def dict_to_manager(sdict, columns, index):
""" create and return the block manager from a dict of series, columns, index """
# from BlockManager perspective
axes = [_ensure_index(columns), _ensure_index(index)]
return create_block_manager_from_arrays([sdict[c] for c in columns], columns, axes)
def _convert_frames(frames, index, columns, fill_value=np.nan, kind='block'):
from pandas.core.panel import _get_combined_index
output = {}
for item, df in compat.iteritems(frames):
if not isinstance(df, SparseDataFrame):
df = SparseDataFrame(df, default_kind=kind,
default_fill_value=fill_value)
output[item] = df
if index is None:
all_indexes = [df.index for df in output.values()]
index = _get_combined_index(all_indexes)
if columns is None:
all_columns = [df.columns for df in output.values()]
columns = _get_combined_index(all_columns)
index = _ensure_index(index)
columns = _ensure_index(columns)
for item, df in compat.iteritems(output):
if not (df.index.equals(index) and df.columns.equals(columns)):
output[item] = df.reindex(index=index, columns=columns)
return output, index, columns
def __setstate__(self, state):
frames, items, major, minor, fv, kind = state
self.default_fill_value = fv
self.default_kind = kind
self._items = _ensure_index(com._unpickle_array(items))
self._major_axis = _ensure_index(com._unpickle_array(major))
self._minor_axis = _ensure_index(com._unpickle_array(minor))
self._frames = frames
def __setstate__(self, state):
frames, items, major, minor, fv, kind = state
from pandas.io.pickle import _unpickle_array
self.default_fill_value = fv
self.default_kind = kind
self._items = _ensure_index(_unpickle_array(items))
self._major_axis = _ensure_index(_unpickle_array(major))
self._minor_axis = _ensure_index(_unpickle_array(minor))
self._frames = frames
def __init__(self, values, items, ref_items, ndim=2):
if issubclass(values.dtype.type, basestring):
values = np.array(values, dtype=object)
assert(values.ndim == ndim)
assert(len(items) == len(values))
self._ref_locs = None
self.values = values
self.ndim = ndim
self.items = _ensure_index(items)
self.ref_items = _ensure_index(ref_items)
def __init__(self, values, items, ref_items, ndim=2, do_integrity_check=False):
if issubclass(values.dtype.type, basestring):
values = np.array(values, dtype=object)
assert values.ndim == ndim
assert len(items) == len(values)
self.values = values
self.ndim = ndim
self.items = _ensure_index(items)
self.ref_items = _ensure_index(ref_items)
if do_integrity_check:
self._check_integrity()
def __init__(self, values, items, ref_items, ndim=2):
if issubclass(values.dtype.type, basestring):
values = np.array(values, dtype=object)
if values.ndim != ndim:
raise AssertionError('Wrong number of dimensions')
if len(items) != len(values):
raise AssertionError('Wrong number of items passed')
self._ref_locs = None
self.values = values
self.ndim = ndim
self.items = _ensure_index(items)
self.ref_items = _ensure_index(ref_items)
def __init__(self, bins, binlabels, filter_empty=False, mutated=False,
indexer=None):
self.bins = _ensure_int64(bins)
self.binlabels = _ensure_index(binlabels)
self._filter_empty_groups = filter_empty
self.mutated = mutated
self.indexer = indexer
def _interleave(self, items):
"""
Return ndarray from blocks with specified item order
Items must be contained in the blocks
"""
dtype = _interleaved_dtype(self.blocks)
items = _ensure_index(items)
result = np.empty(self.shape, dtype=dtype)
itemmask = np.zeros(len(items), dtype=bool)
# By construction, all of the item should be covered by one of the
# blocks
if items.is_unique:
for block in self.blocks:
indexer = items.get_indexer(block.items)
if (indexer == -1).any():
raise AssertionError('Items must contain all block items')
result[indexer] = block.get_values(dtype)
itemmask[indexer] = 1
else:
for block in self.blocks:
mask = items.isin(block.items)
indexer = mask.nonzero()[0]
if (len(indexer) != len(block.items)):
raise AssertionError('All items must be in block items')
result[indexer] = block.get_values(dtype)
itemmask[indexer] = 1
if not itemmask.all():
raise AssertionError('Some items were not contained in blocks')
return result
def _set_levels(self, levels):
from pandas.core.index import _ensure_index
levels = _ensure_index(levels)
if not levels.is_unique:
raise ValueError("Categorical levels must be unique")
self._levels = levels
def __init__(self, frames, items=None, major_axis=None, minor_axis=None,
default_fill_value=np.nan, default_kind='block'):
assert(isinstance(frames, dict))
self.default_fill_value = fill_value = default_fill_value
self.default_kind = kind = default_kind
# pre-filter, if necessary
if items is None:
items = Index(sorted(frames.keys()))
items = _ensure_index(items)
(clean_frames,
major_axis,
minor_axis) = _convert_frames(frames, major_axis,
minor_axis, kind=kind,
fill_value=fill_value)
