index int64 | statement_id int64 | statement string | lineno int64 | end_lineno int64 | comprehension_types list | metadata dict |
|---|---|---|---|---|---|---|
0 | 0 | yield (self.tree.get_text (x) for x in tr) | 87 | 87 | [
"GeneratorExp"
] | {
"hexsha": "f70001f658d4dfaa72dd4f0d1b3176492f6658bb",
"max_stars_repo_name": "CNDB/CNDB",
"max_stars_repo_path": "spider/openwrt.py",
"lang": "Python"
} |
0 | 1 | lq, nlq, etx = (float (x) for x in (lq, nlq, etx)) | 102 | 102 | [
"GeneratorExp"
] | {
"hexsha": "f70001f658d4dfaa72dd4f0d1b3176492f6658bb",
"max_stars_repo_name": "CNDB/CNDB",
"max_stars_repo_path": "spider/openwrt.py",
"lang": "Python"
} |
12 | 0 | images = [self.resize(image=image, size=self.size, resample=self.resample) for image in images] | 139 | 139 | [
"ListComp"
] | {
"hexsha": "f700088372c0eeaff049211c5fe92cdccb5fa804",
"max_stars_repo_name": "djroxx2000/transformers",
"max_stars_repo_path": "src/transformers/models/vit/feature_extraction_vit.py",
"lang": "Python"
} |
12 | 1 | images = [self.normalize(image=image, mean=self.image_mean, std=self.image_std) for image in images] | 141 | 141 | [
"ListComp"
] | {
"hexsha": "f700088372c0eeaff049211c5fe92cdccb5fa804",
"max_stars_repo_name": "djroxx2000/transformers",
"max_stars_repo_path": "src/transformers/models/vit/feature_extraction_vit.py",
"lang": "Python"
} |
13 | 0 | return [read_row() for _ in range(read_val())] | 12 | 12 | [
"ListComp"
] | {
"hexsha": "f700096cbce5db1538215892bb1dcc76b6c37987",
"max_stars_repo_name": "EliahKagan/old-practice-snapshot",
"max_stars_repo_path": "hier/project-euler/euler-067-hackerrank/euler067.py",
"lang": "Python"
} |
13 | 1 | return [make_blank_row(i) for i in range(1, n + 1)] | 18 | 18 | [
"ListComp"
] | {
"hexsha": "f700096cbce5db1538215892bb1dcc76b6c37987",
"max_stars_repo_name": "EliahKagan/old-practice-snapshot",
"max_stars_repo_path": "hier/project-euler/euler-067-hackerrank/euler067.py",
"lang": "Python"
} |
29 | 0 | communities = [f.id for f in gamer.communities.all()] | 170 | 170 | [
"ListComp"
] | {
"hexsha": "f700169f42c4405db98ca51444ca7070b1d5d538",
"max_stars_repo_name": "andrlik/looking-for-group",
"max_stars_repo_path": "looking_for_group/games/api_views.py",
"lang": "Python"
} |
29 | 1 | game_player_ids = [
obj.game.id
for obj in models.Player.objects.filter(gamer=gamer).select_related("game")
] | 171 | 174 | [
"ListComp"
] | {
"hexsha": "f700169f42c4405db98ca51444ca7070b1d5d538",
"max_stars_repo_name": "andrlik/looking-for-group",
"max_stars_repo_path": "looking_for_group/games/api_views.py",
"lang": "Python"
} |
34 | 0 | env_file.writelines((' - %s\n' % ch for ch in self._conda_channels)) | 132 | 132 | [
"GeneratorExp"
] | {
"hexsha": "f7001ad17b839c3551d7b4c8edcc8b1d1d322b6f",
"max_stars_repo_name": "prisae/asv",
"max_stars_repo_path": "asv/plugins/conda.py",
"lang": "Python"
} |
34 | 1 | env_file.writelines((' - %s\n' % s for s in conda_args)) | 140 | 140 | [
"GeneratorExp"
] | {
"hexsha": "f7001ad17b839c3551d7b4c8edcc8b1d1d322b6f",
