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799 lines
25 KiB
799 lines
25 KiB
cimport cython
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import numpy as np
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cimport numpy as cnp
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from numpy cimport (
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float32_t,
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float64_t,
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int8_t,
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int16_t,
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int32_t,
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int64_t,
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intp_t,
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ndarray,
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uint8_t,
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uint16_t,
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uint32_t,
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uint64_t,
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)
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cnp.import_array()
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from pandas._libs cimport util
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from pandas._libs.hashtable cimport HashTable
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from pandas._libs.tslibs.nattype cimport c_NaT as NaT
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from pandas._libs.tslibs.period cimport is_period_object
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from pandas._libs.tslibs.timedeltas cimport _Timedelta
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from pandas._libs.tslibs.timestamps cimport _Timestamp
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from pandas._libs import (
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algos,
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hashtable as _hash,
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)
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from pandas._libs.lib cimport eq_NA_compat
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from pandas._libs.missing cimport (
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C_NA as NA,
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checknull,
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is_matching_na,
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)
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cdef inline bint is_definitely_invalid_key(object val):
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try:
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hash(val)
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except TypeError:
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return True
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return False
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cdef ndarray _get_bool_indexer(ndarray values, object val):
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"""
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Return a ndarray[bool] of locations where val matches self.values.
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If val is not NA, this is equivalent to `self.values == val`
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"""
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# Caller is responsible for ensuring _check_type has already been called
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cdef:
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ndarray[uint8_t, ndim=1, cast=True] indexer
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Py_ssize_t i
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object item
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if values.descr.type_num == cnp.NPY_OBJECT:
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# i.e. values.dtype == object
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if not checknull(val):
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indexer = eq_NA_compat(values, val)
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else:
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# We need to check for _matching_ NA values
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indexer = np.empty(len(values), dtype=np.uint8)
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for i in range(len(values)):
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item = values[i]
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indexer[i] = is_matching_na(item, val)
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else:
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if util.is_nan(val):
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indexer = np.isnan(values)
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else:
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indexer = values == val
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return indexer.view(bool)
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# Don't populate hash tables in monotonic indexes larger than this
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_SIZE_CUTOFF = 1_000_000
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cdef _unpack_bool_indexer(ndarray[uint8_t, ndim=1, cast=True] indexer, object val):
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"""
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Possibly unpack a boolean mask to a single indexer.
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"""
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# Returns ndarray[bool] or int
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cdef:
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ndarray[intp_t, ndim=1] found
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int count
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found = np.where(indexer)[0]
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count = len(found)
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if count > 1:
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return indexer
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if count == 1:
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return int(found[0])
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raise KeyError(val)
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@cython.freelist(32)
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cdef class IndexEngine:
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cdef readonly:
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ndarray values
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HashTable mapping
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bint over_size_threshold
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cdef:
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bint unique, monotonic_inc, monotonic_dec
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bint need_monotonic_check, need_unique_check
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object _np_type
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def __init__(self, ndarray values):
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self.values = values
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self.over_size_threshold = len(values) >= _SIZE_CUTOFF
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self.clear_mapping()
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self._np_type = values.dtype.type
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def __contains__(self, val: object) -> bool:
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# We assume before we get here:
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# - val is hashable
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self._ensure_mapping_populated()
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return val in self.mapping
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cpdef get_loc(self, object val):
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# -> Py_ssize_t | slice | ndarray[bool]
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cdef:
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Py_ssize_t loc
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if is_definitely_invalid_key(val):
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raise TypeError(f"'{val}' is an invalid key")
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self._check_type(val)
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if self.over_size_threshold and self.is_monotonic_increasing:
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if not self.is_unique:
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return self._get_loc_duplicates(val)
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values = self.values
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loc = self._searchsorted_left(val)
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if loc >= len(values):
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raise KeyError(val)
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if values[loc] != val:
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raise KeyError(val)
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return loc
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self._ensure_mapping_populated()
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if not self.unique:
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return self._get_loc_duplicates(val)
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try:
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return self.mapping.get_item(val)
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except OverflowError as err:
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# GH#41775 OverflowError e.g. if we are uint64 and val is -1
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# or if we are int64 and value is np.iinfo(np.int64).max+1
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# (the uint64 with -1 case should actually be excluded by _check_type)
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raise KeyError(val) from err
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cdef Py_ssize_t _searchsorted_left(self, val) except? -1:
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"""
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See ObjectEngine._searchsorted_left.__doc__.