self._frames = clean_frames
# do we want to fill missing ones?
for item in items:
if item not in clean_frames:
raise Exception('column %s not found in data' % item)
self._items = items
self.major_axis = major_axis
self.minor_axis = minor_axis
def reindex(self, index=None, method=None, copy=True, limit=None):
"""
Conform SparseSeries to new Index
See Series.reindex docstring for general behavior
Returns
-------
reindexed : SparseSeries
"""
new_index = _ensure_index(index)
if self.index.equals(new_index):
if copy:
return self.copy()
else:
return self
if len(self.index) == 0:
# FIXME: inelegant / slow
values = np.empty(len(new_index), dtype=np.float64)
values.fill(nan)
return SparseSeries(values, index=new_index, fill_value=self.fill_value)
new_index, fill_vec = self.index.reindex(index, method=method, limit=limit)
new_values = common.take_1d(self.values, fill_vec)
return SparseSeries(new_values, index=new_index, fill_value=self.fill_value, name=self.name)
def __init__(self, frame, buf=None, columns=None, col_space=None,
header=True, index=True, na_rep='NaN', formatters=None,
justify=None, float_format=None, sparsify=None,
index_names=True, line_width=None, **kwds):
self.frame = frame
self.buf = buf if buf is not None else StringIO()
self.show_index_names = index_names
if sparsify is None:
sparsify = get_option("print.multi_sparse")
self.sparsify = sparsify
self.float_format = float_format
self.formatters = formatters if formatters is not None else {}
self.na_rep = na_rep
self.col_space = col_space
self.header = header
self.index = index
self.line_width = line_width
if justify is None:
self.justify = get_option("print.colheader_justify")
else:
self.justify = justify
self.kwds = kwds
if columns is not None:
self.columns = _ensure_index(columns)
self.frame = self.frame[self.columns]
else:
self.columns = frame.columns
def __init__(self, data=None, index=None, columns=None,
default_kind='block', default_fill_value=None):
if default_fill_value is None:
default_fill_value = np.nan
self.default_kind = default_kind
self.default_fill_value = default_fill_value
if isinstance(data, dict):
sdict, columns, index = self._init_dict(data, index, columns)
elif isinstance(data, (np.ndarray, list)):
sdict, columns, index = self._init_matrix(data, index, columns)
elif isinstance(data, DataFrame):
sdict, columns, index = self._init_dict(data, data.index,
data.columns)
elif data is None:
sdict = {}
if index is None:
index = Index([])
else:
index = _ensure_index(index)
if columns is None:
columns = Index([])
else:
for c in columns:
sdict[c] = SparseSeries(np.nan, index=index,
kind=self.default_kind,
fill_value=self.default_fill_value)
self._series = sdict
self.columns = columns
self.index = index
def __init__(
self,
frame,
buf=None,
columns=None,
col_space=None,
na_rep="NaN",
formatters=None,
float_format=None,
sparsify=True,
index_names=True,
):
self.frame = frame
self.buf = buf if buf is not None else StringIO()
self.show_index_names = index_names
self.sparsify = sparsify
self.float_format = float_format
self.formatters = formatters
self.na_rep = na_rep
self.col_space = col_space
if columns is not None:
self.columns = _ensure_index(columns)
else:
self.columns = frame.columns
def __init__(self, frame, buf=None, columns=None, col_space=None,
header=True, index=True, na_rep='NaN', formatters=None,
justify=None, float_format=None, sparsify=True,
index_names=True, **kwds):
self.frame = frame
self.buf = buf if buf is not None else StringIO()
self.show_index_names = index_names
self.sparsify = sparsify
self.float_format = float_format
self.formatters = formatters
self.na_rep = na_rep
self.col_space = col_space
self.header = header
self.index = index
if justify is None:
self.justify = com.print_config.colheader_justify
else:
self.justify = justify
self.kwds = kwds
if columns is not None:
self.columns = _ensure_index(columns)
else:
self.columns = frame.columns
def reindex_items(self, new_items, copy=True):
"""
"""
new_items = _ensure_index(new_items)
data = self
if not data.is_consolidated():
data = data.consolidate()
return data.reindex_items(new_items)
