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interval.py
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interval.py
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""" define the IntervalIndex """
import numpy as np
import warnings
from pandas.core.dtypes.missing import notna, isna
from pandas.core.dtypes.generic import ABCDatetimeIndex, ABCPeriodIndex
from pandas.core.dtypes.dtypes import IntervalDtype
from pandas.core.dtypes.cast import (
maybe_convert_platform, find_common_type, maybe_downcast_to_dtype)
from pandas.core.dtypes.common import (
_ensure_platform_int,
is_list_like,
is_datetime_or_timedelta_dtype,
is_datetime64tz_dtype,
is_categorical_dtype,
is_string_dtype,
is_integer_dtype,
is_float_dtype,
is_interval_dtype,
is_object_dtype,
is_scalar,
is_float,
is_number,
is_integer,
pandas_dtype)
from pandas.core.indexes.base import (
Index, _ensure_index,
default_pprint, _index_shared_docs)
from pandas._libs import Timestamp, Timedelta
from pandas._libs.interval import (
Interval, IntervalMixin, IntervalTree,
intervals_to_interval_bounds)
from pandas.core.indexes.datetimes import date_range
from pandas.core.indexes.timedeltas import timedelta_range
from pandas.core.indexes.multi import MultiIndex
from pandas.compat.numpy import function as nv
import pandas.core.common as com
from pandas.util._decorators import cache_readonly, Appender
from pandas.core.config import get_option
from pandas.tseries.frequencies import to_offset
from pandas.tseries.offsets import DateOffset
import pandas.core.indexes.base as ibase
_index_doc_kwargs = dict(ibase._index_doc_kwargs)
_index_doc_kwargs.update(
dict(klass='IntervalIndex',
target_klass='IntervalIndex or list of Intervals'))
_VALID_CLOSED = set(['left', 'right', 'both', 'neither'])
def _get_next_label(label):
dtype = getattr(label, 'dtype', type(label))
if isinstance(label, (Timestamp, Timedelta)):
dtype = 'datetime64'
if is_datetime_or_timedelta_dtype(dtype) or is_datetime64tz_dtype(dtype):
return label + np.timedelta64(1, 'ns')
elif is_integer_dtype(dtype):
return label + 1
elif is_float_dtype(dtype):
return np.nextafter(label, np.infty)
else:
raise TypeError('cannot determine next label for type {typ!r}'
.format(typ=type(label)))
def _get_prev_label(label):
dtype = getattr(label, 'dtype', type(label))
if isinstance(label, (Timestamp, Timedelta)):
dtype = 'datetime64'
if is_datetime_or_timedelta_dtype(dtype) or is_datetime64tz_dtype(dtype):
return label - np.timedelta64(1, 'ns')
elif is_integer_dtype(dtype):
return label - 1
elif is_float_dtype(dtype):
return np.nextafter(label, -np.infty)
else:
raise TypeError('cannot determine next label for type {typ!r}'
.format(typ=type(label)))
def _get_interval_closed_bounds(interval):
"""
Given an Interval or IntervalIndex, return the corresponding interval with
closed bounds.
"""
left, right = interval.left, interval.right
if interval.open_left:
left = _get_next_label(left)
if interval.open_right:
right = _get_prev_label(right)
return left, right
def maybe_convert_platform_interval(values):
"""
Try to do platform conversion, with special casing for IntervalIndex.
Wrapper around maybe_convert_platform that alters the default return
dtype in certain cases to be compatible with IntervalIndex. For example,
empty lists return with integer dtype instead of object dtype, which is
prohibited for IntervalIndex.
Parameters
----------
values : array-like
Returns
-------
array
"""
if is_categorical_dtype(values):
# GH 21243/21253
values = np.array(values)
if isinstance(values, (list, tuple)) and len(values) == 0:
# GH 19016
# empty lists/tuples get object dtype by default, but this is not
# prohibited for IntervalIndex, so coerce to integer instead
return np.array([], dtype=np.int64)
return maybe_convert_platform(values)
def _new_IntervalIndex(cls, d):
"""
This is called upon unpickling, rather than the default which doesn't have
arguments and breaks __new__
"""
return cls.from_arrays(**d)
class IntervalIndex(IntervalMixin, Index):
"""
Immutable Index implementing an ordered, sliceable set. IntervalIndex
represents an Index of Interval objects that are all closed on the same
side.
