-
Notifications
You must be signed in to change notification settings - Fork 0
/
incorrect.json
297 lines (297 loc) · 30.5 KB
/
incorrect.json
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
[
"import numpy as np\narr = np.array(iterable)\nt = arr[arr > 0]",
"import numpy as np\nt = np.array([i for i in iterable if cond(i)])",
"t = [i for i in iterable if cond(i)]\nt = sorted(t)",
"t = []\nfor i in iterable:\n if cond(i):\n t.append(i)\nt.sort()",
"import numpy as np\nt = np.array([i for i in iterable if cond(i)])\nt.sort()",
"import numpy as np\nt = np.array([i for i in iterable if cond(i)])\nt = np.sort(t)",
"import numpy as np\narr = np.array(iterable)\nt = np.where(arr > 0, arr, 0)",
"import numpy as np\narr = np.array(iterable)\nt = np.extract(arr > 0, arr)",
"import numpy as np\narr = np.array(iterable)\nt = np.select([arr > 0], [arr], default=0)",
"import numpy as np\narr = np.array(iterable)\nt = np.fromiter((x for x in arr if x > 0), dtype=arr.dtype)",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x > 0])",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x if x > 0 else 0 for x in arr])",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x > 0], dtype=arr.dtype)",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x > 0], dtype=np.int64)",
"import numpy as np\narr = np.array(iterable)\nt = arr[cond(arr)]",
"t = tuple(filter(cond, iterable))",
"import numpy as np\narr = np.array(iterable)\nt = arr[np.where(cond(arr))]",
"import numpy as np\narr = np.array(iterable)\nt = np.extract(cond(arr), arr)",
"import itertools\ndef filter_func(x):\n return not cond(x)\nt = []\nfor item in iterable:\n if filter_func(item):\n t.append(item)",
"import itertools\ndef filter_func(x):\n return not cond(x)\nt = [item for item in iterable if filter_func(item)]",
"import numpy as np\nt = np.array(list(filter(cond, iterable)))",
"from itertools import filterfalse\ndef cond(x):\n return x % 2 == 0\nt = list(filterfalse(cond, iterable))",
"import numpy as np\ndef cond(x):\n return x % 2 == 0\nt = np.vectorize(cond)(iterable)\nt = list(iterable[t])",
"import numpy as np\nt = np.array([i for i in iterable if cond(i)], dtype=np.int64)",
"import numpy as np\nt = np.array([i for i in iterable if cond(i)], dtype=np.float64)",
"import numpy as np\nt = np.array([i for i in iterable if cond(i)], dtype=np.str_)",
"import numpy as np\nt = np.array([i for i in iterable if cond(i)], dtype=np.bool_)",
"import numpy as np\nt = np.array([i for i in iterable if cond(i)], dtype=np.uint8)",
"import numpy as np\narr = np.array(iterable)\nt = arr[arr > 0].tolist()",
"import numpy as np\narr = np.array(iterable)\nt = np.sort(arr[arr > 0])",
"t = sorted([i for i in iterable if cond(i)])",
"import numpy as np\narr = np.array(iterable)\nt = np.sort(arr[arr < 10])",
"import itertools\narr = list(itertools.filterfalse(lambda x: not cond(x), iterable))\nt = sorted(arr)",
"import numpy as np\narr = np.array(iterable)\nmask = np.array([cond(i) for i in iterable])\nt = np.sort(arr[mask])",
"import numpy as np\narr = np.array(iterable)\nmask = np.array([cond(i) for i in iterable])\nt = arr[mask]\nt.sort()",
"import numpy as np\narr = np.array(iterable)\nt = np.sort(arr[cond(arr)])",
"import itertools\narr = list(iterable)\nt = sorted(list(itertools.compress(arr, [cond(i) for i in arr])))",
"import numpy as np\ncond = lambda x: x > 0\nt = np.array([x for x in iterable if cond(x)])\nt.sort()",
"import numpy as np\ncond = lambda x: x > 0\nfiltered_list = [x for x in iterable if cond(x)]\nt = sorted(filtered_list)",
"import numpy as np\ncond = lambda x: x > 0\nt = iterable[iterable.apply(cond)].sort_values().tolist()",
