codes, fill_value=-1)
else:
codes = self.categories.get_indexer(target)
indexer, _ = self._engine.get_indexer_non_unique(codes)
return _ensure_platform_int(indexer)
def _reindex_with_indexers(self, reindexers, method=None, fill_value=None,
limit=None, copy=False, allow_dups=False):
if method is not None or limit is not None:
raise NotImplementedError("cannot reindex with a method or limit "
"with sparse")
if fill_value is None:
fill_value = np.nan
index, row_indexer = reindexers.get(0, (None, None))
columns, col_indexer = reindexers.get(1, (None, None))
if columns is None:
columns = self.columns
new_arrays = {}
for col in columns:
if col not in self:
continue
if row_indexer is not None:
new_arrays[col] = algos.take_1d(self[col].get_values(),
row_indexer,
fill_value=fill_value)
else:
new_arrays[col] = self[col]
return self._constructor(new_arrays, index=index,
columns=columns).__finalize__(self)
def _list_of_series_to_arrays(data, columns, coerce_float=False, dtype=None):
if columns is None:
columns = _get_objs_combined_axis(data, sort=False)
indexer_cache = {}
aligned_values = []
for s in data:
index = getattr(s, 'index', None)
if index is None:
index = ibase.default_index(len(s))
if id(index) in indexer_cache:
indexer = indexer_cache[id(index)]
else:
indexer = indexer_cache[id(index)] = index.get_indexer(columns)
values = com.values_from_object(s)
aligned_values.append(algorithms.take_1d(values, indexer))
values = np.vstack(aligned_values)
if values.dtype == np.object_:
content = list(values.T)
return _convert_object_array(content, columns, dtype=dtype,
coerce_float=coerce_float)
else:
return values.T, columns
def test_1d_other_dtypes(self):
arr = np.random.randn(10).astype(np.float32)
indexer = [1, 2, 3, -1]
result = algos.take_1d(arr, indexer)
expected = arr.take(indexer)
expected[-1] = np.nan
tm.assert_almost_equal(result, expected)
def _take_new_index(obj, indexer, new_index, axis=0):
from pandas.core.api import Series, DataFrame
if isinstance(obj, Series):
new_values = algos.take_1d(obj.values, indexer)
return Series(new_values, index=new_index, name=obj.name)
elif isinstance(obj, DataFrame):
if axis == 1:
raise NotImplementedError("axis 1 is not supported")
return DataFrame(obj._data.reindex_indexer(new_axis=new_index, indexer=indexer, axis=1))
else:
raise ValueError("'obj' should be either a Series or a DataFrame")
def test_1d_with_out(self, dtype_can_hold_na, writeable):
dtype, can_hold_na = dtype_can_hold_na
data = np.random.randint(0, 2, 4).astype(dtype)
data.flags.writeable = writeable
indexer = [2, 1, 0, 1]
out = np.empty(4, dtype=dtype)
algos.take_1d(data, indexer, out=out)
expected = data.take(indexer)
tm.assert_almost_equal(out, expected)
indexer = [2, 1, 0, -1]
out = np.empty(4, dtype=dtype)
if can_hold_na:
algos.take_1d(data, indexer, out=out)
expected = data.take(indexer)
expected[3] = np.nan
tm.assert_almost_equal(out, expected)
else:
with pytest.raises(TypeError, match=self.fill_error):
algos.take_1d(data, indexer, out=out)
# No Exception otherwise.
data.take(indexer, out=out)
def union_categoricals(to_union):
"""
Combine list-like of Categoricals, unioning categories. All
must have the same dtype, and none can be ordered.
.. versionadded:: 0.19.0
Parameters
----------
to_union : list-like of Categoricals
Returns
-------
Categorical
A single array, categories will be ordered as they
appear in the list
Raises
------
TypeError
If any of the categoricals are ordered or all do not
have the same dtype
ValueError
Emmpty list of categoricals passed
"""
from pandas import Index, Categorical
if len(to_union) == 0:
raise ValueError("No Categoricals to union")
first = to_union[0]
if any(c.ordered for c in to_union):
raise TypeError("Can only combine unordered Categoricals")
if not all(is_dtype_equal(c.categories.dtype, first.categories.dtype) for c in to_union):
raise TypeError("dtype of categories must be the same")
cats = first.categories
unique_cats = cats.append([c.categories for c in to_union[1:]]).unique()
categories = Index(unique_cats)
new_codes = []
for c in to_union:
if len(c.categories) > 0:
indexer = categories.get_indexer(c.categories)
new_codes.append(take_1d(indexer, c.codes, fill_value=-1))
else:
# must be all NaN
new_codes.append(c.codes)
new_codes = np.concatenate(new_codes)
return Categorical(new_codes, categories=categories, ordered=False, fastpath=True)
def _test_dtype(dtype, can_hold_na, writeable=True):
data = np.random.randint(0, 2, 4).astype(dtype)
data.flags.writeable = writeable
indexer = [2, 1, 0, 1]
out = np.empty(4, dtype=dtype)
algos.take_1d(data, indexer, out=out)
expected = data.take(indexer)
tm.assert_almost_equal(out, expected)
indexer = [2, 1, 0, -1]
out = np.empty(4, dtype=dtype)
if can_hold_na:
algos.take_1d(data, indexer, out=out)
expected = data.take(indexer)
expected[3] = np.nan
tm.assert_almost_equal(out, expected)
else:
with tm.assertRaisesRegexp(TypeError, self.fill_error):
algos.take_1d(data, indexer, out=out)
# no exception o/w
data.take(indexer, out=out)
def parallel_take1d():
take_1d(df["col"].values, indexer)
def _map_values(self, mapper, na_action=None):
"""An internal function that maps values using the input
correspondence (which can be a dict, Series, or function).
Parameters
----------
mapper : function, dict, or Series
The input correspondence object
na_action : {None, 'ignore'}
If 'ignore', propagate NA values, without passing them to the
mapping function
Returns
-------
applied : Union[Index, MultiIndex], inferred
The output of the mapping function applied to the index.
If the function returns a tuple with more than one element
a MultiIndex will be returned.