"max_stars_repo_name": "prisae/asv",
"max_stars_repo_path": "asv/plugins/conda.py",
"lang": "Python"
} |
34 | 2 | env_file.writelines((' - %s\n' % s for s in pip_args)) | 145 | 145 | [
"GeneratorExp"
] | {
"hexsha": "f7001ad17b839c3551d7b4c8edcc8b1d1d322b6f",
"max_stars_repo_name": "prisae/asv",
"max_stars_repo_path": "asv/plugins/conda.py",
"lang": "Python"
} |
36 | 0 | overhead = np.median([self._timer.timeit(0) for _ in range(5)]) | 286 | 286 | [
"ListComp"
] | {
"hexsha": "f7001b697392ceebda04fd774fb9d56f47820f4b",
"max_stars_repo_name": "GOOGLE-M/SGC",
"max_stars_repo_path": "venv/lib/python3.7/site-packages/torch/utils/benchmark/utils/timer.py",
"lang": "Python"
} |
41 | 0 | x = tuple([i.detach() for i in x]) | 225 | 225 | [
"ListComp"
] | {
"hexsha": "f7001e1d779abcb0aeb35035bb723969df9248a9",
"max_stars_repo_name": "mohammedshariqnawaz/Pedestron",
"max_stars_repo_path": "mmdet/models/detectors/csp.py",
"lang": "Python"
} |
41 | 1 | bbox_list = [
bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes)[0]
for det_bboxes, det_labels in bbox_list
] | 229 | 232 | [
"ListComp"
] | {
"hexsha": "f7001e1d779abcb0aeb35035bb723969df9248a9",
"max_stars_repo_name": "mohammedshariqnawaz/Pedestron",
"max_stars_repo_path": "mmdet/models/detectors/csp.py",
"lang": "Python"
} |
41 | 2 | gt_bboxes_ignore = [None for _ in range(num_imgs)] | 239 | 239 | [
"ListComp"
] | {
"hexsha": "f7001e1d779abcb0aeb35035bb723969df9248a9",
"max_stars_repo_name": "mohammedshariqnawaz/Pedestron",
"max_stars_repo_path": "mmdet/models/detectors/csp.py",
"lang": "Python"
} |
41 | 3 | samp_list = [res.bboxes for res in sampling_results] | 256 | 256 | [
"ListComp"
] | {
"hexsha": "f7001e1d779abcb0aeb35035bb723969df9248a9",
"max_stars_repo_name": "mohammedshariqnawaz/Pedestron",
"max_stars_repo_path": "mmdet/models/detectors/csp.py",
"lang": "Python"
} |
41 | 4 | pred_scores = torch.cat([torch.tensor(bbox[:, 4]).float().cuda() for bbox in bbox_list], dim=0) | 262 | 262 | [
"ListComp"
] | {
"hexsha": "f7001e1d779abcb0aeb35035bb723969df9248a9",
"max_stars_repo_name": "mohammedshariqnawaz/Pedestron",
"max_stars_repo_path": "mmdet/models/detectors/csp.py",
"lang": "Python"
} |
41 | 5 | pred_rois = bbox2roi([torch.tensor(bbox).float().cuda() for bbox in bbox_list]) | 263 | 263 | [
"ListComp"
] | {
"hexsha": "f7001e1d779abcb0aeb35035bb723969df9248a9",
"max_stars_repo_name": "mohammedshariqnawaz/Pedestron",
"max_stars_repo_path": "mmdet/models/detectors/csp.py",
"lang": "Python"
} |
41 | 6 | bbox_list = [
bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes)[0]
for det_bboxes, det_labels in bbox_list
] | 311 | 314 | [
"ListComp"
] | {
"hexsha": "f7001e1d779abcb0aeb35035bb723969df9248a9",
"max_stars_repo_name": "mohammedshariqnawaz/Pedestron",
"max_stars_repo_path": "mmdet/models/detectors/csp.py",
"lang": "Python"
} |
41 | 7 | bbox_list = [torch.tensor(bbox).float().cuda() for bbox in bbox_list] | 316 | 316 | [
"ListComp"
] | {
"hexsha": "f7001e1d779abcb0aeb35035bb723969df9248a9",
