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MarcoGorelliMarcoGorelli
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CLN, STYLE: remove unused variables and imports in Cython files (#48290)
* remove unused variables and imports * wip * bump to 0.1.4
1 parent b8ae9bb commit 6dc589b

23 files changed

+20
-102
lines changed

‎.pre-commit-config.yaml

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@@ -26,6 +26,10 @@ repos:
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hooks:
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- id: codespell
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types_or: [python, rst, markdown]
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- repo: https://github.com/MarcoGorelli/cython-lint
30+
rev: v0.1.4
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hooks:
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- id: cython-lint
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.3.0
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hooks:

‎pandas/_libs/algos.pyx

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@@ -14,18 +14,12 @@ import numpy as np
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cimport numpy as cnp
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from numpy cimport (
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NPY_COMPLEX64,
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NPY_COMPLEX128,
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NPY_FLOAT32,
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NPY_FLOAT64,
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NPY_INT8,
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NPY_INT16,
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NPY_INT32,
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NPY_INT64,
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NPY_OBJECT,
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NPY_UINT8,
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NPY_UINT16,
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NPY_UINT32,
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NPY_UINT64,
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float32_t,
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float64_t,

‎pandas/_libs/groupby.pyx

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@@ -17,14 +17,10 @@ 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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from numpy.math cimport NAN
@@ -38,7 +34,6 @@ from pandas._libs.algos cimport (
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)
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from pandas._libs.algos import (
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ensure_platform_int,
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groupsort_indexer,
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rank_1d,
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take_2d_axis1_bool_bool,

‎pandas/_libs/hashing.pyx

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@@ -13,7 +13,6 @@ from numpy cimport (
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import_array,
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ndarray,
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uint8_t,
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uint32_t,
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uint64_t,
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)
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@@ -158,7 +157,6 @@ cdef uint64_t low_level_siphash(uint8_t* data, size_t datalen,
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cdef int i
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cdef uint8_t* end = data + datalen - (datalen % sizeof(uint64_t))
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cdef int left = datalen & 7
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cdef int left_byte
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cdef int cROUNDS = 2
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cdef int dROUNDS = 4
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‎pandas/_libs/hashtable.pyx

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@@ -15,13 +15,7 @@ from libc.stdlib cimport (
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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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float64_t,
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ndarray,
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uint8_t,
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uint32_t,
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)
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from numpy.math cimport NAN
18+
from numpy cimport ndarray
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cnp.import_array()
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@@ -37,9 +31,6 @@ from pandas._libs.khash cimport (
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kh_needed_n_buckets,
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kh_python_hash_equal,
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kh_python_hash_func,
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kh_str_t,
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khcomplex64_t,
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khcomplex128_t,
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khiter_t,
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)
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from pandas._libs.missing cimport checknull

‎pandas/_libs/index.pyx

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@@ -4,17 +4,10 @@ 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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@@ -35,7 +28,6 @@ from pandas._libs import (
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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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)

‎pandas/_libs/internals.pyx

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@@ -226,7 +226,7 @@ cdef class BlockPlacement:
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"""
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cdef:
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slice nv, s = self._ensure_has_slice()
229-
Py_ssize_t other_int, start, stop, step
229+
Py_ssize_t start, stop, step
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ndarray[intp_t, ndim=1] newarr
231231

232232
if s is not None:

‎pandas/_libs/interval.pyx

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@@ -1,4 +1,3 @@
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import inspect
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import numbers
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from operator import (
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le,
@@ -13,15 +12,7 @@ from cpython.datetime cimport (
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import_datetime()
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cimport cython
16-
from cpython.object cimport (
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Py_EQ,
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Py_GE,
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Py_GT,
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Py_LE,
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Py_LT,
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Py_NE,
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PyObject_RichCompare,
24-
)
15+
from cpython.object cimport PyObject_RichCompare
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from cython cimport Py_ssize_t
2617

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import numpy as np
@@ -31,9 +22,7 @@ from numpy cimport (
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NPY_QUICKSORT,
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PyArray_ArgSort,
3324
PyArray_Take,
34-
float32_t,
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float64_t,
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int32_t,
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int64_t,
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ndarray,
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uint64_t,

‎pandas/_libs/lib.pyx

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@@ -47,7 +47,6 @@ from numpy cimport (
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PyArray_IterNew,
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complex128_t,
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flatiter,
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float32_t,
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float64_t,
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int64_t,
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intp_t,