# TODO: this part could be faster (!)
new_items, indexer = self.items.reindex(new_items)
# could have some pathological (MultiIndex) issues here
new_blocks = []
if indexer is None:
for blk in self.blocks:
if copy:
new_blocks.append(blk.reindex_items_from(new_items))
else:
new_blocks.append(blk)
else:
for block in self.blocks:
newb = block.reindex_items_from(new_items, copy=copy)
if len(newb.items) > 0:
new_blocks.append(newb)
mask = indexer == -1
if mask.any():
extra_items = new_items[mask]
na_block = self._make_na_block(extra_items, new_items)
new_blocks.append(na_block)
new_blocks = _consolidate(new_blocks, new_items)
return BlockManager(new_blocks, [new_items] + self.axes[1:])
def _has_valid_type(self, key, axis):
ax = self.obj._get_axis(axis)
# valid for a label where all labels are in the index
# slice of lables (where start-end in labels)
# slice of integers (only if in the lables)
# boolean
if isinstance(key, slice):
if ax.is_floating():
# allowing keys to be slicers with no fallback
pass
else:
if key.start is not None:
if key.start not in ax:
raise KeyError("start bound [%s] is not the [%s]" % (key.start,self.obj._get_axis_name(axis)))
if key.stop is not None:
if key.stop not in ax:
raise KeyError("stop bound [%s] is not in the [%s]" % (key.stop,self.obj._get_axis_name(axis)))
elif com._is_bool_indexer(key):
return True
elif _is_list_like(key):
# mi is just a passthru
if isinstance(key, tuple) and isinstance(ax, MultiIndex):
return True
# require all elements in the index
idx = _ensure_index(key)
if not idx.isin(ax).all():
raise KeyError("[%s] are not in ALL in the [%s]" % (key,self.obj._get_axis_name(axis)))
return True
else:
def error():
if isnull(key):
raise ValueError("cannot use label indexing with a null key")
raise KeyError("the label [%s] is not in the [%s]" % (key,self.obj._get_axis_name(axis)))
try:
key = self._convert_scalar_indexer(key, axis)
if not key in ax:
error()
except (TypeError) as e:
# python 3 type errors should be raised
if 'unorderable' in str(e): # pragma: no cover
error()
raise
except:
error()
return True
def _get_concat_axis(self):
if self._is_series:
if self.axis == 0:
indexes = [x.index for x in self.objs]
elif self.keys is None:
names = []
for x in self.objs:
if not isinstance(x, Series):
raise TypeError(
"Cannot concatenate type 'Series' " "with object of type " "%r" % type(x).__name__
)
if x.name is not None:
names.append(x.name)
else:
return Index(np.arange(len(self.objs)))
return Index(names)
else:
return _ensure_index(self.keys)
else:
indexes = [x._data.axes[self.axis] for x in self.objs]
if self.keys is None:
concat_axis = _concat_indexes(indexes)
else:
concat_axis = _make_concat_multiindex(indexes, self.keys, self.levels, self.names)
self._maybe_check_integrity(concat_axis)
return concat_axis
def remove_unused_categories(self, inplace=False):
""" Removes categories which are not used.
Parameters
----------
inplace : boolean (default: False)
Whether or not to drop unused categories inplace or return a copy of this categorical
with unused categories dropped.
Returns
-------
cat : Categorical with unused categories dropped or None if inplace.