"max_stars_repo_name": "mohammedshariqnawaz/Pedestron",
"max_stars_repo_path": "mmdet/models/detectors/csp.py",
"lang": "Python"
} |
41 | 8 | bbox_list = [bbox/im_scale for bbox in bbox_list] | 318 | 318 | [
"ListComp"
] | {
"hexsha": "f7001e1d779abcb0aeb35035bb723969df9248a9",
"max_stars_repo_name": "mohammedshariqnawaz/Pedestron",
"max_stars_repo_path": "mmdet/models/detectors/csp.py",
"lang": "Python"
} |
41 | 9 | bbox_results = [
bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes)
for det_bboxes, det_labels in bbox_list
] | 336 | 339 | [
"ListComp"
] | {
"hexsha": "f7001e1d779abcb0aeb35035bb723969df9248a9",
"max_stars_repo_name": "mohammedshariqnawaz/Pedestron",
"max_stars_repo_path": "mmdet/models/detectors/csp.py",
"lang": "Python"
} |
41 | 10 | bbox_results = [
bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes)
for det_bboxes, det_labels in bbox_list
] | 346 | 349 | [
"ListComp"
] | {
"hexsha": "f7001e1d779abcb0aeb35035bb723969df9248a9",
"max_stars_repo_name": "mohammedshariqnawaz/Pedestron",
"max_stars_repo_path": "mmdet/models/detectors/csp.py",
"lang": "Python"
} |
45 | 0 | myString = ', '.join('"{0}"'.format(s) for s in df.symbol.unique()) | 15 | 15 | [
"GeneratorExp"
] | {
"hexsha": "f7001f45079e3103298a8ceb0386c7b776820464",
"max_stars_repo_name": "brettelliot/event-study",
"max_stars_repo_path": "examples/earnings_surprises/earnings-converter.py",
"lang": "Python"
} |
48 | 0 | self._window_blocks = {
field: ExpiringCache(LRU(sid_cache_size))
for field in self.FIELDS
} | 332 | 335 | [
"DictComp"
] | {
"hexsha": "f7002044d0369ad65533164d260b2c8f91cb7841",
"max_stars_repo_name": "SJCosgrove/quantoipian",
"max_stars_repo_path": "zipline/data/history_loader.py",
"lang": "Python"
} |
48 | 1 | return [asset_windows[asset] for asset in assets] | 470 | 470 | [
"ListComp"
] | {
"hexsha": "f7002044d0369ad65533164d260b2c8f91cb7841",
"max_stars_repo_name": "SJCosgrove/quantoipian",
"max_stars_repo_path": "zipline/data/history_loader.py",
"lang": "Python"
} |
48 | 2 | return concatenate(
[window.get(end_ix) for window in block],
axis=1,
) | 553 | 556 | [
"ListComp"
] | {
"hexsha": "f7002044d0369ad65533164d260b2c8f91cb7841",
"max_stars_repo_name": "SJCosgrove/quantoipian",
"max_stars_repo_path": "zipline/data/history_loader.py",
"lang": "Python"
} |
60 | 0 | cls_map = {name: i for i, name in enumerate(config.classes)} | 163 | 163 | [
"DictComp"
] | {
"hexsha": "f70024e5f14d8c48a9b1684bda03d5b19a8c5e49",
"max_stars_repo_name": "taroxd/mindspore",
"max_stars_repo_path": "model_zoo/official/cv/ssd/src/dataset.py",
"lang": "Python"
} |
64 | 0 | self.assertTrue(any([p.name == option_name for p in self.vdq.__click_params__]), msg=f"Can not find {option_name} in option parameters") | 35 | 35 | [
"ListComp"
] | {
"hexsha": "f7002809c326a21adb3489f8362fe6d0df39aa6a",
"max_stars_repo_name": "grizmin/ssm-port-forwarding",
"max_stars_repo_path": "ssmpfwd/test/test_helpers.py",
"lang": "Python"
} |