"import numpy as np\ncond = lambda x: x > 0\nt = [v for (k, v) in iterable.items() if cond(v)]\nt.sort()",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if cond(i)]))",
"t = [i for i in iterable if cond(i)]\nt.sort()",
"import numpy as np\nt = np.sort(np.array(list(filter(cond, iterable))))",
"import numpy as np\nt = np.sort(np.array(list(map(lambda x: x, filter(cond, iterable)))))",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if cond(i)], dtype=np.int))",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if cond(i)], dtype=np.float))",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if cond(i)], dtype=np.str))",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if cond(i)], dtype=np.bool))",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if cond(i)], dtype=np.uint8))",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if cond(i)], dtype=np.int32))",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if cond(i)], dtype=np.float64))",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if cond(i)], dtype=np.complex128))",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if cond(i)], dtype=np.object))",
"import numpy as np\nt = np.sort(np.array([i for i in filter(cond, iterable)]))",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if i % 2 == 0]))",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if i > 0]))",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if i < 10]))",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if i % 2 == 0 and i < 10]))",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if i % 2 == 0 or i < 10]))",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if i % 2 == 0 and i < 10 and (i > 0)]))",
"import numpy as np\nt = np.sort(np.array([i for i in iterable if i % 2 == 0 or (i < 10 and i > 0)]))",
"import numpy as np\narr = np.array(iterable)\nt = arr[arr.astype(bool)]",
"t = [i for i in iterable if cond(i) == True]",
"t = [i for i in iterable if cond(i) is True]",
"t = [i for i in iterable if cond(i) == 1]",
"import numpy as np\narr = np.array(iterable)\nmask = np.fromiter(map(cond, iterable), dtype=bool)\nt = arr[mask]",
"import numpy as np\nt = np.array([cond(i) for i in iterable if cond(i)])",
"import itertools\ndef filter_condition(x):\n return cond(x)\nfilter_condition = lambda x: cond(x)\nt = list(itertools.filterfalse(filter_condition, iterable))",
"from itertools import filterfalse\ndef filter_condition(x):\n return cond(x)\nfilter_condition = lambda x: cond(x)\nt = list(filterfalse(filter_condition, iterable))",
"from itertools import filterfalse\ndef filter_condition(x):\n return cond(x)\nt = list(filterfalse(filter_condition, iterable))",
"import itertools\ndef filter_condition(x):\n return cond(x)\nt = list(itertools.filterfalse(filter_condition, iterable))",
"from itertools import filterfalse\nfilter_condition = lambda x: cond(x)\nt = list(filterfalse(filter_condition, iterable))",
"import itertools\nfilter_condition = lambda x: cond(x)\nt = list(itertools.filterfalse(filter_condition, iterable))",
"import numpy as np\ncount = np.sum(arr[arr > 0])",
"t = [x for x in arr if x > 0]",
"import numpy as np\narr = np.array(iterable)\nt = arr[arr > 0].copy()",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x > 0], dtype=np.float64)",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x > 0], dtype=np.str_)",
"import numpy as np\narr = np.array(iterable)\npositive_values = np.where(arr > 0, arr, 0)\nt = positive_values.copy()",
"import numpy as np\narr = np.array(iterable)\npositive_values = np.extract(arr > 0, arr)\nt = positive_values.copy()",
"import numpy as np\narr = np.array(iterable)\npositive_values = arr[arr > 0].copy()\nt = positive_values.copy()",
"import numpy as np\narr = np.array(iterable)\npositive_values = np.array([x for x in arr if x > 0])\nt = positive_values.copy()",
