""" define the IntervalIndex """ from __future__ import annotations from operator import ( le, lt, ) import textwrap from typing import ( Any, Hashable, Literal, ) import numpy as np from pandas._libs import lib from pandas._libs.interval import ( Interval, IntervalMixin, IntervalTree, ) from pandas._libs.tslibs import ( BaseOffset, Timedelta, Timestamp, to_offset, ) from pandas._typing import ( Dtype, DtypeObj, IntervalClosedType, npt, ) from pandas.errors import InvalidIndexError from pandas.util._decorators import ( Appender, cache_readonly, ) from pandas.util._exceptions import rewrite_exception from pandas.core.dtypes.cast import ( find_common_type, infer_dtype_from_scalar, maybe_box_datetimelike, maybe_downcast_numeric, maybe_upcast_numeric_to_64bit, ) from pandas.core.dtypes.common import ( ensure_platform_int, is_datetime64tz_dtype, is_datetime_or_timedelta_dtype, is_dtype_equal, is_float, is_float_dtype, is_integer, is_integer_dtype, is_interval_dtype, is_list_like, is_number, is_object_dtype, is_scalar, ) from pandas.core.dtypes.dtypes import IntervalDtype from pandas.core.dtypes.missing import is_valid_na_for_dtype from pandas.core.algorithms import unique from pandas.core.arrays.interval import ( IntervalArray, _interval_shared_docs, ) import pandas.core.common as com from pandas.core.indexers import is_valid_positional_slice import pandas.core.indexes.base as ibase from pandas.core.indexes.base import ( Index, _index_shared_docs, ensure_index, maybe_extract_name, ) from pandas.core.indexes.datetimes import ( DatetimeIndex, date_range, ) from pandas.core.indexes.extension import ( ExtensionIndex, inherit_names, ) from pandas.core.indexes.multi import MultiIndex from pandas.core.indexes.timedeltas import ( TimedeltaIndex, timedelta_range, ) _index_doc_kwargs = dict(ibase._index_doc_kwargs) _index_doc_kwargs.update( { "klass": "IntervalIndex", "qualname": "IntervalIndex", "target_klass": "IntervalIndex or list of Intervals", "name": textwrap.dedent( """\ name : object, optional Name to be stored in the index. """ ), } ) 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(f"cannot determine next label for type {repr(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(f"cannot determine next label for type {repr(type(label))}") 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) @Appender( _interval_shared_docs["class"] % { "klass": "IntervalIndex", "summary": "Immutable index of intervals that are closed on the same side.", "name": _index_doc_kwargs["name"], "versionadded": "0.20.0", "extra_attributes": "is_overlapping\nvalues\n", "extra_methods": "", "examples": textwrap.dedent( """\ 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]], dtype='interval[int64, right]') 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. """ ), } ) @inherit_names(["set_closed", "to_tuples"], IntervalArray, wrap=True) @inherit_names( [ "__array__", "overlaps", "contains", "closed_left", "closed_right", "open_left", "open_right", "is_empty", ], IntervalArray, ) @inherit_names(["is_non_overlapping_monotonic", "closed"], IntervalArray, cache=True) class IntervalIndex(ExtensionIndex): _typ = "intervalindex" # annotate properties pinned via inherit_names closed: IntervalClosedType is_non_overlapping_monotonic: bool closed_left: bool closed_right: bool open_left: bool open_right: bool _data: IntervalArray _values: IntervalArray _can_hold_strings = False _data_cls = IntervalArray # -------------------------------------------------------------------- # Constructors def __new__( cls, data, closed=None, dtype: Dtype | None = None, copy: bool = False, name: Hashable = None, verify_integrity: bool = True, ) -> IntervalIndex: name = maybe_extract_name(name, data, cls) with rewrite_exception("IntervalArray", cls.