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alkaline-ml / pandas python
Repository URL to install this package:
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Version:
0.23.4 ▾
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# pylint: disable=E1101,E1103,W0232
import datetime
import warnings
from sys import getsizeof
import numpy as np
from pandas._libs import algos as libalgos, index as libindex, lib, Timestamp
from pandas.compat import range, zip, lrange, lzip, map
from pandas.compat.numpy import function as nv
from pandas import compat
from pandas.core.dtypes.dtypes import (
ExtensionDtype, PandasExtensionDtype)
from pandas.core.dtypes.common import (
_ensure_int64,
_ensure_platform_int,
is_categorical_dtype,
is_object_dtype,
is_hashable,
is_iterator,
is_list_like,
pandas_dtype,
is_scalar)
from pandas.core.dtypes.missing import isna, array_equivalent
from pandas.errors import PerformanceWarning, UnsortedIndexError
from pandas.util._decorators import Appender, cache_readonly, deprecate_kwarg
import pandas.core.common as com
import pandas.core.missing as missing
import pandas.core.algorithms as algos
from pandas.io.formats.printing import pprint_thing
from pandas.core.config import get_option
from pandas.core.indexes.base import (
Index, _ensure_index,
InvalidIndexError,
_index_shared_docs)
from pandas.core.indexes.frozen import (
FrozenNDArray, FrozenList, _ensure_frozen)
import pandas.core.indexes.base as ibase
_index_doc_kwargs = dict(ibase._index_doc_kwargs)
_index_doc_kwargs.update(
dict(klass='MultiIndex',
target_klass='MultiIndex or list of tuples'))
class MultiIndexUIntEngine(libindex.BaseMultiIndexCodesEngine,
libindex.UInt64Engine):
"""
This class manages a MultiIndex by mapping label combinations to positive
integers.
"""
_base = libindex.UInt64Engine
def _codes_to_ints(self, codes):
"""
Transform combination(s) of uint64 in one uint64 (each), in a strictly
monotonic way (i.e. respecting the lexicographic order of integer
combinations): see BaseMultiIndexCodesEngine documentation.
Parameters
----------
codes : 1- or 2-dimensional array of dtype uint64
Combinations of integers (one per row)
Returns
------
int_keys : scalar or 1-dimensional array, of dtype uint64
Integer(s) representing one combination (each)
"""
# Shift the representation of each level by the pre-calculated number
# of bits:
codes <<= self.offsets
# Now sum and OR are in fact interchangeable. This is a simple
# composition of the (disjunct) significant bits of each level (i.e.
# each column in "codes") in a single positive integer:
if codes.ndim == 1:
# Single key
return np.bitwise_or.reduce(codes)
# Multiple keys
return np.bitwise_or.reduce(codes, axis=1)
class MultiIndexPyIntEngine(libindex.BaseMultiIndexCodesEngine,
libindex.ObjectEngine):
"""
This class manages those (extreme) cases in which the number of possible
label combinations overflows the 64 bits integers, and uses an ObjectEngine
containing Python integers.
"""
_base = libindex.ObjectEngine
def _codes_to_ints(self, codes):
"""
Transform combination(s) of uint64 in one Python integer (each), in a
strictly monotonic way (i.e. respecting the lexicographic order of
integer combinations): see BaseMultiIndexCodesEngine documentation.
Parameters
----------
codes : 1- or 2-dimensional array of dtype uint64
Combinations of integers (one per row)
Returns
------
int_keys : int, or 1-dimensional array of dtype object
Integer(s) representing one combination (each)
"""
# Shift the representation of each level by the pre-calculated number
# of bits. Since this can overflow uint64, first make sure we are
# working with Python integers:
codes = codes.astype('object') << self.offsets
# Now sum and OR are in fact interchangeable. This is a simple
# composition of the (disjunct) significant bits of each level (i.e.
# each column in "codes") in a single positive integer (per row):
if codes.ndim == 1:
# Single key
return np.bitwise_or.reduce(codes)
# Multiple keys
return np.bitwise_or.reduce(codes, axis=1)
class MultiIndex(Index):
"""
A multi-level, or hierarchical, index object for pandas objects
Parameters
----------
levels : sequence of arrays
The unique labels for each level
labels : sequence of arrays
Integers for each level designating which label at each location
sortorder : optional int
Level of sortedness (must be lexicographically sorted by that
level)
names : optional sequence of objects
Names for each of the index levels. (name is accepted for compat)
copy : boolean, default False
Copy the meta-data
verify_integrity : boolean, default True
Check that the levels/labels are consistent and valid
Examples
---------
A new ``MultiIndex`` is typically constructed using one of the helper
methods :meth:`MultiIndex.from_arrays`, :meth:`MultiIndex.from_product`
and :meth:`MultiIndex.from_tuples`. For example (using ``.from_arrays``):
>>> arrays = [[1, 1, 2, 2], ['red', 'blue', 'red', 'blue']]
>>> pd.MultiIndex.from_arrays(arrays, names=('number', 'color'))
MultiIndex(levels=[[1, 2], ['blue', 'red']],
labels=[[0, 0, 1, 1], [1, 0, 1, 0]],
names=['number', 'color'])
See further examples for how to construct a MultiIndex in the doc strings
of the mentioned helper methods.
Notes
-----
See the `user guide
<http://pandas.pydata.org/pandas-docs/stable/advanced.html>`_ for more.
See Also
--------
MultiIndex.from_arrays : Convert list of arrays to MultiIndex
MultiIndex.from_product : Create a MultiIndex from the cartesian product
of iterables
MultiIndex.from_tuples : Convert list of tuples to a MultiIndex
Index : The base pandas Index type
Attributes
----------
names
levels
labels
nlevels
levshape
Methods
-------
from_arrays
from_tuples
from_product
set_levels
set_labels
to_hierarchical
to_frame
is_lexsorted
sortlevel
droplevel
swaplevel
reorder_levels
remove_unused_levels
"""
# initialize to zero-length tuples to make everything work
_typ = 'multiindex'
_names = FrozenList()
_levels = FrozenList()
_labels = FrozenList()
_comparables = ['names']
rename = Index.set_names
def __new__(cls, levels=None, labels=None, sortorder=None, names=None,
dtype=None, copy=False, name=None,
verify_integrity=True, _set_identity=True):
# compat with Index
if name is not None:
names = name
if levels is None or labels is None:
raise TypeError("Must pass both levels and labels")
if len(levels) != len(labels):
raise ValueError('Length of levels and labels must be the same.')
if len(levels) == 0:
raise ValueError('Must pass non-zero number of levels/labels')
result = object.__new__(MultiIndex)
# we've already validated levels and labels, so shortcut here
result._set_levels(levels, copy=copy, validate=False)
result._set_labels(labels, copy=copy, validate=False)
if names is not None:
# handles name validation
result._set_names(names)
if sortorder is not None:
result.sortorder = int(sortorder)
else:
result.sortorder = sortorder
if verify_integrity:
result._verify_integrity()
if _set_identity:
result._reset_identity()
return result
def _verify_integrity(self, labels=None, levels=None):
"""
Parameters
----------
labels : optional list
Labels to check for validity. Defaults to current labels.
levels : optional list
Levels to check for validity. Defaults to current levels.
Raises
------
ValueError
If length of levels and labels don't match, if any label would
exceed level bounds, or there are any duplicate levels.
"""
# NOTE: Currently does not check, among other things, that cached
# nlevels matches nor that sortorder matches actually sortorder.
labels = labels or self.labels
levels = levels or self.levels
if len(levels) != len(labels):
raise ValueError("Length of levels and labels must match. NOTE:"
" this index is in an inconsistent state.")
label_length = len(self.labels[0])
for i, (level, label) in enumerate(zip(levels, labels)):
if len(label) != label_length:
raise ValueError("Unequal label lengths: %s" %
([len(lab) for lab in labels]))
if len(label) and label.max() >= len(level):
raise ValueError("On level %d, label max (%d) >= length of"
" level (%d). NOTE: this index is in an"
" inconsistent state" % (i, label.max(),
len(level)))
if not level.is_unique:
raise ValueError("Level values must be unique: {values} on "
"level {level}".format(
values=[value for value in level],
level=i))
@property
def levels(self):
return self._levels
def _set_levels(self, levels, level=None, copy=False, validate=True,
verify_integrity=False):
# This is NOT part of the levels property because it should be
# externally not allowed to set levels. User beware if you change
# _levels directly
if validate and len(levels) == 0:
raise ValueError('Must set non-zero number of levels.')
if validate and level is None and len(levels) != self.nlevels:
raise ValueError('Length of levels must match number of levels.')
if validate and level is not None and len(levels) != len(level):
raise ValueError('Length of levels must match length of level.')
if level is None:
new_levels = FrozenList(
_ensure_index(lev, copy=copy)._shallow_copy()
for lev in levels)
else:
level = [self._get_level_number(l) for l in level]
new_levels = list(self._levels)
for l, v in zip(level, levels):
new_levels[l] = _ensure_index(v, copy=copy)._shallow_copy()
new_levels = FrozenList(new_levels)
if verify_integrity:
self._verify_integrity(levels=new_levels)
names = self.names
self._levels = new_levels
if any(names):
self._set_names(names)
self._tuples = None
self._reset_cache()
def set_levels(self, levels, level=None, inplace=False,
verify_integrity=True):
"""
Set new levels on MultiIndex. Defaults to returning
new index.