"import numpy as np\narr = np.array(iterable)\npositive_values = np.array([x if x > 0 else 0 for x in arr])\nt = positive_values.copy()",
"import numpy as np\narr = np.array(iterable)\npositive_values = np.array([x for x in arr if x > 0], dtype=arr.dtype)\nt = positive_values.copy()",
"import numpy as np\narr = np.array(iterable)\npositive_values = np.array([x for x in arr if x > 0], dtype=np.int64)\nt = positive_values.copy()",
"import numpy as np\narr = np.array(iterable)\npositive_values = np.array([x for x in arr if x > 0], dtype=np.float64)\nt = positive_values.copy()",
"import numpy as np\narr = np.array(iterable)\npositive_values = np.array([x for x in arr if x > 0], dtype=np.str_)\nt = positive_values.copy()",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x > 0])\nt = np.where(t > 0, t, 0)",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x > 0])\nt = np.extract(t > 0, t)",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x > 0])\nt = np.array([x if x > 0 else 0 for x in t])",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x > 0])\nt = np.array([x for x in t if x > 0], dtype=t.dtype)",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x > 0])\nt = np.array([x for x in t if x > 0], dtype=np.int64)",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x > 0])\nt = np.array([x for x in t if x > 0], dtype=np.float64)",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x > 0])\nt = np.array([x for x in t if x > 0], dtype=np.str_)",
"import itertools\nt = [x for x in iterable if not cond(x)]",
"import itertools\nt = []\nfor x in iterable:\n if not cond(x):\n t.append(x)",
"import itertools\nt = list(filter(lambda x: not cond(x), iterable))",
"import itertools\ndef filter_func(x):\n return not cond(x)\nt = list(filter(filter_func, iterable))",
"import itertools\nt = list(itertools.filterfalse(lambda x: cond(x), iterable))",
"import numpy as np\nt = np.array(iterable)[np.array(iterable).astype(bool)]",
"import numpy as np\nt = np.array(iterable)[np.array(iterable).nonzero()]",
"import numpy as np\narr = np.array(iterable)\nt = arr[arr.nonzero()]",
"import numpy as np\nt = np.array(iterable)[np.where(np.array(iterable))]",
"import numpy as np\narr = np.array(iterable)\nt = arr[np.where(arr)]",
"import numpy as np\narr = np.array(iterable)\nt = arr[list(filter(bool, arr))]",
"import numpy as np\narr = np.array(iterable)\nt = np.extract(arr.astype(bool), arr)",
"import numpy as np\narr = np.array(iterable)\nbool_arr = arr.astype(bool)\nt = arr[bool_arr]",
"import numpy as np\narr = np.array(iterable)\nbool_arr = np.where(arr, True, False)\nt = arr[bool_arr]",
"import numpy as np\nt = np.array([i for i in iterable if cond(i) == True])",
"t = []\nfor i in iterable:\n if cond(i) == True:\n t.append(i)",
"t = list(filter(lambda x: cond(x) == True, iterable))",
"t = []\nfor i in iterable:\n if cond(i) is True:\n t.append(i)",
"import itertools\nt = list(itertools.filterfalse(lambda x: cond(x) is False, iterable))",
"import functools\nt = functools.reduce(lambda a, b: a + [b] if cond(b) is True else a, iterable, [])",
"import numpy as np\nt = np.array([i for i in iterable if cond(i) == 1])",
"t = list(filter(lambda x: cond(x) == 1, iterable))",
"t = []\nfor i in iterable:\n if cond(i) == 1:\n t.append(i)",
"import itertools\niterable = list(iterable)\nt = list(itertools.filterfalse(lambda x: cond(x) != 1, iterable))",
"import functools\nis_cond_true = functools.partial(cond, val=1)\nt = list(filter(is_cond_true, iterable))",
"import numpy as np\nt = np.array(list(filter(lambda x: cond(x) == 1, iterable)))",
"import numpy as np\narr = np.array(iterable)\nt = arr[arr >= 0]",
"import numpy as np\narr = np.array(iterable)\nt = np.array([max(x, 0) for x in arr])",
