8000 CLN: use float64_t consistently instead of double, double_t by jbrockmendel · Pull Request #23583 · pandas-dev/pandas · GitHub
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CLN: use float64_t consistently instead of double, double_t #23583

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Merged
merged 19 commits into from
Nov 11, 2018
Merged

CLN: use float64_t consistently instead of double, double_t #23583

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fa38001
use float64_t instead of double
jbrockmendel Nov 7, 2018
af27870
make memoryview arguments const where needed; use C NAT lookups inste…
jbrockmendel Nov 7, 2018
83ca237
Merge branch 'master' of https://github.com/pandas-dev/pandas into cln1
jbrockmendel Nov 7, 2018
367969e
remove boundschecks
jbrockmendel Nov 8, 2018
16d75a3
Merge branch 'master' of https://github.com/pandas-dev/pandas into cln1
jbrockmendel Nov 8, 2018
4f4d1cf
de-duplicate using checknull_with_nat
jbrockmendel Nov 8, 2018
d1511f7
remove non-standard imports of np.nan
jbrockmendel Nov 8, 2018
c9ef170
revert not-worth it NAT, remove extraneous nan
jbrockmendel Nov 8, 2018
1445830
Merge branch 'master' of https://github.com/pandas-dev/pandas into cln1
jbrockmendel Nov 8, 2018
d46d516
standardize iNaT-->NPY_NAT
jbrockmendel Nov 8, 2018
f2f4b8d
comment cleanup
jbrockmendel Nov 8, 2018
e7adaf2
Merge branch 'master' of https://github.com/pandas-dev/pandas into cln1
jbrockmendel Nov 8, 2018
03ae9cf
remove unncessary cpdef
jbrockmendel Nov 8, 2018
9f51831
Merge branch 'master' of ht 8000 tps://github.com/pandas-dev/pandas into cln1
jbrockmendel Nov 8, 2018
33fc12d
delete unused or commented-out
jbrockmendel Nov 8, 2018
8bc71b8
remove unused
jbrockmendel Nov 8, 2018
5e50897
whitespace fixup
jbrockmendel Nov 8, 2018
52ede45
Merge branch 'master' of https://github.com/pandas-dev/pandas into cln1
jbrockmendel Nov 10, 2018
23344b2
remove unliked comment
jbrockmendel Nov 10, 2018
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standardize iNaT-->NPY_NAT
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@jbrockmendel
jbrockmendel committed Nov 8, 2018
commit d46d516b8d85d03cc6089906a7bd55cdb34d795b
10 changes: 5 additions & 5 deletions pandas/_libs/algos.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@ cdef float64_t FP_ERR = 1e-13

cdef float64_t NaN = <float64_t>np.NaN

cdef int64_t iNaT = get_nat()
cdef int64_t NPY_NAT = get_nat()

tiebreakers = {
'average': TIEBREAK_AVERAGE,
Expand Down Expand Up @@ -810,23 +810,23 @@ def is_monotonic(ndarray[algos_t, ndim=1] arr, bint timelike):
n = len(arr)

if n == 1:
if arr[0] != arr[0] or (timelike and <int64_t>arr[0] == iNaT):
if arr[0] != arr[0] or (timelike and <int64_t>arr[0] == NPY_NAT):
# single value is NaN
return False, False, True
else:
return True, True, True
elif n < 2:
return True, True, True

if timelike and <int64_t>arr[0] == iNaT:
if timelike and <int64_t>arr[0] == NPY_NAT:
return False, False, True

if algos_t is not object:
with nogil:
prev = arr[0]
for i in range(1, n):
cur = arr[i]
if timelike and <int64_t>cur == iNaT:
if timelike and <int64_t>cur 8000 == NPY_NAT:
is_monotonic_inc = 0
is_monotonic_dec = 0
break
Expand All @@ -851,7 +851,7 @@ def is_monotonic(ndarray[algos_t, ndim=1] arr, bint timelike):
prev = arr[0]
for i in range(1, n):
cur = arr[i]
if timelike and <int64_t>cur == iNaT:
if timelike and <int64_t>cur == NPY_NAT:
is_monotonic_inc = 0
is_monotonic_dec = 0
break
Expand Down
6 changes: 3 additions & 3 deletions pandas/_libs/algos_rank_helper.pxi.in
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Expand Up @@ -74,9 +74,9 @@ def rank_1d_{{dtype}}(object in_arr, ties_method='average',
{{elif dtype == 'float64'}}
mask = np.isnan(values)
{{elif dtype == 'int64'}}
mask = values == iNaT
mask = values == NPY_NAT