.. versionadded:: 0.20.0
.. warning::
The indexing behaviors are provisional and may change in
a future version of pandas.
Parameters
----------
data : array-like (1-dimensional)
Array-like containing Interval objects from which to build the
IntervalIndex
closed : {'left', 'right', 'both', 'neither'}, default 'right'
Whether the intervals are closed on the left-side, right-side, both or
neither.
name : object, optional
Name to be stored in the index.
copy : boolean, default False
Copy the meta-data
dtype : dtype or None, default None
If None, dtype will be inferred
.. versionadded:: 0.23.0
Attributes
----------
closed
is_non_overlapping_monotonic
left
length
mid
right
values
Methods
-------
contains
from_arrays
from_breaks
from_tuples
get_indexer
get_loc
Examples
---------
A new ``IntervalIndex`` is typically constructed using
:func:`interval_range`:
>>> pd.interval_range(start=0, end=5)
IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]]
closed='right', dtype='interval[int64]')
It may also be constructed using one of the constructor
methods: :meth:`IntervalIndex.from_arrays`,
:meth:`IntervalIndex.from_breaks`, and :meth:`IntervalIndex.from_tuples`.
See further examples in the doc strings of ``interval_range`` and the
mentioned constructor methods.
Notes
------
See the `user guide
<http://pandas.pydata.org/pandas-docs/stable/advanced.html#intervalindex>`_
for more.
See Also
--------
Index : The base pandas Index type
Interval : A bounded slice-like interval; the elements of an IntervalIndex
interval_range : Function to create a fixed frequency IntervalIndex
cut, qcut : Convert arrays of continuous data into Categoricals/Series of
Intervals
"""
_typ = 'intervalindex'
_comparables = ['name']
_attributes = ['name', 'closed']
# we would like our indexing holder to defer to us
_defer_to_indexing = True
_mask = None
def __new__(cls, data, closed=None, dtype=None, copy=False,
name=None, fastpath=False, verify_integrity=True):
if fastpath:
return cls._simple_new(data.left, data.right, closed, name,
copy=copy, verify_integrity=False)
if name is None and hasattr(data, 'name'):
name = data.name
if isinstance(data, IntervalIndex):
left = data.left
right = data.right
closed = data.closed
else:
# don't allow scalars
if is_scalar(data):
cls._scalar_data_error(data)
data = maybe_convert_platform_interval(data)
left, right, infer_closed = intervals_to_interval_bounds(data)
if (com._all_not_none(closed, infer_closed) and
closed != infer_closed):
# GH 18421
msg = ("conflicting values for closed: constructor got "
"'{closed}', inferred from data '{infer_closed}'"
.format(closed=closed, infer_closed=infer_closed))
raise ValueError(msg)
closed = closed or infer_closed
return cls._simple_new(left, right, closed, name, copy=copy,
dtype=dtype, verify_integrity=verify_integrity)
@classmethod
def _simple_new(cls, left, right, closed=None, name=None, copy=False,
dtype=None, verify_integrity=True):
result = IntervalMixin.__new__(cls)
closed = closed or 'right'
left = _ensure_index(left, copy=copy)
right = _ensure_index(right, copy=copy)
if dtype is not None:
# GH 19262: dtype must be an IntervalDtype to override inferred
dtype = pandas_dtype(dtype)
if not is_interval_dtype(dtype):
msg = 'dtype must be an IntervalDtype, got {dtype}'
raise TypeError(msg.format(dtype=dtype))
elif dtype.subtype is not None:
left = left.astype(dtype.subtype)
right = right.astype(dtype.subtype)
# coerce dtypes to match if needed
if is_float_dtype(left) and is_integer_dtype(right):
right = right.astype(left.dtype)
elif is_float_dtype(right) and is_integer_dtype(left):
left = left.astype(right.dtype)