See also
--------
rename_categories
reorder_categories
add_categories
remove_categories
set_categories
"""
cat = self if inplace else self.copy()
_used = sorted(np.unique(cat._codes))
new_categories = cat.categories.take(com._ensure_platform_int(_used))
new_categories = _ensure_index(new_categories)
cat._codes = _get_codes_for_values(cat.__array__(), new_categories)
cat._categories = new_categories
if not inplace:
return cat
def _extract_axis(data, axis=0, intersect=False):
if len(data) == 0:
index = Index([])
elif len(data) > 0:
raw_lengths = []
indexes = []
have_raw_arrays = False
have_frames = False
for v in data.values():
if isinstance(v, DataFrame):
have_frames = True
indexes.append(v._get_axis(axis))
else:
have_raw_arrays = True
raw_lengths.append(v.shape[axis])
if have_frames:
index = _get_combined_index(indexes, intersect=intersect)
if have_raw_arrays:
lengths = list(set(raw_lengths))
if len(lengths) > 1:
raise ValueError('ndarrays must match shape on axis %d' % axis)
if have_frames:
assert(lengths[0] == len(index))
else:
index = Index(np.arange(lengths[0]))
return _ensure_index(index)
def _get_concat_axis(self):
if self._is_series:
if self.axis == 0:
indexes = [x.index for x in self.objs]
elif self.keys is None:
names = []
for x in self.objs:
if x.name is not None:
names.append(x.name)
else:
return Index(np.arange(len(self.objs)))
return Index(names)
else:
return _ensure_index(self.keys)
else:
indexes = [x._data.axes[self.axis] for x in self.objs]
if self.keys is None:
concat_axis = _concat_indexes(indexes)
else:
concat_axis = _make_concat_multiindex(indexes, self.keys,
self.levels, self.names)
self._maybe_check_integrity(concat_axis)
return concat_axis
def reindex_axis(self, new_axis, method=None, axis=0, copy=True):
new_axis = _ensure_index(new_axis)
cur_axis = self.axes[axis]
if new_axis.equals(cur_axis):
if copy:
result = self.copy(deep=True)
result.axes[axis] = new_axis
if axis == 0:
# patch ref_items, #1823
for blk in result.blocks:
blk.ref_items = new_axis
return result
else:
return self
if axis == 0:
if method is not None:
raise AssertionError('method argument not supported for '
'axis == 0')
return self.reindex_items(new_axis)
new_axis, indexer = cur_axis.reindex(new_axis, method)
return self.reindex_indexer(new_axis, indexer, axis=axis)
def _interleave(self, items):
"""
Return ndarray from blocks with specified item order
Items must be contained in the blocks
"""
dtype = _interleaved_dtype(self.blocks)
items = _ensure_index(items)
result = np.empty(self.shape, dtype=dtype)
itemmask = np.zeros(len(items), dtype=bool)
# By construction, all of the item should be covered by one of the
# blocks
if items.is_unique:
for block in self.blocks:
indexer = items.get_indexer(block.items)
assert((indexer != -1).all())
result[indexer] = block.get_values(dtype)
itemmask[indexer] = 1
else:
for block in self.blocks:
mask = items.isin(block.items)
indexer = mask.nonzero()[0]
assert(len(indexer) == len(block.items))
result[indexer] = block.get_values(dtype)
itemmask[indexer] = 1
assert(itemmask.all())
return result
def set_axis(self, axis, value):
cur_axis = self.axes[axis]
if len(value) != len(cur_axis):
raise Exception("Length mismatch (%d vs %d)" % (len(value), len(cur_axis)))
self.axes[axis] = _ensure_index(value)
if axis == 0:
for block in self.blocks:
block.set_ref_items(self.items, maybe_rename=True)
def remove_unused_levels(self):
""" Removes levels which are not used.
The level removal is done inplace.