Parameters
----------
levels : sequence or list of sequence
new level(s) to apply
level : int, level name, or sequence of int/level names (default None)
level(s) to set (None for all levels)
inplace : bool
if True, mutates in place
verify_integrity : bool (default True)
if True, checks that levels and labels are compatible
Returns
-------
new index (of same type and class...etc)
Examples
--------
>>> idx = MultiIndex.from_tuples([(1, u'one'), (1, u'two'),
(2, u'one'), (2, u'two')],
names=['foo', 'bar'])
>>> idx.set_levels([['a','b'], [1,2]])
MultiIndex(levels=[[u'a', u'b'], [1, 2]],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]],
names=[u'foo', u'bar'])
>>> idx.set_levels(['a','b'], level=0)
MultiIndex(levels=[[u'a', u'b'], [u'one', u'two']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]],
names=[u'foo', u'bar'])
>>> idx.set_levels(['a','b'], level='bar')
MultiIndex(levels=[[1, 2], [u'a', u'b']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]],
names=[u'foo', u'bar'])
>>> idx.set_levels([['a','b'], [1,2]], level=[0,1])
MultiIndex(levels=[[u'a', u'b'], [1, 2]],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]],
names=[u'foo', u'bar'])
"""
if level is not None and not is_list_like(level):
if not is_list_like(levels):
raise TypeError("Levels must be list-like")
if is_list_like(levels[0]):
raise TypeError("Levels must be list-like")
level = [level]
levels = [levels]
elif level is None or is_list_like(level):
if not is_list_like(levels) or not is_list_like(levels[0]):
raise TypeError("Levels must be list of lists-like")
if inplace:
idx = self
else:
idx = self._shallow_copy()
idx._reset_identity()
idx._set_levels(levels, level=level, validate=True,
verify_integrity=verify_integrity)
if not inplace:
return idx
@property
def labels(self):
return self._labels
def _set_labels(self, labels, level=None, copy=False, validate=True,
verify_integrity=False):
if validate and level is None and len(labels) != self.nlevels:
raise ValueError("Length of labels must match number of levels")
if validate and level is not None and len(labels) != len(level):
raise ValueError('Length of labels must match length of levels.')
if level is None:
new_labels = FrozenList(
_ensure_frozen(lab, lev, copy=copy)._shallow_copy()
for lev, lab in zip(self.levels, labels))
else:
level = [self._get_level_number(l) for l in level]
new_labels = list(self._labels)
for lev_idx, lab in zip(level, labels):
lev = self.levels[lev_idx]
new_labels[lev_idx] = _ensure_frozen(
lab, lev, copy=copy)._shallow_copy()
new_labels = FrozenList(new_labels)
if verify_integrity:
self._verify_integrity(labels=new_labels)
self._labels = new_labels
self._tuples = None
self._reset_cache()
def set_labels(self, labels, level=None, inplace=False,
verify_integrity=True):
"""
Set new labels on MultiIndex. Defaults to returning
new index.
Parameters
----------
labels : sequence or list of sequence
new labels to apply
level : int, level name, or sequence of int/level names (default None)
level(s) to set (None for all levels)
inplace : bool
if True, mutates in place
verify_integrity : bool (default True)
if True, checks that levels and labels are compatible
Returns
-------
new index (of same type and class...etc)
Examples
--------
>>> idx = MultiIndex.from_tuples([(1, u'one'), (1, u'two'),
(2, u'one'), (2, u'two')],
names=['foo', 'bar'])
>>> idx.set_labels([[1,0,1,0], [0,0,1,1]])
MultiIndex(levels=[[1, 2], [u'one', u'two']],
labels=[[1, 0, 1, 0], [0, 0, 1, 1]],
names=[u'foo', u'bar'])
>>> idx.set_labels([1,0,1,0], level=0)
MultiIndex(levels=[[1, 2], [u'one', u'two']],
labels=[[1, 0, 1, 0], [0, 1, 0, 1]],
names=[u'foo', u'bar'])
>>> idx.set_labels([0,0,1,1], level='bar')
MultiIndex(levels=[[1, 2], [u'one', u'two']],
labels=[[0, 0, 1, 1], [0, 0, 1, 1]],
names=[u'foo', u'bar'])
>>> idx.set_labels([[1,0,1,0], [0,0,1,1]], level=[0,1])
MultiIndex(levels=[[1, 2], [u'one', u'two']],
labels=[[1, 0, 1, 0], [0, 0, 1, 1]],
names=[u'foo', u'bar'])
"""
if level is not None and not is_list_like(level):
if not is_list_like(labels):
raise TypeError("Labels must be list-like")
if is_list_like(labels[0]):
raise TypeError("Labels must be list-like")
level = [level]
labels = [labels]
elif level is None or is_list_like(level):
if not is_list_like(labels) or not is_list_like(labels[0]):
raise TypeError("Labels must be list of lists-like")
if inplace:
idx = self
else:
idx = self._shallow_copy()
idx._reset_identity()
idx._set_labels(labels, level=level, verify_integrity=verify_integrity)
if not inplace:
return idx
def copy(self, names=None, dtype=None, levels=None, labels=None,
deep=False, _set_identity=False, **kwargs):
"""
Make a copy of this object. Names, dtype, levels and labels can be
passed and will be set on new copy.
Parameters
----------
names : sequence, optional
dtype : numpy dtype or pandas type, optional
levels : sequence, optional
labels : sequence, optional
Returns
-------
copy : MultiIndex
Notes
-----
In most cases, there should be no functional difference from using
``deep``, but if ``deep`` is passed it will attempt to deepcopy.
This could be potentially expensive on large MultiIndex objects.
"""
name = kwargs.get('name')
names = self._validate_names(name=name, names=names, deep=deep)
if deep:
from copy import deepcopy
if levels is None:
levels = deepcopy(self.levels)
if labels is None:
labels = deepcopy(self.labels)
else:
if levels is None:
levels = self.levels
if labels is None:
labels = self.labels
return MultiIndex(levels=levels, labels=labels, names=names,
sortorder=self.sortorder, verify_integrity=False,
_set_identity=_set_identity)
def __array__(self, dtype=None):
""" the array interface, return my values """
return self.values
def view(self, cls=None):
""" this is defined as a copy with the same identity """
result = self.copy()
result._id = self._id
return result
def _shallow_copy_with_infer(self, values=None, **kwargs):
# On equal MultiIndexes the difference is empty.
# Therefore, an empty MultiIndex is returned GH13490
if len(values) == 0:
return MultiIndex(levels=[[] for _ in range(self.nlevels)],
labels=[[] for _ in range(self.nlevels)],
**kwargs)
return self._shallow_copy(values, **kwargs)
@Appender(_index_shared_docs['__contains__'] % _index_doc_kwargs)
def __contains__(self, key):
hash(key)
try:
self.get_loc(key)
return True
except (LookupError, TypeError):
return False
contains = __contains__
@Appender(_index_shared_docs['_shallow_copy'])
def _shallow_copy(self, values=None, **kwargs):
if values is not None:
names = kwargs.pop('names', kwargs.pop('name', self.names))
# discards freq
kwargs.pop('freq', None)
return MultiIndex.from_tuples(values, names=names, **kwargs)
return self.view()
@cache_readonly
def dtype(self):
return np.dtype('O')
def _is_memory_usage_qualified(self):
""" return a boolean if we need a qualified .info display """
def f(l):
return 'mixed' in l or 'string' in l or 'unicode' in l
return any(f(l) for l in self._inferred_type_levels)
@Appender(Index.memory_usage.__doc__)
def memory_usage(self, deep=False):
# we are overwriting our base class to avoid
# computing .values here which could materialize
# a tuple representation uncessarily
return self._nbytes(deep)
@cache_readonly
def nbytes(self):
""" return the number of bytes in the underlying data """
return self._nbytes(False)
def _nbytes(self, deep=False):
"""
return the number of bytes in the underlying data
deeply introspect the level data if deep=True
include the engine hashtable
*this is in internal routine*
"""
# for implementations with no useful getsizeof (PyPy)
objsize = 24
level_nbytes = sum(i.memory_usage(deep=deep) for i in self.levels)
label_nbytes = sum(i.nbytes for i in self.labels)
names_nbytes = sum(getsizeof(i, objsize) for i in self.names)
result = level_nbytes + label_nbytes + names_nbytes
# include our engine hashtable
result += self._engine.sizeof(deep=deep)
return result
def _format_attrs(self):
"""
Return a list of tuples of the (attr,formatted_value)
"""
attrs = [
('levels', ibase.default_pprint(self._levels,
max_seq_items=False)),
('labels', ibase.default_pprint(self._labels,
max_seq_items=False))]
if com._any_not_none(*self.names):
attrs.append(('names', ibase.default_pprint(self.names)))
if self.sortorder is not None:
attrs.append(('sortorder', ibase.default_pprint(self.sortorder)))
return attrs
def _format_space(self):
return "\n%s" % (' ' * (len(self.__class__.__name__) + 1))
def _format_data(self, name=None):
# we are formatting thru the attributes
return None
def __len__(self):
return len(self.labels[0])
def _get_names(self):
return FrozenList(level.name for level in self.levels)
def _set_names(self, names, level=None, validate=True):
"""
Set new names on index. Each name has to be a hashable type.
Parameters
----------
values : str or sequence
name(s) to set
level : int, level name, or sequence of int/level names (default None)
If the index is a MultiIndex (hierarchical), level(s) to set (None
for all levels). Otherwise level must be None
validate : boolean, default True
validate that the names match level lengths
Raises
------
TypeError if each name is not hashable.
Notes
-----
sets names on levels. WARNING: mutates!