if type(left) != type(right):
msg = ('must not have differing left [{ltype}] and right '
'[{rtype}] types')
raise ValueError(msg.format(ltype=type(left).__name__,
rtype=type(right).__name__))
elif is_categorical_dtype(left.dtype) or is_string_dtype(left.dtype):
# GH 19016
msg = ('category, object, and string subtypes are not supported '
'for IntervalIndex')
raise TypeError(msg)
elif isinstance(left, ABCPeriodIndex):
msg = 'Period dtypes are not supported, use a PeriodIndex instead'
raise ValueError(msg)
elif (isinstance(left, ABCDatetimeIndex) and
str(left.tz) != str(right.tz)):
msg = ("left and right must have the same time zone, got "
"'{left_tz}' and '{right_tz}'")
raise ValueError(msg.format(left_tz=left.tz, right_tz=right.tz))
result._left = left
result._right = right
result._closed = closed
result.name = name
if verify_integrity:
result._validate()
result._reset_identity()
return result
@Appender(_index_shared_docs['_shallow_copy'])
def _shallow_copy(self, left=None, right=None, **kwargs):
if left is None:
# no values passed
left, right = self.left, self.right
elif right is None:
# only single value passed, could be an IntervalIndex
# or array of Intervals
if not isinstance(left, IntervalIndex):
left = self._constructor(left)
left, right = left.left, left.right
else:
# both left and right are values
pass
attributes = self._get_attributes_dict()
attributes.update(kwargs)
attributes['verify_integrity'] = False
return self._simple_new(left, right, **attributes)
def _validate(self):
"""
Verify that the IntervalIndex is valid.
"""
if self.closed not in _VALID_CLOSED:
raise ValueError("invalid option for 'closed': {closed}"
.format(closed=self.closed))
if len(self.left) != len(self.right):
raise ValueError('left and right must have the same length')
left_mask = notna(self.left)
right_mask = notna(self.right)
if not (left_mask == right_mask).all():
raise ValueError('missing values must be missing in the same '
'location both left and right sides')
if not (self.left[left_mask] <= self.right[left_mask]).all():
raise ValueError('left side of interval must be <= right side')
self._mask = ~left_mask
@cache_readonly
def hasnans(self):
"""
Return if the IntervalIndex has any nans; enables various performance
speedups
"""
return self._isnan.any()
@cache_readonly
def _isnan(self):
"""Return a mask indicating if each value is NA"""
if self._mask is None:
self._mask = isna(self.left)
return self._mask
@cache_readonly
def _engine(self):
return IntervalTree(self.left, self.right, closed=self.closed)
@property
def _constructor(self):
return type(self)
def __contains__(self, key):
"""
return a boolean if this key is IN the index
We *only* accept an Interval
Parameters
----------
key : Interval
Returns
-------
boolean
"""
if not isinstance(key, Interval):
return False
try:
self.get_loc(key)
return True
except KeyError:
return False
def contains(self, key):
"""
Return a boolean indicating if the key is IN the index
We accept / allow keys to be not *just* actual
objects.
Parameters
----------
key : int, float, Interval
Returns
-------
boolean
"""
try:
self.get_loc(key)
return True
except KeyError:
return False
@classmethod
def from_breaks(cls, breaks, closed='right', name=None, copy=False,
dtype=None):
"""
Construct an IntervalIndex from an array of splits
Parameters
----------
breaks : array-like (1-dimensional)
Left and right bounds for each interval.
closed : {'left', 'right', 'both', 'neither'}, default 'right'
Whether the intervals are closed on the left-side, right-side, both
or neither.
name : object, optional
Name to be stored in the index.
