[CLN] More cython cleanups, with bonus type annotations

pandas/_libs/algos_common_helper.pxi.in

@@ -45,7 +45,7 @@ def get_dispatch(dtypes):
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-cpdef map_indices_{{name}}(ndarray[{{c_type}}] index):
+def map_indices_{{name}}(ndarray[{{c_type}}] index):
     """
     Produce a dict mapping the values of the input array to their respective
     locations.
@@ -55,8 +55,9 @@ cpdef map_indices_{{name}}(ndarray[{{c_type}}] index):
 
     Better to do this with Cython because of the enormous speed boost.
     """
-    cdef Py_ssize_t i, length
-    cdef dict result = {}
+    cdef:
+        Py_ssize_t i, length
+        dict result = {}
 
     length = len(index)
 
@@ -541,7 +542,7 @@ def put2d_{{name}}_{{dest_type}}(ndarray[{{c_type}}, ndim=2, cast=True] values,
 cdef int PLATFORM_INT = (<ndarray> np.arange(0, dtype=np.intp)).descr.type_num
 
 
-cpdef ensure_platform_int(object arr):
+def ensure_platform_int(object arr):
     # GH3033, GH1392
     # platform int is the size of the int pointer, e.g. np.intp
     if util.is_array(arr):
@@ -553,7 +554,7 @@ cpdef ensure_platform_int(object arr):
         return np.array(arr, dtype=np.intp)
 
 
-cpdef ensure_object(object arr):
+def ensure_object(object arr):
     if util.is_array(arr):
         if (<ndarray> arr).descr.type_num == NPY_OBJECT:
             return arr
@@ -586,7 +587,7 @@ def get_dispatch(dtypes):
 
 {{for name, c_type, dtype in get_dispatch(dtypes)}}
 
-cpdef ensure_{{name}}(object arr, copy=True):
+def ensure_{{name}}(object arr, copy=True):
     if util.is_array(arr):
         if (<ndarray> arr).descr.type_num == NPY_{{c_type}}:
             return arr

pandas/_libs/groupby.pyx

@@ -67,6 +67,7 @@ cdef inline float64_t median_linear(float64_t* a, int n) nogil:
     return result
 
 
+# TODO: Is this redundant with algos.kth_smallest?
 cdef inline float64_t kth_smallest_c(float64_t* a,
                                      Py_ssize_t k,
                                      Py_ssize_t n) nogil:

pandas/_libs/hashing.pyx

@@ -132,6 +132,7 @@ cdef inline void _sipround(uint64_t* v0, uint64_t* v1,
     v2[0] = _rotl(v2[0], 32)
 
 
+# TODO: This appears unused; remove?
 cpdef uint64_t siphash(bytes data, bytes key) except? 0:
     if len(key) != 16:
         raise ValueError("key should be a 16-byte bytestring, "

pandas/_libs/index.pyx

@@ -49,7 +49,7 @@ cpdef get_value_at(ndarray arr, object loc, object tz=None):
     return util.get_value_at(arr, loc)
 
 
-cpdef object get_value_box(ndarray arr, object loc):
+def get_value_box(arr: ndarray, loc: object) -> object:
     cdef:
         Py_ssize_t i, sz
 

pandas/_libs/internals.pyx

@@ -184,7 +184,7 @@ cdef class BlockPlacement:
         return self._as_slice
 
 
-cpdef slice_canonize(slice s):
+cdef slice_canonize(slice s):
     """
     Convert slice to canonical bounded form.
     """
@@ -255,7 +255,7 @@ cpdef Py_ssize_t slice_len(
     return length
 
 
-cpdef slice_get_indices_ex(slice slc, Py_ssize_t objlen=PY_SSIZE_T_MAX):
+cdef slice_get_indices_ex(slice slc, Py_ssize_t objlen=PY_SSIZE_T_MAX):
     """
     Get (start, stop, step, length) tuple for a slice.
 

pandas/_libs/interval.pyx

@@ -362,8 +362,8 @@ cdef class Interval(IntervalMixin):
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-cpdef intervals_to_interval_bounds(ndarray intervals,
-                                   bint validate_closed=True):
+def intervals_to_interval_bounds(ndarray intervals,
+                                 bint validate_closed=True):
     """
     Parameters
     ----------
@@ -415,4 +415,5 @@ cpdef intervals_to_interval_bounds(ndarray intervals,
 
     return left, right, closed
 
+
 include "intervaltree.pxi"

pandas/_libs/lib.pyx

@@ -107,7 +107,7 @@ def memory_usage_of_objects(object[:] arr):
 # ----------------------------------------------------------------------
 
 
-cpdef bint is_scalar(object val):
+def is_scalar(val: object) -> bint:
     """
     Return True if given value is scalar.
 