"""
# we can fastpath dict/Series to an efficient map
# as we know that we are not going to have to yield
# python types
if isinstance(mapper, dict):
if hasattr(mapper, '__missing__'):
# If a dictionary subclass defines a default value method,
# convert mapper to a lookup function (GH #15999).
dict_with_default = mapper
mapper = lambda x: dict_with_default[x]
else:
# Dictionary does not have a default. Thus it's safe to
# convert to an Series for efficiency.
# we specify the keys here to handle the
# possibility that they are tuples
from pandas import Series
mapper = Series(mapper)
if isinstance(mapper, ABCSeries):
# Since values were input this means we came from either
# a dict or a series and mapper should be an index
if is_extension_type(self.dtype):
values = self._values
else:
values = self.values
indexer = mapper.index.get_indexer(values)
new_values = algorithms.take_1d(mapper._values, indexer)
return new_values
# we must convert to python types
if is_extension_type(self.dtype):
values = self._values
if na_action is not None:
raise NotImplementedError
map_f = lambda values, f: values.map(f)
else:
values = self.astype(object)
values = getattr(values, 'values', values)
if na_action == 'ignore':
def map_f(values, f):
return lib.map_infer_mask(values, f,
isna(values).view(np.uint8))
else:
map_f = lib.map_infer
# mapper is a function
new_values = map_f(values, mapper)
return new_values
def union_categoricals(to_union, sort_categories=False, ignore_order=False):
"""
Combine list-like of Categorical-like, unioning categories. All
categories must have the same dtype.
.. versionadded:: 0.19.0
Parameters
----------
to_union : list-like of Categorical, CategoricalIndex,
or Series with dtype='category'
sort_categories : boolean, default False
If true, resulting categories will be lexsorted, otherwise
they will be ordered as they appear in the data.
ignore_order: boolean, default False
If true, the ordered attribute of the Categoricals will be ignored.
Results in an unordered categorical.
.. versionadded:: 0.20.0
Returns
-------
result : Categorical
Raises
------
TypeError
- all inputs do not have the same dtype
- all inputs do not have the same ordered property
- all inputs are ordered and their categories are not identical
- sort_categories=True and Categoricals are ordered
ValueError
Empty list of categoricals passed
Notes
-----
To learn more about categories, see `link
<http://pandas.pydata.org/pandas-docs/stable/categorical.html#unioning>`__
Examples
--------
>>> from pandas.api.types import union_categoricals
If you want to combine categoricals that do not necessarily have
the same categories, `union_categoricals` will combine a list-like
of categoricals. The new categories will be the union of the
categories being combined.
>>> a = pd.Categorical(["b", "c"])
>>> b = pd.Categorical(["a", "b"])
>>> union_categoricals([a, b])
[b, c, a, b]
Categories (3, object): [b, c, a]
By default, the resulting categories will be ordered as they appear
in the `categories` of the data. If you want the categories to be
lexsorted, use `sort_categories=True` argument.
>>> union_categoricals([a, b], sort_categories=True)
[b, c, a, b]
Categories (3, object): [a, b, c]
`union_categoricals` also works with the case of combining two
categoricals of the same categories and order information (e.g. what
you could also `append` for).
>>> a = pd.Categorical(["a", "b"], ordered=True)
>>> b = pd.Categorical(["a", "b", "a"], ordered=True)
>>> union_categoricals([a, b])
[a, b, a, b, a]
Categories (2, object): [a < b]
Raises `TypeError` because the categories are ordered and not identical.
>>> a = pd.Categorical(["a", "b"], ordered=True)
>>> b = pd.Categorical(["a", "b", "c"], ordered=True)
>>> union_categoricals([a, b])
TypeError: to union ordered Categoricals, all categories must be the same
New in version 0.20.0
Ordered categoricals with different categories or orderings can be
combined by using the `ignore_ordered=True` argument.
>>> a = pd.Categorical(["a", "b", "c"], ordered=True)
>>> b = pd.Categorical(["c", "b", "a"], ordered=True)
>>> union_categoricals([a, b], ignore_order=True)
[a, b, c, c, b, a]
Categories (3, object): [a, b, c]
`union_categoricals` also works with a `CategoricalIndex`, or `Series`
containing categorical data, but note that the resulting array will
always be a plain `Categorical`
>>> a = pd.Series(["b", "c"], dtype='category')
>>> b = pd.Series(["a", "b"], dtype='category')
>>> union_categoricals([a, b])
[b, c, a, b]
Categories (3, object): [b, c, a]
"""
from pandas import Index, Categorical, CategoricalIndex, Series
from pandas.core.categorical import _recode_for_categories
if len(to_union) == 0:
raise ValueError('No Categoricals to union')
def _maybe_unwrap(x):
if isinstance(x, (CategoricalIndex, Series)):
return x.values
elif isinstance(x, Categorical):
return x
else:
raise TypeError("all components to combine must be Categorical")
to_union = [_maybe_unwrap(x) for x in to_union]
first = to_union[0]
if not all(
is_dtype_equal(other.categories.dtype, first.categories.dtype)
for other in to_union[1:]):
raise TypeError("dtype of categories must be the same")
ordered = False
if all(first.is_dtype_equal(other) for other in to_union[1:]):
# identical categories - fastpath
categories = first.categories
ordered = first.ordered
new_codes = np.concatenate([c.codes for c in to_union])
if sort_categories and not ignore_order and ordered:
raise TypeError("Cannot use sort_categories=True with "
"ordered Categoricals")
if sort_categories and not categories.is_monotonic_increasing:
categories = categories.sort_values()
indexer = categories.get_indexer(first.categories)
from pandas.core.algorithms import take_1d
new_codes = take_1d(indexer, new_codes, fill_value=-1)
elif ignore_order or all(not c.ordered for c in to_union):
# different categories - union and recode
cats = first.categories.append([c.categories for c in to_union[1:]])
categories = Index(cats.unique())
if sort_categories:
categories = categories.sort_values()
new_codes = []
for c in to_union:
new_codes.append(
_recode_for_categories(c.codes, c.categories, categories))
new_codes = np.concatenate(new_codes)
else:
# ordered - to show a proper error message
if all(c.ordered for c in to_union):
msg = ("to union ordered Categoricals, "
"all categories must be the same")
raise TypeError(msg)
else:
raise TypeError('Categorical.ordered must be the same')
if ignore_order:
ordered = False
return Categorical(new_codes,
categories=categories,
ordered=ordered,
fastpath=True)
def _map_values(self, mapper, na_action=None):
"""
An internal function that maps values using the input
correspondence (which can be a dict, Series, or function).