64 | 1 | self.assertTrue(any([p.flag_value == flag_value for p in self.vdq.__click_params__]), msg=f"Can not find {flag_value} in option flag values") | 39 | 39 | [
"ListComp"
] | {
"hexsha": "f7002809c326a21adb3489f8362fe6d0df39aa6a",
"max_stars_repo_name": "grizmin/ssm-port-forwarding",
"max_stars_repo_path": "ssmpfwd/test/test_helpers.py",
"lang": "Python"
} |
64 | 2 | self.assertTrue(any([p.flag_value == flag_value for p in self.vdq.__click_params__]), msg=f"Can not find {flag_value} in option flag values") | 43 | 43 | [
"ListComp"
] | {
"hexsha": "f7002809c326a21adb3489f8362fe6d0df39aa6a",
"max_stars_repo_name": "grizmin/ssm-port-forwarding",
"max_stars_repo_path": "ssmpfwd/test/test_helpers.py",
"lang": "Python"
} |
64 | 3 | self.assertTrue(any([p.flag_value == flag_value for p in self.vdq.__click_params__]), msg=f"Can not find {flag_value} in option flag values") | 47 | 47 | [
"ListComp"
] | {
"hexsha": "f7002809c326a21adb3489f8362fe6d0df39aa6a",
"max_stars_repo_name": "grizmin/ssm-port-forwarding",
"max_stars_repo_path": "ssmpfwd/test/test_helpers.py",
"lang": "Python"
} |
65 | 0 | I0_modulation_err = np.array([val.m.s for val in I0_modulation]) | 39 | 39 | [
"ListComp"
] | {
"hexsha": "f70028f9fa4d86978ac4bf40e069c11a32974d6b",
"max_stars_repo_name": "doronbehar/lab4",
"max_stars_repo_path": "x2.ESR/ESRB.py",
"lang": "Python"
} |
65 | 1 | I0_modulation_raw = np.array([val.m.n for val in I0_modulation]) | 40 | 40 | [
"ListComp"
] | {
"hexsha": "f70028f9fa4d86978ac4bf40e069c11a32974d6b",
"max_stars_repo_name": "doronbehar/lab4",
"max_stars_repo_path": "x2.ESR/ESRB.py",
"lang": "Python"
} |
65 | 2 | absorption_deriviative_raw = np.array([val.m.n for val in absorption_deriviative]) | 43 | 43 | [
"ListComp"
] | {
"hexsha": "f70028f9fa4d86978ac4bf40e069c11a32974d6b",
"max_stars_repo_name": "doronbehar/lab4",
"max_stars_repo_path": "x2.ESR/ESRB.py",
"lang": "Python"
} |
65 | 3 | absorption_deriviative_err = np.array([val.m.s for val in absorption_deriviative]) | 44 | 44 | [
"ListComp"
] | {
"hexsha": "f70028f9fa4d86978ac4bf40e069c11a32974d6b",
"max_stars_repo_name": "doronbehar/lab4",
"max_stars_repo_path": "x2.ESR/ESRB.py",
"lang": "Python"
} |
70 | 0 | d[ 6 ] = HydrusSerialisable.SerialisableDictionary( { i : 'test' + str( i ) for i in range( 20 ) } ) | 97 | 97 | [
"DictComp"
] | {
"hexsha": "f7002c90467435a91e99ee2aae11e1a594ffba14",
"max_stars_repo_name": "baibhavvishalpani/hydrus",
"max_stars_repo_path": "hydrus/test/TestHydrusSerialisable.py",
"lang": "Python"
} |
70 | 1 | d[ ClientSearch.Predicate( ClientSearch.PREDICATE_TYPE_TAG, 'test pred 2' ) ] = HydrusSerialisable.SerialisableList( [ ClientSearch.Predicate( ClientSearch.PREDICATE_TYPE_TAG, 'test' + str( i ) ) for i in range( 10 ) ] ) | 100 | 100 | [
"ListComp"
] | {
"hexsha": "f7002c90467435a91e99ee2aae11e1a594ffba14",
"max_stars_repo_name": "baibhavvishalpani/hydrus",
"max_stars_repo_path": "hydrus/test/TestHydrusSerialisable.py",
"lang": "Python"
} |
70 | 2 | db[ HydrusData.GenerateKey() ] = [ HydrusData.GenerateKey() for i in range( 10 ) ] | 116 | 116 | [