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"""
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# Caller is responsible for ensuring _check_type has already been called
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loc = self.values.searchsorted(self._np_type(val), side="left")
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return loc
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cdef inline _get_loc_duplicates(self, object val):
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# -> Py_ssize_t | slice | ndarray[bool]
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cdef:
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Py_ssize_t diff, left, right
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if self.is_monotonic_increasing:
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values = self.values
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try:
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left = values.searchsorted(val, side='left')
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right = values.searchsorted(val, side='right')
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except TypeError:
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# e.g. GH#29189 get_loc(None) with a Float64Index
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# 2021-09-29 Now only reached for object-dtype
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raise KeyError(val)
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diff = right - left
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if diff == 0:
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raise KeyError(val)
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elif diff == 1:
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return left
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else:
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return slice(left, right)
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return self._maybe_get_bool_indexer(val)
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cdef _maybe_get_bool_indexer(self, object val):
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# Returns ndarray[bool] or int
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cdef:
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ndarray[uint8_t, ndim=1, cast=True] indexer
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indexer = _get_bool_indexer(self.values, val)
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return _unpack_bool_indexer(indexer, val)
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def sizeof(self, deep: bool = False) -> int:
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""" return the sizeof our mapping """
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if not self.is_mapping_populated:
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return 0
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return self.mapping.sizeof(deep=deep)
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def __sizeof__(self) -> int:
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return self.sizeof()
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@property
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def is_unique(self) -> bool:
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if self.need_unique_check:
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self._do_unique_check()
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return self.unique == 1
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cdef inline _do_unique_check(self):
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# this de-facto the same
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self._ensure_mapping_populated()
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@property
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def is_monotonic_increasing(self) -> bool:
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if self.need_monotonic_check:
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self._do_monotonic_check()
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return self.monotonic_inc == 1
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@property
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def is_monotonic_decreasing(self) -> bool:
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if self.need_monotonic_check:
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self._do_monotonic_check()
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return self.monotonic_dec == 1
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cdef inline _do_monotonic_check(self):
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cdef:
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bint is_unique
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try:
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values = self.values
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self.monotonic_inc, self.monotonic_dec, is_unique = \
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self._call_monotonic(values)
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except TypeError:
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self.monotonic_inc = 0
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self.monotonic_dec = 0
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is_unique = 0
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self.need_monotonic_check = 0
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# we can only be sure of uniqueness if is_unique=1
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if is_unique:
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self.unique = 1
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self.need_unique_check = 0
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cdef _call_monotonic(self, values):
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return algos.is_monotonic(values, timelike=False)
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cdef _make_hash_table(self, Py_ssize_t n):
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raise NotImplementedError # pragma: no cover
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cdef _check_type(self, object val):
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hash(val)
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@property
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def is_mapping_populated(self) -> bool:
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return self.mapping is not None
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cdef inline _ensure_mapping_populated(self):
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# this populates the mapping
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# if its not already populated
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# also satisfies the need_unique_check
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if not self.is_mapping_populated:
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values = self.values
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self.mapping = self._make_hash_table(len(values))
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self.mapping.map_locations(values)
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if len(self.mapping) == len(values):
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self.unique = 1
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self.need_unique_check = 0
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def clear_mapping(self):
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self.mapping = None
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self.need_monotonic_check = 1
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self.need_unique_check = 1
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self.unique = 0
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self.monotonic_inc = 0
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self.monotonic_dec = 0
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def get_indexer(self, ndarray values) -> np.ndarray:
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self._ensure_mapping_populated()
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return self.mapping.lookup(values)
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def get_indexer_non_unique(self, ndarray targets):
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"""
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Return an indexer suitable for taking from a non unique index
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return the labels in the same order as the target
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and a missing indexer into the targets (which correspond
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to the -1 indices in the results
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Returns
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-------
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indexer : np.ndarray[np.intp]
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missing : np.ndarray[np.intp]
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"""
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cdef:
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ndarray values
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ndarray[intp_t] result, missing
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set stargets, remaining_stargets, found_nas
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dict d = {}
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object val