‎pandas/_libs/sparse.pyx

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@@ -3,10 +3,8 @@ import numpy as np
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cimport numpy as cnp
55
from numpy cimport (
6-
float32_t,
76
float64_t,
87
int8_t,
9-
int16_t,
108
int32_t,
119
int64_t,
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ndarray,
@@ -127,7 +125,7 @@ cdef class IntIndex(SparseIndex):
127125

128126
cpdef IntIndex intersect(self, SparseIndex y_):
129127
cdef:
130-
Py_ssize_t out_length, xi, yi = 0, result_indexer = 0
128+
Py_ssize_t xi, yi = 0, result_indexer = 0
131129
int32_t xind
132130
ndarray[int32_t, ndim=1] xindices, yindices, new_indices
133131
IntIndex y
@@ -205,7 +203,7 @@ cdef class IntIndex(SparseIndex):
205203
Vectorized lookup, returns ndarray[int32_t]
206204
"""
207205
cdef:
208-
Py_ssize_t n, i, ind_val
206+
Py_ssize_t n
209207
ndarray[int32_t, ndim=1] inds
210208
ndarray[uint8_t, ndim=1, cast=True] mask
211209
ndarray[int32_t, ndim=1] masked
@@ -232,7 +230,7 @@ cdef class IntIndex(SparseIndex):
232230

233231
cpdef get_blocks(ndarray[int32_t, ndim=1] indices):
234232
cdef:
235-
Py_ssize_t init_len, i, npoints, result_indexer = 0
233+
Py_ssize_t i, npoints, result_indexer = 0
236234
int32_t block, length = 1, cur, prev
237235
ndarray[int32_t, ndim=1] locs, lens
238236

@@ -606,7 +604,7 @@ cdef class BlockUnion(BlockMerge):
606604
cdef:
607605
ndarray[int32_t, ndim=1] xstart, xend, ystart
608606
ndarray[int32_t, ndim=1] yend, out_bloc, out_blen
609-
int32_t nstart, nend, diff
607+
int32_t nstart, nend
610608
Py_ssize_t max_len, result_indexer = 0
611609

612610
xstart = self.xstart
@@ -659,7 +657,7 @@ cdef class BlockUnion(BlockMerge):
659657
"""
660658
cdef:
661659
ndarray[int32_t, ndim=1] xstart, xend, ystart, yend
662-
int32_t xi, yi, xnblocks, ynblocks, nend
660+
int32_t xi, yi, ynblocks, nend
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664662
if mode != 0 and mode != 1:
665663
raise Exception('Mode must be 0 or 1')

‎pandas/_libs/tslib.pyx

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@@ -474,12 +474,10 @@ cpdef array_to_datetime(
474474
Py_ssize_t i, n = len(values)
475475
object val, tz
476476
ndarray[int64_t] iresult
477-
ndarray[object] oresult
478477
npy_datetimestruct dts
479478
NPY_DATETIMEUNIT out_bestunit
480479
bint utc_convert = bool(utc)
481480
bint seen_integer = False
482-
bint seen_string = False
483481
bint seen_datetime = False
484482
bint seen_datetime_offset = False
485483
bint is_raise = errors=='raise'
@@ -489,7 +487,7 @@ cpdef array_to_datetime(
489487
_TSObject _ts
490488
int64_t value
491489
int out_local = 0, out_tzoffset = 0
492-
float offset_seconds, tz_offset
490+
float tz_offset
493491
set out_tzoffset_vals = set()
494492
bint string_to_dts_failed
495493
datetime py_dt
@@ -572,7 +570,6 @@ cpdef array_to_datetime(
572570

573571
elif isinstance(val, str):
574572
# string
575-
seen_string = True
576573
if type(val) is not str:
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# GH#32264 np.str_ object
578575
val = str(val)

‎pandas/_libs/tslibs/conversion.pyx

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@@ -1,20 +1,14 @@
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import inspect
2-
3-
cimport cython
4-
52
import warnings
63