Parameters
----------
mapper : function, dict, or Series
The input correspondence object
na_action : {None, 'ignore'}
If 'ignore', propagate NA values, without passing them to the
mapping function
Returns
-------
Union[Index, MultiIndex], inferred
The output of the mapping function applied to the index.
If the function returns a tuple with more than one element
a MultiIndex will be returned.
"""
# we can fastpath dict/Series to an efficient map
# as we know that we are not going to have to yield
# python types
if is_dict_like(mapper):
if isinstance(mapper, dict) and hasattr(mapper, "__missing__"):
# If a dictionary subclass defines a default value method,
# convert mapper to a lookup function (GH #15999).
dict_with_default = mapper
mapper = lambda x: dict_with_default[x]
else:
# Dictionary does not have a default. Thus it's safe to
# convert to an Series for efficiency.
# we specify the keys here to handle the
# possibility that they are tuples
# The return value of mapping with an empty mapper is
# expected to be pd.Series(np.nan, ...). As np.nan is
# of dtype float64 the return value of this method should
# be float64 as well
mapper = create_series_with_explicit_dtype(
mapper, dtype_if_empty=np.float64
)
if isinstance(mapper, ABCSeries):
# Since values were input this means we came from either
# a dict or a series and mapper should be an index
if is_categorical_dtype(self._values):
# use the built in categorical series mapper which saves
# time by mapping the categories instead of all values
return self._values.map(mapper)
if is_extension_array_dtype(self.dtype):
values = self._values
else:
values = self.values
indexer = mapper.index.get_indexer(values)
new_values = algorithms.take_1d(mapper._values, indexer)
return new_values
# we must convert to python types
if is_extension_array_dtype(self.dtype) and hasattr(self._values, "map"):
# GH#23179 some EAs do not have `map`
values = self._values
if na_action is not None:
raise NotImplementedError
map_f = lambda values, f: values.map(f)
else:
values = self.astype(object)
values = getattr(values, "values", values)
if na_action == "ignore":
def map_f(values, f):
return lib.map_infer_mask(values, f, isna(values).view(np.uint8))
else:
map_f = lib.map_infer
# mapper is a function
new_values = map_f(values, mapper)
return new_values
def parallel_take1d():
take_1d(df['col'].values, indexer)
def union_categoricals(to_union, sort_categories=False, ignore_order=False):
"""
Combine list-like of Categorical-like, unioning categories. All
categories must have the same dtype.
.. versionadded:: 0.19.0
Parameters
----------
to_union : list-like of Categorical, CategoricalIndex,
or Series with dtype='category'
sort_categories : boolean, default False
If true, resulting categories will be lexsorted, otherwise
they will be ordered as they appear in the data.
ignore_order: boolean, default False
If true, the ordered attribute of the Categoricals will be ignored.
Results in an unordered categorical.
.. versionadded:: 0.20.0
Returns
-------
result : Categorical
Raises
------
TypeError
- all inputs do not have the same dtype
- all inputs do not have the same ordered property
- all inputs are ordered and their categories are not identical
- sort_categories=True and Categoricals are ordered
ValueError
Empty list of categoricals passed
"""
from pandas import Index, Categorical, CategoricalIndex, Series
if len(to_union) == 0:
raise ValueError('No Categoricals to union')
def _maybe_unwrap(x):
if isinstance(x, (CategoricalIndex, Series)):
return x.values
elif isinstance(x, Categorical):
return x
else:
raise TypeError("all components to combine must be Categorical")
to_union = [_maybe_unwrap(x) for x in to_union]
first = to_union[0]
if not all(
is_dtype_equal(other.categories.dtype, first.categories.dtype)
for other in to_union[1:]):
raise TypeError("dtype of categories must be the same")
ordered = False
if all(first.is_dtype_equal(other) for other in to_union[1:]):
# identical categories - fastpath
categories = first.categories
ordered = first.ordered
new_codes = np.concatenate([c.codes for c in to_union])
if sort_categories and not ignore_order and ordered:
raise TypeError("Cannot use sort_categories=True with "
"ordered Categoricals")
if sort_categories and not categories.is_monotonic_increasing:
categories = categories.sort_values()
indexer = categories.get_indexer(first.categories)
new_codes = take_1d(indexer, new_codes, fill_value=-1)
elif ignore_order or all(not c.ordered for c in to_union):
# different categories - union and recode
cats = first.categories.append([c.categories for c in to_union[1:]])
categories = Index(cats.unique())
if sort_categories:
categories = categories.sort_values()
new_codes = []
for c in to_union:
if len(c.categories) > 0:
indexer = categories.get_indexer(c.categories)
new_codes.append(take_1d(indexer, c.codes, fill_value=-1))
else:
# must be all NaN
new_codes.append(c.codes)
new_codes = np.concatenate(new_codes)
else:
# ordered - to show a proper error message
if all(c.ordered for c in to_union):
msg = ("to union ordered Categoricals, "
"all categories must be the same")
raise TypeError(msg)
else:
raise TypeError('Categorical.ordered must be the same')
if ignore_order:
ordered = False
return Categorical(new_codes,
categories=categories,
ordered=ordered,
fastpath=True)
def get_level_values(num):
unique = vals.levels[num] # .values
labels = vals.labels[num]
filled = algos.take_1d(unique._values, labels,
fill_value=unique._na_value)
return filled
def parallel_take1d():
take_1d(df['col'].values, indexer)
def _get_mgr_concatenation_plan(mgr, indexers):
"""
Construct concatenation plan for given block manager and indexers.