# create copy in case of iNaT
# create copy in case of NPY_NAT
# values are mutated inplace
if mask.any():
values = values.copy()
Expand Down Expand Up @@ -257,7 +257,7 @@ def rank_2d_{{dtype}}(object in_arr, axis=0, ties_method='average',
{{elif dtype == 'float64'}}
mask = np.isnan(values)
{{elif dtype == 'int64'}}
mask = values == iNaT
mask = values == NPY_NAT
{{endif}}

np.putmask(values, mask, nan_value)
Expand Down
2 changes: 1 addition & 1 deletion pandas/_libs/groupby.pyx
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Expand Up @@ -19,7 +19,7 @@ from algos cimport (swap, TiebreakEnumType, TIEBREAK_AVERAGE, TIEBREAK_MIN,
TIEBREAK_MAX, TIEBREAK_FIRST, TIEBREAK_DENSE)
from algos import take_2d_axis1_float64_float64, groupsort_indexer, tiebreakers

cdef int64_t iNaT = get_nat()
cdef int64_t NPY_NAT = get_nat()

cdef float64_t NaN = <float64_t>np.NaN

Expand Down
14 changes: 7 additions & 7 deletions pandas/_libs/groupby_helper.pxi.in
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Expand Up @@ -277,7 +277,7 @@ def group_ohlc_{{name}}(ndarray[{{c_type}}, ndim=2] out,
# name, c_type, nan_val
dtypes = [('float64', 'float64_t', 'NAN'),
('float32', 'float32_t', 'NAN'),
('int64', 'int64_t', 'iNaT'),
('int64', 'int64_t', 'NPY_NAT'),
('object', 'object', 'NAN')]

def get_dispatch(dtypes):
Expand Down Expand Up @@ -630,7 +630,7 @@ def group_max(ndarray[groupby_t, ndim=2] out,
if groupby_t is int64_t:
# Note: evaluated at compile-time
maxx[:] = -_int64_max
nan_val = iNaT
nan_val = NPY_NAT
else:
maxx[:] = -np.inf
nan_val = NAN
Expand Down Expand Up @@ -692,7 +692,7 @@ def group_min(ndarray[groupby_t, ndim=2] out,
minx = np.empty_like(out)
if groupby_t is int64_t:
minx[:] = _int64_max
nan_val = iNaT
nan_val = NPY_NAT
else:
minx[:] = np.inf
nan_val = NAN
Expand Down Expand Up @@ -762,8 +762,8 @@ def group_cummin(ndarray[groupby_t, ndim=2] out,

# val = nan
if groupby_t is int64_t:
if is_datetimelike and val == iNaT:
out[i, j] = iNaT
if is_datetimelike and val == NPY_NAT:
out[i, j] = NPY_NAT
else:
mval = accum[lab, j]
if val < mval:
Expand Down Expand Up @@ -809,8 +809,8 @@ def group_cummax(ndarray[groupby_t, ndim=2] out,
val = values[i, j]

if groupby_t is int64_t:
if is_datetimelike and val == iNaT:
out[i, j] = iNaT
if is_datetimelike and val == NPY_NAT:
out[i, j] = NPY_NAT
else:
mval = accum[lab, j]
if val > mval:
Expand Down
2 changes: 1 addition & 1 deletion pandas/_libs/hashtable.pyx
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Expand Up @@ -42,7 +42,7 @@ cimport util
from missing cimport checknull


cdef int64_t iNaT = util.get_nat()
cdef int64_t NPY_NAT = util.get_nat()
_SIZE_HINT_LIMIT = (1 << 20) + 7


Expand Down
2 changes: 1 addition & 1 deletion pandas/_libs/hashtable_class_helper.pxi.in
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Expand Up @@ -253,7 +253,7 @@ cdef class HashTable:
# name, dtype, float_group, default_na_value
dtypes = [('Float64', 'float64', True, 'np.nan'),
('UInt64', 'uint64', False, 0),
('Int64', 'int64', False, 'iNaT')]
('Int64', 'int64', False, 'NPY_NAT')]

}}

Expand Down
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