@@ -137,7 +137,7 @@ cpdef bint is_scalar(object val):
             or util.is_period_object(val)
             or is_decimal(val)
             or is_interval(val)
-            or is_offset(val))
+            or util.is_offset_object(val))
 
 
 def item_from_zerodim(object val):
@@ -457,7 +457,7 @@ def maybe_booleans_to_slice(ndarray[uint8_t] mask):
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-cpdef bint array_equivalent_object(object[:] left, object[:] right):
+def array_equivalent_object(left: object[:], right: object[:]) -> bint:
     """ perform an element by element comparion on 1-d object arrays
         taking into account nan positions """
     cdef:
@@ -499,7 +499,7 @@ def astype_intsafe(ndarray[object] arr, new_dtype):
     return result
 
 
-cpdef ndarray[object] astype_unicode(ndarray arr):
+def astype_unicode(arr: ndarray) -> ndarray[object]:
     cdef:
         Py_ssize_t i, n = arr.size
         ndarray[object] result = np.empty(n, dtype=object)
@@ -512,7 +512,7 @@ cpdef ndarray[object] astype_unicode(ndarray arr):
     return result
 
 
-cpdef ndarray[object] astype_str(ndarray arr):
+def astype_str(arr: ndarray) -> ndarray[object]:
     cdef:
         Py_ssize_t i, n = arr.size
         ndarray[object] result = np.empty(n, dtype=object)
@@ -797,19 +797,19 @@ def indices_fast(object index, ndarray[int64_t] labels, list keys,
 
 # core.common import for fast inference checks
 
-cpdef bint is_float(object obj):
+def is_float(obj: object) -> bint:
     return util.is_float_object(obj)
 
 
-cpdef bint is_integer(object obj):
+def is_integer(obj: object) -> bint:
     return util.is_integer_object(obj)
 
 
-cpdef bint is_bool(object obj):
+def is_bool(obj: object) -> bint:
     return util.is_bool_object(obj)
 
 
-cpdef bint is_complex(object obj):
+def is_complex(obj: object) -> bint:
     return util.is_complex_object(obj)
 
 
@@ -821,15 +821,11 @@ cpdef bint is_interval(object obj):
     return getattr(obj, '_typ', '_typ') == 'interval'
 
 
-cpdef bint is_period(object val):
+def is_period(val: object) -> bint:
     """ Return a boolean if this is a Period object """
     return util.is_period_object(val)
 
 
-cdef inline bint is_offset(object val):
-    return getattr(val, '_typ', '_typ') == 'dateoffset'
-
-
 _TYPE_MAP = {
     'categorical': 'categorical',
     'category': 'categorical',
@@ -1231,7 +1227,7 @@ def infer_dtype(object value, bint skipna=False):
         if is_bytes_array(values, skipna=skipna):
             return 'bytes'
 
-    elif is_period(val):
+    elif util.is_period_object(val):
         if is_period_array(values):
             return 'period'
 
@@ -1249,7 +1245,7 @@ def infer_dtype(object value, bint skipna=False):
     return 'mixed'
 
 
-cpdef object infer_datetimelike_array(object arr):
+def infer_datetimelike_array(arr: object) -> object:
     """
     infer if we have a datetime or timedelta array
     - date: we have *only* date and maybe strings, nulls
@@ -1586,7 +1582,7 @@ cpdef bint is_datetime64_array(ndarray values):
     return validator.validate(values)
 
 
-cpdef bint is_datetime_with_singletz_array(ndarray values):
+def is_datetime_with_singletz_array(values: ndarray) -> bint:
     """
     Check values have the same tzinfo attribute.
     Doesn't check values are datetime-like types.
@@ -1622,7 +1618,8 @@ cdef class TimedeltaValidator(TemporalValidator):
         return is_null_timedelta64(value)
 
 
-cpdef bint is_timedelta_array(ndarray values):
+# TODO: Not used outside of tests; remove?
+def is_timedelta_array(values: ndarray) -> bint:
     cdef:
         TimedeltaValidator validator = TimedeltaValidator(len(values),
                                                           skipna=True)
@@ -1634,7 +1631,8 @@ cdef class Timedelta64Validator(TimedeltaValidator):
         return util.is_timedelta64_object(value)
 
 
-cpdef bint is_timedelta64_array(ndarray values):
+# TODO: Not used outside of tests; remove?
+def is_timedelta64_array(values: ndarray) -> bint:
     cdef:
         Timedelta64Validator validator = Timedelta64Validator(len(values),
                                                               skipna=True)
@@ -1678,7 +1676,7 @@ cpdef bint is_time_array(ndarray values, bint skipna=False):
 
 cdef class PeriodValidator(TemporalValidator):
     cdef inline bint is_value_typed(self, object value) except -1:
-        return is_period(value)
+        return util.is_period_object(value)
 
     cdef inline bint is_valid_null(self, object value) except -1:
         return is_null_period(value)

pandas/_libs/parsers.pyx

@@ -29,7 +29,7 @@ cdef extern from "Python.h":
 
 import numpy as np
 cimport numpy as cnp
-from numpy cimport ndarray, uint8_t, uint64_t, int64_t
+from numpy cimport ndarray, uint8_t, uint64_t, int64_t, float64_t
 cnp.import_array()
 