Note that you generally want to set this *after* changing levels, so
that it only acts on copies
"""
# GH 15110
# Don't allow a single string for names in a MultiIndex
if names is not None and not is_list_like(names):
raise ValueError('Names should be list-like for a MultiIndex')
names = list(names)
if validate and level is not None and len(names) != len(level):
raise ValueError('Length of names must match length of level.')
if validate and level is None and len(names) != self.nlevels:
raise ValueError('Length of names must match number of levels in '
'MultiIndex.')
if level is None:
level = range(self.nlevels)
else:
level = [self._get_level_number(l) for l in level]
# set the name
for l, name in zip(level, names):
if name is not None:
# GH 20527
# All items in 'names' need to be hashable:
if not is_hashable(name):
raise TypeError('{}.name must be a hashable type'
.format(self.__class__.__name__))
self.levels[l].rename(name, inplace=True)
names = property(fset=_set_names, fget=_get_names,
doc="Names of levels in MultiIndex")
def _format_native_types(self, na_rep='nan', **kwargs):
new_levels = []
new_labels = []
# go through the levels and format them
for level, label in zip(self.levels, self.labels):
level = level._format_native_types(na_rep=na_rep, **kwargs)
# add nan values, if there are any
mask = (label == -1)
if mask.any():
nan_index = len(level)
level = np.append(level, na_rep)
label = label.values()
label[mask] = nan_index
new_levels.append(level)
new_labels.append(label)
# reconstruct the multi-index
mi = MultiIndex(levels=new_levels, labels=new_labels, names=self.names,
sortorder=self.sortorder, verify_integrity=False)
return mi.values
@Appender(_index_shared_docs['_get_grouper_for_level'])
def _get_grouper_for_level(self, mapper, level):
indexer = self.labels[level]
level_index = self.levels[level]
if mapper is not None:
# Handle group mapping function and return
level_values = self.levels[level].take(indexer)
grouper = level_values.map(mapper)
return grouper, None, None
labels, uniques = algos.factorize(indexer, sort=True)
if len(uniques) > 0 and uniques[0] == -1:
# Handle NAs
mask = indexer != -1
ok_labels, uniques = algos.factorize(indexer[mask],
sort=True)
labels = np.empty(len(indexer), dtype=indexer.dtype)
labels[mask] = ok_labels
labels[~mask] = -1
if len(uniques) < len(level_index):
# Remove unobserved levels from level_index
level_index = level_index.take(uniques)
grouper = level_index.take(labels)
return grouper, labels, level_index
@property
def _constructor(self):
return MultiIndex.from_tuples
@cache_readonly
def inferred_type(self):
return 'mixed'
@staticmethod
def _from_elements(values, labels=None, levels=None, names=None,
sortorder=None):
return MultiIndex(levels, labels, names, sortorder=sortorder)
def _get_level_number(self, level):
try:
count = self.names.count(level)
if count > 1:
raise ValueError('The name %s occurs multiple times, use a '
'level number' % level)
level = self.names.index(level)
except ValueError:
if not isinstance(level, int):
raise KeyError('Level %s not found' % str(level))
elif level < 0:
level += self.nlevels
if level < 0:
orig_level = level - self.nlevels
raise IndexError('Too many levels: Index has only %d '
'levels, %d is not a valid level number' %
(self.nlevels, orig_level))
# Note: levels are zero-based
elif level >= self.nlevels:
raise IndexError('Too many levels: Index has only %d levels, '
'not %d' % (self.nlevels, level + 1))
return level
_tuples = None
@cache_readonly
def _engine(self):
# Calculate the number of bits needed to represent labels in each
# level, as log2 of their sizes (including -1 for NaN):
sizes = np.ceil(np.log2([len(l) + 1 for l in self.levels]))
# Sum bit counts, starting from the _right_....
lev_bits = np.cumsum(sizes[::-1])[::-1]
# ... in order to obtain offsets such that sorting the combination of
# shifted codes (one for each level, resulting in a unique integer) is
# equivalent to sorting lexicographically the codes themselves. Notice
# that each level needs to be shifted by the number of bits needed to
# represent the _previous_ ones:
offsets = np.concatenate([lev_bits[1:], [0]]).astype('uint64')
# Check the total number of bits needed for our representation:
if lev_bits[0] > 64:
# The levels would overflow a 64 bit uint - use Python integers:
return MultiIndexPyIntEngine(self.levels, self.labels, offsets)
return MultiIndexUIntEngine(self.levels, self.labels, offsets)
@property
def values(self):
if self._tuples is not None:
return self._tuples
values = []
for i in range(self.nlevels):
vals = self._get_level_values(i)
if is_categorical_dtype(vals):
vals = vals.get_values()
if (isinstance(vals.dtype, (PandasExtensionDtype, ExtensionDtype))
or hasattr(vals, '_box_values')):
vals = vals.astype(object)
vals = np.array(vals, copy=False)
values.append(vals)
self._tuples = lib.fast_zip(values)
return self._tuples
# fml
@property
def _is_v1(self):
return False
@property
def _is_v2(self):
return False
@property
def _has_complex_internals(self):
# to disable groupby tricks
return True
@cache_readonly
def is_monotonic_increasing(self):
"""
return if the index is monotonic increasing (only equal or
increasing) values.
"""
# reversed() because lexsort() wants the most significant key last.
values = [self._get_level_values(i).values
for i in reversed(range(len(self.levels)))]
try:
sort_order = np.lexsort(values)
return Index(sort_order).is_monotonic
except TypeError:
# we have mixed types and np.lexsort is not happy
return Index(self.values).is_monotonic
@cache_readonly
def is_monotonic_decreasing(self):
"""
return if the index is monotonic decreasing (only equal or
decreasing) values.
"""
# monotonic decreasing if and only if reverse is monotonic increasing
return self[::-1].is_monotonic_increasing
@cache_readonly
def _have_mixed_levels(self):
""" return a boolean list indicated if we have mixed levels """
return ['mixed' in l for l in self._inferred_type_levels]
@cache_readonly
def _inferred_type_levels(self):
""" return a list of the inferred types, one for each level """
return [i.inferred_type for i in self.levels]
@cache_readonly
def _hashed_values(self):
""" return a uint64 ndarray of my hashed values """
from pandas.core.util.hashing import hash_tuples
return hash_tuples(self)
def _hashed_indexing_key(self, key):
"""
validate and return the hash for the provided key
*this is internal for use for the cython routines*
Parameters
----------
key : string or tuple
Returns
-------
np.uint64
Notes
-----
we need to stringify if we have mixed levels
"""
from pandas.core.util.hashing import hash_tuples, hash_tuple
if not isinstance(key, tuple):
return hash_tuples(key)
if not len(key) == self.nlevels:
raise KeyError
def f(k, stringify):
if stringify and not isinstance(k, compat.string_types):
k = str(k)
return k
key = tuple([f(k, stringify)
for k, stringify in zip(key, self._have_mixed_levels)])
return hash_tuple(key)
@Appender(Index.duplicated.__doc__)
def duplicated(self, keep='first'):
from pandas.core.sorting import get_group_index
from pandas._libs.hashtable import duplicated_int64
shape = map(len, self.levels)
ids = get_group_index(self.labels, shape, sort=False, xnull=False)
return duplicated_int64(ids, keep)
def fillna(self, value=None, downcast=None):
"""
fillna is not implemented for MultiIndex
"""
raise NotImplementedError('isna is not defined for MultiIndex')
@Appender(_index_shared_docs['dropna'])
def dropna(self, how='any'):
nans = [label == -1 for label in self.labels]
if how == 'any':
indexer = np.any(nans, axis=0)
elif how == 'all':
indexer = np.all(nans, axis=0)
else:
raise ValueError("invalid how option: {0}".format(how))
new_labels = [label[~indexer] for label in self.labels]
return self.copy(labels=new_labels, deep=True)
def get_value(self, series, key):
# somewhat broken encapsulation
from pandas.core.indexing import maybe_droplevels
# Label-based
s = com._values_from_object(series)
k = com._values_from_object(key)
def _try_mi(k):
# TODO: what if a level contains tuples??
loc = self.get_loc(k)
new_values = series._values[loc]
new_index = self[loc]
new_index = maybe_droplevels(new_index, k)
return series._constructor(new_values, index=new_index,
name=series.name).__finalize__(self)
try:
return self._engine.get_value(s, k)
except KeyError as e1:
try:
return _try_mi(key)
except KeyError:
pass
try:
return libindex.get_value_at(s, k)
except IndexError:
raise
except TypeError:
# generator/iterator-like
if is_iterator(key):
raise InvalidIndexError(key)
else:
raise e1
except Exception: # pragma: no cover
raise e1
except TypeError:
# a Timestamp will raise a TypeError in a multi-index
# rather than a KeyError, try it here
# note that a string that 'looks' like a Timestamp will raise
# a KeyError! (GH5725)
if (isinstance(key, (datetime.datetime, np.datetime64)) or
(compat.PY3 and isinstance(key, compat.string_types))):
try:
return _try_mi(key)
except (KeyError):
raise
except:
pass
try:
return _try_mi(Timestamp(key))
except:
pass
raise InvalidIndexError(key)
def _get_level_values(self, level, unique=False):
"""
Return vector of label values for requested level,
equal to the length of the index
**this is an internal method**
Parameters
----------
level : int level
unique : bool, default False
if True, drop duplicated values
Returns
-------
values : ndarray
"""
values = self.levels[level]
labels = self.labels[level]
if unique:
labels = algos.unique(labels)
filled = algos.take_1d(values._values, labels,
fill_value=values._na_value)
values = values._shallow_copy(filled)
return values
def get_level_values(self, level):
"""
Return vector of label values for requested level,
equal to the length of the index.
Parameters
----------
level : int or str
``level`` is either the integer position of the level in the
MultiIndex, or the name of the level.
Returns
-------
values : Index
``values`` is a level of this MultiIndex converted to
a single :class:`Index` (or subclass thereof).