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Py_ssize_t count = 0, count_missing = 0
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Py_ssize_t i, j, n, n_t, n_alloc, start, end
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bint check_na_values = False
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values = self.values
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stargets = set(targets)
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n = len(values)
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n_t = len(targets)
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if n > 10_000:
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n_alloc = 10_000
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else:
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n_alloc = n
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result = np.empty(n_alloc, dtype=np.intp)
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missing = np.empty(n_t, dtype=np.intp)
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# map each starget to its position in the index
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if (
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stargets and
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len(stargets) < 5 and
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not any([checknull(t) for t in stargets]) and
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self.is_monotonic_increasing
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):
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# if there are few enough stargets and the index is monotonically
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# increasing, then use binary search for each starget
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remaining_stargets = set()
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for starget in stargets:
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try:
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start = values.searchsorted(starget, side='left')
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end = values.searchsorted(starget, side='right')
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except TypeError: # e.g. if we tried to search for string in int array
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remaining_stargets.add(starget)
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else:
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if start != end:
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d[starget] = list(range(start, end))
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stargets = remaining_stargets
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if stargets:
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# otherwise, map by iterating through all items in the index
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# short-circuit na check
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if values.dtype == object:
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check_na_values = True
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# keep track of nas in values
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found_nas = set()
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for i in range(n):
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val = values[i]
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# GH#43870
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# handle lookup for nas
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# (ie. np.nan, float("NaN"), Decimal("NaN"), dt64nat, td64nat)
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if check_na_values and checknull(val):
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match = [na for na in found_nas if is_matching_na(val, na)]
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# matching na not found
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if not len(match):
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found_nas.add(val)
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# add na to stargets to utilize `in` for stargets/d lookup
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match_stargets = [
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x for x in stargets if is_matching_na(val, x)
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]
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if len(match_stargets):
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# add our 'standardized' na
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stargets.add(val)
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# matching na found
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else:
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assert len(match) == 1
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val = match[0]
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if val in stargets:
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if val not in d:
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d[val] = []
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d[val].append(i)
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for i in range(n_t):
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val = targets[i]
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# ensure there are nas in values before looking for a matching na
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if check_na_values and checknull(val):
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match = [na for na in found_nas if is_matching_na(val, na)]
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if len(match):
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assert len(match) == 1
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val = match[0]
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# found
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if val in d:
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key = val
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for j in d[key]:
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# realloc if needed
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if count >= n_alloc:
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n_alloc += 10_000
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result = np.resize(result, n_alloc)
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result[count] = j
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count += 1
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# value not found
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else:
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if count >= n_alloc:
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n_alloc += 10_000
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result = np.resize(result, n_alloc)
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result[count] = -1
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count += 1
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missing[count_missing] = i
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count_missing += 1
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return result[0:count], missing[0:count_missing]
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|
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|
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cdef Py_ssize_t _bin_search(ndarray values, object val) except -1:
|
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# GH#1757 ndarray.searchsorted is not safe to use with array of tuples
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# (treats a tuple `val` as a sequence of keys instead of a single key),
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# so we implement something similar.
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# This is equivalent to the stdlib's bisect.bisect_left
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|
|
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cdef:
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Py_ssize_t mid = 0, lo = 0, hi = len(values) - 1
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object pval
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|
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if hi == 0 or (hi > 0 and val > values[hi]):
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return len(values)
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|
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while lo < hi:
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mid = (lo + hi) // 2
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pval = values[mid]
|
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if val < pval:
|
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hi = mid
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elif val > pval:
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lo = mid + 1
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else:
|
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while mid > 0 and val == values[mid - 1]:
|
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mid -= 1
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return mid
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|
|
|
if val <= values[mid]:
|
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return mid
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else:
|
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return mid + 1
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|
|
|
|
cdef class ObjectEngine(IndexEngine):
|
|
"""
|
|
Index Engine for use with object-dtype Index, namely the base class Index.