copy : boolean, default False
copy the data
dtype : dtype or None, default None
If None, dtype will be inferred
.. versionadded:: 0.23.0
Examples
--------
>>> pd.IntervalIndex.from_breaks([0, 1, 2, 3])
IntervalIndex([(0, 1], (1, 2], (2, 3]]
closed='right',
dtype='interval[int64]')
See Also
--------
interval_range : Function to create a fixed frequency IntervalIndex
IntervalIndex.from_arrays : Construct an IntervalIndex from a left and
right array
IntervalIndex.from_tuples : Construct an IntervalIndex from a
list/array of tuples
"""
breaks = maybe_convert_platform_interval(breaks)
return cls.from_arrays(breaks[:-1], breaks[1:], closed,
name=name, copy=copy, dtype=dtype)
@classmethod
def from_arrays(cls, left, right, closed='right', name=None, copy=False,
dtype=None):
"""
Construct from two arrays defining the left and right bounds.
Parameters
----------
left : array-like (1-dimensional)
Left bounds for each interval.
right : array-like (1-dimensional)
Right bounds for each interval.
closed : {'left', 'right', 'both', 'neither'}, default 'right'
Whether the intervals are closed on the left-side, right-side, both
or neither.
name : object, optional
Name to be stored in the index.
copy : boolean, default False
Copy the data.
dtype : dtype, optional
If None, dtype will be inferred.
.. versionadded:: 0.23.0
Returns
-------
index : IntervalIndex
Notes
-----
Each element of `left` must be less than or equal to the `right`
element at the same position. If an element is missing, it must be
missing in both `left` and `right`. A TypeError is raised when
using an unsupported type for `left` or `right`. At the moment,
'category', 'object', and 'string' subtypes are not supported.
Raises
------
ValueError
When a value is missing in only one of `left` or `right`.
When a value in `left` is greater than the corresponding value
in `right`.
See Also
--------
interval_range : Function to create a fixed frequency IntervalIndex.
IntervalIndex.from_breaks : Construct an IntervalIndex from an array of
splits.
IntervalIndex.from_tuples : Construct an IntervalIndex from a
list/array of tuples.
Examples
--------
>>> pd.IntervalIndex.from_arrays([0, 1, 2], [1, 2, 3])
IntervalIndex([(0, 1], (1, 2], (2, 3]]
closed='right',
dtype='interval[int64]')
If you want to segment different groups of people based on
ages, you can apply the method as follows:
>>> ages = pd.IntervalIndex.from_arrays([0, 2, 13],
... [2, 13, 19], closed='left')
>>> ages
IntervalIndex([[0, 2), [2, 13), [13, 19)]
closed='left',
dtype='interval[int64]')
>>> s = pd.Series(['baby', 'kid', 'teen'], ages)
>>> s
[0, 2) baby
[2, 13) kid
[13, 19) teen
dtype: object
Values may be missing, but they must be missing in both arrays.
>>> pd.IntervalIndex.from_arrays([0, np.nan, 13],
... [2, np.nan, 19])
IntervalIndex([(0.0, 2.0], nan, (13.0, 19.0]]
closed='right',
dtype='interval[float64]')
"""
left = maybe_convert_platform_interval(left)
right = maybe_convert_platform_interval(right)
return cls._simple_new(left, right, closed, name=name, copy=copy,
dtype=dtype, verify_integrity=True)
@classmethod
def from_intervals(cls, data, closed=None, name=None, copy=False,
dtype=None):
"""
Construct an IntervalIndex from a 1d array of Interval objects
.. deprecated:: 0.23.0
Parameters
----------
data : array-like (1-dimensional)
Array of Interval objects. All intervals must be closed on the same
sides.
name : object, optional
Name to be stored in the index.