"""
_used = sorted(np.unique(self._codes))
new_levels = self.levels.take(_used)
new_levels = _ensure_index(new_levels)
self._codes = _get_codes_for_values(self.__array__(), new_levels)
self._levels = new_levels
def __init__(self, labels, levels, name=None):
from pandas.core.index import _ensure_index
levels = _ensure_index(levels)
if not levels.is_unique:
raise ValueError('Factor levels must be unique')
self.labels = labels
self.levels = levels
self.name = name
def __set__(self, obj, value):
value = _ensure_index(value)
if isinstance(value, MultiIndex):
raise NotImplementedError
for v in compat.itervalues(obj._frames):
setattr(v, self.frame_attr, value)
setattr(obj, self.cache_field, value)
def __init__(self, blocks, axes, do_integrity_check=True):
self.axes = [_ensure_index(ax) for ax in axes]
self.blocks = blocks
ndim = len(axes)
for block in blocks:
assert(ndim == block.values.ndim)
if do_integrity_check:
self._verify_integrity()
def __setstate__(self, state):
# discard anything after 3rd, support beta pickling format for a little
# while longer
ax_arrays, bvalues, bitems = state[:3]
self.axes = [_ensure_index(ax) for ax in ax_arrays]
blocks = []
for values, items in zip(bvalues, bitems):
blk = make_block(values, items, self.axes[0], do_integrity_check=True)
blocks.append(blk)
self.blocks = blocks
def __new__(cls,
data,
index=None,
sparse_index=None,
kind='block',
fill_value=None,
name=None,
copy=False):
is_sparse_array = isinstance(data, SparseArray)
if fill_value is None:
if is_sparse_array:
fill_value = data.fill_value
else:
fill_value = nan
if is_sparse_array:
if isinstance(data, SparseSeries) and index is None:
index = data.index
elif index is not None:
assert (len(index) == len(data))
sparse_index = data.sp_index
values = np.asarray(data)
elif isinstance(data, (Series, dict)):
if index is None:
index = data.index
data = Series(data)
values, sparse_index = make_sparse(data,
kind=kind,
fill_value=fill_value)
elif np.isscalar(data): # pragma: no cover
if index is None:
raise Exception('must pass index!')
values = np.empty(len(index))
values.fill(data)
# TODO: more efficient
values, sparse_index = make_sparse(values,
kind=kind,
fill_value=fill_value)
else:
# array-like
if sparse_index is None:
values, sparse_index = make_sparse(data,
kind=kind,
fill_value=fill_value)
else:
values = data
assert (len(values) == sparse_index.npoints)
if index is None:
index = Index(np.arange(sparse_index.length))
index = _ensure_index(index)
# Create array, do *not* copy data by default
if copy:
subarr = np.array(values, dtype=np.float64, copy=True)
else:
subarr = np.asarray(values, dtype=np.float64)
if index.is_all_dates:
cls = SparseTimeSeries
# Change the class of the array to be the subclass type.
output = subarr.view(cls)
output.sp_index = sparse_index
output.fill_value = np.float64(fill_value)
output.index = index
output.name = name
return output
def _make_concat_multiindex(indexes, keys, levels=None, names=None):
if ((levels is None and isinstance(keys[0], tuple))
or (levels is not None and len(levels) > 1)):
zipped = zip(*keys)
if names is None:
names = [None] * len(zipped)
if levels is None:
levels = [Factor(zp).levels for zp in zipped]
else:
levels = [_ensure_index(x) for x in levels]
else:
zipped = [keys]
if names is None:
names = [None]
if levels is None:
levels = [_ensure_index(keys)]
else:
levels = [_ensure_index(x) for x in levels]
if not _all_indexes_same(indexes):
label_list = []
# things are potentially different sizes, so compute the exact labels
# for each level and pass those to MultiIndex.from_arrays
for hlevel, level in zip(zipped, levels):
to_concat = []
for key, index in zip(hlevel, indexes):
i = level.get_loc(key)
to_concat.append(np.repeat(i, len(index)))
label_list.append(np.concatenate(to_concat))
concat_index = _concat_indexes(indexes)
# these go at the end
if isinstance(concat_index, MultiIndex):
levels.extend(concat_index.levels)
label_list.extend(concat_index.labels)
else:
factor = Factor(concat_index)
levels.append(factor.levels)
label_list.append(factor.labels)
# also copies
names = names + _get_consensus_names(indexes)
return MultiIndex(levels=levels, labels=label_list, names=names)
new_index = indexes[0]
n = len(new_index)
kpieces = len(indexes)
# also copies
new_names = list(names)
new_levels = list(levels)
# construct labels
new_labels = []