"ListComp"
] | {
"hexsha": "f7002c90467435a91e99ee2aae11e1a594ffba14",
"max_stars_repo_name": "baibhavvishalpani/hydrus",
"max_stars_repo_path": "hydrus/test/TestHydrusSerialisable.py",
"lang": "Python"
} |
70 | 3 | db[ 2 ] = [ HydrusData.GenerateKey() for i in range( 10 ) ] | 118 | 118 | [
"ListComp"
] | {
"hexsha": "f7002c90467435a91e99ee2aae11e1a594ffba14",
"max_stars_repo_name": "baibhavvishalpani/hydrus",
"max_stars_repo_path": "hydrus/test/TestHydrusSerialisable.py",
"lang": "Python"
} |
75 | 0 | return [Request(x, callback=self.parse_item) for x in links] | 22 | 22 | [
"ListComp"
] | {
"hexsha": "f7002fa28c4f96c4ce9de895ed3dc6923730e7d5",
"max_stars_repo_name": "fictivekin/openrecipes",
"max_stars_repo_path": "scrapy_proj/openrecipes/spiders/elanaspantry_feedspider.py",
"lang": "Python"
} |
77 | 0 | class_sample_count = torch.LongTensor(
[(bin_labels == t).sum() for t in torch.arange(nbins)]) | 49 | 50 | [
"ListComp"
] | {
"hexsha": "f700308f76753f938a995240fe09d0f4b13796ba",
"max_stars_repo_name": "ayushkarnawat/profit",
"max_stars_repo_path": "examples/gb1/train_oracle.py",
"lang": "Python"
} |
77 | 1 | stratified = {split: Subset(dataset, sorted(idx))
for split, idx in zip(splits, subset_idx)} | 65 | 66 | [
"DictComp"
] | {
"hexsha": "f700308f76753f938a995240fe09d0f4b13796ba",
"max_stars_repo_name": "ayushkarnawat/profit",
"max_stars_repo_path": "examples/gb1/train_oracle.py",
"lang": "Python"
} |
78 | 0 | results = [i for i in pager] | 812 | 812 | [
"ListComp"
] | {
"hexsha": "f70031964499de2478621f668a6bbbbe0d067348",
"max_stars_repo_name": "LaudateCorpus1/python-deploy",
"max_stars_repo_path": "tests/unit/gapic/deploy_v1/test_cloud_deploy.py",
"lang": "Python"
} |
78 | 1 | assert all(isinstance(i, cloud_deploy.DeliveryPipeline) for i in results) | 814 | 814 | [
"GeneratorExp"
] | {
"hexsha": "f70031964499de2478621f668a6bbbbe0d067348",
"max_stars_repo_name": "LaudateCorpus1/python-deploy",
"max_stars_repo_path": "tests/unit/gapic/deploy_v1/test_cloud_deploy.py",
"lang": "Python"
} |
78 | 2 | assert all(isinstance(i, cloud_deploy.DeliveryPipeline) for i in responses) | 898 | 898 | [
"GeneratorExp"
] | {
"hexsha": "f70031964499de2478621f668a6bbbbe0d067348",
"max_stars_repo_name": "LaudateCorpus1/python-deploy",
"max_stars_repo_path": "tests/unit/gapic/deploy_v1/test_cloud_deploy.py",
"lang": "Python"
} |
78 | 3 | results = [i for i in pager] | 2,089 | 2,089 | [
"ListComp"
] | {
"hexsha": "f70031964499de2478621f668a6bbbbe0d067348",
"max_stars_repo_name": "LaudateCorpus1/python-deploy",
"max_stars_repo_path": "tests/unit/gapic/deploy_v1/test_cloud_deploy.py",
"lang": "Python"
} |
78 | 4 | assert all(isinstance(i, cloud_deploy.Target) for i in results) | 2,091 | 2,091 | [
"GeneratorExp"
] | {
"hexsha": "f70031964499de2478621f668a6bbbbe0d067348",
"max_stars_repo_name": "LaudateCorpus1/python-deploy",
"max_stars_repo_path": "tests/unit/gapic/deploy_v1/test_cloud_deploy.py",
"lang": "Python"