Parameters
----------
mgr : BlockManager
indexers : dict of {axis: indexer}
Returns
-------
plan : list of (BlockPlacement, JoinUnit) tuples
"""
# Calculate post-reindex shape , save for item axis which will be separate
# for each block anyway.
mgr_shape_list = list(mgr.shape)
for ax, indexer in indexers.items():
mgr_shape_list[ax] = len(indexer)
mgr_shape = tuple(mgr_shape_list)
if 0 in indexers:
ax0_indexer = indexers.pop(0)
blknos = algos.take_1d(mgr.blknos, ax0_indexer, fill_value=-1)
blklocs = algos.take_1d(mgr.blklocs, ax0_indexer, fill_value=-1)
else:
if mgr._is_single_block:
blk = mgr.blocks[0]
return [(blk.mgr_locs, JoinUnit(blk, mgr_shape, indexers))]
ax0_indexer = None
blknos = mgr.blknos
blklocs = mgr.blklocs
plan = []
for blkno, placements in libinternals.get_blkno_placements(blknos,
group=False):
assert placements.is_slice_like
join_unit_indexers = indexers.copy()
shape_list = list(mgr_shape)
shape_list[0] = len(placements)
shape = tuple(shape_list)
if blkno == -1:
unit = JoinUnit(None, shape)
else:
blk = mgr.blocks[blkno]
ax0_blk_indexer = blklocs[placements.indexer]
unit_no_ax0_reindexing = (
len(placements) == len(blk.mgr_locs) and
# Fastpath detection of join unit not
# needing to reindex its block: no ax0
# reindexing took place and block
# placement was sequential before.
((ax0_indexer is None and blk.mgr_locs.is_slice_like
and blk.mgr_locs.as_slice.step == 1) or
# Slow-ish detection: all indexer locs
# are sequential (and length match is
# checked above).
(np.diff(ax0_blk_indexer) == 1).all()))
# Omit indexer if no item reindexing is required.
if unit_no_ax0_reindexing:
join_unit_indexers.pop(0, None)
else:
join_unit_indexers[0] = ax0_blk_indexer
unit = JoinUnit(blk, shape, join_unit_indexers)
plan.append((placements, unit))
return plan
def union_categoricals(to_union, sort_categories=False, ignore_order=False):
"""
Combine list-like of Categorical-like, unioning categories. All
categories must have the same dtype.
.. versionadded:: 0.19.0
Parameters
----------
to_union : list-like of Categorical, CategoricalIndex,
or Series with dtype='category'
sort_categories : boolean, default False
If true, resulting categories will be lexsorted, otherwise
they will be ordered as they appear in the data.
ignore_order: boolean, default False
If true, the ordered attribute of the Categoricals will be ignored.
Results in an unordered categorical.
.. versionadded:: 0.20.0
Returns
-------
result : Categorical
Raises
------
TypeError
- all inputs do not have the same dtype
- all inputs do not have the same ordered property
- all inputs are ordered and their categories are not identical
- sort_categories=True and Categoricals are ordered
ValueError
Empty list of categoricals passed
"""
from pandas import Index, Categorical, CategoricalIndex, Series
if len(to_union) == 0:
raise ValueError('No Categoricals to union')
def _maybe_unwrap(x):
if isinstance(x, (CategoricalIndex, Series)):
return x.values
elif isinstance(x, Categorical):
return x
else:
raise TypeError("all components to combine must be Categorical")
to_union = [_maybe_unwrap(x) for x in to_union]
first = to_union[0]
if not all(is_dtype_equal(other.categories.dtype, first.categories.dtype)
for other in to_union[1:]):
raise TypeError("dtype of categories must be the same")
ordered = False
if all(first.is_dtype_equal(other) for other in to_union[1:]):
# identical categories - fastpath
categories = first.categories
ordered = first.ordered
new_codes = np.concatenate([c.codes for c in to_union])
if sort_categories and not ignore_order and ordered:
raise TypeError("Cannot use sort_categories=True with "
"ordered Categoricals")
if sort_categories and not categories.is_monotonic_increasing:
categories = categories.sort_values()
indexer = categories.get_indexer(first.categories)
new_codes = take_1d(indexer, new_codes, fill_value=-1)
elif ignore_order or all(not c.ordered for c in to_union):
# different categories - union and recode
cats = first.categories.append([c.categories for c in to_union[1:]])
categories = Index(cats.unique())
if sort_categories:
categories = categories.sort_values()
new_codes = []
for c in to_union:
if len(c.categories) > 0:
indexer = categories.get_indexer(c.categories)
new_codes.append(take_1d(indexer, c.codes, fill_value=-1))
else:
# must be all NaN
new_codes.append(c.codes)
new_codes = np.concatenate(new_codes)
else:
# ordered - to show a proper error message
if all(c.ordered for c in to_union):
msg = ("to union ordered Categoricals, "
"all categories must be the same")
raise TypeError(msg)
else:
raise TypeError('Categorical.ordered must be the same')
if ignore_order:
ordered = False
return Categorical(new_codes, categories=categories, ordered=ordered,
fastpath=True)
def _map_values(self, mapper, na_action=None):
"""
An internal function that maps values using the input
correspondence (which can be a dict, Series, or function).
Parameters
----------
mapper : function, dict, or Series
The input correspondence object
na_action : {None, 'ignore'}
If 'ignore', propagate NA values, without passing them to the
mapping function
Returns
-------
Union[Index, MultiIndex], inferred
The output of the mapping function applied to the index.
If the function returns a tuple with more than one element
a MultiIndex will be returned.