Examples
---------
Create a MultiIndex:
>>> mi = pd.MultiIndex.from_arrays((list('abc'), list('def')))
>>> mi.names = ['level_1', 'level_2']
Get level values by supplying level as either integer or name:
>>> mi.get_level_values(0)
Index(['a', 'b', 'c'], dtype='object', name='level_1')
>>> mi.get_level_values('level_2')
Index(['d', 'e', 'f'], dtype='object', name='level_2')
"""
level = self._get_level_number(level)
values = self._get_level_values(level)
return values
@Appender(_index_shared_docs['index_unique'] % _index_doc_kwargs)
def unique(self, level=None):
if level is None:
return super(MultiIndex, self).unique()
else:
level = self._get_level_number(level)
return self._get_level_values(level=level, unique=True)
def format(self, space=2, sparsify=None, adjoin=True, names=False,
na_rep=None, formatter=None):
if len(self) == 0:
return []
stringified_levels = []
for lev, lab in zip(self.levels, self.labels):
na = na_rep if na_rep is not None else _get_na_rep(lev.dtype.type)
if len(lev) > 0:
formatted = lev.take(lab).format(formatter=formatter)
# we have some NA
mask = lab == -1
if mask.any():
formatted = np.array(formatted, dtype=object)
formatted[mask] = na
formatted = formatted.tolist()
else:
# weird all NA case
formatted = [pprint_thing(na if isna(x) else x,
escape_chars=('\t', '\r', '\n'))
for x in algos.take_1d(lev._values, lab)]
stringified_levels.append(formatted)
result_levels = []
for lev, name in zip(stringified_levels, self.names):
level = []
if names:
level.append(pprint_thing(name,
escape_chars=('\t', '\r', '\n'))
if name is not None else '')
level.extend(np.array(lev, dtype=object))
result_levels.append(level)
if sparsify is None:
sparsify = get_option("display.multi_sparse")
if sparsify:
sentinel = ''
# GH3547
# use value of sparsify as sentinel, unless it's an obvious
# "Truthey" value
if sparsify not in [True, 1]:
sentinel = sparsify
# little bit of a kludge job for #1217
result_levels = _sparsify(result_levels, start=int(names),
sentinel=sentinel)
if adjoin:
from pandas.io.formats.format import _get_adjustment
adj = _get_adjustment()
return adj.adjoin(space, *result_levels).split('\n')
else:
return result_levels
def _to_safe_for_reshape(self):
""" convert to object if we are a categorical """
return self.set_levels([i._to_safe_for_reshape() for i in self.levels])
def to_frame(self, index=True):
"""
Create a DataFrame with the levels of the MultiIndex as columns.
.. versionadded:: 0.20.0
Parameters
----------
index : boolean, default True
Set the index of the returned DataFrame as the original MultiIndex.
Returns
-------
DataFrame : a DataFrame containing the original MultiIndex data.
"""
from pandas import DataFrame
result = DataFrame({(name or level):
self._get_level_values(level)
for name, level in
zip(self.names, range(len(self.levels)))},
copy=False)
if index:
result.index = self
return result
def to_hierarchical(self, n_repeat, n_shuffle=1):
"""
Return a MultiIndex reshaped to conform to the
shapes given by n_repeat and n_shuffle.
Useful to replicate and rearrange a MultiIndex for combination
with another Index with n_repeat items.
Parameters
----------
n_repeat : int
Number of times to repeat the labels on self
n_shuffle : int
Controls the reordering of the labels. If the result is going
to be an inner level in a MultiIndex, n_shuffle will need to be
greater than one. The size of each label must divisible by
n_shuffle.
Returns
-------
MultiIndex
Examples
--------
>>> idx = MultiIndex.from_tuples([(1, u'one'), (1, u'two'),
(2, u'one'), (2, u'two')])
>>> idx.to_hierarchical(3)
MultiIndex(levels=[[1, 2], [u'one', u'two']],
labels=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1]])
"""
levels = self.levels
labels = [np.repeat(x, n_repeat) for x in self.labels]
# Assumes that each label is divisible by n_shuffle
labels = [x.reshape(n_shuffle, -1).ravel(order='F') for x in labels]
names = self.names
return MultiIndex(levels=levels, labels=labels, names=names)
@property
def is_all_dates(self):
return False
def is_lexsorted(self):
"""
Return True if the labels are lexicographically sorted
"""
return self.lexsort_depth == self.nlevels
@cache_readonly
def lexsort_depth(self):
if self.sortorder is not None:
if self.sortorder == 0:
return self.nlevels
else:
return 0
int64_labels = [_ensure_int64(lab) for lab in self.labels]
for k in range(self.nlevels, 0, -1):
if libalgos.is_lexsorted(int64_labels[:k]):
return k
return 0
@classmethod
def from_arrays(cls, arrays, sortorder=None, names=None):
"""
Convert arrays to MultiIndex
Parameters
----------
arrays : list / sequence of array-likes
Each array-like gives one level's value for each data point.
len(arrays) is the number of levels.
sortorder : int or None
Level of sortedness (must be lexicographically sorted by that
level)
Returns
-------
index : MultiIndex
Examples
--------
>>> arrays = [[1, 1, 2, 2], ['red', 'blue', 'red', 'blue']]
>>> MultiIndex.from_arrays(arrays, names=('number', 'color'))
See Also
--------
MultiIndex.from_tuples : Convert list of tuples to MultiIndex
MultiIndex.from_product : Make a MultiIndex from cartesian product
of iterables
"""
if not is_list_like(arrays):
raise TypeError("Input must be a list / sequence of array-likes.")
elif is_iterator(arrays):
arrays = list(arrays)
# Check if lengths of all arrays are equal or not,
# raise ValueError, if not
for i in range(1, len(arrays)):
if len(arrays[i]) != len(arrays[i - 1]):
raise ValueError('all arrays must be same length')
from pandas.core.arrays.categorical import _factorize_from_iterables
labels, levels = _factorize_from_iterables(arrays)
if names is None:
names = [getattr(arr, "name", None) for arr in arrays]
return MultiIndex(levels=levels, labels=labels, sortorder=sortorder,
names=names, verify_integrity=False)
@classmethod
def from_tuples(cls, tuples, sortorder=None, names=None):
"""
Convert list of tuples to MultiIndex
Parameters
----------
tuples : list / sequence of tuple-likes
Each tuple is the index of one row/column.
sortorder : int or None
Level of sortedness (must be lexicographically sorted by that
level)
Returns
-------
index : MultiIndex
Examples
--------
>>> tuples = [(1, u'red'), (1, u'blue'),
(2, u'red'), (2, u'blue')]
>>> MultiIndex.from_tuples(tuples, names=('number', 'color'))
See Also
--------
MultiIndex.from_arrays : Convert list of arrays to MultiIndex
MultiIndex.from_product : Make a MultiIndex from cartesian product
of iterables
"""
if not is_list_like(tuples):
raise TypeError('Input must be a list / sequence of tuple-likes.')
elif is_iterator(tuples):
tuples = list(tuples)
if len(tuples) == 0:
if names is None:
msg = 'Cannot infer number of levels from empty list'
raise TypeError(msg)
arrays = [[]] * len(names)
elif isinstance(tuples, (np.ndarray, Index)):
if isinstance(tuples, Index):
tuples = tuples._values
arrays = list(lib.tuples_to_object_array(tuples).T)
elif isinstance(tuples, list):
arrays = list(lib.to_object_array_tuples(tuples).T)
else:
arrays = lzip(*tuples)
return MultiIndex.from_arrays(arrays, sortorder=sortorder, names=names)
@classmethod
def from_product(cls, iterables, sortorder=None, names=None):
"""
Make a MultiIndex from the cartesian product of multiple iterables
Parameters
----------
iterables : list / sequence of iterables
Each iterable has unique labels for each level of the index.
sortorder : int or None
Level of sortedness (must be lexicographically sorted by that
level).
names : list / sequence of strings or None
Names for the levels in the index.
Returns
-------
index : MultiIndex
Examples
--------
>>> numbers = [0, 1, 2]
>>> colors = [u'green', u'purple']
>>> MultiIndex.from_product([numbers, colors],
names=['number', 'color'])
MultiIndex(levels=[[0, 1, 2], [u'green', u'purple']],
labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
names=[u'number', u'color'])
See Also
--------
MultiIndex.from_arrays : Convert list of arrays to MultiIndex
MultiIndex.from_tuples : Convert list of tuples to MultiIndex
"""
from pandas.core.arrays.categorical import _factorize_from_iterables
from pandas.core.reshape.util import cartesian_product
if not is_list_like(iterables):
raise TypeError("Input must be a list / sequence of iterables.")
elif is_iterator(iterables):
iterables = list(iterables)
labels, levels = _factorize_from_iterables(iterables)
labels = cartesian_product(labels)
return MultiIndex(levels, labels, sortorder=sortorder, names=names)
def _sort_levels_monotonic(self):
"""
.. versionadded:: 0.20.0
This is an *internal* function.
create a new MultiIndex from the current to monotonically sorted
items IN the levels. This does not actually make the entire MultiIndex
monotonic, JUST the levels.
The resulting MultiIndex will have the same outward
appearance, meaning the same .values and ordering. It will also
be .equals() to the original.
Returns
-------
MultiIndex
Examples
--------
>>> i = pd.MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
>>> i
MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
>>> i.sort_monotonic()
MultiIndex(levels=[['a', 'b'], ['aa', 'bb']],
labels=[[0, 0, 1, 1], [1, 0, 1, 0]])
"""
if self.is_lexsorted() and self.is_monotonic:
return self
new_levels = []
new_labels = []
for lev, lab in zip(self.levels, self.labels):
if not lev.is_monotonic:
try:
# indexer to reorder the levels
indexer = lev.argsort()
except TypeError:
pass
else:
lev = lev.take(indexer)
# indexer to reorder the labels
indexer = _ensure_int64(indexer)
ri = lib.get_reverse_indexer(indexer, len(indexer))
lab = algos.take_1d(ri, lab)
new_levels.append(lev)
new_labels.append(lab)
return MultiIndex(new_levels, new_labels,
names=self.names, sortorder=self.sortorder,
verify_integrity=False)
def remove_unused_levels(self):
"""
create a new MultiIndex from the current that removing
unused levels, meaning that they are not expressed in the labels
The resulting MultiIndex will have the same outward
appearance, meaning the same .values and ordering. It will also
be .equals() to the original.