__name__): array = IntervalArray( data, closed=closed, copy=copy, dtype=dtype, verify_integrity=verify_integrity, ) return cls._simple_new(array, name) @classmethod @Appender( _interval_shared_docs["from_breaks"] % { "klass": "IntervalIndex", "name": textwrap.dedent( """ name : str, optional Name of the resulting IntervalIndex.""" ), "examples": textwrap.dedent( """\ Examples -------- >>> pd.IntervalIndex.from_breaks([0, 1, 2, 3]) IntervalIndex([(0, 1], (1, 2], (2, 3]], dtype='interval[int64, right]') """ ), } ) def from_breaks( cls, breaks, closed: IntervalClosedType | None = "right", name: Hashable = None, copy: bool = False, dtype: Dtype | None = None, ) -> IntervalIndex: with rewrite_exception("IntervalArray", cls.__name__): array = IntervalArray.from_breaks( breaks, closed=closed, copy=copy, dtype=dtype ) return cls._simple_new(array, name=name) @classmethod @Appender( _interval_shared_docs["from_arrays"] % { "klass": "IntervalIndex", "name": textwrap.dedent( """ name : str, optional Name of the resulting IntervalIndex.""" ), "examples": textwrap.dedent( """\ Examples -------- >>> pd.IntervalIndex.from_arrays([0, 1, 2], [1, 2, 3]) IntervalIndex([(0, 1], (1, 2], (2, 3]], dtype='interval[int64, right]') """ ), } ) def from_arrays( cls, left, right, closed: IntervalClosedType = "right", name: Hashable = None, copy: bool = False, dtype: Dtype | None = None, ) -> IntervalIndex: with rewrite_exception("IntervalArray", cls.__name__): array = IntervalArray.from_arrays( left, right, closed, copy=copy, dtype=dtype ) return cls._simple_new(array, name=name) @classmethod @Appender( _interval_shared_docs["from_tuples"] % { "klass": "IntervalIndex", "name": textwrap.dedent( """ name : str, optional Name of the resulting IntervalIndex.""" ), "examples": textwrap.dedent( """\ Examples -------- >>> pd.IntervalIndex.from_tuples([(0, 1), (1, 2)]) IntervalIndex([(0, 1], (1, 2]], dtype='interval[int64, right]') """ ), } ) def from_tuples( cls, data, closed: IntervalClosedType = "right", name: Hashable = None, copy: bool = False, dtype: Dtype | None = None, ) -> IntervalIndex: with rewrite_exception("IntervalArray", cls.__name__): arr = IntervalArray.from_tuples(data, closed=closed, copy=copy, dtype=dtype) return cls._simple_new(arr, name=name) # -------------------------------------------------------------------- # error: Return type "IntervalTree" of "_engine" incompatible with return type # "Union[IndexEngine, ExtensionEngine]" in supertype "Index" @cache_readonly def _engine(self) -> IntervalTree: # type: ignore[override] # IntervalTree does not supports numpy array unless they are 64 bit left = self._maybe_convert_i8(self.left) left = maybe_upcast_numeric_to_64bit(left) right = self._maybe_convert_i8(self.right) right = maybe_upcast_numeric_to_64bit(right) return IntervalTree(left, right, closed=self.closed) def __contains__(self, key: Any) -> bool: """ return a boolean if this key is IN the index We *only* accept an Interval Parameters ---------- key : Interval Returns ------- bool """ hash(key) if not isinstance(key, Interval): if is_valid_na_for_dtype(key, self.dtype): return self.hasnans return False try: self.get_loc(key) return True except KeyError: return False @cache_readonly def _multiindex(self) -> MultiIndex: return MultiIndex.from_arrays([self.left, self.right], names=["left", "right"]) def __reduce__(self): d = { "left": self.left, "right": self.right, "closed": self.closed, "name": self.name, } return _new_IntervalIndex, (type(self), d), None @property def inferred_type(self) -> str: """Return a string of the type inferred from the values""" return "interval" # Cannot determine type of "memory_usage" @Appender(Index.memory_usage.__doc__) # type: ignore[has-type] def memory_usage(self, deep: bool = False) -> int: # 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) # IntervalTree doesn't have a is_monotonic_decreasing, so have to override # the Index implementation @cache_readonly def is_monotonic_decreasing(self) -> bool: """ Return True if the IntervalIndex is monotonic decreasing (only equal or decreasing values), else False """ return self[::-1].is_monotonic_increasing @cache_readonly def is_unique(self) -> bool: """ Return True if the IntervalIndex contains unique elements, else False. """ left = self.left right = self.right if self.isna().sum() > 1: return False if left.is_unique or right.is_unique: return True seen_pairs = set() check_idx = np.where(left.duplicated(keep=False))[0] for