# do something a bit more speedy
for hlevel, level in zip(zipped, levels):
mapped = level.get_indexer(hlevel)
new_labels.append(np.repeat(mapped, n))
if isinstance(new_index, MultiIndex):
new_levels.extend(new_index.levels)
new_labels.extend([np.tile(lab, kpieces) for lab in new_index.labels])
new_names.extend(new_index.names)
else:
new_levels.append(new_index)
new_names.append(new_index.name)
new_labels.append(np.tile(np.arange(n), kpieces))
return MultiIndex(levels=new_levels, labels=new_labels, names=new_names)
def __setstate__(self, state):
items, ref_items, values = state
self.items = _ensure_index(items)
self.ref_items = _ensure_index(ref_items)
self.values = values
self.ndim = values.ndim
def set_items_norename(self, value):
value = _ensure_index(value)
self.axes[0] = value
for block in self.blocks:
block.set_ref_items(value, maybe_rename=False)
def __init__(self,
data=None,
index=None,
sparse_index=None,
kind='block',
fill_value=None,
name=None,
dtype=None,
copy=False,
fastpath=False):
# we are called internally, so short-circuit
if fastpath:
# data is an ndarray, index is defined
if not isinstance(data, SingleBlockManager):
data = SingleBlockManager(data, index, fastpath=True)
if copy:
data = data.copy()
else:
if data is None:
data = []
if isinstance(data, Series) and name is None:
name = data.name
if isinstance(data, SparseArray):
if index is not None:
assert (len(index) == len(data))
sparse_index = data.sp_index
if fill_value is None:
fill_value = data.fill_value
data = np.asarray(data)
elif isinstance(data, SparseSeries):
if index is None:
index = data.index.view()
if fill_value is None:
fill_value = data.fill_value
# extract the SingleBlockManager
data = data._data
elif isinstance(data, (Series, dict)):
data = Series(data, index=index)
index = data.index.view()
res = make_sparse(data, kind=kind, fill_value=fill_value)
data, sparse_index, fill_value = res
elif isinstance(data, (tuple, list, np.ndarray)):
# array-like
if sparse_index is None:
res = make_sparse(data, kind=kind, fill_value=fill_value)
data, sparse_index, fill_value = res
else:
assert (len(data) == sparse_index.npoints)
elif isinstance(data, SingleBlockManager):
if dtype is not None:
data = data.astype(dtype)
if index is None:
index = data.index.view()
else:
data = data.reindex(index, copy=False)
else:
length = len(index)
if data == fill_value or (isna(data) and isna(fill_value)):
if kind == 'block':
sparse_index = BlockIndex(length, [], [])
else:
sparse_index = IntIndex(length, [])
data = np.array([])
else:
if kind == 'block':
locs, lens = ([0], [length]) if length else ([], [])
sparse_index = BlockIndex(length, locs, lens)
else:
sparse_index = IntIndex(length, index)
v = data
data = np.empty(length)
data.fill(v)
if index is None:
index = com._default_index(sparse_index.length)
index = _ensure_index(index)
# create/copy the manager
if isinstance(data, SingleBlockManager):
if copy:
data = data.copy()
else:
# create a sparse array
if not isinstance(data, SparseArray):
data = SparseArray(data,
sparse_index=sparse_index,
fill_value=fill_value,
dtype=dtype,
copy=copy)
data = SingleBlockManager(data, index)
generic.NDFrame.__init__(self, data)
self.index = index
self.name = name
def _make_concat_multiindex(indexes, keys, levels=None, names=None):
if ((levels is None and isinstance(keys[0], tuple))
or (levels is not None and len(levels) > 1)):
zipped = lzip(*keys)
if names is None:
names = [None] * len(zipped)
if levels is None:
levels = [Categorical.from_array(zp).levels for zp in zipped]
else:
levels = [_ensure_index(x) for x in levels]
else:
zipped = [keys]
if names is None:
names = [None]
if levels is None:
levels = [_ensure_index(keys)]
else:
levels = [_ensure_index(x) for x in levels]
if not _all_indexes_same(indexes):
label_list = []
# things are potentially different sizes, so compute the exact labels
# for each level and pass those to MultiIndex.from_arrays
for hlevel, level in zip(zipped, levels):
to_concat = []
for key, index in zip(hlevel, indexes):
try:
i = level.get_loc(key)
except KeyError:
raise ValueError('Key %s not in level %s' %
(str(key), str(level)))
to_concat.append(np.repeat(i, len(index)))
label_list.append(np.concatenate(to_concat))
concat_index = _concat_indexes(indexes)
# these go at the end
if isinstance(concat_index, MultiIndex):
levels.extend(concat_index.levels)