.. versionadded:: 0.20.0
Returns
-------
MultiIndex
Examples
--------
>>> i = pd.MultiIndex.from_product([range(2), list('ab')])
MultiIndex(levels=[[0, 1], ['a', 'b']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
>>> i[2:]
MultiIndex(levels=[[0, 1], ['a', 'b']],
labels=[[1, 1], [0, 1]])
The 0 from the first level is not represented
and can be removed
>>> i[2:].remove_unused_levels()
MultiIndex(levels=[[1], ['a', 'b']],
labels=[[0, 0], [0, 1]])
"""
new_levels = []
new_labels = []
changed = False
for lev, lab in zip(self.levels, self.labels):
# Since few levels are typically unused, bincount() is more
# efficient than unique() - however it only accepts positive values
# (and drops order):
uniques = np.where(np.bincount(lab + 1) > 0)[0] - 1
has_na = int(len(uniques) and (uniques[0] == -1))
if len(uniques) != len(lev) + has_na:
# We have unused levels
changed = True
# Recalculate uniques, now preserving order.
# Can easily be cythonized by exploiting the already existing
# "uniques" and stop parsing "lab" when all items are found:
uniques = algos.unique(lab)
if has_na:
na_idx = np.where(uniques == -1)[0]
# Just ensure that -1 is in first position:
uniques[[0, na_idx[0]]] = uniques[[na_idx[0], 0]]
# labels get mapped from uniques to 0:len(uniques)
# -1 (if present) is mapped to last position
label_mapping = np.zeros(len(lev) + has_na)
# ... and reassigned value -1:
label_mapping[uniques] = np.arange(len(uniques)) - has_na
lab = label_mapping[lab]
# new levels are simple
lev = lev.take(uniques[has_na:])
new_levels.append(lev)
new_labels.append(lab)
result = self._shallow_copy()
if changed:
result._reset_identity()
result._set_levels(new_levels, validate=False)
result._set_labels(new_labels, validate=False)
return result
@property
def nlevels(self):
"""Integer number of levels in this MultiIndex."""
return len(self.levels)
@property
def levshape(self):
"""A tuple with the length of each level."""
return tuple(len(x) for x in self.levels)
def __reduce__(self):
"""Necessary for making this object picklable"""
d = dict(levels=[lev for lev in self.levels],
labels=[label for label in self.labels],
sortorder=self.sortorder, names=list(self.names))
return ibase._new_Index, (self.__class__, d), None
def __setstate__(self, state):
"""Necessary for making this object picklable"""
if isinstance(state, dict):
levels = state.get('levels')
labels = state.get('labels')
sortorder = state.get('sortorder')
names = state.get('names')
elif isinstance(state, tuple):
nd_state, own_state = state
levels, labels, sortorder, names = own_state
self._set_levels([Index(x) for x in levels], validate=False)
self._set_labels(labels)
self._set_names(names)
self.sortorder = sortorder
self._verify_integrity()
self._reset_identity()
def __getitem__(self, key):
if is_scalar(key):
retval = []
for lev, lab in zip(self.levels, self.labels):
if lab[key] == -1:
retval.append(np.nan)
else:
retval.append(lev[lab[key]])
return tuple(retval)
else:
if com.is_bool_indexer(key):
key = np.asarray(key)
sortorder = self.sortorder
else:
# cannot be sure whether the result will be sorted
sortorder = None
if isinstance(key, Index):
key = np.asarray(key)
new_labels = [lab[key] for lab in self.labels]
return MultiIndex(levels=self.levels, labels=new_labels,
names=self.names, sortorder=sortorder,
verify_integrity=False)
@Appender(_index_shared_docs['take'] % _index_doc_kwargs)
def take(self, indices, axis=0, allow_fill=True,
fill_value=None, **kwargs):
nv.validate_take(tuple(), kwargs)
indices = _ensure_platform_int(indices)
taken = self._assert_take_fillable(self.labels, indices,
allow_fill=allow_fill,
fill_value=fill_value,
na_value=-1)
return MultiIndex(levels=self.levels, labels=taken,
names=self.names, verify_integrity=False)
def _assert_take_fillable(self, values, indices, allow_fill=True,
fill_value=None, na_value=None):
""" Internal method to handle NA filling of take """
# only fill if we are passing a non-None fill_value
if allow_fill and fill_value is not None:
if (indices < -1).any():
msg = ('When allow_fill=True and fill_value is not None, '
'all indices must be >= -1')
raise ValueError(msg)
taken = [lab.take(indices) for lab in self.labels]
mask = indices == -1
if mask.any():
masked = []
for new_label in taken:
label_values = new_label.values()
label_values[mask] = na_value
masked.append(FrozenNDArray(label_values))
taken = masked
else:
taken = [lab.take(indices) for lab in self.labels]
return taken
def append(self, other):
"""
Append a collection of Index options together
Parameters
----------
other : Index or list/tuple of indices
Returns
-------
appended : Index
"""
if not isinstance(other, (list, tuple)):
other = [other]
if all((isinstance(o, MultiIndex) and o.nlevels >= self.nlevels)
for o in other):
arrays = []
for i in range(self.nlevels):
label = self._get_level_values(i)
appended = [o._get_level_values(i) for o in other]
arrays.append(label.append(appended))
return MultiIndex.from_arrays(arrays, names=self.names)
to_concat = (self.values, ) + tuple(k._values for k in other)
new_tuples = np.concatenate(to_concat)
# if all(isinstance(x, MultiIndex) for x in other):
try:
return MultiIndex.from_tuples(new_tuples, names=self.names)
except:
return Index(new_tuples)
def argsort(self, *args, **kwargs):
return self.values.argsort(*args, **kwargs)
@deprecate_kwarg(old_arg_name='n', new_arg_name='repeats')
def repeat(self, repeats, *args, **kwargs):
nv.validate_repeat(args, kwargs)
return MultiIndex(levels=self.levels,
labels=[label.view(np.ndarray).repeat(repeats)
for label in self.labels], names=self.names,
sortorder=self.sortorder, verify_integrity=False)
def where(self, cond, other=None):
raise NotImplementedError(".where is not supported for "
"MultiIndex operations")
def drop(self, labels, level=None, errors='raise'):
"""
Make new MultiIndex with passed list of labels deleted
Parameters
----------
labels : array-like
Must be a list of tuples
level : int or level name, default None
Returns
-------
dropped : MultiIndex
"""
if level is not None:
return self._drop_from_level(labels, level)
try:
if not isinstance(labels, (np.ndarray, Index)):
labels = com._index_labels_to_array(labels)
indexer = self.get_indexer(labels)
mask = indexer == -1
if mask.any():
if errors != 'ignore':
raise ValueError('labels %s not contained in axis' %
labels[mask])
except Exception:
pass
inds = []
for label in labels:
try:
loc = self.get_loc(label)
# get_loc returns either an integer, a slice, or a boolean
# mask
if isinstance(loc, int):
inds.append(loc)
elif isinstance(loc, slice):
inds.extend(lrange(loc.start, loc.stop))
elif com.is_bool_indexer(loc):
if self.lexsort_depth == 0:
warnings.warn('dropping on a non-lexsorted multi-index'
' without a level parameter may impact '
'performance.',
PerformanceWarning,
stacklevel=3)
loc = loc.nonzero()[0]
inds.extend(loc)
else:
msg = 'unsupported indexer of type {}'.format(type(loc))
raise AssertionError(msg)
except KeyError:
if errors != 'ignore':
raise
return self.delete(inds)
def _drop_from_level(self, labels, level):
labels = com._index_labels_to_array(labels)
i = self._get_level_number(level)
index = self.levels[i]
values = index.get_indexer(labels)
mask = ~algos.isin(self.labels[i], values)
return self[mask]
def droplevel(self, level=0):
"""
Return Index with requested level removed. If MultiIndex has only 2
levels, the result will be of Index type not MultiIndex.
Parameters
----------
level : int/level name or list thereof
Notes
-----
Does not check if result index is unique or not
Returns
-------
index : Index or MultiIndex
"""
levels = level
if not isinstance(levels, (tuple, list)):
levels = [level]
new_levels = list(self.levels)
new_labels = list(self.labels)
new_names = list(self.names)
levnums = sorted(self._get_level_number(lev) for lev in levels)[::-1]
for i in levnums:
new_levels.pop(i)
new_labels.pop(i)
new_names.pop(i)
if len(new_levels) == 1:
# set nan if needed
mask = new_labels[0] == -1
result = new_levels[0].take(new_labels[0])
if mask.any():
result = result.putmask(mask, np.nan)
result.name = new_names[0]
return result
else:
return MultiIndex(levels=new_levels, labels=new_labels,
names=new_names, verify_integrity=False)
def swaplevel(self, i=-2, j=-1):
"""
Swap level i with level j.
Calling this method does not change the ordering of the values.
Parameters
----------
i : int, str, default -2
First level of index to be swapped. Can pass level name as string.
Type of parameters can be mixed.
j : int, str, default -1
Second level of index to be swapped. Can pass level name as string.
Type of parameters can be mixed.
Returns
-------
MultiIndex
A new MultiIndex
.. versionchanged:: 0.18.1
The indexes ``i`` and ``j`` are now optional, and default to
the two innermost levels of the index.