"from itertools import filterfalse\nfiltered_iterable = filterfalse(cond, iterable)\nt = list(filtered_iterable)",
"import itertools\ndef filter_func(x):\n return not cond(x)\nt = []\nfor item in iterable:\n if not filter_func(item):\n continue\n t.append(item)",
"import itertools\ndef filter_func(x):\n return not cond(x)\nt = []\nfor item in iterable:\n if filter_func(item):\n t.append(item)\n else:\n continue",
"import itertools\ndef filter_func(x):\n return not cond(x)\nt = []\nfor item in iterable:\n if not filter_func(item):\n continue\n t.append(item)\nelse:\n pass",
"import itertools\ndef filter_func(x):\n return not cond(x)\nt = []\nfor item in iterable:\n if filter_func(item):\n t.append(item)\n else:\n pass",
"import itertools\ndef filter_func(x):\n return not cond(x)\nt = []\nfor item in iterable:\n if not filter_func(item):\n continue\n t.append(item)\nelse:\n break",
"import itertools\ndef filter_func(x):\n return not cond(x)\nt = []\nfor item in iterable:\n if filter_func(item):\n t.append(item)\n else:\n break",
"import itertools\ndef filter_func(x):\n return not cond(x)\nt = []\nfor item in iterable:\n if not filter_func(item):\n continue\n t.append(item)\nelse:\n break\nt = list(t)",
"import itertools\ndef filter_func(x):\n return not cond(x)\nt = []\nfor item in iterable:\n if filter_func(item):\n t.append(item)\n else:\n break\nt = list(t)",
"import itertools\ndef filter_func(x):\n return not cond(x)\nt = []\nfor item in iterable:\n if not filter_func(item):\n continue\n t.append(item)\nelse:\n break\nt = list(t)\nt = list(t)",
"import itertools\ndef filter_func(x):\n return not cond(x)\nt = []\nfor item in iterable:\n if filter_func(item):\n t.append(item)\n else:\n break\nt = list(t)\nt = list(t)",
"import itertools\ndef filter_func(x):\n return not cond(x)\nt = []\nfor item in iterable:\n if not filter_func(item):\n continue\n t.append(item)\nelse:\n break\nt = list(t)\nt = list(t)\nt = list(t)",
"import itertools\ndef filter_func(x):\n return not cond(x)\nt = []\nfor item in iterable:\n if filter_func(item):\n t.append(item)\n else:\n break\nt = list(t)\nt = list(t)\nt = list(t)",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x != 0])",
"import numpy as np\narr = np.array(iterable)\nt = np.array(list(filter(lambda x: x != 0, arr)))",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x])",
"import numpy as np\narr = np.array(iterable)\nt = np.array(list(filter(None, arr)))",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x != 0 or x is True])",
"import numpy as np\narr = np.array(iterable)\nt = np.array(list(filter(lambda x: x != 0 or x is True, arr)))",
"import numpy as np\narr = np.array(iterable)\nt = list(arr[arr.map(cond)])",
"import numpy as np\narr = np.array(iterable)\nt = list(arr[np.vectorize(cond)(arr)])",
"import numpy as np\narr = np.array(iterable)\nt = list(filter(cond, arr))",
"import numpy as np\narr = np.array(iterable)\nt = list(arr[np.fromiter(map(cond, arr), dtype=bool)])",
"import numpy as np\narr = np.array(iterable)\nt = list(arr[np.vectorize(lambda x: cond(x))(arr)])",
"import numpy as np\narr = np.array(iterable)\nt = list(arr[np.frompyfunc(cond, 1, 1)(arr)])",
"import numpy as np\narr = np.array(iterable)\nt = arr[arr.apply(cond)]",
"import numpy as np\narr = np.array(iterable)\nt = list(filter(lambda x: x > 0, arr))",
"import numpy as np\narr = np.array(iterable)\nt = [x for x in arr if x > 0]",
"import numpy as np\narr = np.array(iterable)\nt = np.where(arr > 0, arr, 0).tolist()",
"import numpy as np\narr = np.array(iterable)\npositive_values = np.extract(arr > 0, arr)\nt = positive_values.tolist()",
"import numpy as np\narr = np.array(iterable)\npositive_values = np.where(arr > 0, arr, 0)\nt = positive_values.tolist()",