label_list.extend(concat_index.labels)
else:
factor = Categorical.from_array(concat_index)
levels.append(factor.levels)
label_list.append(factor.labels)
if len(names) == len(levels):
names = list(names)
else:
# make sure that all of the passed indices have the same nlevels
if not len(set([i.nlevels for i in indexes])) == 1:
raise AssertionError("Cannot concat indices that do"
" not have the same number of levels")
# also copies
names = names + _get_consensus_names(indexes)
return MultiIndex(levels=levels, labels=label_list, names=names)
new_index = indexes[0]
n = len(new_index)
kpieces = len(indexes)
# also copies
new_names = list(names)
new_levels = list(levels)
# construct labels
new_labels = []
# do something a bit more speedy
for hlevel, level in zip(zipped, levels):
hlevel = _ensure_index(hlevel)
mapped = level.get_indexer(hlevel)
mask = mapped == -1
if mask.any():
raise ValueError('Values not found in passed level: %s' %
str(hlevel[mask]))
new_labels.append(np.repeat(mapped, n))
if isinstance(new_index, MultiIndex):
new_levels.extend(new_index.levels)
new_labels.extend([np.tile(lab, kpieces) for lab in new_index.labels])
else:
new_levels.append(new_index)
new_labels.append(np.tile(np.arange(n), kpieces))
if len(new_names) < len(new_levels):
new_names.extend(new_index.names)
return MultiIndex(levels=new_levels, labels=new_labels, names=new_names)
def __init__(self,
data=None,
index=None,
columns=None,
default_kind=None,
default_fill_value=None,
dtype=None,
copy=False):
# pick up the defaults from the Sparse structures
if isinstance(data, SparseDataFrame):
if index is None:
index = data.index
if columns is None:
columns = data.columns
if default_fill_value is None:
default_fill_value = data.default_fill_value
if default_kind is None:
default_kind = data.default_kind
elif isinstance(data, (SparseSeries, SparseArray)):
if index is None:
index = data.index
if default_fill_value is None:
default_fill_value = data.fill_value
if columns is None and hasattr(data, 'name'):
columns = [data.name]
if columns is None:
raise Exception("cannot pass a series w/o a name or columns")
data = {columns[0]: data}
if default_fill_value is None:
default_fill_value = np.nan
if default_kind is None:
default_kind = 'block'
self._default_kind = default_kind
self._default_fill_value = default_fill_value
if is_scipy_sparse(data):
mgr = self._init_spmatrix(data,
index,
columns,
dtype=dtype,
fill_value=default_fill_value)
elif isinstance(data, dict):
mgr = self._init_dict(data, index, columns, dtype=dtype)
elif isinstance(data, (np.ndarray, list)):
mgr = self._init_matrix(data, index, columns, dtype=dtype)
elif isinstance(data, SparseDataFrame):
mgr = self._init_mgr(data._data,
dict(index=index, columns=columns),
dtype=dtype,
copy=copy)
elif isinstance(data, DataFrame):
mgr = self._init_dict(data, data.index, data.columns, dtype=dtype)
elif isinstance(data, BlockManager):
mgr = self._init_mgr(data,
axes=dict(index=index, columns=columns),
dtype=dtype,
copy=copy)
elif data is None:
data = DataFrame()
if index is None:
index = Index([])
else:
index = _ensure_index(index)
if columns is None:
columns = Index([])
else:
for c in columns:
data[c] = SparseArray(np.nan,
index=index,
kind=self._default_kind,
fill_value=self._default_fill_value)
mgr = to_manager(data, columns, index)
if dtype is not None:
mgr = mgr.astype(dtype)
generic.NDFrame.__init__(self, mgr)
def __new__(cls,
data,
index=None,
sparse_index=None,
kind='block',
fill_value=None,
name=None,
copy=False):
is_sparse_array = isinstance(data, SparseArray)
if fill_value is None:
if is_sparse_array:
fill_value = data.fill_value
else:
fill_value = nan
if is_sparse_array:
if isinstance(data, SparseSeries) and index is None:
index = data.index
elif index is not None:
if not (len(index) == len(data)):
raise AssertionError()
sparse_index = data.sp_index
values = np.asarray(data)
elif isinstance(data, (Series, dict)):
if index is None:
index = data.index
data = Series(data)
values, sparse_index = make_sparse(data,
kind=kind,
fill_value=fill_value)
elif isinstance(data, (tuple, list, np.ndarray)):
# array-like
if sparse_index is None:
values, sparse_index = make_sparse(data,
kind=kind,
fill_value=fill_value)
else:
values = data
if not (len(values) == sparse_index.npoints):
raise AssertionError()
else:
if index is None:
raise TypeError('must pass index!')