 from util cimport UINT64_MAX, INT64_MAX, INT64_MIN
@@ -694,7 +694,7 @@ cdef class TextReader:
             if ptr == NULL:
                 if not os.path.exists(source):
                     raise compat.FileNotFoundError(
-                        'File %s does not exist' % source)
+                        'File {source} does not exist'.format(source=source))
                 raise IOError('Initializing from file failed')
 
             self.parser.source = ptr
@@ -772,9 +772,10 @@ cdef class TextReader:
 
                     if name == '':
                         if self.has_mi_columns:
-                            name = 'Unnamed: %d_level_%d' % (i, level)
+                            name = ('Unnamed: {i}_level_{lvl}'
+                                    .format(i=i, lvl=level))
                         else:
-                            name = 'Unnamed: %d' % i
+                            name = 'Unnamed: {i}'.format(i=i)
                         unnamed_count += 1
 
                     count = counts.get(name, 0)
@@ -849,8 +850,8 @@ cdef class TextReader:
             #                        'data has %d fields'
             #                        % (passed_count, field_count))
 
-            if self.has_usecols and self.allow_leading_cols and \
-                    not callable(self.usecols):
+            if (self.has_usecols and self.allow_leading_cols and
+                    not callable(self.usecols)):
                 nuse = len(self.usecols)
                 if nuse == passed_count:
                     self.leading_cols = 0
@@ -1027,17 +1028,19 @@ cdef class TextReader:
 
         if self.table_width - self.leading_cols > num_cols:
             raise ParserError(
-                "Too many columns specified: expected %s and found %s" %
-                (self.table_width - self.leading_cols, num_cols))
+                "Too many columns specified: expected {expected} and "
+                "found {found}"
+                .format(expected=self.table_width - self.leading_cols,
+                        found=num_cols))
 
         results = {}
         nused = 0
         for i in range(self.table_width):
             if i < self.leading_cols:
                 # Pass through leading columns always
                 name = i
-            elif self.usecols and not callable(self.usecols) and \
-                    nused == len(self.usecols):
+            elif (self.usecols and not callable(self.usecols) and
+                    nused == len(self.usecols)):
                 # Once we've gathered all requested columns, stop. GH5766
                 break
             else:
@@ -1103,7 +1106,7 @@ cdef class TextReader:
                 col_res = _maybe_upcast(col_res)
 
             if col_res is None:
-                raise ParserError('Unable to parse column %d' % i)
+                raise ParserError('Unable to parse column {i}'.format(i=i))
 
             results[i] = col_res
 
@@ -1222,8 +1225,8 @@ cdef class TextReader:
         elif dtype.kind == 'U':
             width = dtype.itemsize
             if width > 0:
-                raise TypeError("the dtype %s is not "
-                                "supported for parsing" % dtype)
+                raise TypeError("the dtype {dtype} is not "
+                                "supported for parsing".format(dtype=dtype))
 
             # unicode variable width
             return self._string_convert(i, start, end, na_filter,
@@ -1241,12 +1244,12 @@ cdef class TextReader:
             return self._string_convert(i, start, end, na_filter,
                                         na_hashset)
         elif is_datetime64_dtype(dtype):
-            raise TypeError("the dtype %s is not supported "
+            raise TypeError("the dtype {dtype} is not supported "
                             "for parsing, pass this column "
-                            "using parse_dates instead" % dtype)
+                            "using parse_dates instead".format(dtype=dtype))
         else:
-            raise TypeError("the dtype %s is not "
-                            "supported for parsing" % dtype)
+            raise TypeError("the dtype {dtype} is not "
+                            "supported for parsing".format(dtype=dtype))
 
     cdef _string_convert(self, Py_ssize_t i, int64_t start, int64_t end,
                          bint na_filter, kh_str_t *na_hashset):
@@ -2058,7 +2061,7 @@ cdef kh_float64_t* kset_float64_from_list(values) except NULL:
         khiter_t k
         kh_float64_t *table
         int ret = 0
-        cnp.float64_t val
+        float64_t val
         object value
 
     table = kh_init_float64()
@@ -2101,7 +2104,7 @@ cdef raise_parser_error(object base, parser_t *parser):
                 Py_XDECREF(type)
                 raise old_exc
 
-    message = '%s. C error: ' % base
+    message = '{base}. C error: '.format(base=base)
     if parser.error_msg != NULL:
         if PY3:
             message += parser.error_msg.decode('utf-8')

pandas/_libs/tslib.pyx

@@ -301,7 +301,7 @@ def format_array_from_datetime(ndarray[int64_t] values, object tz=None,
     return result
 
 
-cpdef array_with_unit_to_datetime(ndarray values, unit, errors='coerce'):
+def array_with_unit_to_datetime(ndarray values, unit, errors='coerce'):
     """
     convert the ndarray according to the unit
     if errors: