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Variable Name Cleanup
1 parent 64929a5 commit 27cb6b5

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2 files changed

+34
-34
lines changed

2 files changed

+34
-34
lines changed

pandas/_libs/groupby.pyx

+11-11
Original file line numberDiff line numberDiff line change
@@ -133,7 +133,7 @@ def group_rank_object(ndarray[float64_t, ndim=2] out,
133133
cdef:
134134
int tiebreak
135135
Py_ssize_t i, j, N, K
136-
int64_t val_start=0, grp_start=0, dups=0, sum_ranks=0, vals_seen=1
136+
int64_t val_start=0, grp_start=0, dups=0, sum_ranks=0, grp_vals_seen=1
137137
int64_t grp_na_count=0
138138
ndarray[int64_t] _as
139139
ndarray[object] _values
@@ -145,16 +145,16 @@ def group_rank_object(ndarray[float64_t, ndim=2] out,
145145
keep_na = kwargs['na_option'] == 'keep'
146146
N, K = (<object> values).shape
147147

148-
_values = np.array(values[:, 0], copy=True)
149-
mask = missing.isnaobj(_values)
148+
masked_vals = np.array(values[:, 0], copy=True)
149+
mask = missing.isnaobj(masked_vals)
150150

151151
if ascending ^ (kwargs['na_option'] == 'top'):
152-
nan_value = np.inf
153-
order = (_values, mask, labels)
152+
nan_fill_val = np.inf
153+
order = (masked_vals, mask, labels)
154154
else:
155-
nan_value = -np.inf
156-
order = (_values, ~mask, labels)
157-
np.putmask(_values, mask, nan_value)
155+
nan_fill_val = -np.inf
156+
order = (masked_vals, ~mask, labels)
157+
np.putmask(masked_vals, mask, nan_fill_val)
158158
try:
159159
_as = np.lexsort(order)
160160
except TypeError:
@@ -194,15 +194,15 @@ def group_rank_object(ndarray[float64_t, ndim=2] out,
194194
out[_as[j], 0] = 2 * i - j - dups + 2 - grp_start
195195
elif tiebreak == TIEBREAK_DENSE:
196196
for j in range(i - dups + 1, i + 1):
197-
out[_as[j], 0] = vals_seen
197+
out[_as[j], 0] = grp_vals_seen
198198

199199
if (i == N - 1 or (
200200
(values[_as[i], 0] != values[_as[i+1], 0]) and not
201201
(values[_as[i], 0] is np.nan and values[_as[i+1], 0] is np.nan)
202202
)):
203203
dups = sum_ranks = 0
204204
val_start = i
205-
vals_seen += 1
205+
grp_vals_seen += 1
206206

207207
if i == N - 1 or labels[_as[i]] != labels[_as[i+1]]:
208208
if pct:
@@ -211,7 +211,7 @@ def group_rank_object(ndarray[float64_t, ndim=2] out,
211211
- grp_na_count)
212212
grp_na_count = 0
213213
grp_start = i + 1
214-
vals_seen = 1
214+
grp_vals_seen = 1
215215

216216

217217
cdef inline float64_t median_linear(float64_t* a, int n) nogil:

pandas/_libs/groupby_helper.pxi.in

+23-23
Original file line numberDiff line numberDiff line change
@@ -457,42 +457,42 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
457457
cdef:
458458
int tiebreak
459459
Py_ssize_t i, j, N, K
460-
int64_t val_start=0, grp_start=0, dups=0, sum_ranks=0, vals_seen=1
460+
int64_t val_start=0, grp_start=0, dups=0, sum_ranks=0, grp_vals_seen=1
461461
int64_t grp_na_count=0
462462
ndarray[int64_t] _as
463-
ndarray[{{c_type}}] _values
463+
ndarray[{{c_type}}] masked_vals
464464
ndarray[uint8_t] mask
465465
bint pct, ascending, keep_na
466-
{{c_type}} nan_value
466+
{{c_type}} nan_fill_val
467467