} |
78 | 5 | assert all(isinstance(i, cloud_deploy.Target) for i in responses) | 2,159 | 2,159 | [
"GeneratorExp"
] | {
"hexsha": "f70031964499de2478621f668a6bbbbe0d067348",
"max_stars_repo_name": "LaudateCorpus1/python-deploy",
"max_stars_repo_path": "tests/unit/gapic/deploy_v1/test_cloud_deploy.py",
"lang": "Python"
} |
78 | 6 | results = [i for i in pager] | 3,270 | 3,270 | [
"ListComp"
] | {
"hexsha": "f70031964499de2478621f668a6bbbbe0d067348",
"max_stars_repo_name": "LaudateCorpus1/python-deploy",
"max_stars_repo_path": "tests/unit/gapic/deploy_v1/test_cloud_deploy.py",
"lang": "Python"
} |
78 | 7 | assert all(isinstance(i, cloud_deploy.Release) for i in results) | 3,272 | 3,272 | [
"GeneratorExp"
] | {
"hexsha": "f70031964499de2478621f668a6bbbbe0d067348",
"max_stars_repo_name": "LaudateCorpus1/python-deploy",
"max_stars_repo_path": "tests/unit/gapic/deploy_v1/test_cloud_deploy.py",
"lang": "Python"
} |
78 | 8 | assert all(isinstance(i, cloud_deploy.Release) for i in responses) | 3,340 | 3,340 | [
"GeneratorExp"
] | {
"hexsha": "f70031964499de2478621f668a6bbbbe0d067348",
"max_stars_repo_name": "LaudateCorpus1/python-deploy",
"max_stars_repo_path": "tests/unit/gapic/deploy_v1/test_cloud_deploy.py",
"lang": "Python"
} |
78 | 9 | results = [i for i in pager] | 4,253 | 4,253 | [
"ListComp"
] | {
"hexsha": "f70031964499de2478621f668a6bbbbe0d067348",
"max_stars_repo_name": "LaudateCorpus1/python-deploy",
"max_stars_repo_path": "tests/unit/gapic/deploy_v1/test_cloud_deploy.py",
"lang": "Python"
} |
78 | 10 | assert all(isinstance(i, cloud_deploy.Rollout) for i in results) | 4,255 | 4,255 | [
"GeneratorExp"
] | {
"hexsha": "f70031964499de2478621f668a6bbbbe0d067348",
"max_stars_repo_name": "LaudateCorpus1/python-deploy",
"max_stars_repo_path": "tests/unit/gapic/deploy_v1/test_cloud_deploy.py",
"lang": "Python"
} |
78 | 11 | assert all(isinstance(i, cloud_deploy.Rollout) for i in responses) | 4,323 | 4,323 | [
"GeneratorExp"
] | {
"hexsha": "f70031964499de2478621f668a6bbbbe0d067348",
"max_stars_repo_name": "LaudateCorpus1/python-deploy",
"max_stars_repo_path": "tests/unit/gapic/deploy_v1/test_cloud_deploy.py",
"lang": "Python"
} |
80 | 0 | self.ids = [os.path.join(dir, data_rank, mode, filename) for filename in os.listdir(os.path.join(dir, data_rank, mode))] | 34 | 34 | [
"ListComp"
] | {
"hexsha": "f70033d1cbc2d6abea9a13563db9c1b94096e116",
"max_stars_repo_name": "Theia-4869/U-RISC",
"max_stars_repo_path": "utils/dataset.py",
"lang": "Python"
} |
82 | 0 | fpolicies = {k: int(v) for k, v in
policies.items() if k.endswith("max")} | 405 | 406 | [
"DictComp"
] | {
"hexsha": "f70034b5d8bc1589a710450b847c2f39ab19cddb",
"max_stars_repo_name": "traghavendra/cinder-train",
"max_stars_repo_path": "cinder/volume/drivers/datera/datera_iscsi.py",
"lang": "Python"
} |
90 | 0 | return [Evaluator.evaluate(m, y, y_pred) for m in metric] | 265 | 265 | [
"ListComp"
] | {
"hexsha": "f700384c604ac91f885c84ab6ed838d3ba8c4771",
"max_stars_repo_name": "GZHoffie/analytics-zoo",