idx in check_idx: pair = (left[idx], right[idx]) if pair in seen_pairs: return False seen_pairs.add(pair) return True @property def is_overlapping(self) -> bool: """ Return True if the IntervalIndex has overlapping intervals, else False. Two intervals overlap if they share a common point, including closed endpoints. Intervals that only have an open endpoint in common do not overlap. Returns ------- bool Boolean indicating if the IntervalIndex has overlapping intervals. See Also -------- Interval.overlaps : Check whether two Interval objects overlap. IntervalIndex.overlaps : Check an IntervalIndex elementwise for overlaps. Examples -------- >>> index = pd.IntervalIndex.from_tuples([(0, 2), (1, 3), (4, 5)]) >>> index IntervalIndex([(0, 2], (1, 3], (4, 5]], dtype='interval[int64, right]') >>> index.is_overlapping True Intervals that share closed endpoints overlap: >>> index = pd.interval_range(0, 3, closed='both') >>> index IntervalIndex([[0, 1], [1, 2], [2, 3]], dtype='interval[int64, both]') >>> index.is_overlapping True Intervals that only have an open endpoint in common do not overlap: >>> index = pd.interval_range(0, 3, closed='left') >>> index IntervalIndex([[0, 1), [1, 2), [2, 3)], dtype='interval[int64, left]') >>> index.is_overlapping False """ # GH 23309 return self._engine.is_overlapping def _needs_i8_conversion(self, key) -> bool: """ Check if a given key needs i8 conversion. Conversion is necessary for Timestamp, Timedelta, DatetimeIndex, and TimedeltaIndex keys. An Interval-like requires conversion if its endpoints are one of the aforementioned types. Assumes that any list-like data has already been cast to an Index. Parameters ---------- key : scalar or Index-like The key that should be checked for i8 conversion Returns ------- bool """ if is_interval_dtype(key) or isinstance(key, Interval): return self._needs_i8_conversion(key.left) i8_types = (Timestamp, Timedelta, DatetimeIndex, TimedeltaIndex) return isinstance(key, i8_types) def _maybe_convert_i8(self, key): """ Maybe convert a given key to its equivalent i8 value(s). Used as a preprocessing step prior to IntervalTree queries (self._engine), which expects numeric data. Parameters ---------- key : scalar or list-like The key that should maybe be converted to i8. Returns ------- scalar or list-like The original key if no conversion occurred, int if converted scalar, Index with an int64 dtype if converted list-like. """ if is_list_like(key): key = ensure_index(key) key = maybe_upcast_numeric_to_64bit(key) if not self._needs_i8_conversion(key): return key scalar = is_scalar(key) if is_interval_dtype(key) or isinstance(key, Interval): # convert left/right and reconstruct left = self._maybe_convert_i8(key.left) right = self._maybe_convert_i8(key.right) constructor = Interval if scalar else IntervalIndex.from_arrays # error: "object" not callable return constructor( left, right, closed=self.closed ) # type: ignore[operator] if scalar: # Timestamp/Timedelta key_dtype, key_i8 = infer_dtype_from_scalar(key, pandas_dtype=True) if lib.is_period(key): key_i8 = key.ordinal elif isinstance(key_i8, Timestamp): key_i8 = key_i8._value elif isinstance(key_i8, (np.datetime64, np.timedelta64)): key_i8 = key_i8.view("i8") else: # DatetimeIndex/TimedeltaIndex key_dtype, key_i8 = key.dtype, Index(key.asi8) if key.hasnans: # convert NaT from its i8 value to np.nan so it's not viewed # as a valid value, maybe causing errors (e.g. is_overlapping) key_i8 = key_i8.where(~key._isnan) # ensure consistency with IntervalIndex subtype # error: Item "ExtensionDtype"/"dtype[Any]" of "Union[dtype[Any], # ExtensionDtype]" has no attribute "subtype" subtype = self.dtype.subtype # type: ignore[union-attr] if not is_dtype_equal(subtype, key_dtype): raise ValueError( f"Cannot index an IntervalIndex of subtype {subtype} with " f"values of dtype {key_dtype}" ) return key_i8 def _searchsorted_monotonic(self, label, side: Literal["left", "right"] = "left"): 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, IntervalIndex)): raise NotImplementedError("Interval objects are not currently supported") # 