See Also
--------
Series.swaplevel : Swap levels i and j in a MultiIndex
Dataframe.swaplevel : Swap levels i and j in a MultiIndex on a
particular axis
Examples
--------
>>> mi = pd.MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
... labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
>>> mi
MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
>>> mi.swaplevel(0, 1)
MultiIndex(levels=[['bb', 'aa'], ['a', 'b']],
labels=[[0, 1, 0, 1], [0, 0, 1, 1]])
"""
new_levels = list(self.levels)
new_labels = list(self.labels)
new_names = list(self.names)
i = self._get_level_number(i)
j = self._get_level_number(j)
new_levels[i], new_levels[j] = new_levels[j], new_levels[i]
new_labels[i], new_labels[j] = new_labels[j], new_labels[i]
new_names[i], new_names[j] = new_names[j], new_names[i]
return MultiIndex(levels=new_levels, labels=new_labels,
names=new_names, verify_integrity=False)
def reorder_levels(self, order):
"""
Rearrange levels using input order. May not drop or duplicate levels
Parameters
----------
"""
order = [self._get_level_number(i) for i in order]
if len(order) != self.nlevels:
raise AssertionError('Length of order must be same as '
'number of levels (%d), got %d' %
(self.nlevels, len(order)))
new_levels = [self.levels[i] for i in order]
new_labels = [self.labels[i] for i in order]
new_names = [self.names[i] for i in order]
return MultiIndex(levels=new_levels, labels=new_labels,
names=new_names, verify_integrity=False)
def __getslice__(self, i, j):
return self.__getitem__(slice(i, j))
def _get_labels_for_sorting(self):
"""
we categorizing our labels by using the
available catgories (all, not just observed)
excluding any missing ones (-1); this is in preparation
for sorting, where we need to disambiguate that -1 is not
a valid valid
"""
from pandas.core.arrays import Categorical
def cats(label):
return np.arange(np.array(label).max() + 1 if len(label) else 0,
dtype=label.dtype)
return [Categorical.from_codes(label, cats(label), ordered=True)
for label in self.labels]
def sortlevel(self, level=0, ascending=True, sort_remaining=True):
"""
Sort MultiIndex at the requested level. The result will respect the
original ordering of the associated factor at that level.
Parameters
----------
level : list-like, int or str, default 0
If a string is given, must be a name of the level
If list-like must be names or ints of levels.
ascending : boolean, default True
False to sort in descending order
Can also be a list to specify a directed ordering
sort_remaining : sort by the remaining levels after level.
Returns
-------
sorted_index : pd.MultiIndex
Resulting index
indexer : np.ndarray
Indices of output values in original index
"""
from pandas.core.sorting import indexer_from_factorized
if isinstance(level, (compat.string_types, int)):
level = [level]
level = [self._get_level_number(lev) for lev in level]
sortorder = None
# we have a directed ordering via ascending
if isinstance(ascending, list):
if not len(level) == len(ascending):
raise ValueError("level must have same length as ascending")
from pandas.core.sorting import lexsort_indexer
indexer = lexsort_indexer([self.labels[lev] for lev in level],
orders=ascending)
# level ordering
else:
labels = list(self.labels)
shape = list(self.levshape)
# partition labels and shape
primary = tuple(labels.pop(lev - i) for i, lev in enumerate(level))
primshp = tuple(shape.pop(lev - i) for i, lev in enumerate(level))
if sort_remaining:
primary += primary + tuple(labels)
primshp += primshp + tuple(shape)
else:
sortorder = level[0]
indexer = indexer_from_factorized(primary, primshp,
compress=False)
if not ascending:
indexer = indexer[::-1]
indexer = _ensure_platform_int(indexer)
new_labels = [lab.take(indexer) for lab in self.labels]
new_index = MultiIndex(labels=new_labels, levels=self.levels,
names=self.names, sortorder=sortorder,
verify_integrity=False)
return new_index, indexer
def _convert_listlike_indexer(self, keyarr, kind=None):
"""
Parameters
----------
keyarr : list-like
Indexer to convert.
Returns
-------
tuple (indexer, keyarr)
indexer is an ndarray or None if cannot convert
keyarr are tuple-safe keys
"""
indexer, keyarr = super(MultiIndex, self)._convert_listlike_indexer(
keyarr, kind=kind)
# are we indexing a specific level
if indexer is None and len(keyarr) and not isinstance(keyarr[0],
tuple):
level = 0
_, indexer = self.reindex(keyarr, level=level)
# take all
if indexer is None:
indexer = np.arange(len(self))
check = self.levels[0].get_indexer(keyarr)
mask = check == -1
if mask.any():
raise KeyError('%s not in index' % keyarr[mask])
return indexer, keyarr
@Appender(_index_shared_docs['get_indexer'] % _index_doc_kwargs)
def get_indexer(self, target, method=None, limit=None, tolerance=None):
method = missing.clean_reindex_fill_method(method)
target = _ensure_index(target)
# empty indexer
if is_list_like(target) and not len(target):
return _ensure_platform_int(np.array([]))
if not isinstance(target, MultiIndex):
try:
target = MultiIndex.from_tuples(target)
except (TypeError, ValueError):
# let's instead try with a straight Index
if method is None:
return Index(self.values).get_indexer(target,
method=method,
limit=limit,
tolerance=tolerance)
if not self.is_unique:
raise Exception('Reindexing only valid with uniquely valued Index '
'objects')
if method == 'pad' or method == 'backfill':
if tolerance is not None:
raise NotImplementedError("tolerance not implemented yet "
'for MultiIndex')
indexer = self._engine.get_indexer(target, method, limit)
elif method == 'nearest':
raise NotImplementedError("method='nearest' not implemented yet "
'for MultiIndex; see GitHub issue 9365')
else:
indexer = self._engine.get_indexer(target)
return _ensure_platform_int(indexer)
@Appender(_index_shared_docs['get_indexer_non_unique'] % _index_doc_kwargs)
def get_indexer_non_unique(self, target):
return super(MultiIndex, self).get_indexer_non_unique(target)
def reindex(self, target, method=None, level=None, limit=None,
tolerance=None):
"""
Create index with target's values (move/add/delete values as necessary)
Returns
-------
new_index : pd.MultiIndex
Resulting index
indexer : np.ndarray or None
Indices of output values in original index
"""
# GH6552: preserve names when reindexing to non-named target
# (i.e. neither Index nor Series).
preserve_names = not hasattr(target, 'names')
if level is not None:
if method is not None:
raise TypeError('Fill method not supported if level passed')
# GH7774: preserve dtype/tz if target is empty and not an Index.
# target may be an iterator
target = ibase._ensure_has_len(target)
if len(target) == 0 and not isinstance(target, Index):
idx = self.levels[level]
attrs = idx._get_attributes_dict()
attrs.pop('freq', None) # don't preserve freq
target = type(idx)._simple_new(np.empty(0, dtype=idx.dtype),
**attrs)
else:
target = _ensure_index(target)
target, indexer, _ = self._join_level(target, level, how='right',
return_indexers=True,
keep_order=False)
else:
target = _ensure_index(target)
if self.equals(target):
indexer = None
else:
if self.is_unique:
indexer = self.get_indexer(target, method=method,
limit=limit,
tolerance=tolerance)
else:
raise Exception("cannot handle a non-unique multi-index!")
if not isinstance(target, MultiIndex):
if indexer is None:
target = self
elif (indexer >= 0).all():
target = self.take(indexer)
else:
# hopefully?
target = MultiIndex.from_tuples(target)
if (preserve_names and target.nlevels == self.nlevels and
target.names != self.names):
target = target.copy(deep=False)
target.names = self.names
return target, indexer
def get_slice_bound(self, label, side, kind):
if not isinstance(label, tuple):
label = label,
return self._partial_tup_index(label, side=side)
def slice_locs(self, start=None, end=None, step=None, kind=None):
"""
For an ordered MultiIndex, compute the slice locations for input
labels.
The input labels can be tuples representing partial levels, e.g. for a
MultiIndex with 3 levels, you can pass a single value (corresponding to
the first level), or a 1-, 2-, or 3-tuple.
Parameters
----------
start : label or tuple, default None
If None, defaults to the beginning
end : label or tuple
If None, defaults to the end
step : int or None
Slice step
kind : string, optional, defaults None
Returns
-------
(start, end) : (int, int)
Notes
-----
This method only works if the MultiIndex is properly lex-sorted. So,
if only the first 2 levels of a 3-level MultiIndex are lexsorted,
you can only pass two levels to ``.slice_locs``.
Examples
--------
>>> mi = pd.MultiIndex.from_arrays([list('abbd'), list('deff')],
... names=['A', 'B'])
Get the slice locations from the beginning of 'b' in the first level
until the end of the multiindex:
>>> mi.slice_locs(start='b')
(1, 4)
Like above, but stop at the end of 'b' in the first level and 'f' in
the second level:
>>> mi.slice_locs(start='b', end=('b', 'f'))
(1, 3)
See Also
--------
MultiIndex.get_loc : Get location for a label or a tuple of labels.
MultiIndex.get_locs : Get location for a label/slice/list/mask or a
sequence of such.
"""
# This function adds nothing to its parent implementation (the magic
# happens in get_slice_bound method), but it adds meaningful doc.
return super(MultiIndex, self).slice_locs(start, end, step, kind=kind)
def _partial_tup_index(self, tup, side='left'):
if len(tup) > self.lexsort_depth:
raise UnsortedIndexError(
'Key length (%d) was greater than MultiIndex'
' lexsort depth (%d)' %
(len(tup), self.lexsort_depth))
n = len(tup)
start, end = 0, len(self)
zipped = zip(tup, self.levels, self.labels)
for k, (lab, lev, labs) in enumerate(zipped):
section = labs[start:end]
if lab not in lev:
if not lev.is_type_compatible(lib.infer_dtype([lab])):
raise TypeError('Level type mismatch: %s' % lab)
# short circuit
loc = lev.searchsorted(lab, side=side)
if side == 'right' and loc >= 0:
loc -= 1
return start + section.searchsorted(loc, side=side)
idx = lev.get_loc(lab)
if k < n - 1:
end = start + section.searchsorted(idx, side='right')
start = start + section.searchsorted(idx, side='left')
else:
return start + section.searchsorted(idx, side=side)
def get_loc(self, key, method=None):
"""
Get location for a label or a tuple of labels as an integer, slice or
boolean mask.
Parameters
----------
key : label or tuple of labels (one for each level)
method : None
Returns
-------
loc : int, slice object or boolean mask
If the key is past the lexsort depth, the return may be a
boolean mask array, otherwise it is always a slice or int.
Examples
---------
>>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')])
>>> mi.get_loc('b')
slice(1, 3, None)
>>> mi.get_loc(('b', 'e'))
1
Notes
------
The key cannot be a slice, list of same-level labels, a boolean mask,
or a sequence of such. If you want to use those, use
:meth:`MultiIndex.get_locs` instead.