468468
tiebreak = tiebreakers[kwargs['ties_method']]
469469
ascending = kwargs['ascending']
470470
pct = kwargs['pct']
471471
keep_na = kwargs['na_option'] == 'keep'
472472
N, K = (<object> values).shape
473473

474-
_values = np.array(values[:, 0], copy=True)
474+
masked_vals = np.array(values[:, 0], copy=True)
475475
{{if name=='int64'}}
476-
mask = (_values == {{nan_val}}).astype(np.uint8)
476+
mask = (masked_vals == {{nan_val}}).astype(np.uint8)
477477
{{else}}
478-
mask = np.isnan(_values).astype(np.uint8)
478+
mask = np.isnan(masked_vals).astype(np.uint8)
479479
{{endif}}
480480

481481
if ascending ^ (kwargs['na_option'] == 'top'):
482482
{{if name == 'int64'}}
483-
nan_value = np.iinfo(np.int64).max
483+
nan_fill_val = np.iinfo(np.int64).max
484484
{{else}}
485-
nan_value = np.inf
485+
nan_fill_val = np.inf
486486
{{endif}}
487-
order = (_values, mask, labels)
487+
order = (masked_vals, mask, labels)
488488
else:
489489
{{if name == 'int64'}}
490-
nan_value = np.iinfo(np.int64).min
490+
nan_fill_val = np.iinfo(np.int64).min
491491
{{else}}
492-
nan_value = -np.inf
492+
nan_fill_val = -np.inf
493493
{{endif}}
494-
order = (_values, ~mask, labels)
495-
np.putmask(_values, mask, nan_value)
494+
order = (masked_vals, ~mask, labels)
495+
np.putmask(masked_vals, mask, nan_fill_val)
496496

497497
_as = np.lexsort(order)
498498

@@ -504,7 +504,7 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
504504
dups += 1
505505
sum_ranks += i - grp_start + 1
506506

507-
if keep_na and _values[_as[i]] == nan_value:
507+
if keep_na and masked_vals[_as[i]] == nan_fill_val:
508508
grp_na_count += 1
509509
out[_as[i], 0] = nan
510510
else:
@@ -525,24 +525,24 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
525525
out[_as[j], 0] = 2 * i - j - dups + 2 - grp_start
526526
elif tiebreak == TIEBREAK_DENSE:
527527
for j in range(i - dups + 1, i + 1):
528-
out[_as[j], 0] = vals_seen
528+
out[_as[j], 0] = grp_vals_seen
529529

530530
{{if name=='int64'}}
531531
if (i == N - 1 or (
532-
(_values[_as[i]] != _values[_as[i+1]]) and not
533-
(_values[_as[i]] == nan_value and
534-
_values[_as[i+1]] == nan_value
532+
(masked_vals[_as[i]] != masked_vals[_as[i+1]]) and not
533+
(masked_vals[_as[i]] == nan_fill_val and
534+
masked_vals[_as[i+1]] == nan_fill_val
535535
))):
536536
{{else}}
537537
if (i == N - 1 or (
538-
(_values[_as[i]] != _values[_as[i+1]]) and not
539-
(isnan(_values[_as[i]]) and
540-
isnan(_values[_as[i+1]])
538+
(masked_vals[_as[i]] != masked_vals[_as[i+1]]) and not
539+
(isnan(masked_vals[_as[i]]) and
540+
isnan(masked_vals[_as[i+1]])
541541
))):
542542
{{endif}}
543543
dups = sum_ranks = 0
544544
val_start = i
545-
vals_seen += 1
545+
grp_vals_seen += 1
546546

547547
# Move to the next group, cleaning up any values
548548
if i == N - 1 or labels[_as[i]] != labels[_as[i+1]]:
@@ -552,7 +552,7 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
552552
- grp_na_count)
553553
grp_na_count = 0
554554
grp_start = i + 1
555-
vals_seen = 1
555+
grp_vals_seen = 1
556556

557557
{{endfor}}
558558

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