length = len(index)
if data == fill_value or (isnull(data) and isnull(fill_value)):
if kind == 'block':
sparse_index = BlockIndex(length, [], [])
else:
sparse_index = IntIndex(length, [])
values = np.array([])
else:
if kind == 'block':
locs, lens = ([0], [length]) if length else ([], [])
sparse_index = BlockIndex(length, locs, lens)
else:
sparse_index = IntIndex(length, index)
values = np.empty(length)
values.fill(data)
if index is None:
index = com._default_index(sparse_index.length)
index = _ensure_index(index)
# Create array, do *not* copy data by default
if copy:
subarr = np.array(values, dtype=np.float64, copy=True)
else:
subarr = np.asarray(values, dtype=np.float64)
if index.is_all_dates:
cls = SparseTimeSeries
# Change the class of the array to be the subclass type.
output = subarr.view(cls)
output.sp_index = sparse_index
output.fill_value = np.float64(fill_value)
output.index = index
output.name = name
return output
def _set_columns(self, cols):
if len(cols) != len(self._series):
raise Exception('Columns length %d did not match data %d!' %
(len(cols), len(self._series)))
self._columns = _ensure_index(cols)
def _has_valid_type(self, key, axis):
ax = self.obj._get_axis(axis)
# valid for a label where all labels are in the index
# slice of lables (where start-end in labels)
# slice of integers (only if in the lables)
# boolean
if isinstance(key, slice):
if ax.is_floating():
# allowing keys to be slicers with no fallback
pass
else:
if key.start is not None:
if key.start not in ax:
raise KeyError(
"start bound [%s] is not the [%s]" %
(key.start, self.obj._get_axis_name(axis)))
if key.stop is not None:
if key.stop not in ax:
raise KeyError(
"stop bound [%s] is not in the [%s]" %
(key.stop, self.obj._get_axis_name(axis)))
elif com._is_bool_indexer(key):
return True
elif _is_list_like(key):
# mi is just a passthru
if isinstance(key, tuple) and isinstance(ax, MultiIndex):
return True
# require all elements in the index
idx = _ensure_index(key)
if not idx.isin(ax).all():
raise KeyError("[%s] are not in ALL in the [%s]" %
(key, self.obj._get_axis_name(axis)))
return True
else:
def error():
if isnull(key):
raise ValueError(
"cannot use label indexing with a null key")
raise KeyError("the label [%s] is not in the [%s]" %
(key, self.obj._get_axis_name(axis)))
try:
key = self._convert_scalar_indexer(key, axis)
if not key in ax:
error()
except (TypeError) as e:
# python 3 type errors should be raised
if 'unorderable' in str(e): # pragma: no cover
error()
raise
except:
error()
return True
def _validate_levels(cls, levels):
"""" Validates that we have good levels """
levels = _ensure_index(levels)
if not levels.is_unique:
raise ValueError('Categorical levels must be unique')
return levels
def _set_index(self, index):
self._index = _ensure_index(index)
for v in self._series.values():
v.index = self._index