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: label = _get_next_label(label) else: sub_idx = self.left if self.open_left: label = _get_prev_label(label) return sub_idx._searchsorted_monotonic(label, side) # -------------------------------------------------------------------- # Indexing Methods def get_loc(self, key) -> int | slice | np.ndarray: """ Get integer location, slice or boolean mask for requested label. Parameters ---------- key : label Returns ------- int if unique index, slice if monotonic index, else mask Examples -------- >>> i1, i2 = pd.Interval(0, 1), pd.Interval(1, 2) >>> index = pd.IntervalIndex([i1, i2]) >>> index.get_loc(1) 0 You can also supply a point inside an interval. >>> index.get_loc(1.5) 1 If a label is in several intervals, you get the locations of all the relevant intervals. >>> i3 = pd.Interval(0, 2) >>> overlapping_index = pd.IntervalIndex([i1, i2, i3]) >>> overlapping_index.get_loc(0.5) array([ True, False, True]) Only exact matches will be returned if an interval is provided. >>> index.get_loc(pd.Interval(0, 1)) 0 """ self._check_indexing_error(key) if isinstance(key, Interval): if self.closed != key.closed: raise KeyError(key) mask = (self.left == key.left) & (self.right == key.right) elif is_valid_na_for_dtype(key, self.dtype): mask = self.isna() else: # assume scalar op_left = le if self.closed_left else lt op_right = le if self.closed_right else lt try: mask = op_left(self.left, key) & op_right(key, self.right) except TypeError as err: # scalar is not comparable to II subtype --> invalid label raise KeyError(key) from err matches = mask.sum() if matches == 0: raise KeyError(key) if matches == 1: return mask.argmax() res = lib.maybe_booleans_to_slice(mask.view("u1")) if isinstance(res, slice) and res.stop is None: # TODO: DO this in maybe_booleans_to_slice? res = slice(res.start, len(self), res.step) return res def _get_indexer( self, target: Index, method: str | None = None, limit: int | None = None, tolerance: Any | None = None, ) -> npt.NDArray[np.intp]: if isinstance(target, IntervalIndex): # We only get here with not self.is_overlapping # -> at most one match per interval in target # want exact matches -> need both left/right to match, so defer to # left/right get_indexer, compare elementwise, equality -> match indexer = self._get_indexer_unique_sides(target) elif not is_object_dtype(target.dtype): # homogeneous scalar index: use IntervalTree # we should always have self._should_partial_index(target) here target = self._maybe_convert_i8(target) indexer = self._engine.get_indexer(target.values) else: # heterogeneous scalar index: defer elementwise to get_loc # we should always have self._should_partial_index(target) here return self._get_indexer_pointwise(target)[0] return ensure_platform_int(indexer) @Appender(_index_shared_docs["get_indexer_non_unique"] % _index_doc_kwargs) def get_indexer_non_unique( self, target: Index ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: target = ensure_index(target) if not self._should_compare(target) and not self._should_partial_index(target): # e.g. IntervalIndex with different closed or incompatible subtype # -> no matches return self._get_indexer_non_comparable(target, None, unique=False) elif isinstance(target, IntervalIndex): if self.left.is_unique and self.right.is_unique: # fastpath available even if we don't have self._index_as_unique indexer = self._get_indexer_unique_sides(target) missing = (indexer == -1).nonzero()[0] else: return self._get_indexer_pointwise(target) elif is_object_dtype(target.dtype) or not self._should_partial_index(target): # target might contain intervals: defer elementwise to get_loc return self._get_indexer_pointwise(target) else: # Note: this case behaves differently from other Index subclasses # because IntervalIndex does partial-int indexing target = self._maybe_convert_i8(target) indexer, missing = self._engine.get_indexer_non_unique(target.values) return ensure_platform_int(indexer), ensure_platform_int(missing) def _get_indexer_unique_sides(self, target: IntervalIndex) -> npt.NDArray[np.intp]: """ _get_indexer specialized to the case