"""
# we can fastpath dict/Series to an efficient map
# as we know that we are not going to have to yield
# python types
if is_dict_like(mapper):
if isinstance(mapper, dict) and hasattr(mapper, "__missing__"):
# If a dictionary subclass defines a default value method,
# convert mapper to a lookup function (GH #15999).
dict_with_default = mapper
mapper = lambda x: dict_with_default[x]
else:
# Dictionary does not have a default. Thus it's safe to
# convert to an Series for efficiency.
# we specify the keys here to handle the
# possibility that they are tuples
# The return value of mapping with an empty mapper is
# expected to be pd.Series(np.nan, ...). As np.nan is
# of dtype float64 the return value of this method should
# be float64 as well
mapper = create_series_with_explicit_dtype(
mapper, dtype_if_empty=np.float64)
if isinstance(mapper, ABCSeries):
# Since values were input this means we came from either
# a dict or a series and mapper should be an index
if is_categorical_dtype(self.dtype):
# use the built in categorical series mapper which saves
# time by mapping the categories instead of all values
# pandas\core\base.py:893: error: Incompatible types in
# assignment (expression has type "Categorical", variable has
# type "IndexOpsMixin") [assignment]
self = cast("Categorical", self) # type: ignore[assignment]
# pandas\core\base.py:894: error: Item "ExtensionArray" of
# "Union[ExtensionArray, Any]" has no attribute "map"
# [union-attr]
return self._values.map(mapper) # type: ignore[union-attr]
values = self._values
indexer = mapper.index.get_indexer(values)
new_values = algorithms.take_1d(mapper._values, indexer)
return new_values
# we must convert to python types
if is_extension_array_dtype(self.dtype) and hasattr(
self._values, "map"):
# GH#23179 some EAs do not have `map`
values = self._values
if na_action is not None:
raise NotImplementedError
map_f = lambda values, f: values.map(f)
else:
# pandas\core\base.py:1142: error: "IndexOpsMixin" has no attribute
# "astype" [attr-defined]
values = self.astype(object)._values # type: ignore[attr-defined]
if na_action == "ignore":
map_f = lambda values, f: lib.map_infer_mask(
values, f,
isna(values).view(np.uint8))
elif na_action is None:
map_f = lib.map_infer
else:
msg = ("na_action must either be 'ignore' or None, "
f"{na_action} was passed")
raise ValueError(msg)
# mapper is a function
new_values = map_f(values, mapper)
return new_values
def _map_values(self, mapper, na_action=None):
"""An internal function that maps values using the input
correspondence (which can be a dict, Series, or function).
Parameters
----------
mapper : function, dict, or Series
The input correspondence object
na_action : {None, 'ignore'}
If 'ignore', propagate NA values, without passing them to the
mapping function
Returns
-------
applied : Union[Index, MultiIndex], inferred
The output of the mapping function applied to the index.
If the function returns a tuple with more than one element
a MultiIndex will be returned.
"""
# we can fastpath dict/Series to an efficient map
# as we know that we are not going to have to yield
# python types
if isinstance(mapper, dict):
if hasattr(mapper, '__missing__'):
# If a dictionary subclass defines a default value method,
# convert mapper to a lookup function (GH #15999).
dict_with_default = mapper
mapper = lambda x: dict_with_default[x]
else:
# Dictionary does not have a default. Thus it's safe to
# convert to an Series for efficiency.
# we specify the keys here to handle the
# possibility that they are tuples
from pandas import Series
mapper = Series(mapper)
if isinstance(mapper, ABCSeries):
# Since values were input this means we came from either
# a dict or a series and mapper should be an index
if is_extension_type(self.dtype):
values = self._values
else:
values = self.values
indexer = mapper.index.get_indexer(values)
new_values = algorithms.take_1d(mapper._values, indexer)
return new_values
# we must convert to python types
if is_extension_type(self.dtype):
values = self._values
if na_action is not None:
raise NotImplementedError
map_f = lambda values, f: values.map(f)
else:
values = self.astype(object)
values = getattr(values, 'values', values)
if na_action == 'ignore':
def map_f(values, f):
return lib.map_infer_mask(values, f,
isna(values).view(np.uint8))
else:
map_f = lib.map_infer
# mapper is a function
new_values = map_f(values, mapper)
return new_values
def _get_ilevel_values(index, level):
# accept level number only
unique = index.levels[level]
level_codes = index.codes[level]
filled = take_1d(unique._values, level_codes, fill_value=unique._na_value)
return unique._shallow_copy(filled, name=index.names[level])
def union_categoricals(to_union, sort_categories=False, ignore_order=False):
"""
Combine list-like of Categorical-like, unioning categories. All
categories must have the same dtype.
.. versionadded:: 0.19.0
Parameters
----------
to_union : list-like of Categorical, CategoricalIndex,
or Series with dtype='category'
sort_categories : boolean, default False
If true, resulting categories will be lexsorted, otherwise
they will be ordered as they appear in the data.
ignore_order: boolean, default False
If true, the ordered attribute of the Categoricals will be ignored.
Results in an unordered categorical.
.. versionadded:: 0.20.0
Returns
-------
result : Categorical
Raises
------
TypeError
- all inputs do not have the same dtype
- all inputs do not have the same ordered property
- all inputs are ordered and their categories are not identical
- sort_categories=True and Categoricals are ordered
ValueError
Empty list of categoricals passed
Notes
-----
To learn more about categories, see `link
<http://pandas.pydata.org/pandas-docs/stable/categorical.html#unioning>`__
Examples
--------
>>> from pandas.api.types import union_categoricals
If you want to combine categoricals that do not necessarily have
the same categories, `union_categoricals` will combine a list-like
of categoricals. The new categories will be the union of the
categories being combined.
>>> a = pd.Categorical(["b", "c"])
>>> b = pd.Categorical(["a", "b"])
>>> union_categoricals([a, b])
[b, c, a, b]
Categories (3, object): [b, c, a]
By default, the resulting categories will be ordered as they appear
in the `categories` of the data. If you want the categories to be
lexsorted, use `sort_categories=True` argument.
>>> union_categoricals([a, b], sort_categories=True)
[b, c, a, b]
Categories (3, object): [a, b, c]
`union_categoricals` also works with the case of combining two
categoricals of the same categories and order information (e.g. what
you could also `append` for).
>>> a = pd.Categorical(["a", "b"], ordered=True)
>>> b = pd.Categorical(["a", "b", "a"], ordered=True)
>>> union_categoricals([a, b])
[a, b, a, b, a]
Categories (2, object): [a < b]
Raises `TypeError` because the categories are ordered and not identical.
>>> a = pd.Categorical(["a", "b"], ordered=True)
>>> b = pd.Categorical(["a", "b", "c"], ordered=True)
>>> union_categoricals([a, b])
TypeError: to union ordered Categoricals, all categories must be the same
New in version 0.20.0
Ordered categoricals with different categories or orderings can be
combined by using the `ignore_ordered=True` argument.