copy : boolean, default False
by-default copy the data, this is compat only and ignored
dtype : dtype or None, default None
If None, dtype will be inferred
.. versionadded:: 0.23.0
Examples
--------
>>> pd.IntervalIndex.from_intervals([pd.Interval(0, 1),
... pd.Interval(1, 2)])
IntervalIndex([(0, 1], (1, 2]]
closed='right', dtype='interval[int64]')
The generic Index constructor work identically when it infers an array
of all intervals:
>>> pd.Index([pd.Interval(0, 1), pd.Interval(1, 2)])
IntervalIndex([(0, 1], (1, 2]]
closed='right', dtype='interval[int64]')
See Also
--------
interval_range : Function to create a fixed frequency IntervalIndex
IntervalIndex.from_arrays : Construct an IntervalIndex from a left and
right array
IntervalIndex.from_breaks : Construct an IntervalIndex from an array of
splits
IntervalIndex.from_tuples : Construct an IntervalIndex from a
list/array of tuples
"""
msg = ('IntervalIndex.from_intervals is deprecated and will be '
'removed in a future version; use IntervalIndex(...) instead')
warnings.warn(msg, FutureWarning, stacklevel=2)
return cls(data, closed=closed, name=name, copy=copy, dtype=dtype)
@classmethod
def from_tuples(cls, data, closed='right', name=None, copy=False,
dtype=None):
"""
Construct an IntervalIndex from a list/array of tuples
Parameters
----------
data : array-like (1-dimensional)
Array of tuples
closed : {'left', 'right', 'both', 'neither'}, default 'right'
Whether the intervals are closed on the left-side, right-side, both
or neither.
name : object, optional
Name to be stored in the index.
copy : boolean, default False
by-default copy the data, this is compat only and ignored
dtype : dtype or None, default None
If None, dtype will be inferred
.. versionadded:: 0.23.0
Examples
--------
>>> pd.IntervalIndex.from_tuples([(0, 1), (1, 2)])
IntervalIndex([(0, 1], (1, 2]],
closed='right', dtype='interval[int64]')
See Also
--------
interval_range : Function to create a fixed frequency IntervalIndex
IntervalIndex.from_arrays : Construct an IntervalIndex from a left and
right array
IntervalIndex.from_breaks : Construct an IntervalIndex from an array of
splits
"""
if len(data):
left, right = [], []
else:
left = right = data
for d in data:
if isna(d):
lhs = rhs = np.nan
else:
try:
# need list of length 2 tuples, e.g. [(0, 1), (1, 2), ...]
lhs, rhs = d
except ValueError:
msg = ('IntervalIndex.from_tuples requires tuples of '
'length 2, got {tpl}').format(tpl=d)
raise ValueError(msg)
except TypeError:
msg = ('IntervalIndex.from_tuples received an invalid '
'item, {tpl}').format(tpl=d)
raise TypeError(msg)
left.append(lhs)
right.append(rhs)
return cls.from_arrays(left, right, closed, name=name, copy=False,
dtype=dtype)
def to_tuples(self, na_tuple=True):
"""
Return an Index of tuples of the form (left, right)
Parameters
----------
na_tuple : boolean, default True
Returns NA as a tuple if True, ``(nan, nan)``, or just as the NA
value itself if False, ``nan``.
.. versionadded:: 0.23.0
Examples
--------
>>> idx = pd.IntervalIndex.from_arrays([0, np.nan, 2], [1, np.nan, 3])
>>> idx.to_tuples()
Index([(0.0, 1.0), (nan, nan), (2.0, 3.0)], dtype='object')
>>> idx.to_tuples(na_tuple=False)
Index([(0.0, 1.0), nan, (2.0, 3.0)], dtype='object')
"""
tuples = com._asarray_tuplesafe(zip(self.left, self.right))
if not na_tuple:
# GH 18756
tuples = np.where(~self._isnan, tuples, np.nan)
return Index(tuples)
@cache_readonly
def _multiindex(self):
return MultiIndex.from_arrays([self.left, self.right],
names=['left', 'right'])
@property
def left(self):
"""
Return the left endpoints of each Interval in the IntervalIndex as
an Index
"""
return self._left
@property
def right(self):
"""
Return the right endpoints of each Interval in the IntervalIndex as
an Index
"""
return self._right
@property
def closed(self):
"""
Whether the intervals are closed on the left-side, right-side, both or
neither
"""
return self._closed
@property
def length(self):
"""
Return an Index with entries denoting the length of each Interval in
the IntervalIndex
"""
try:
return self.right - self.left
except TypeError:
# length not defined for some types, e.g. string
msg = ('IntervalIndex contains Intervals without defined length, '
'e.g. Intervals with string endpoints')
raise TypeError(msg)
@property
def size(self):
# Avoid materializing self.values
return self.left.size
@property
def shape(self):
# Avoid materializing self.values
return self.left.shape
def __len__(self):
return len(self.left)
@cache_readonly
def values(self):
"""
Return the IntervalIndex's data as a numpy array of Interval
objects (with dtype='object')
"""
left = self.left
right = self.right
mask = self._isnan
closed = self._closed
result = np.empty(len(left), dtype=object)
for i in range(len(left)):
if mask[i]:
result[i] = np.nan
else:
result[i] = Interval(left[i], right[i], closed)
return result
def __array__(self, result=None):
""" the array interface, return my values """
return self.values
def __array_wrap__(self, result, context=None):
# we don't want the superclass implementation
return result
def _array_values(self):
return self.values
def __reduce__(self):
d = dict(left=self.left,
right=self.right)
d.update(self._get_attributes_dict())
return _new_IntervalIndex, (self.__class__, d), None
@Appender(_index_shared_docs['copy'])
def copy(self, deep=False, name=None):
left = self.left.copy(deep=True) if deep else self.left
right = self.right.copy(deep=True) if deep else self.right
name = name if name is not None else self.name
closed = self.closed
return type(self).from_arrays(left, right, closed=closed, name=name)
@Appender(_index_shared_docs['astype'])
def astype(self, dtype, copy=True):
dtype = pandas_dtype(dtype)
if is_interval_dtype(dtype) and dtype != self.dtype:
try:
new_left = self.left.astype(dtype.subtype)
new_right = self.right.astype(dtype.subtype)
except TypeError:
msg = ('Cannot convert {dtype} to {new_dtype}; subtypes are '
'incompatible')
raise TypeError(msg.format(dtype=self.dtype, new_dtype=dtype))
return self._shallow_copy(new_left, new_right)
return super(IntervalIndex, self).astype(dtype, copy=copy)
@cache_readonly
def dtype(self):
"""Return the dtype object of the underlying data"""
return IntervalDtype.construct_from_string(str(self.left.dtype))
@property
def inferred_type(self):
"""Return a string of the type inferred from the values"""
return 'interval'
@Appender(Index.memory_usage.__doc__)
def memory_usage(self, deep=False):
# we don't use an explicit engine
# so return the bytes here
return (self.left.memory_usage(deep=deep) +
self.right.memory_usage(deep=deep))
@cache_readonly
def mid(self):
"""
Return the midpoint of each Interval in the IntervalIndex as an Index
"""
try:
return 0.5 * (self.left + self.right)
except TypeError:
# datetime safe version
return self.left + 0.5 * self.length
@cache_readonly
def is_monotonic(self):
"""
Return True if the IntervalIndex is monotonic increasing (only equal or
increasing values), else False
"""
return self._multiindex.is_monotonic
@cache_readonly
def is_monotonic_increasing(self):
"""
Return True if the IntervalIndex is monotonic increasing (only equal or
increasing values), else False
"""
return self._multiindex.is_monotonic_increasing
@cache_readonly
def is_monotonic_decreasing(self):
"""
Return True if the IntervalIndex is monotonic decreasing (only equal or
decreasing values), else False
"""
return self._multiindex.is_monotonic_decreasing
@cache_readonly
def is_unique(self):
"""
Return True if the IntervalIndex contains unique elements, else False
"""
return self._multiindex.is_unique
@cache_readonly
def is_non_overlapping_monotonic(self):
"""
Return True if the IntervalIndex is non-overlapping (no Intervals share
points) and is either monotonic increasing or monotonic decreasing,
else False
"""