where both of our sides are unique. """ # Caller is responsible for checking # `self.left.is_unique and self.right.is_unique` left_indexer = self.left.get_indexer(target.left) right_indexer = self.right.get_indexer(target.right) indexer = np.where(left_indexer == right_indexer, left_indexer, -1) return indexer def _get_indexer_pointwise( self, target: Index ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: """ pointwise implementation for get_indexer and get_indexer_non_unique. """ indexer, missing = [], [] for i, key in enumerate(target): try: locs = self.get_loc(key) if isinstance(locs, slice): # Only needed for get_indexer_non_unique locs = np.arange(locs.start, locs.stop, locs.step, dtype="intp") elif lib.is_integer(locs): locs = np.array(locs, ndmin=1) else: # otherwise we have ndarray[bool] locs = np.where(locs)[0] except KeyError: missing.append(i) locs = np.array([-1]) except InvalidIndexError: # i.e. non-scalar key e.g. a tuple. # see test_append_different_columns_types_raises missing.append(i) locs = np.array([-1]) indexer.append(locs) indexer = np.concatenate(indexer) return ensure_platform_int(indexer), ensure_platform_int(missing) @cache_readonly def _index_as_unique(self) -> bool: return not self.is_overlapping and self._engine._na_count < 2 _requires_unique_msg = ( "cannot handle overlapping indices; use IntervalIndex.get_indexer_non_unique" ) def _convert_slice_indexer(self, key: slice, kind: str): if not (key.step is None or key.step == 1): # GH#31658 if label-based, we require step == 1, # if positional, we disallow float start/stop msg = "label-based slicing with step!=1 is not supported for IntervalIndex" if kind == "loc": raise ValueError(msg) if kind == "getitem": if not is_valid_positional_slice(key): # i.e. this cannot be interpreted as a positional slice raise ValueError(msg) return super()._convert_slice_indexer(key, kind) @cache_readonly def _should_fallback_to_positional(self) -> bool: # integer lookups in Series.__getitem__ are unambiguously # positional in this case # error: Item "ExtensionDtype"/"dtype[Any]" of "Union[dtype[Any], # ExtensionDtype]" has no attribute "subtype" return self.dtype.subtype.kind in ["m", "M"] # type: ignore[union-attr] def _maybe_cast_slice_bound(self, label, side: str): return getattr(self, side)._maybe_cast_slice_bound(label, side) def _is_comparable_dtype(self, dtype: DtypeObj) -> bool: if not isinstance(dtype, IntervalDtype): return False common_subtype = find_common_type([self.dtype, dtype]) return not is_object_dtype(common_subtype) # -------------------------------------------------------------------- @cache_readonly def left(self) -> Index: return Index(self._data.left, copy=False) @cache_readonly def right(self) -> Index: return Index(self._data.right, copy=False) @cache_readonly def mid(self) -> Index: return Index(self._data.mid, copy=False) @property def length(self) -> Index: return Index(self._data.length, copy=False) # -------------------------------------------------------------------- # Rendering Methods # __repr__ associated methods are based on MultiIndex def _format_with_header(self, header: list[str], na_rep: str) -> list[str]: # matches base class except for whitespace padding return header + list(self._format_native_types(na_rep=na_rep)) def _format_native_types( self, *, na_rep: str = "NaN", quoting=None, **kwargs ) -> npt.NDArray[np.object_]: # GH 28210: use base method but with different default na_rep return super()._format_native_types(na_rep=na_rep, quoting=quoting, **kwargs) def _format_data(self, name=None) -> str: # TODO: integrate with categorical and make generic # name argument is unused here; just for compat with base / categorical return f"{self._data._format_data()},{self._format_space()}" # -------------------------------------------------------------------- # Set Operations def _intersection(self, other, sort): """ intersection specialized to the case with matching dtypes. """ # For IntervalIndex we also know other.closed == self.closed if self.left.is_unique and self.right.is_unique: taken = self._intersection_unique(other) elif other.left.is_unique