See also
--------
Index.get_loc : get_loc method for (single-level) index.
MultiIndex.slice_locs : Get slice location given start label(s) and
end label(s).
MultiIndex.get_locs : Get location for a label/slice/list/mask or a
sequence of such.
"""
if method is not None:
raise NotImplementedError('only the default get_loc method is '
'currently supported for MultiIndex')
def _maybe_to_slice(loc):
"""convert integer indexer to boolean mask or slice if possible"""
if not isinstance(loc, np.ndarray) or loc.dtype != 'int64':
return loc
loc = lib.maybe_indices_to_slice(loc, len(self))
if isinstance(loc, slice):
return loc
mask = np.empty(len(self), dtype='bool')
mask.fill(False)
mask[loc] = True
return mask
if not isinstance(key, tuple):
loc = self._get_level_indexer(key, level=0)
# _get_level_indexer returns an empty slice if the key has
# been dropped from the MultiIndex
if isinstance(loc, slice) and loc.start == loc.stop:
raise KeyError(key)
return _maybe_to_slice(loc)
keylen = len(key)
if self.nlevels < keylen:
raise KeyError('Key length ({0}) exceeds index depth ({1})'
''.format(keylen, self.nlevels))
if keylen == self.nlevels and self.is_unique:
return self._engine.get_loc(key)
# -- partial selection or non-unique index
# break the key into 2 parts based on the lexsort_depth of the index;
# the first part returns a continuous slice of the index; the 2nd part
# needs linear search within the slice
i = self.lexsort_depth
lead_key, follow_key = key[:i], key[i:]
start, stop = (self.slice_locs(lead_key, lead_key)
if lead_key else (0, len(self)))
if start == stop:
raise KeyError(key)
if not follow_key:
return slice(start, stop)
warnings.warn('indexing past lexsort depth may impact performance.',
PerformanceWarning, stacklevel=10)
loc = np.arange(start, stop, dtype='int64')
for i, k in enumerate(follow_key, len(lead_key)):
mask = self.labels[i][loc] == self.levels[i].get_loc(k)
if not mask.all():
loc = loc[mask]
if not len(loc):
raise KeyError(key)
return (_maybe_to_slice(loc) if len(loc) != stop - start else
slice(start, stop))
def get_loc_level(self, key, level=0, drop_level=True):
"""
Get both the location for the requested label(s) and the
resulting sliced index.
Parameters
----------
key : label or sequence of labels
level : int/level name or list thereof, optional
drop_level : bool, default True
if ``False``, the resulting index will not drop any level.
Returns
-------
loc : A 2-tuple where the elements are:
Element 0: int, slice object or boolean array
Element 1: The resulting sliced multiindex/index. If the key
contains all levels, this will be ``None``.
Examples
--------
>>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')],
... names=['A', 'B'])
>>> mi.get_loc_level('b')
(slice(1, 3, None), Index(['e', 'f'], dtype='object', name='B'))
>>> mi.get_loc_level('e', level='B')
(array([False, True, False], dtype=bool),
Index(['b'], dtype='object', name='A'))
>>> mi.get_loc_level(['b', 'e'])
(1, None)
See Also
---------
MultiIndex.get_loc : Get location for a label or a tuple of labels.
MultiIndex.get_locs : Get location for a label/slice/list/mask or a
sequence of such
"""
def maybe_droplevels(indexer, levels, drop_level):
if not drop_level:
return self[indexer]
# kludgearound
orig_index = new_index = self[indexer]
levels = [self._get_level_number(i) for i in levels]
for i in sorted(levels, reverse=True):
try:
new_index = new_index.droplevel(i)
except:
# no dropping here
return orig_index
return new_index
if isinstance(level, (tuple, list)):
if len(key) != len(level):
raise AssertionError('Key for location must have same '
'length as number of levels')
result = None
for lev, k in zip(level, key):
loc, new_index = self.get_loc_level(k, level=lev)
if isinstance(loc, slice):
mask = np.zeros(len(self), dtype=bool)
mask[loc] = True
loc = mask
result = loc if result is None else result & loc
return result, maybe_droplevels(result, level, drop_level)
level = self._get_level_number(level)
# kludge for #1796
if isinstance(key, list):
key = tuple(key)
if isinstance(key, tuple) and level == 0:
try:
if key in self.levels[0]:
indexer = self._get_level_indexer(key, level=level)
new_index = maybe_droplevels(indexer, [0], drop_level)
return indexer, new_index
except TypeError:
pass
if not any(isinstance(k, slice) for k in key):
# partial selection
# optionally get indexer to avoid re-calculation
def partial_selection(key, indexer=None):
if indexer is None:
indexer = self.get_loc(key)
ilevels = [i for i in range(len(key))
if key[i] != slice(None, None)]
return indexer, maybe_droplevels(indexer, ilevels,
drop_level)
if len(key) == self.nlevels and self.is_unique:
# Complete key in unique index -> standard get_loc
return (self._engine.get_loc(key), None)
else:
return partial_selection(key)
else:
indexer = None
for i, k in enumerate(key):
if not isinstance(k, slice):
k = self._get_level_indexer(k, level=i)
if isinstance(k, slice):
# everything
if k.start == 0 and k.stop == len(self):
k = slice(None, None)
else:
k_index = k
if isinstance(k, slice):
if k == slice(None, None):
continue
else:
raise TypeError(key)
if indexer is None:
indexer = k_index
else: # pragma: no cover
indexer &= k_index
if indexer is None:
indexer = slice(None, None)
ilevels = [i for i in range(len(key))
if key[i] != slice(None, None)]
return indexer, maybe_droplevels(indexer, ilevels, drop_level)
else:
indexer = self._get_level_indexer(key, level=level)
return indexer, maybe_droplevels(indexer, [level], drop_level)
def _get_level_indexer(self, key, level=0, indexer=None):
# return an indexer, boolean array or a slice showing where the key is
# in the totality of values
# if the indexer is provided, then use this
level_index = self.levels[level]
labels = self.labels[level]
def convert_indexer(start, stop, step, indexer=indexer, labels=labels):
# given the inputs and the labels/indexer, compute an indexer set
# if we have a provided indexer, then this need not consider
# the entire labels set
r = np.arange(start, stop, step)
if indexer is not None and len(indexer) != len(labels):
# we have an indexer which maps the locations in the labels
# that we have already selected (and is not an indexer for the
# entire set) otherwise this is wasteful so we only need to
# examine locations that are in this set the only magic here is
# that the result are the mappings to the set that we have
# selected
from pandas import Series
mapper = Series(indexer)
indexer = labels.take(_ensure_platform_int(indexer))
result = Series(Index(indexer).isin(r).nonzero()[0])
m = result.map(mapper)._ndarray_values
else:
m = np.zeros(len(labels), dtype=bool)
m[np.in1d(labels, r,
assume_unique=Index(labels).is_unique)] = True
return m
if isinstance(key, slice):
# handle a slice, returnig a slice if we can
# otherwise a boolean indexer
try:
if key.start is not None:
start = level_index.get_loc(key.start)
else:
start = 0
if key.stop is not None:
stop = level_index.get_loc(key.stop)
else:
stop = len(level_index) - 1
step = key.step
except KeyError:
# we have a partial slice (like looking up a partial date
# string)
start = stop = level_index.slice_indexer(key.start, key.stop,
key.step, kind='loc')
step = start.step
if isinstance(start, slice) or isinstance(stop, slice):
# we have a slice for start and/or stop
# a partial date slicer on a DatetimeIndex generates a slice
# note that the stop ALREADY includes the stopped point (if
# it was a string sliced)
return convert_indexer(start.start, stop.stop, step)
elif level > 0 or self.lexsort_depth == 0 or step is not None:
# need to have like semantics here to right
# searching as when we are using a slice
# so include the stop+1 (so we include stop)
return convert_indexer(start, stop + 1, step)
else:
# sorted, so can return slice object -> view
i = labels.searchsorted(start, side='left')
j = labels.searchsorted(stop, side='right')
return slice(i, j, step)
else:
loc = level_index.get_loc(key)
if isinstance(loc, slice):
return loc
elif level > 0 or self.lexsort_depth == 0:
return np.array(labels == loc, dtype=bool)
i = labels.searchsorted(loc, side='left')
j = labels.searchsorted(loc, side='right')
return slice(i, j)
def get_locs(self, seq):
"""
Get location for a given label/slice/list/mask or a sequence of such as
an array of integers.
Parameters
----------
seq : label/slice/list/mask or a sequence of such
You should use one of the above for each level.
If a level should not be used, set it to ``slice(None)``.
Returns
-------
locs : array of integers suitable for passing to iloc
Examples
---------
>>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')])
>>> mi.get_locs('b')
array([1, 2], dtype=int64)
>>> mi.get_locs([slice(None), ['e', 'f']])
array([1, 2], dtype=int64)
>>> mi.get_locs([[True, False, True], slice('e', 'f')])
array([2], dtype=int64)
See also
--------
MultiIndex.get_loc : Get location for a label or a tuple of labels.
MultiIndex.slice_locs : Get slice location given start label(s) and
end label(s).