74
import numpy as np
85

96
from pandas.util._exceptions import find_stack_level
107

118
cimport numpy as cnp
12-
from cpython.object cimport PyObject
139
from numpy cimport (
1410
int32_t,
1511
int64_t,
16-
intp_t,
17-
ndarray,
1812
)
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2014
cnp.import_array()
@@ -42,33 +36,25 @@ from pandas._libs.tslibs.dtypes cimport (
4236
from pandas._libs.tslibs.np_datetime cimport (
4337
NPY_DATETIMEUNIT,
4438
NPY_FR_ns,
45-
astype_overflowsafe,
4639
check_dts_bounds,
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dtstruct_to_dt64,
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get_datetime64_unit,
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get_datetime64_value,
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get_implementation_bounds,
51-
get_unit_from_dtype,
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npy_datetime,
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npy_datetimestruct,
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npy_datetimestruct_to_datetime,
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pandas_datetime_to_datetimestruct,
56-
pydatetime_to_dt64,
5748
pydatetime_to_dtstruct,
5849
string_to_dts,
5950
)
6051

61-
from pandas._libs.tslibs.np_datetime import (
62-
OutOfBoundsDatetime,
63-
OutOfBoundsTimedelta,
64-
)
52+
from pandas._libs.tslibs.np_datetime import OutOfBoundsDatetime
6553

6654
from pandas._libs.tslibs.timezones cimport (
6755
get_utcoffset,
6856
is_utc,
6957
maybe_get_tz,
70-
tz_compare,
71-
utc_pytz as UTC,
7258
)
7359
from pandas._libs.tslibs.util cimport (
7460
is_datetime64_object,
@@ -82,7 +68,6 @@ from pandas._libs.tslibs.nattype cimport (
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NPY_NAT,
8369
c_NaT as NaT,
8470
c_nat_strings as nat_strings,
85-
checknull_with_nat,
8671
)
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from pandas._libs.tslibs.tzconversion cimport (
8873
Localizer,

‎pandas/_libs/tslibs/dtypes.pyx

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# period frequency constants corresponding to scikits timeseries
22
# originals
3-
cimport cython
4-
53
from enum import Enum
64

75
from pandas._libs.tslibs.np_datetime cimport (

‎pandas/_libs/tslibs/fields.pyx

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@@ -37,15 +37,12 @@ from pandas._libs.tslibs.ccalendar cimport (
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get_iso_calendar,
3838
get_lastbday,
3939
get_week_of_year,
40-
is_leapyear,
4140
iso_calendar_t,
42-
month_offset,
4341
)
4442
from pandas._libs.tslibs.nattype cimport NPY_NAT
4543
from pandas._libs.tslibs.np_datetime cimport (
4644
NPY_DATETIMEUNIT,
4745
NPY_FR_ns,
48-
get_unit_from_dtype,
4946
npy_datetimestruct,
5047
pandas_datetime_to_datetimestruct,
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pandas_timedelta_to_timedeltastruct,

‎pandas/_libs/tslibs/nattype.pyx

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@@ -15,10 +15,6 @@ from cpython.datetime cimport (
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import_datetime()
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from cpython.object cimport (
1717
Py_EQ,
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Py_GE,
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Py_GT,
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Py_LE,
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Py_LT,
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Py_NE,
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PyObject_RichCompare,
2420
)

‎pandas/_libs/tslibs/offsets.pyx

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import inspect
2-
import operator
32
import re
43
import time
54
import warnings

‎pandas/_libs/tslibs/parsing.pyx

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@@ -503,7 +503,7 @@ cdef inline object _parse_dateabbr_string(object date_string, datetime default,
503503
cdef:
504504
object ret
505505
# year initialized to prevent compiler warnings
506-
int year = -1, quarter = -1, month, mnum
506+
int year = -1, quarter = -1, month
507507
Py_ssize_t date_len
508508

509509
# special handling for possibilities eg, 2Q2005, 2Q05, 2005Q1, 05Q1

‎pandas/_libs/tslibs/period.pyx

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2525
from cpython.datetime cimport (
2626
PyDate_Check,
2727
PyDateTime_Check,
28-
PyDelta_Check,
2928
datetime,
3029
import_datetime,
3130
)
@@ -50,7 +49,6 @@ from pandas._libs.missing cimport C_NA
5049
from pandas._libs.tslibs.np_datetime cimport (
5150
NPY_DATETIMEUNIT,
5251
NPY_FR_D,
53-
NPY_FR_us,
5452
astype_overflowsafe,
5553
check_dts_bounds,
5654
get_timedelta64_value,
@@ -780,7 +778,7 @@ cdef int64_t get_period_ordinal(npy_datetimestruct *dts, int freq) nogil:
780778
"""
781779
cdef:
782780
int64_t unix_date
783-
int freq_group, fmonth, mdiff
781+
int freq_group, fmonth
784782
NPY_DATETIMEUNIT unit
785783

786784
freq_group = get_freq_group(freq)

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