clear out a bunch of algos, de-duplicate a bunch of core.missing (pan…

…das-dev#24652)

pandas/_libs/algos.pyx

@@ -369,31 +369,6 @@ ctypedef fused algos_t:
     uint8_t
 
 
-# TODO: unused; needed?
-@cython.wraparound(False)
-@cython.boundscheck(False)
-cpdef map_indices(ndarray[algos_t] index):
-    """
-    Produce a dict mapping the values of the input array to their respective
-    locations.
-
-    Example:
-        array(['hi', 'there']) --> {'hi' : 0 , 'there' : 1}
-
-    Better to do this with Cython because of the enormous speed boost.
-    """
-    cdef:
-        Py_ssize_t i, length
-        dict result = {}
-
-    length = len(index)
-
-    for i in range(length):
-        result[index[i]] = i
-
-    return result
-
-
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def pad(ndarray[algos_t] old, ndarray[algos_t] new, limit=None):
@@ -458,20 +433,6 @@ def pad(ndarray[algos_t] old, ndarray[algos_t] new, limit=None):
     return indexer
 
 
-pad_float64 = pad["float64_t"]
-pad_float32 = pad["float32_t"]
-pad_object = pad["object"]
-pad_int64 = pad["int64_t"]
-pad_int32 = pad["int32_t"]
-pad_int16 = pad["int16_t"]
-pad_int8 = pad["int8_t"]
-pad_uint64 = pad["uint64_t"]
-pad_uint32 = pad["uint32_t"]
-pad_uint16 = pad["uint16_t"]
-pad_uint8 = pad["uint8_t"]
-pad_bool = pad["uint8_t"]
-
-
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def pad_inplace(ndarray[algos_t] values,
@@ -509,15 +470,6 @@ def pad_inplace(ndarray[algos_t] values,
             val = values[i]
 
 
-pad_inplace_float64 = pad_inplace["float64_t"]
-pad_inplace_float32 = pad_inplace["float32_t"]
-pad_inplace_object = pad_inplace["object"]
-pad_inplace_int64 = pad_inplace["int64_t"]
-pad_inplace_int32 = pad_inplace["int32_t"]
-pad_inplace_uint64 = pad_inplace["uint64_t"]
-pad_inplace_bool = pad_inplace["uint8_t"]
-
-
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def pad_2d_inplace(ndarray[algos_t, ndim=2] values,
@@ -557,15 +509,6 @@ def pad_2d_inplace(ndarray[algos_t, ndim=2] values,
                 val = values[j, i]
 
 
-pad_2d_inplace_float64 = pad_2d_inplace["float64_t"]
-pad_2d_inplace_float32 = pad_2d_inplace["float32_t"]
-pad_2d_inplace_object = pad_2d_inplace["object"]
-pad_2d_inplace_int64 = pad_2d_inplace["int64_t"]
-pad_2d_inplace_int32 = pad_2d_inplace["int32_t"]
-pad_2d_inplace_uint64 = pad_2d_inplace["uint64_t"]
-pad_2d_inplace_bool = pad_2d_inplace["uint8_t"]
-
-
 """
 Backfilling logic for generating fill vector
 
@@ -657,20 +600,6 @@ def backfill(ndarray[algos_t] old, ndarray[algos_t] new, limit=None):
     return indexer
 
 
-backfill_float64 = backfill["float64_t"]
-backfill_float32 = backfill["float32_t"]
-backfill_object = backfill["object"]
-backfill_int64 = backfill["int64_t"]
-backfill_int32 = backfill["int32_t"]
-backfill_int16 = backfill["int16_t"]
-backfill_int8 = backfill["int8_t"]
-backfill_uint64 = backfill["uint64_t"]
-backfill_uint32 = backfill["uint32_t"]
-backfill_uint16 = backfill["uint16_t"]
-backfill_uint8 = backfill["uint8_t"]
-backfill_bool = backfill["uint8_t"]
-
-
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def backfill_inplace(ndarray[algos_t] values,
@@ -708,15 +637,6 @@ def backfill_inplace(ndarray[algos_t] values,
             val = values[i]
 
 
-backfill_inplace_float64 = backfill_inplace["float64_t"]
-backfill_inplace_float32 = backfill_inplace["float32_t"]
-backfill_inplace_object = backfill_inplace["object"]
-backfill_inplace_int64 = backfill_inplace["int64_t"]
-backfill_inplace_int32 = backfill_inplace["int32_t"]
-backfill_inplace_uint64 = backfill_inplace["uint64_t"]
-backfill_inplace_bool = backfill_inplace["uint8_t"]
-
-
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def backfill_2d_inplace(ndarray[algos_t, ndim=2] values,
@@ -756,15 +676,6 @@ def backfill_2d_inplace(ndarray[algos_t, ndim=2] values,
                 val = values[j, i]
 
 
-backfill_2d_inplace_float64 = backfill_2d_inplace["float64_t"]
-backfill_2d_inplace_float32 = backfill_2d_inplace["float32_t"]
-backfill_2d_inplace_object = backfill_2d_inplace["object"]
-backfill_2d_inplace_int64 = backfill_2d_inplace["int64_t"]
-backfill_2d_inplace_int32 = backfill_2d_inplace["int32_t"]
-backfill_2d_inplace_uint64 = backfill_2d_inplace["uint64_t"]
-backfill_2d_inplace_bool = backfill_2d_inplace["uint8_t"]
-
-
 @cython.wraparound(False)
 @cython.boundscheck(False)
 def arrmap(ndarray[algos_t] index, object func):
@@ -875,20 +786,6 @@ def is_monotonic(ndarray[algos_t, ndim=1] arr, bint timelike):
     return is_monotonic_inc, is_monotonic_dec, is_strict_monotonic
 
 
-is_monotonic_float64 = is_monotonic["float64_t"]
-is_monotonic_float32 = is_monotonic["float32_t"]
-is_monotonic_object = is_monotonic["object"]
-is_monotonic_int64 = is_monotonic["int64_t"]
-is_monotonic_int32 = is_monotonic["int32_t"]
-is_monotonic_int16 = is_monotonic["int16_t"]
-is_monotonic_int8 = is_monotonic["int8_t"]
-is_monotonic_uint64 = is_monotonic["uint64_t"]
-is_monotonic_uint32 = is_monotonic["uint32_t"]
-is_monotonic_uint16 = is_monotonic["uint16_t"]
-is_monotonic_uint8 = is_monotonic["uint8_t"]
-is_monotonic_bool = is_monotonic["uint8_t"]
-
-
 # generated from template
 include "algos_common_helper.pxi"
 include "algos_rank_helper.pxi"

pandas/_libs/algos_common_helper.pxi.in

@@ -70,18 +70,6 @@ def diff_2d_{{name}}(ndarray[{{c_type}}, ndim=2] arr,
                 for j in range(start, stop):
                     out[i, j] = arr[i, j] - arr[i, j - periods]
 
-
-def put2d_{{name}}_{{dest_name}}(ndarray[{{c_type}}, ndim=2, cast=True] values,
-                                 ndarray[int64_t] indexer, Py_ssize_t loc,
-                                 ndarray[{{dest_type}}] out):
-    cdef:
-        Py_ssize_t i, j, k
-
-    k = len(values)
-    for j in range(k):
-        i = indexer[j]
-        out[i] = values[j, loc]
-
 {{endfor}}
 
 # ----------------------------------------------------------------------

pandas/_libs/index.pyx

@@ -392,7 +392,7 @@ cdef class DatetimeEngine(Int64Engine):
         return self.vgetter().view('i8')
 
     def _call_monotonic(self, values):
-        return algos.is_monotonic_int64(values, timelike=True)
+        return algos.is_monotonic(values, timelike=True)
 
     cpdef get_loc(self, object val):
         if is_definitely_invalid_key(val):
@@ -451,14 +451,13 @@ cdef class DatetimeEngine(Int64Engine):
         if other.dtype != self._get_box_dtype():
             return np.repeat(-1, len(other)).astype('i4')
         other = np.asarray(other).view('i8')
-        return algos.pad_int64(self._get_index_values(), other, limit=limit)
+        return algos.pad(self._get_index_values(), other, limit=limit)
 
     def get_backfill_indexer(self, other, limit=None):
         if other.dtype != self._get_box_dtype():
             return np.repeat(-1, len(other)).astype('i4')
         other = np.asarray(other).view('i8')
-        return algos.backfill_int64(self._get_index_values(), other,
-                                    limit=limit)
+        return algos.backfill(self._get_index_values(), other, limit=limit)
 
 
 cdef class TimedeltaEngine(DatetimeEngine):
@@ -492,15 +491,15 @@ cdef class PeriodEngine(Int64Engine):
         freq = super(PeriodEngine, self).vgetter().freq
         ordinal = periodlib.extract_ordinals(other, freq)
 
-        return algos.pad_int64(self._get_index_values(),
-                               np.asarray(ordinal), limit=limit)
+        return algos.pad(self._get_index_values(),
+                         np.asarray(ordinal), limit=limit)
 
     def get_backfill_indexer(self, other, limit=None):
         freq = super(PeriodEngine, self).vgetter().freq
         ordinal = periodlib.extract_ordinals(other, freq)
 
-        return algos.backfill_int64(self._get_index_values(),
-                                    np.asarray(ordinal), limit=limit)
+        return algos.backfill(self._get_index_values(),
+                              np.asarray(ordinal), limit=limit)
 
     def get_indexer_non_unique(self, targets):
         freq = super(PeriodEngine, self).vgetter().freq

pandas/_libs/index_class_helper.pxi.in

@@ -31,15 +31,13 @@ dtypes = [('Float64', 'float64', 'float64_t', 'Float64', 'float64'),
 cdef class {{name}}Engine(IndexEngine):
 
     def _call_monotonic(self, values):
-        return algos.is_monotonic_{{dtype}}(values, timelike=False)
+        return algos.is_monotonic(values, timelike=False)
 
     def get_backfill_indexer(self, other, limit=None):
-        return algos.backfill_{{dtype}}(self._get_index_values(),
-                                        other, limit=limit)
+        return algos.backfill(self._get_index_values(), other, limit=limit)
 
     def get_pad_indexer(self, other, limit=None):
-        return algos.pad_{{dtype}}(self._get_index_values(),
-                                   other, limit=limit)
+        return algos.pad(self._get_index_values(), other, limit=limit)
 
     cdef _make_hash_table(self, n):
         return _hash.{{hashtable_name}}HashTable(n)