>>> a = pd.Categorical(["a", "b", "c"], ordered=True)
>>> b = pd.Categorical(["c", "b", "a"], ordered=True)
>>> union_categoricals([a, b], ignore_order=True)
[a, b, c, c, b, a]
Categories (3, object): [a, b, c]
`union_categoricals` also works with a `CategoricalIndex`, or `Series`
containing categorical data, but note that the resulting array will
always be a plain `Categorical`
>>> a = pd.Series(["b", "c"], dtype='category')
>>> b = pd.Series(["a", "b"], dtype='category')
>>> union_categoricals([a, b])
[b, c, a, b]
Categories (3, object): [b, c, a]
"""
from pandas import Index, Categorical, CategoricalIndex, Series
from pandas.core.categorical import _recode_for_categories
if len(to_union) == 0:
raise ValueError('No Categoricals to union')
def _maybe_unwrap(x):
if isinstance(x, (CategoricalIndex, Series)):
return x.values
elif isinstance(x, Categorical):
return x
else:
raise TypeError("all components to combine must be Categorical")
to_union = [_maybe_unwrap(x) for x in to_union]
first = to_union[0]
if not all(is_dtype_equal(other.categories.dtype, first.categories.dtype)
for other in to_union[1:]):
raise TypeError("dtype of categories must be the same")
ordered = False
if all(first.is_dtype_equal(other) for other in to_union[1:]):
# identical categories - fastpath
categories = first.categories
ordered = first.ordered
new_codes = np.concatenate([c.codes for c in to_union])
if sort_categories and not ignore_order and ordered:
raise TypeError("Cannot use sort_categories=True with "
"ordered Categoricals")
if sort_categories and not categories.is_monotonic_increasing:
categories = categories.sort_values()
indexer = categories.get_indexer(first.categories)
from pandas.core.algorithms import take_1d
new_codes = take_1d(indexer, new_codes, fill_value=-1)
elif ignore_order or all(not c.ordered for c in to_union):
# different categories - union and recode
cats = first.categories.append([c.categories for c in to_union[1:]])
categories = Index(cats.unique())
if sort_categories:
categories = categories.sort_values()
new_codes = []
for c in to_union:
new_codes.append(_recode_for_categories(c.codes, c.categories,
categories))
new_codes = np.concatenate(new_codes)
else:
# ordered - to show a proper error message
if all(c.ordered for c in to_union):
msg = ("to union ordered Categoricals, "
"all categories must be the same")
raise TypeError(msg)
else:
raise TypeError('Categorical.ordered must be the same')
if ignore_order:
ordered = False
return Categorical(new_codes, categories=categories, ordered=ordered,
fastpath=True)
def union_categoricals(to_union):
"""
Combine list-like of Categoricals, unioning categories. All
must have the same dtype, and none can be ordered.
.. versionadded:: 0.19.0
Parameters
----------
to_union : list-like of Categoricals
Returns
-------
Categorical
A single array, categories will be ordered as they
appear in the list
Raises
------
TypeError
- all inputs do not have the same dtype
- all inputs do not have the same ordered property
- all inputs are ordered and their categories are not identical
ValueError
Emmpty list of categoricals passed
"""
from pandas import Index, Categorical
if len(to_union) == 0:
raise ValueError('No Categoricals to union')
first = to_union[0]
if not all(
is_dtype_equal(c.categories.dtype, first.categories.dtype)
for c in to_union):
raise TypeError("dtype of categories must be the same")
if all(first.is_dtype_equal(other) for other in to_union[1:]):
return Categorical(np.concatenate([c.codes for c in to_union]),
categories=first.categories,
ordered=first.ordered,
fastpath=True)
elif all(not c.ordered for c in to_union):
# not ordered
pass
else:
# to show a proper error message
if all(c.ordered for c in to_union):
msg = ("to union ordered Categoricals, "
"all categories must be the same")
raise TypeError(msg)
else:
raise TypeError('Categorical.ordered must be the same')
cats = first.categories
unique_cats = cats.append([c.categories for c in to_union[1:]]).unique()
categories = Index(unique_cats)
new_codes = []
for c in to_union:
if len(c.categories) > 0:
indexer = categories.get_indexer(c.categories)
new_codes.append(take_1d(indexer, c.codes, fill_value=-1))
else:
# must be all NaN
new_codes.append(c.codes)
new_codes = np.concatenate(new_codes)
return Categorical(new_codes,
categories=categories,
ordered=False,
fastpath=True)
def _(groupby):
src = groupby._orig_obj
ids, _, ngroup = groupby._orig_grouper.group_info
out = algorithms.take_1d(groupby.obj._values, ids)
return Series(out, index=src.index, name=src.name)
def get_mgr_concatenation_plan(mgr, indexers):
"""
Construct concatenation plan for given block manager and indexers.
Parameters
----------
mgr : BlockManager
indexers : dict of {axis: indexer}
Returns
-------
plan : list of (BlockPlacement, JoinUnit) tuples
"""
# Calculate post-reindex shape , save for item axis which will be separate
# for each block anyway.
mgr_shape = list(mgr.shape)
for ax, indexer in indexers.items():
mgr_shape[ax] = len(indexer)
mgr_shape = tuple(mgr_shape)
if 0 in indexers:
ax0_indexer = indexers.pop(0)
blknos = algos.take_1d(mgr._blknos, ax0_indexer, fill_value=-1)
blklocs = algos.take_1d(mgr._blklocs, ax0_indexer, fill_value=-1)
else:
if mgr._is_single_block:
blk = mgr.blocks[0]
return [(blk.mgr_locs, JoinUnit(blk, mgr_shape, indexers))]
ax0_indexer = None
blknos = mgr._blknos
blklocs = mgr._blklocs
plan = []
for blkno, placements in libinternals.get_blkno_placements(blknos,
mgr.nblocks,
group=False):
assert placements.is_slice_like
join_unit_indexers = indexers.copy()
shape = list(mgr_shape)
shape[0] = len(placements)
shape = tuple(shape)
if blkno == -1:
unit = JoinUnit(None, shape)
else:
blk = mgr.blocks[blkno]
ax0_blk_indexer = blklocs[placements.indexer]