|
|
"""
|
|
cdef _make_hash_table(self, Py_ssize_t n):
|
|
return _hash.PyObjectHashTable(n)
|
|
|
|
cdef Py_ssize_t _searchsorted_left(self, val) except? -1:
|
|
# using values.searchsorted here would treat a tuple `val` as a sequence
|
|
# instead of a single key, so we use a different implementation
|
|
try:
|
|
loc = _bin_search(self.values, val)
|
|
except TypeError as err:
|
|
raise KeyError(val) from err
|
|
return loc
|
|
|
|
|
|
cdef class DatetimeEngine(Int64Engine):
|
|
|
|
cdef int64_t _unbox_scalar(self, scalar) except? -1:
|
|
# NB: caller is responsible for ensuring tzawareness compat
|
|
# before we get here
|
|
if not (isinstance(scalar, _Timestamp) or scalar is NaT):
|
|
raise TypeError(scalar)
|
|
return scalar.value
|
|
|
|
def __contains__(self, val: object) -> bool:
|
|
# We assume before we get here:
|
|
# - val is hashable
|
|
self._unbox_scalar(val)
|
|
try:
|
|
self.get_loc(val)
|
|
return True
|
|
except KeyError:
|
|
return False
|
|
|
|
cdef _call_monotonic(self, values):
|
|
return algos.is_monotonic(values, timelike=True)
|
|
|
|
cpdef get_loc(self, object val):
|
|
# NB: the caller is responsible for ensuring that we are called
|
|
# with either a Timestamp or NaT (Timedelta or NaT for TimedeltaEngine)
|
|
|
|
cdef:
|
|
Py_ssize_t loc
|
|
|
|
if is_definitely_invalid_key(val):
|
|
raise TypeError(f"'{val}' is an invalid key")
|
|
|
|
try:
|
|
conv = self._unbox_scalar(val)
|
|
except TypeError:
|
|
raise KeyError(val)
|
|
|
|
# Welcome to the spaghetti factory
|
|
if self.over_size_threshold and self.is_monotonic_increasing:
|
|
if not self.is_unique:
|
|
return self._get_loc_duplicates(conv)
|
|
values = self.values
|
|
|
|
loc = values.searchsorted(conv, side='left')
|
|
|
|
if loc == len(values) or values[loc] != conv:
|
|
raise KeyError(val)
|
|
return loc
|
|
|
|
self._ensure_mapping_populated()
|
|
if not self.unique:
|
|
return self._get_loc_duplicates(conv)
|
|
|
|
try:
|
|
return self.mapping.get_item(conv)
|
|
except KeyError:
|
|
raise KeyError(val)
|
|
|
|
|
|
cdef class TimedeltaEngine(DatetimeEngine):
|
|
|
|
cdef int64_t _unbox_scalar(self, scalar) except? -1:
|
|
if not (isinstance(scalar, _Timedelta) or scalar is NaT):
|
|
raise TypeError(scalar)
|
|
return scalar.value
|
|
|
|
|
|
cdef class PeriodEngine(Int64Engine):
|
|
|
|
cdef int64_t _unbox_scalar(self, scalar) except? -1:
|
|
if scalar is NaT:
|
|
return scalar.value
|
|
if is_period_object(scalar):
|
|
# NB: we assume that we have the correct freq here.
|
|
return scalar.ordinal
|
|
raise TypeError(scalar)
|
|
|
|
cpdef get_loc(self, object val):
|
|
# NB: the caller is responsible for ensuring that we are called
|
|
# with either a Period or NaT
|
|
cdef:
|
|
int64_t conv
|
|
|
|
try:
|
|
conv = self._unbox_scalar(val)
|
|
except TypeError:
|
|
raise KeyError(val)
|
|
|
|
return Int64Engine.get_loc(self, conv)
|
|
|
|
cdef _call_monotonic(self, values):
|
|
return algos.is_monotonic(values, timelike=True)
|
|
|
|
|
|
cdef class BaseMultiIndexCodesEngine:
|
|
"""
|
|
Base class for MultiIndexUIntEngine and MultiIndexPyIntEngine, which
|
|
represent each label in a MultiIndex as an integer, by juxtaposing the bits
|
|
encoding each level, with appropriate offsets.
|
|
|
|
For instance: if 3 levels have respectively 3, 6 and 1 possible values,
|
|
then their labels can be represented using respectively 2, 3 and 1 bits,
|
|
as follows:
|
|
_ _ _ _____ _ __ __ __
|
|
|0|0|0| ... |0| 0|a1|a0| -> offset 0 (first level)
|
|
— — — ————— — —— —— ——
|
|
|0|0|0| ... |0|b2|b1|b0| -> offset 2 (bits required for first level)
|
|
— — — ————— — —— —— ——
|
|
|0|0|0| ... |0| 0| 0|c0| -> offset 5 (bits required for first two levels)
|
|
‾ ‾ ‾ ‾‾‾‾‾ ‾ ‾‾ ‾‾ ‾‾
|
|
and the resulting unsigned integer representation will be:
|
|
_ _ _ _____ _ __ __ __ __ __ __
|
|
|0|0|0| ... |0|c0|b2|b1|b0|a1|a0|
|
|
‾ ‾ ‾ ‾‾‾‾‾ ‾ ‾‾ ‾‾ ‾‾ ‾‾ ‾‾ ‾‾
|
|
|
|
Offsets are calculated at initialization, labels are transformed by method
|
|
_codes_to_ints.
|
|
|
|
Keys are located by first locating each component against the respective
|
|
level, then locating (the integer representation of) codes.
|
|
"""
|
|
def __init__(self, object levels, object labels,
|
|
ndarray[uint64_t, ndim=1] offsets):
|
|
"""
|
|
Parameters
|
|
----------
|
|
levels : list-like of numpy arrays
|
|
Levels of the MultiIndex.
|
|
labels : list-like of numpy arrays of integer dtype
|
|
Labels of the MultiIndex.
|
|
offsets : numpy array of uint64 dtype
|
|
Pre-calculated offsets, one for each level of the index.
|
|
"""
|
|
self.levels = levels
|
|
self.offsets = offsets
|
|
|
|
# Transform labels in a single array, and add 1 so that we are working
|
|
# with positive integers (-1 for NaN becomes 0):
|
|
codes = (np.array(labels, dtype='int64').T + 1).astype('uint64',
|
|
copy=False)
|
|
|
|
# Map each codes combination in the index to an integer unambiguously
|
|
# (no collisions possible), based on the "offsets", which describe the
|
|
# number of bits to switch labels for each level:
|
|
lab_ints = self._codes_to_ints(codes)
|
|
|
|
# Initialize underlying index (e.g. libindex.UInt64Engine) with
|
|
# integers representing labels: we will use its get_loc and get_indexer
|
|
self._base.__init__(self, lab_ints)
|
|
|
|
def _codes_to_ints(self, ndarray[uint64_t] codes) -> np.ndarray:
|
|
raise NotImplementedError("Implemented by subclass") # pragma: no cover
|
|
|
|
def _extract_level_codes(self, target) -> np.ndarray:
|
|
"""
|
|
Map the requested list of (tuple) keys to their integer representations
|
|
for searching in the underlying integer index.
|
|
|
|
Parameters
|
|
----------
|
|
target : MultiIndex
|
|
|
|
Returns
|
|
------
|
|
int_keys : 1-dimensional array of dtype uint64 or object
|
|
Integers representing one combination each
|
|
"""
|
|
zt = [target._get_level_values(i) for i in range(target.nlevels)]
|
|
level_codes = [lev.get_indexer_for(codes) + 1 for lev, codes
|
|
in zip(self.levels, zt)]
|
|
return self._codes_to_ints(np.array(level_codes, dtype='uint64').T)
|
|
|
|
def get_indexer(self, target: np.ndarray) -> np.ndarray:
|
|
"""
|
|
Returns an array giving the positions of each value of `target` in
|
|
`self.values`, where -1 represents a value in `target` which does not
|
|
appear in `self.values`
|
|
|
|
Parameters
|
|
----------
|
|
target : np.ndarray
|
|
|
|
Returns
|
|
-------
|
|
np.ndarray[intp_t, ndim=1] of the indexer of `target` into
|
|
`self.values`
|
|
"""
|
|
return self._base.get_indexer(self, target)
|
|
|
|
def get_indexer_with_fill(self, ndarray target, ndarray values,
|
|
str method, object limit) -> np.ndarray:
|
|
"""
|
|
Returns an array giving the positions of each value of `target` in
|
|
`values`, where -1 represents a value in `target` which does not
|
|
appear in `values`
|
|
|
|
If `method` is "backfill" then the position for a value in `target`
|
|
which does not appear in `values` is that of the next greater value
|
|
in `values` (if one exists), and -1 if there is no such value.
|
|
|
|
Similarly, if the method is "pad" then the position for a value in
|
|
`target` which does not appear in `values` is that of the next smaller
|
|
value in `values` (if one exists), and -1 if there is no such value.