"import numpy as np\niterable = np.array(iterable)\nt = iterable[np.vectorize(cond)(iterable)]",
"import numpy as np\narr = np.array(iterable)\nt = arr[arr.apply(lambda x: cond(x) is True)]",
"import numpy as np\narr = np.array(iterable)\nt = np.zeros(arr.shape)\nt[arr > 0] = arr[arr > 0]",
"import numpy as np\narr = np.array(iterable)\nt = np.zeros(arr.shape)\nfor i in range(arr.shape[0]):\n for j in range(arr.shape[1]):\n if arr[i][j] > 0:\n t[i][j] = arr[i][j]",
"import numpy as np\narr = np.array(iterable)\nt = np.zeros(arr.shape)\nfor (i, row) in enumerate(arr):\n for (j, val) in enumerate(row):\n if val > 0:\n t[i][j] = val",
"import numpy as np\narr = np.array(iterable)\nt = np.zeros(arr.shape)\nfor i in range(len(arr)):\n for j in range(len(arr[i])):\n if arr[i][j] > 0:\n t[i][j] = arr[i][j]",
"import numpy as np\narr = np.array(iterable)\nt = np.maximum(arr, 0)",
"import numpy as np\narr = np.array(iterable)\nt = np.clip(arr, 0, np.inf)",
"import numpy as np\narr = np.array(iterable)\nt = np.extract(np.greater(arr, 0), arr)",
"import numpy as np\narr = np.array(iterable)\nt = np.extract(arr, arr > 0)",
"import numpy as np\narr = np.array(iterable)\nt = np.extract(arr, np.greater(arr, 0))",
"import numpy as np\narr = np.array(iterable)\nt = np.extract(np.greater(arr, 0), np.greater(arr, 0))",
"import numpy as np\narr = np.array(iterable)\nt = np.extract(arr > 0, np.greater(arr, 0))",
"import numpy as np\narr = np.array(iterable)\nt = np.extract(np.greater(arr, 0), arr > 0)",
"import numpy as np\narr = np.array(iterable)\nt = np.extract(arr > 0, arr > 0)",
"import numpy as np\narr = np.array(iterable)\nt = np.extract(arr > 0, arr[arr > 0])",
"import numpy as np\narr = np.array(iterable)\nt = np.extract(np.greater(arr, 0), arr[arr > 0])",
"import numpy as np\narr = np.array(iterable)\nt = arr[arr > threshold]",
"import numpy as np\narr = np.array(iterable)\nt = arr[arr > threshold].tolist()",
"import pandas as pd\niterable_series = pd.Series(iterable)\nt = iterable_series[iterable_series.apply(cond)]",
"import numpy as np\nmask = np.array([cond(i) for i in iterable])\nt = np.array(iterable)[mask]",
"import numpy as np\nt = np.fromiter(filter(lambda x: cond(x), iterable), dtype=float)",
"import numpy as np\nt = np.fromiter(filter(cond, iterable), dtype=float)",
"filtered_iterable = filter(lambda x: not cond(x), iterable)\nt = list(filtered_iterable)",
"import numpy as np\nnew_t = np.array(iterable)\ncond_t = cond(new_t)\nt = new_t[cond_t]",
"import numpy as np\ndefine_cond = cond\nnew_t = np.array(iterable)\nt = new_t[define_cond(new_t)]",
"import numpy as np\nt = np.array(iterable)\nfiltered_indices = np.where(cond(t))\nt = t[filtered_indices]",
"from itertools import filterfalse\nfrom functools import partial\nt = list(filterfalse(partial(cond), iterable))",
"import numpy as np\nt = np.array([x for x in iterable if x > threshold])",
"import numpy as np\nt = np.array(list(filter(lambda x: x > threshold, iterable)))",
"from collections import deque\nt = deque()\nfor i in iterable:\n if cond(i):\n t.append(i)",
"import numpy as np\nnew_iterable = np.array(iterable)\ncond_arr = np.array([cond(x) for x in iterable])\nt = new_iterable[cond_arr]",
"t = [x for x in arr if x > threshold]",
"import numpy as np\narr = np.array(iterable)\nt = [x.tolist() for x in arr if x > threshold]",
"import numpy as np\ntemp = np.array(iterable)\narr = temp[temp > threshold].tolist()\nt = arr.tolist()",
"from numpy import array\narr = array(iterable)\nt = arr[arr > threshold].tolist()",
"import itertools\nfiltered_list = [x for x in iterable if cond(x)]\nt = iter(filtered_list)",