unit_no_ax0_reindexing = (len(placements) == len(blk.mgr_locs) and
# Fastpath detection of join unit not
# needing to reindex its block: no ax0
# reindexing took place and block
# placement was sequential before.
((ax0_indexer is None and
blk.mgr_locs.is_slice_like and
blk.mgr_locs.as_slice.step == 1) or
# Slow-ish detection: all indexer locs
# are sequential (and length match is
# checked above).
(np.diff(ax0_blk_indexer) == 1).all()))
# Omit indexer if no item reindexing is required.
if unit_no_ax0_reindexing:
join_unit_indexers.pop(0, None)
else:
join_unit_indexers[0] = ax0_blk_indexer
unit = JoinUnit(blk, shape, join_unit_indexers)
plan.append((placements, unit))
return plan
def union_categoricals(to_union):
"""
Combine list-like of Categoricals, unioning categories. All
must have the same dtype, and none can be ordered.
.. versionadded:: 0.19.0
Parameters
----------
to_union : list-like of Categoricals
Returns
-------
Categorical
A single array, categories will be ordered as they
appear in the list
Raises
------
TypeError
- all inputs do not have the same dtype
- all inputs do not have the same ordered property
- all inputs are ordered and their categories are not identical
ValueError
Emmpty list of categoricals passed
"""
from pandas import Index, Categorical
if len(to_union) == 0:
raise ValueError('No Categoricals to union')
first = to_union[0]
if not all(is_dtype_equal(c.categories.dtype, first.categories.dtype)
for c in to_union):
raise TypeError("dtype of categories must be the same")
if all(first.is_dtype_equal(other) for other in to_union[1:]):
return Categorical(np.concatenate([c.codes for c in to_union]),
categories=first.categories, ordered=first.ordered,
fastpath=True)
elif all(not c.ordered for c in to_union):
# not ordered
pass
else:
# to show a proper error message
if all(c.ordered for c in to_union):
msg = ("to union ordered Categoricals, "
"all categories must be the same")
raise TypeError(msg)
else:
raise TypeError('Categorical.ordered must be the same')
cats = first.categories
unique_cats = cats.append([c.categories for c in to_union[1:]]).unique()
categories = Index(unique_cats)
new_codes = []
for c in to_union:
if len(c.categories) > 0:
indexer = categories.get_indexer(c.categories)
new_codes.append(take_1d(indexer, c.codes, fill_value=-1))
else:
# must be all NaN
new_codes.append(c.codes)
new_codes = np.concatenate(new_codes)
return Categorical(new_codes, categories=categories, ordered=False,
fastpath=True)
def safe_sort(values,
labels=None,
na_sentinel=-1,
assume_unique=False,
verify=True):
"""
Sort ``values`` and reorder corresponding ``labels``.
``values`` should be unique if ``labels`` is not None.
Safe for use with mixed types (int, str), orders ints before strs.
.. versionadded:: 0.19.0
Parameters
----------
values : list-like
Sequence; must be unique if ``labels`` is not None.
labels : list_like
Indices to ``values``. All out of bound indices are treated as
"not found" and will be masked with ``na_sentinel``.
na_sentinel : int, default -1
Value in ``labels`` to mark "not found".
Ignored when ``labels`` is None.
assume_unique : bool, default False
When True, ``values`` are assumed to be unique, which can speed up
the calculation. Ignored when ``labels`` is None.
verify : bool, default True
Check if labels are out of bound for the values and put out of bound
labels equal to na_sentinel. If ``verify=False``, it is assumed there
are no out of bound labels. Ignored when ``labels`` is None.
.. versionadded:: 0.25.0
Returns
-------
ordered : ndarray
Sorted ``values``
new_labels : ndarray
Reordered ``labels``; returned when ``labels`` is not None.
Raises
------
TypeError
* If ``values`` is not list-like or if ``labels`` is neither None
nor list-like
* If ``values`` cannot be sorted
ValueError
* If ``labels`` is not None and ``values`` contain duplicates.
"""
if not is_list_like(values):
raise TypeError("Only list-like objects are allowed to be passed to"
"safe_sort as values")
if (not isinstance(values, np.ndarray)
and not is_extension_array_dtype(values)):
# don't convert to string types
dtype, _ = infer_dtype_from_array(values)
values = np.asarray(values, dtype=dtype)
def sort_mixed(values):
# order ints before strings, safe in py3
str_pos = np.array([isinstance(x, str) for x in values], dtype=bool)
nums = np.sort(values[~str_pos])
strs = np.sort(values[str_pos])
return np.concatenate([nums, np.asarray(strs, dtype=object)])
sorter = None
if (not is_extension_array_dtype(values)
and lib.infer_dtype(values, skipna=False) == 'mixed-integer'):
# unorderable in py3 if mixed str/int
ordered = sort_mixed(values)
else:
try:
sorter = values.argsort()
ordered = values.take(sorter)
except TypeError:
# try this anyway
ordered = sort_mixed(values)
# labels:
if labels is None:
return ordered
if not is_list_like(labels):
raise TypeError("Only list-like objects or None are allowed to be"
"passed to safe_sort as labels")
labels = ensure_platform_int(np.asarray(labels))
from pandas import Index
if not assume_unique and not Index(values).is_unique:
raise ValueError("values should be unique if labels is not None")
if sorter is None:
# mixed types
(hash_klass,
_), values = algorithms._get_data_algo(values, algorithms._hashtables)
t = hash_klass(len(values))
t.map_locations(values)
sorter = ensure_platform_int(t.lookup(ordered))
if na_sentinel == -1:
# take_1d is faster, but only works for na_sentinels of -1
order2 = sorter.argsort()
new_labels = algorithms.take_1d(order2, labels, fill_value=-1)
if verify:
mask = (labels < -len(values)) | (labels >= len(values))
else:
mask = None
else:
reverse_indexer = np.empty(len(sorter), dtype=np.int_)
reverse_indexer.put(sorter, np.arange(len(sorter)))
# Out of bound indices will be masked with `na_sentinel` next, so we
# may deal with them here without performance loss using `mode='wrap'`
new_labels = reverse_indexer.take(labels, mode='wrap')
mask = labels == na_sentinel
if verify:
mask = mask | (labels < -len(values)) | (labels >= len(values))
if mask is not None:
np.putmask(new_labels, mask, na_sentinel)
return ordered, ensure_platform_int(new_labels)
def safe_sort(values, labels=None, na_sentinel=-1, assume_unique=False,
verify=True):
"""
Sort ``values`` and reorder corresponding ``labels``.