and other.right.is_unique and self.isna().sum() <= 1: # Swap other/self if other is unique and self does not have # multiple NaNs taken = other._intersection_unique(self) else: # duplicates taken = self._intersection_non_unique(other) if sort is None: taken = taken.sort_values() return taken def _intersection_unique(self, other: IntervalIndex) -> IntervalIndex: """ Used when the IntervalIndex does not have any common endpoint, no matter left or right. Return the intersection with another IntervalIndex. Parameters ---------- other : IntervalIndex Returns ------- IntervalIndex """ # Note: this is much more performant than super()._intersection(other) lindexer = self.left.get_indexer(other.left) rindexer = self.right.get_indexer(other.right) match = (lindexer == rindexer) & (lindexer != -1) indexer = lindexer.take(match.nonzero()[0]) indexer = unique(indexer) return self.take(indexer) def _intersection_non_unique(self, other: IntervalIndex) -> IntervalIndex: """ Used when the IntervalIndex does have some common endpoints, on either sides. Return the intersection with another IntervalIndex. Parameters ---------- other : IntervalIndex Returns ------- IntervalIndex """ # Note: this is about 3.25x faster than super()._intersection(other) # in IntervalIndexMethod.time_intersection_both_duplicate(1000) mask = np.zeros(len(self), dtype=bool) if self.hasnans and other.hasnans: first_nan_loc = np.arange(len(self))[self.isna()][0] mask[first_nan_loc] = True other_tups = set(zip(other.left, other.right)) for i, tup in enumerate(zip(self.left, self.right)): if tup in other_tups: mask[i] = True return self[mask] # -------------------------------------------------------------------- def _get_engine_target(self) -> np.ndarray: # Note: we _could_ use libjoin functions by either casting to object # dtype or constructing tuples (faster than constructing Intervals) # but the libjoin fastpaths are no longer fast in these cases. raise NotImplementedError( "IntervalIndex does not use libjoin fastpaths or pass values to " "IndexEngine objects" ) def _from_join_target(self, result): raise NotImplementedError("IntervalIndex does not use libjoin fastpaths") # TODO: arithmetic operations def _is_valid_endpoint(endpoint) -> bool: """ Helper for interval_range to check if start/end are valid types. """ return any( [ is_number(endpoint), isinstance(endpoint, Timestamp), isinstance(endpoint, Timedelta), endpoint is None, ] ) def _is_type_compatible(a, b) -> bool: """ Helper for interval_range to check type compat of start/end/freq. """ is_ts_compat = lambda x: isinstance(x, (Timestamp, BaseOffset)) is_td_compat = lambda x: isinstance(x, (Timedelta, BaseOffset)) return ( (is_number(a) and is_number(b)) or (is_ts_compat(a) and is_ts_compat(b)) or (is_td_compat(a) and is_td_compat(b)) or com.any_none(a, b) ) def interval_range( start=None, end=None, periods=None, freq=None, name: Hashable = None, closed: IntervalClosedType = "right", ) -> IntervalIndex: """ Return a fixed frequency IntervalIndex. Parameters ---------- start : numeric or datetime-like, default None Left bound for generating intervals. end : numeric or datetime-like, default None Right bound for generating intervals. periods : int, default None Number of periods to generate. freq : numeric, str, datetime.timedelta, or DateOffset, default None The length of each interval. Must be consistent with the type of start and end, e.g. 2 for numeric, or '5H' for datetime-like. Default is 1 for numeric and 'D' for datetime-like. name : str, default None Name of the resulting IntervalIndex. closed : {'left', 'right', 'both', 'neither'}, default 'right' Whether the intervals are closed on the left-side, right-side, both or neither. Returns ------- IntervalIndex See Also -------- IntervalIndex : An Index of intervals that are all closed on the same side. Notes ----- Of the four parameters ``start``, ``end``, ``periods``, and ``freq``, exactly three must be specified. If ``freq`` is omitted, the resulting ``IntervalIndex`` will have ``periods`` linearly spaced elements between ``start`` and ``end``, inclusively. To learn more about