"import itertools\niterables = itertools.tee(iterable, 2)\nfiltered = filter(lambda x: x if cond(x) else None, iterables[0])\nt = list(filtered)",
"import pandas as pd\nimport numpy as np\ndef cond(x):\n return x > 0\n\ndef apply_cond(x):\n return iterable_series.apply(cond)\niterable_series = pd.Series(iterable)\nt = iterable_series[apply_cond(iterable_series)]",
"import pandas as pd\ndef cond(x):\n return x > 0\n\ndef apply_cond(series):\n return series.apply(cond)\niterable_series = pd.Series(iterable)\nt = iterable_series[apply_cond(iterable_series)]",
"import pandas as pd\ndef cond(x):\n return x > 0\n\ndef apply_cond(series):\n return series.apply(cond)\n\ndef get_filtered_series(series):\n return series[apply_cond(series)]\niterable_series = pd.Series(iterable)\nt = get_filtered_series(iterable_series)",
"import pandas as pd\niterable_series = pd.Series(iterable)\nt = iterable_series.loc[iterable_series.apply(cond)]",
"import pandas as pd\niterable_series = pd.Series(iterable)\nt = iterable_series[cond(iterable_series)]",
"import pandas as pd\niterable_series = pd.Series(iterable)\nt = iterable_series[iterable_series.apply(lambda x: cond(x))]",
"import numpy as np\nmask = np.array([True if cond(i) else False for i in iterable])\nt = np.array(iterable)[mask]",
"import itertools\nfrom functools import partial\ndef cond2(x):\n return cond(x)\nt = list(itertools.filterfalse(partial(cond2), iterable))",
"import itertools\ncond_iter = itertools.filterfalse(lambda x: cond(x), iterable)\nt = list(cond_iter)",
"import itertools\ncond_iter = [x for x in iterable if not cond(x)]\nt = list(cond_iter)",
"import itertools\ncond_iter = list(itertools.filterfalse(lambda x: cond(x), iterable))\nt = cond_iter",
"import itertools\ncond_iter = list(filter(lambda x: not cond(x), iterable))\nt = cond_iter",
"import itertools\ncond_iter = [x for x in iterable if not cond(x)]\nt = cond_iter",
"import numpy as np\nfiltered_values = list(filter(cond, iterable))\nt = np.array(filtered_values)",
"import numpy as np\narr = np.array(iterable)\nt = arr[arr < 0]",
"import numpy as np\narr = np.array(iterable)\nmask = np.vectorize(cond)(arr)\nt = arr[mask]",
"import numpy as np\narr = np.array(iterable)\nt = np.extract(np.vectorize(cond)(arr), arr)",
"import numpy as np\narr = np.array(iterable)\nt = np.where(arr < 0, arr, 0)",
"import numpy as np\narr = np.array(iterable)\nt = np.extract(arr < 0, arr)",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x < 0])",
"import numpy as np\narr = np.array(iterable)\nt = np.array(list(filter(lambda x: x < 0, arr)))",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x if x < 0 else 0 for x in arr])",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x for x in arr if x < 0], dtype=arr.dtype)",
"import numpy as np\narr = np.array(iterable)\nt = np.array([x if x < 0 else 0 for x in arr], dtype=arr.dtype)",
"import numpy as np\ncount = np.count_nonzero(arr[arr < 0])\nt = count",
"import numpy as np\nt = np.count_nonzero(arr[arr < 0])",
"import numpy as np\nt = len(arr[arr < 0])",
"count = 0\nfor i in arr:\n if i < 0:\n count += 1\nt = count",
"count = 0\nfor i in arr:\n count += 1 if i < 0 else 0\nt = count",
"count = sum((1 for i in arr if i < 0))\nt = count",
"import numpy as np\nfiltered_arr = np.fromiter((x for x in iterable if cond(x)), dtype=int)\nt = filtered_arr.tolist()",
"import itertools\niterable = iter(iterable)\nt = [x for x in iterable if not cond(x)]",
"import numpy as np\nfiltered = filter(cond, iterable)\nt = np.array(list(filtered))",
"import numpy as np\nt = np.array([])\nfor x in iterable:\n if cond(x):\n t = np.append(t, x)",
"import numpy as np\nt = np.array([])\nfor x in iterable:\n if cond(x):\n np.append(t, x)",
"import numpy as np\nt = np.array([])\nfor x in iterable:\n if cond(x):\n np.concatenate((t, np.array([x])), axis=0)",