"""
from .numeric import Int64Index
# must be lexsorted to at least as many levels
true_slices = [i for (i, s) in enumerate(com.is_true_slices(seq)) if s]
if true_slices and true_slices[-1] >= self.lexsort_depth:
raise UnsortedIndexError('MultiIndex slicing requires the index '
'to be lexsorted: slicing on levels {0}, '
'lexsort depth {1}'
.format(true_slices, self.lexsort_depth))
# indexer
# this is the list of all values that we want to select
n = len(self)
indexer = None
def _convert_to_indexer(r):
# return an indexer
if isinstance(r, slice):
m = np.zeros(n, dtype=bool)
m[r] = True
r = m.nonzero()[0]
elif com.is_bool_indexer(r):
if len(r) != n:
raise ValueError("cannot index with a boolean indexer "
"that is not the same length as the "
"index")
r = r.nonzero()[0]
return Int64Index(r)
def _update_indexer(idxr, indexer=indexer):
if indexer is None:
indexer = Index(np.arange(n))
if idxr is None:
return indexer
return indexer & idxr
for i, k in enumerate(seq):
if com.is_bool_indexer(k):
# a boolean indexer, must be the same length!
k = np.asarray(k)
indexer = _update_indexer(_convert_to_indexer(k),
indexer=indexer)
elif is_list_like(k):
# a collection of labels to include from this level (these
# are or'd)
indexers = None
for x in k:
try:
idxrs = _convert_to_indexer(
self._get_level_indexer(x, level=i,
indexer=indexer))
indexers = (idxrs if indexers is None
else indexers | idxrs)
except KeyError:
# ignore not founds
continue
if indexers is not None:
indexer = _update_indexer(indexers, indexer=indexer)
else:
# no matches we are done
return Int64Index([])._ndarray_values
elif com.is_null_slice(k):
# empty slice
indexer = _update_indexer(None, indexer=indexer)
elif isinstance(k, slice):
# a slice, include BOTH of the labels
indexer = _update_indexer(_convert_to_indexer(
self._get_level_indexer(k, level=i, indexer=indexer)),
indexer=indexer)
else:
# a single label
indexer = _update_indexer(_convert_to_indexer(
self.get_loc_level(k, level=i, drop_level=False)[0]),
indexer=indexer)
# empty indexer
if indexer is None:
return Int64Index([])._ndarray_values
return indexer._ndarray_values
def truncate(self, before=None, after=None):
"""
Slice index between two labels / tuples, return new MultiIndex
Parameters
----------
before : label or tuple, can be partial. Default None
None defaults to start
after : label or tuple, can be partial. Default None
None defaults to end
Returns
-------
truncated : MultiIndex
"""
if after and before and after < before:
raise ValueError('after < before')
i, j = self.levels[0].slice_locs(before, after)
left, right = self.slice_locs(before, after)
new_levels = list(self.levels)
new_levels[0] = new_levels[0][i:j]
new_labels = [lab[left:right] for lab in self.labels]
new_labels[0] = new_labels[0] - i
return MultiIndex(levels=new_levels, labels=new_labels,
verify_integrity=False)
def equals(self, other):
"""
Determines if two MultiIndex objects have the same labeling information
(the levels themselves do not necessarily have to be the same)
See also
--------
equal_levels
"""
if self.is_(other):
return True
if not isinstance(other, Index):
return False
if not isinstance(other, MultiIndex):
other_vals = com._values_from_object(_ensure_index(other))
return array_equivalent(self._ndarray_values, other_vals)
if self.nlevels != other.nlevels:
return False
if len(self) != len(other):
return False
for i in range(self.nlevels):
slabels = self.labels[i]
slabels = slabels[slabels != -1]
svalues = algos.take_nd(np.asarray(self.levels[i]._values),
slabels, allow_fill=False)
olabels = other.labels[i]
olabels = olabels[olabels != -1]
ovalues = algos.take_nd(
np.asarray(other.levels[i]._values),
olabels, allow_fill=False)
# since we use NaT both datetime64 and timedelta64
# we can have a situation where a level is typed say
# timedelta64 in self (IOW it has other values than NaT)
# but types datetime64 in other (where its all NaT)
# but these are equivalent
if len(svalues) == 0 and len(ovalues) == 0:
continue
if not array_equivalent(svalues, ovalues):
return False
return True
def equal_levels(self, other):
"""
Return True if the levels of both MultiIndex objects are the same
"""
if self.nlevels != other.nlevels:
return False
for i in range(self.nlevels):
if not self.levels[i].equals(other.levels[i]):
return False
return True
def union(self, other):
"""
Form the union of two MultiIndex objects, sorting if possible
Parameters
----------
other : MultiIndex or array / Index of tuples
Returns
-------
Index
>>> index.union(index2)
"""
self._assert_can_do_setop(other)
other, result_names = self._convert_can_do_setop(other)
if len(other) == 0 or self.equals(other):
return self
uniq_tuples = lib.fast_unique_multiple([self._ndarray_values,
other._ndarray_values])
return MultiIndex.from_arrays(lzip(*uniq_tuples), sortorder=0,
names=result_names)
def intersection(self, other):
"""
Form the intersection of two MultiIndex objects, sorting if possible
Parameters
----------
other : MultiIndex or array / Index of tuples
Returns
-------
Index
"""
self._assert_can_do_setop(other)
other, result_names = self._convert_can_do_setop(other)
if self.equals(other):
return self
self_tuples = self._ndarray_values
other_tuples = other._ndarray_values
uniq_tuples = sorted(set(self_tuples) & set(other_tuples))
if len(uniq_tuples) == 0:
return MultiIndex(levels=self.levels,
labels=[[]] * self.nlevels,
names=result_names, verify_integrity=False)
else:
return MultiIndex.from_arrays(lzip(*uniq_tuples), sortorder=0,
names=result_names)
def difference(self, other):
"""
Compute sorted set difference of two MultiIndex objects
Returns
-------
diff : MultiIndex
"""
self._assert_can_do_setop(other)
other, result_names = self._convert_can_do_setop(other)
if len(other) == 0:
return self
if self.equals(other):
return MultiIndex(levels=self.levels,
labels=[[]] * self.nlevels,
names=result_names, verify_integrity=False)
difference = sorted(set(self._ndarray_values) -
set(other._ndarray_values))
if len(difference) == 0:
return MultiIndex(levels=[[]] * self.nlevels,
labels=[[]] * self.nlevels,
names=result_names, verify_integrity=False)
else:
return MultiIndex.from_tuples(difference, sortorder=0,
names=result_names)
@Appender(_index_shared_docs['astype'])
def astype(self, dtype, copy=True):
dtype = pandas_dtype(dtype)
if is_categorical_dtype(dtype):
msg = '> 1 ndim Categorical are not supported at this time'
raise NotImplementedError(msg)
elif not is_object_dtype(dtype):
msg = ('Setting {cls} dtype to anything other than object '
'is not supported').format(cls=self.__class__)
raise TypeError(msg)
elif copy is True:
return self._shallow_copy()
return self
def _convert_can_do_setop(self, other):
result_names = self.names
if not hasattr(other, 'names'):
if len(other) == 0:
other = MultiIndex(levels=[[]] * self.nlevels,
labels=[[]] * self.nlevels,
verify_integrity=False)
else:
msg = 'other must be a MultiIndex or a list of tuples'
try:
other = MultiIndex.from_tuples(other)
except:
raise TypeError(msg)
else:
result_names = self.names if self.names == other.names else None
return other, result_names
def insert(self, loc, item):
"""
Make new MultiIndex inserting new item at location
Parameters
----------
loc : int
item : tuple
Must be same length as number of levels in the MultiIndex
Returns
-------
new_index : Index
"""
# Pad the key with empty strings if lower levels of the key
# aren't specified:
if not isinstance(item, tuple):
item = (item, ) + ('', ) * (self.nlevels - 1)
elif len(item) != self.nlevels:
raise ValueError('Item must have length equal to number of '
'levels.')
new_levels = []
new_labels = []
for k, level, labels in zip(item, self.levels, self.labels):
if k not in level:
# have to insert into level
# must insert at end otherwise you have to recompute all the
# other labels
lev_loc = len(level)
level = level.insert(lev_loc, k)
else:
lev_loc = level.get_loc(k)
new_levels.append(level)
new_labels.append(np.insert(_ensure_int64(labels), loc, lev_loc))
return MultiIndex(levels=new_levels, labels=new_labels,
names=self.names, verify_integrity=False)
def delete(self, loc):
"""
Make new index with passed location deleted
Returns
-------
new_index : MultiIndex
"""
new_labels = [np.delete(lab, loc) for lab in self.labels]
return MultiIndex(levels=self.levels, labels=new_labels,
names=self.names, verify_integrity=False)
get_major_bounds = slice_locs
__bounds = None
@property
def _bounds(self):
"""
Return or compute and return slice points for level 0, assuming
sortedness
"""
if self.__bounds is None:
inds = np.arange(len(self.levels[0]))
self.__bounds = self.labels[0].searchsorted(inds)
return self.__bounds
def _wrap_joined_index(self, joined, other):
names = self.names if self.names == other.names else None
return MultiIndex.from_tuples(joined, names=names)
@Appender(Index.isin.__doc__)
def isin(self, values, level=None):
if level is None:
values = MultiIndex.from_tuples(values,
names=self.names).values
return algos.isin(self.values, values)
else:
num = self._get_level_number(level)
levs = self.levels[num]
labs = self.labels[num]
sought_labels = levs.isin(values).nonzero()[0]
if levs.size == 0:
return np.zeros(len(labs), dtype=np.bool_)
else:
return np.lib.arraysetops.in1d(labs, sought_labels)
def _reference_duplicate_name(self, name):
"""
Returns True if the name refered to in self.names is duplicated.
"""
# count the times name equals an element in self.names.
return sum(name == n for n in self.names) > 1
MultiIndex._add_numeric_methods_disabled()
MultiIndex._add_numeric_methods_add_sub_disabled()
MultiIndex._add_logical_methods_disabled()
def _sparsify(label_list, start=0, sentinel=''):
pivoted = lzip(*label_list)
k = len(label_list)
result = pivoted[:start + 1]
prev = pivoted[start]
for cur in pivoted[start + 1:]:
sparse_cur = []
for i, (p, t) in enumerate(zip(prev, cur)):
if i == k - 1:
sparse_cur.append(t)
result.append(sparse_cur)
break
if p == t:
sparse_cur.append(sentinel)
else:
sparse_cur.extend(cur[i:])
result.append(sparse_cur)
break
prev = cur
return lzip(*result)
def _get_na_rep(dtype):
return {np.datetime64: 'NaT', np.timedelta64: 'NaT'}.get(dtype, 'NaN')