datetime-like frequency strings, please see `this link `__. Examples -------- Numeric ``start`` and ``end`` is supported. >>> pd.interval_range(start=0, end=5) IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]], dtype='interval[int64, right]') Additionally, datetime-like input is also supported. >>> pd.interval_range(start=pd.Timestamp('2017-01-01'), ... end=pd.Timestamp('2017-01-04')) IntervalIndex([(2017-01-01, 2017-01-02], (2017-01-02, 2017-01-03], (2017-01-03, 2017-01-04]], dtype='interval[datetime64[ns], right]') The ``freq`` parameter specifies the frequency between the left and right. endpoints of the individual intervals within the ``IntervalIndex``. For numeric ``start`` and ``end``, the frequency must also be numeric. >>> pd.interval_range(start=0, periods=4, freq=1.5) IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]], dtype='interval[float64, right]') Similarly, for datetime-like ``start`` and ``end``, the frequency must be convertible to a DateOffset. >>> pd.interval_range(start=pd.Timestamp('2017-01-01'), ... periods=3, freq='MS') IntervalIndex([(2017-01-01, 2017-02-01], (2017-02-01, 2017-03-01], (2017-03-01, 2017-04-01]], dtype='interval[datetime64[ns], right]') Specify ``start``, ``end``, and ``periods``; the frequency is generated automatically (linearly spaced). >>> pd.interval_range(start=0, end=6, periods=4) IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]], dtype='interval[float64, right]') The ``closed`` parameter specifies which endpoints of the individual intervals within the ``IntervalIndex`` are closed. >>> pd.interval_range(end=5, periods=4, closed='both') IntervalIndex([[1, 2], [2, 3], [3, 4], [4, 5]], dtype='interval[int64, both]') """ start = maybe_box_datetimelike(start) end = maybe_box_datetimelike(end) endpoint = start if start is not None else end if freq is None and com.any_none(periods, start, end): freq = 1 if is_number(endpoint) else "D" if com.count_not_none(start, end, periods, freq) != 3: raise ValueError( "Of the four parameters: start, end, periods, and " "freq, exactly three must be specified" ) if not _is_valid_endpoint(start): raise ValueError(f"start must be numeric or datetime-like, got {start}") if not _is_valid_endpoint(end): raise ValueError(f"end must be numeric or datetime-like, got {end}") if is_float(periods): periods = int(periods) elif not is_integer(periods) and periods is not None: raise TypeError(f"periods must be a number, got {periods}") if freq is not None and not is_number(freq): try: freq = to_offset(freq) except ValueError as err: raise ValueError( f"freq must be numeric or convertible to DateOffset, got {freq}" ) from err # verify type compatibility if not all( [ _is_type_compatible(start, end), _is_type_compatible(start, freq), _is_type_compatible(end, freq), ] ): raise TypeError("start, end, freq need to be type compatible") # +1 to convert interval count to breaks count (n breaks = n-1 intervals) if periods is not None: periods += 1 breaks: np.ndarray | TimedeltaIndex | DatetimeIndex if is_number(endpoint): # force consistency between start/end/freq (lower end if freq skips it) if com.all_not_none(start, end, freq): end -= (end - start) % freq # compute the period/start/end if unspecified (at most one) if periods is None: periods = int((end - start) // freq) + 1 elif start is None: start = end - (periods - 1) * freq elif end is None: end = start + (periods - 1) * freq breaks = np.linspace(start, end, periods) if all(is_integer(x) for x in com.not_none(start, end, freq)): # np.linspace always produces float output # error: Argument 1 to "maybe_downcast_numeric" has incompatible type # "Union[ndarray[Any, Any], TimedeltaIndex, DatetimeIndex]"; # expected "ndarray[Any, Any]" [ breaks = maybe_downcast_numeric( breaks, # type: ignore[arg-type] np.dtype("int64"), ) else: # delegate to the appropriate range function if isinstance(endpoint, Timestamp): breaks = date_range(start=start, end=end, periods=periods, freq=freq) else: breaks = timedelta_range(start=start, end=end, periods=periods, freq=freq) return IntervalIndex.from_breaks(breaks, name=name, closed=closed)