"import numpy as np\nt = np.fromiter((x for x in iterable if cond(x)), dtype=float)",
"import numpy as np\nt = np.empty((0,))\nfor x in iterable:\n if cond(x):\n t = np.append(t, x)",
"import numpy as np\ndef filter_func(x):\n if cond(x):\n return True\n else:\n return False\nfiltered_list = filter(filter_func, iterable)\nt = np.array(list(filtered_list))",
"import numpy as np\nt = np.array(list(filter(cond, iterable)), dtype=np.int64)",
"import numpy as np\narr = np.array(iterable)\nfiltered_arr = arr[cond(arr)]\nt = filtered_arr",
"import numpy as np\narr = np.array(iterable)\ntemp_arr = np.copy(arr)\ntemp_arr = temp_arr[cond(arr)]\nt = temp_arr",
"import numpy as np\narr = np.array(iterable)\nfiltered_arr = [x for x in arr if cond(x)]\nt = np.array(filtered_arr)",
"import numpy as np\narr = np.array(iterable)\nfiltered_arr = np.where(cond(arr), arr, 0)\nt = np.array(filtered_arr)",
"import numpy as np\narr = np.array(iterable)\nmask = np.vectorize(cond)(arr)\nt = np.extract(mask, arr)",
"import numpy as np\narr = np.array(iterable)\nmask = np.vectorize(cond)(arr)\nt = np.compress(mask, arr)",
"import numpy as np\narr = np.array(iterable)\nmask = np.vectorize(cond)(arr)\nt = np.where(mask, arr, np.nan)",
"import numpy as np\narr = np.array(iterable)\nmask = np.vectorize(cond)(arr)\nindices = np.where(mask)\nt = arr[indices]",
"import numpy as np\narr = np.array(iterable)\nmask = np.vectorize(cond)(arr)\nindices = np.nonzero(mask)\nt = arr[indices]",
"import numpy as np\narr = np.array(iterable)\nmask = np.vectorize(cond)(arr)\nindices = np.argwhere(mask)\nt = arr[indices]",
"import numpy as np\narr = np.array(iterable)\nmask = np.vectorize(cond)(arr)\nt = np.select([mask], [arr])",
"import numpy as np\narr = np.array(iterable)\nmask = np.vectorize(cond)(arr)\nt = np.piecewise(arr, [mask], [lambda x: x])",
"import numpy as np\narr = np.array(iterable)\nmask = np.vectorize(cond)(arr)\nindices = np.indices(arr.shape)\nt = arr[indices[mask]]",
"import numpy as np\narr = np.array(iterable)\ntemp = np.vectorize(cond)(arr)\nmask = temp.astype(bool)\nt = arr[mask]",
"import numpy as np\narr = np.array(iterable)\nmask = [cond(x) for x in arr]\nt = arr[mask]",
"import numpy as np\narr = np.array(iterable)\nmask = np.where([cond(x) for x in arr])\nt = arr[mask]",
"import numpy as np\narr = np.array(iterable)\nmask = np.where(np.vectorize(cond)(arr))\nt = arr[mask]",
"import numpy as np\narr = np.array(iterable)\nt = arr[arr % 2 == 0]",
"import numpy as np\narr = np.array(iterable)\npositive_values = arr[arr > 0]\nt = positive_values",
"import numpy as np\narr = np.array(iterable)\npositives = [x for x in arr if x > 0]\nt = np.array(positives)",
"import numpy as np\narr = np.array(iterable)\npositives = []\nfor x in arr:\n if x > 0:\n positives.append(x)\nt = np.array(positives)",
"import numpy as np\narr = np.array(iterable)\ntemp_arr = np.where(arr > 0, arr, 0)\nt = temp_arr",
"import numpy as np\narr = np.array(iterable)\npositive_values = arr[arr > 0]\ntemp_arr = positive_values\nt = temp_arr",
"import numpy as np\narr = np.array(iterable)\npositives = [x for x in arr if x > 0]\ntemp_arr = np.array(positives)\nt = temp_arr",
"import numpy as np\narr = np.array(iterable)\npositives = []\nfor x in arr:\n if x > 0:\n positives.append(x)\ntemp_arr = np.array(positives)\nt = temp_arr",
"import numpy as np\narr = np.array(iterable)\npositive_arr = np.where(arr > 0, arr, 0)\nt = positive_arr[positive_arr != 0]",
"import numpy as np\narr = np.array(iterable)\nt = arr[np.where(arr > 0)]",
"import numpy as np\narr = np.array(iterable)\npositive_arr = np.where(arr > 0, arr, 0)\nt = positive_arr[positive_arr > 0]",