# must be increasing (e.g., [0, 1), [1, 2), [2, 3), ... )
# or decreasing (e.g., [-1, 0), [-2, -1), [-3, -2), ...)
# we already require left <= right
# strict inequality for closed == 'both'; equality implies overlapping
# at a point when both sides of intervals are included
if self.closed == 'both':
return bool((self.right[:-1] < self.left[1:]).all() or
(self.left[:-1] > self.right[1:]).all())
# non-strict inequality when closed != 'both'; at least one side is
# not included in the intervals, so equality does not imply overlapping
return bool((self.right[:-1] <= self.left[1:]).all() or
(self.left[:-1] >= self.right[1:]).all())
@Appender(_index_shared_docs['_convert_scalar_indexer'])
def _convert_scalar_indexer(self, key, kind=None):
if kind == 'iloc':
return super(IntervalIndex, self)._convert_scalar_indexer(
key, kind=kind)
return key
def _maybe_cast_slice_bound(self, label, side, kind):
return getattr(self, side)._maybe_cast_slice_bound(label, side, kind)
@Appender(_index_shared_docs['_convert_list_indexer'])
def _convert_list_indexer(self, keyarr, kind=None):
"""
we are passed a list-like indexer. Return the
indexer for matching intervals.
"""
locs = self.get_indexer_for(keyarr)
# we have missing values
if (locs == -1).any():
raise KeyError
return locs
def _maybe_cast_indexed(self, key):
"""
we need to cast the key, which could be a scalar
or an array-like to the type of our subtype
"""
if isinstance(key, IntervalIndex):
return key
subtype = self.dtype.subtype
if is_float_dtype(subtype):
if is_integer(key):
key = float(key)
elif isinstance(key, (np.ndarray, Index)):
key = key.astype('float64')
elif is_integer_dtype(subtype):
if is_integer(key):
key = int(key)
return key
def _check_method(self, method):
if method is None:
return
if method in ['bfill', 'backfill', 'pad', 'ffill', 'nearest']:
msg = 'method {method} not yet implemented for IntervalIndex'
raise NotImplementedError(msg.format(method=method))
raise ValueError("Invalid fill method")
def _searchsorted_monotonic(self, label, side, exclude_label=False):
if not self.is_non_overlapping_monotonic:
raise KeyError('can only get slices from an IntervalIndex if '
'bounds are non-overlapping and all monotonic '
'increasing or decreasing')
if isinstance(label, IntervalMixin):
raise NotImplementedError
# GH 20921: "not is_monotonic_increasing" for the second condition
# instead of "is_monotonic_decreasing" to account for single element
# indexes being both increasing and decreasing
if ((side == 'left' and self.left.is_monotonic_increasing) or
(side == 'right' and not self.left.is_monotonic_increasing)):
sub_idx = self.right
if self.open_right or exclude_label:
label = _get_next_label(label)
else:
sub_idx = self.left
if self.open_left or exclude_label:
label = _get_prev_label(label)
return sub_idx._searchsorted_monotonic(label, side)
def _get_loc_only_exact_matches(self, key):
if isinstance(key, Interval):
if not self.is_unique:
raise ValueError("cannot index with a slice Interval"
" and a non-unique index")
# TODO: this expands to a tuple index, see if we can
# do better
return Index(self._multiindex.values).get_loc(key)
raise KeyError
def _find_non_overlapping_monotonic_bounds(self, key):
if isinstance(key, IntervalMixin):
start = self._searchsorted_monotonic(
key.left, 'left', exclude_label=key.open_left)
stop = self._searchsorted_monotonic(
key.right, 'right', exclude_label=key.open_right)
elif isinstance(key, slice):
# slice
start, stop = key.start, key.stop
if (key.step or 1) != 1:
raise NotImplementedError("cannot slice with a slice step")
if start is None:
start = 0
else:
start = self._searchsorted_monotonic(start, 'left')
if stop is None:
stop = len(self)
else:
stop = self._searchsorted_monotonic(stop, 'right')
else:
# scalar or index-like
start = self._searchsorted_monotonic(key, 'left')
stop = self._searchsorted_monotonic(key, 'right')
return start, stop