|
|
|
|
Parameters
|
|
----------
|
|
target: ndarray[object] of tuples
|
|
need not be sorted, but all must have the same length, which must be
|
|
the same as the length of all tuples in `values`
|
|
values : ndarray[object] of tuples
|
|
must be sorted and all have the same length. Should be the set of
|
|
the MultiIndex's values.
|
|
method: string
|
|
"backfill" or "pad"
|
|
limit: int or None
|
|
if provided, limit the number of fills to this value
|
|
|
|
Returns
|
|
-------
|
|
np.ndarray[intp_t, ndim=1] of the indexer of `target` into `values`,
|
|
filled with the `method` (and optionally `limit`) specified
|
|
"""
|
|
assert method in ("backfill", "pad")
|
|
cdef:
|
|
int64_t i, j, next_code
|
|
int64_t num_values, num_target_values
|
|
ndarray[int64_t, ndim=1] target_order
|
|
ndarray[object, ndim=1] target_values
|
|
ndarray[int64_t, ndim=1] new_codes, new_target_codes
|
|
ndarray[intp_t, ndim=1] sorted_indexer
|
|
|
|
target_order = np.argsort(target).astype('int64')
|
|
target_values = target[target_order]
|
|
num_values, num_target_values = len(values), len(target_values)
|
|
new_codes, new_target_codes = (
|
|
np.empty((num_values,)).astype('int64'),
|
|
np.empty((num_target_values,)).astype('int64'),
|
|
)
|
|
|
|
# `values` and `target_values` are both sorted, so we walk through them
|
|
# and memoize the (ordered) set of indices in the (implicit) merged-and
|
|
# sorted list of the two which belong to each of them
|
|
# the effect of this is to create a factorization for the (sorted)
|
|
# merger of the index values, where `new_codes` and `new_target_codes`
|
|
# are the subset of the factors which appear in `values` and `target`,
|
|
# respectively
|
|
i, j, next_code = 0, 0, 0
|
|
while i < num_values and j < num_target_values:
|
|
val, target_val = values[i], target_values[j]
|
|
if val <= target_val:
|
|
new_codes[i] = next_code
|
|
i += 1
|
|
if target_val <= val:
|
|
new_target_codes[j] = next_code
|
|
j += 1
|
|
next_code += 1
|
|
|
|
# at this point, at least one should have reached the end
|
|
# the remaining values of the other should be added to the end
|
|
assert i == num_values or j == num_target_values
|
|
while i < num_values:
|
|
new_codes[i] = next_code
|
|
i += 1
|
|
next_code += 1
|
|
while j < num_target_values:
|
|
new_target_codes[j] = next_code
|
|
j += 1
|
|
next_code += 1
|
|
|
|
# get the indexer, and undo the sorting of `target.values`
|
|
algo = algos.backfill if method == "backfill" else algos.pad
|
|
sorted_indexer = algo(new_codes, new_target_codes, limit=limit)
|
|
return sorted_indexer[np.argsort(target_order)]
|
|
|
|
def get_loc(self, object key):
|
|
if is_definitely_invalid_key(key):
|
|
raise TypeError(f"'{key}' is an invalid key")
|
|
if not isinstance(key, tuple):
|
|
raise KeyError(key)
|
|
try:
|
|
indices = [0 if checknull(v) else lev.get_loc(v) + 1
|
|
for lev, v in zip(self.levels, key)]
|
|
except KeyError:
|
|
raise KeyError(key)
|
|
|
|
# Transform indices into single integer:
|
|
lab_int = self._codes_to_ints(np.array(indices, dtype='uint64'))
|
|
|
|
return self._base.get_loc(self, lab_int)
|
|
|
|
def get_indexer_non_unique(self, target: np.ndarray) -> np.ndarray:
|
|
indexer = self._base.get_indexer_non_unique(self, target)
|
|
|
|
return indexer
|
|
|
|
def __contains__(self, val: object) -> bool:
|
|
# We assume before we get here:
|
|
# - val is hashable
|
|
# Default __contains__ looks in the underlying mapping, which in this
|
|
# case only contains integer representations.
|
|
try:
|
|
self.get_loc(val)
|
|
return True
|
|
except (KeyError, TypeError, ValueError):
|
|
return False
|
|
|
|
|
|
# Generated from template.
|
|
include "index_class_helper.pxi"
|
|
|