"import numpy as np\narr = np.array(iterable)\npositive_arr = np.where(arr > 0, arr, 0)\nt = np.nonzero(positive_arr)",
"import itertools\nfiltered = list(filter(cond, iterable))\nt = filtered[0]",
"import itertools\nt = next(filter(cond, iterable))",
"t = [x for x in iterable if cond(x)][0]",
"t = next((x for x in iterable if cond(x)))",
"import numpy as np\nt = np.array([i for i in iterable if i in [i for i in iterable if cond(i)]])",
"t = [i if cond(i) else None for i in iterable]\nt = list(filter(None, t))",
"import itertools\ndef cond_wrap(x):\n if cond(x):\n return x\n return None\nt = list(itertools.filterfalse(cond_wrap, iterable))",
"t = list(map(lambda i: i if cond(i) else None, iterable))",
"import itertools\nlist(itertools.filterfalse(lambda x: not cond(x), iterable))",
"import itertools\niterable = itertools.chain(iterable)\nt = [i for i in iterable if i in [i for i in iterable if cond(i)]]",
"import functools\nclass T(object):\n\n def cond(self, x):\n return cond(x)\n\n def reduce_fn(self, acc, x):\n return acc + [x] if self.cond(x) else acc\nauto_t = functools.partial(functools.reduce, T().reduce_fn)\nt = auto_t(iterable)",
"import numpy as np\nc = np.array([i for i in iterable if cond(i)])\ninner_list = []\nfor i in iterable:\n if i in c:\n inner_list.append(i)\nt = np.array(inner_list)",
"from itertools import filterfalse\nfrom functools import partial\ndef cond(i):\n return not i\n\ndef func(filter_obj, i):\n return filter_obj(i)\nt = list(filterfalse(partial(func, filter_obj=cond), iterable))",
"import numpy as np\ncount = np.sum([cond(i) for i in iterable])\nt = np.array([i for i in iterable if cond(i)])",
"import numpy as np\ncount = np.sum([1 for i in iterable if cond(i)])\nt = np.array([i for i in iterable if cond(i)])",
"import numpy as np\nindices = np.where([cond(i) for i in iterable])\ncount = len(indices[0])\nt = np.array([iterable[i] for i in indices[0]])",
"import itertools\nfiltered_iterable = filter(cond, iterable)\ncount = len(list(filtered_iterable))\nt = itertools.islice(filtered_iterable, count)",
"import numpy as np\nmask = np.array([cond(i) for i in iterable])\ncount = np.sum(mask)\nt = np.array(iterable)[mask]",
"import numpy as np\nt = [i for i in iterable if cond(i)]\nt = list(filter(None, t))",
"t = []\nfor i in iterable:\n if cond(i):\n t.append(i)\nt = list(filter(None, t))",
"import itertools\nt = list(itertools.filterfalse(lambda x: x is None, [i if cond(i) else None for i in iterable]))",
"t = tuple(filter(lambda x: x is not None, [i if cond(i) else None for i in iterable]))",
"import itertools\ndef cond_wrap(x):\n if cond(x):\n return x\n return None\nt = list(filter(lambda x: cond(x) == False, iterable))",
"from itertools import filterfalse\nt = list(filterfalse(lambda x: cond(x), iterable))",
"t = [i if cond(i) else None for i in iterable]",
"import numpy as np\narr = np.array(iterable)\nt = arr[np.where(np.vectorize(cond)(arr))]",
"t = []\nfor i in iterable:\n if cond(i):\n t.append(i)\nt = list(filter(lambda i: i is not None, t))",
"t = []\nfor i in iterable:\n if cond(i):\n t.append(i)\nt = filter(None, t)",
"import numpy as np\nt = np.where([cond(i) for i in iterable])",
"import numpy as np\nt = np.array([i for i in iterable if cond(i) is True])",
"import itertools\nlist(filter(lambda x: cond(x), iterable))",
"result = []\nfor i in iterable:\n if cond(i):\n result.append(i)\nlist(result)",
"import itertools\nfiltered_list = list(itertools.filterfalse(lambda x: not cond(x), iterable))\nt = filtered_list\n\ndef good(x):\n if cond(x):\n return x\nintermediate_answer = [x for x in iterable if good(x)]\nt = [i for i in intermediate_answer if i != None]",
"import numpy as np\nfiltered_arr = np.array(list(filter(cond, iterable)))\nt = filtered_arr"
]