``values`` should be unique if ``labels`` is not None.
Safe for use with mixed types (int, str), orders ints before strs.
.. versionadded:: 0.19.0
Parameters
----------
values : list-like
Sequence; must be unique if ``labels`` is not None.
labels : list_like
Indices to ``values``. All out of bound indices are treated as
"not found" and will be masked with ``na_sentinel``.
na_sentinel : int, default -1
Value in ``labels`` to mark "not found".
Ignored when ``labels`` is None.
assume_unique : bool, default False
When True, ``values`` are assumed to be unique, which can speed up
the calculation. Ignored when ``labels`` is None.
verify : bool, default True
Check if labels are out of bound for the values and put out of bound
labels equal to na_sentinel. If ``verify=False``, it is assumed there
are no out of bound labels. Ignored when ``labels`` is None.
.. versionadded:: 0.25.0
Returns
-------
ordered : ndarray
Sorted ``values``
new_labels : ndarray
Reordered ``labels``; returned when ``labels`` is not None.
Raises
------
TypeError
* If ``values`` is not list-like or if ``labels`` is neither None
nor list-like
* If ``values`` cannot be sorted
ValueError
* If ``labels`` is not None and ``values`` contain duplicates.
"""
if not is_list_like(values):
raise TypeError("Only list-like objects are allowed to be passed to"
"safe_sort as values")
if (not isinstance(values, np.ndarray)
and not is_extension_array_dtype(values)):
# don't convert to string types
dtype, _ = infer_dtype_from_array(values)
values = np.asarray(values, dtype=dtype)
def sort_mixed(values):
# order ints before strings, safe in py3
str_pos = np.array([isinstance(x, str) for x in values],
dtype=bool)
nums = np.sort(values[~str_pos])
strs = np.sort(values[str_pos])
return np.concatenate([nums, np.asarray(strs, dtype=object)])
sorter = None
if (not is_extension_array_dtype(values)
and lib.infer_dtype(values, skipna=False) == 'mixed-integer'):
# unorderable in py3 if mixed str/int
ordered = sort_mixed(values)
else:
try:
sorter = values.argsort()
ordered = values.take(sorter)
except TypeError:
# try this anyway
ordered = sort_mixed(values)
# labels:
if labels is None:
return ordered
if not is_list_like(labels):
raise TypeError("Only list-like objects or None are allowed to be"
"passed to safe_sort as labels")
labels = ensure_platform_int(np.asarray(labels))
from pandas import Index
if not assume_unique and not Index(values).is_unique:
raise ValueError("values should be unique if labels is not None")
if sorter is None:
# mixed types
(hash_klass, _), values = algorithms._get_data_algo(
values, algorithms._hashtables)
t = hash_klass(len(values))
t.map_locations(values)
sorter = ensure_platform_int(t.lookup(ordered))
if na_sentinel == -1:
# take_1d is faster, but only works for na_sentinels of -1
order2 = sorter.argsort()
new_labels = algorithms.take_1d(order2, labels, fill_value=-1)
if verify:
mask = (labels < -len(values)) | (labels >= len(values))
else:
mask = None
else:
reverse_indexer = np.empty(len(sorter), dtype=np.int_)
reverse_indexer.put(sorter, np.arange(len(sorter)))
# Out of bound indices will be masked with `na_sentinel` next, so we
# may deal with them here without performance loss using `mode='wrap'`
new_labels = reverse_indexer.take(labels, mode='wrap')
mask = labels == na_sentinel
if verify:
mask = mask | (labels < -len(values)) | (labels >= len(values))
if mask is not None:
np.putmask(new_labels, mask, na_sentinel)
return ordered, ensure_platform_int(new_labels)
def _maybe_add_join_keys(self, result, left_indexer, right_indexer):
left_has_missing = None
right_has_missing = None
keys = zip(self.join_names, self.left_on, self.right_on)
for i, (name, lname, rname) in enumerate(keys):
if not _should_fill(lname, rname):
continue
take_left, take_right = None, None
if name in result:
if left_indexer is not None and right_indexer is not None:
if name in self.left:
if left_has_missing is None:
left_has_missing = any(left_indexer == -1)
if left_has_missing:
take_right = self.right_join_keys[i]
if not com.is_dtype_equal(result[name].dtype,
self.left[name].dtype):
take_left = self.left[name]._values
elif name in self.right:
if right_has_missing is None:
right_has_missing = any(right_indexer == -1)
if right_has_missing:
take_left = self.left_join_keys[i]
if not com.is_dtype_equal(result[name].dtype,
self.right[name].dtype):
take_right = self.right[name]._values
elif left_indexer is not None \
and isinstance(self.left_join_keys[i], np.ndarray):
take_left = self.left_join_keys[i]
take_right = self.right_join_keys[i]
if take_left is not None or take_right is not None:
if take_left is None:
lvals = result[name]._values
else:
lfill = na_value_for_dtype(take_left.dtype)
lvals = algos.take_1d(take_left, left_indexer,
fill_value=lfill)
if take_right is None:
rvals = result[name]._values
else:
rfill = na_value_for_dtype(take_right.dtype)
rvals = algos.take_1d(take_right, right_indexer,
fill_value=rfill)
# if we have an all missing left_indexer
# make sure to just use the right values
mask = left_indexer == -1
if mask.all():
key_col = rvals
else:
key_col = Index(lvals).where(~mask, rvals)
if name in result:
result[name] = key_col
else:
result.insert(i, name or 'key_%d' % i, key_col)