"max_stars_repo_path": "pyzoo/zoo/zouwu/model/Seq2Seq.py",
"lang": "Python"
} |
90 | 1 | return [np.array([Evaluator.evaluate(m, y[:, i, :], y_pred[:, i, :])
for i in range(self.future_seq_len)])
for m in metric] | 267 | 269 | [
"ListComp"
] | {
"hexsha": "f700384c604ac91f885c84ab6ed838d3ba8c4771",
"max_stars_repo_name": "GZHoffie/analytics-zoo",
"max_stars_repo_path": "pyzoo/zoo/zouwu/model/Seq2Seq.py",
"lang": "Python"
} |
90 | 2 | result = np.array([self.predict(x, mc=True) for i in range(n_iter)]) | 283 | 283 | [
"ListComp"
] | {
"hexsha": "f700384c604ac91f885c84ab6ed838d3ba8c4771",
"max_stars_repo_name": "GZHoffie/analytics-zoo",
"max_stars_repo_path": "pyzoo/zoo/zouwu/model/Seq2Seq.py",
"lang": "Python"
} |
104 | 0 | dist_info = dict(
line.strip().split('=', 1) for line in f.readlines()) | 412 | 413 | [
"GeneratorExp"
] | {
"hexsha": "f70045418bc49a61f9a7a48205189a79ca91e491",
"max_stars_repo_name": "marijnfs/onnxruntime",
"max_stars_repo_path": "tools/ci_build/build.py",
"lang": "Python"
} |
104 | 1 | return (
os.path.exists('/.dockerenv') or
os.path.isfile(path) and any('docker' in line for line in open(path))
) | 463 | 466 | [
"GeneratorExp"
] | {
"hexsha": "f70045418bc49a61f9a7a48205189a79ca91e491",
"max_stars_repo_name": "marijnfs/onnxruntime",
"max_stars_repo_path": "tools/ci_build/build.py",
"lang": "Python"
} |
104 | 2 | raise BuildError(
"iOS build on MacOS canceled due to missing arguments: " +
', '.join(
val for val, cond in zip(arg_names, needed_args)
if not cond)) | 771 | 775 | [
"GeneratorExp"
] | {
"hexsha": "f70045418bc49a61f9a7a48205189a79ca91e491",
"max_stars_repo_name": "marijnfs/onnxruntime",
"max_stars_repo_path": "tools/ci_build/build.py",
"lang": "Python"
} |
104 | 3 | raise BuildError(
"iOS build canceled due to missing arguments: " +
', '.join(
val for val, cond in zip(arg_names, needed_args)
if not cond)) | 806 | 810 | [
"GeneratorExp"
] | {
"hexsha": "f70045418bc49a61f9a7a48205189a79ca91e491",
"max_stars_repo_name": "marijnfs/onnxruntime",
"max_stars_repo_path": "tools/ci_build/build.py",
"lang": "Python"
} |
104 | 4 | cmake_args += ["-D{}".format(define) for define in cmake_extra_defines] | 853 | 853 | [
"ListComp"
] | {
"hexsha": "f70045418bc49a61f9a7a48205189a79ca91e491",
"max_stars_repo_name": "marijnfs/onnxruntime",
"max_stars_repo_path": "tools/ci_build/build.py",
"lang": "Python"
} |
107 | 0 | main([int(x) for x in sys.argv[1].split(',')], int(sys.argv[2])) | 82 | 82 | [
"ListComp"
] | {
"hexsha": "f70047cdafe4dcd083f47814ee7d17be097fee36",
"max_stars_repo_name": "not-sponsored/Guide-to-Data-Structures-and-Algorithms-Exercises",
"max_stars_repo_path": "algorithms/quicksort.py",
"lang": "Python"
} |
109 | 0 | return [w for w in cls._split_words(text) if w] | 800 | 800 | [
"ListComp"
] | {
"hexsha": "f70049a62ff8108e599465f06904de5438b65282",
"max_stars_repo_name": "lordloki/upbge",
"max_stars_repo_path": "release/scripts/modules/bl_i18n_utils/utils_spell_check.py",
"lang": "Python"
} |
110 | 0 | t_prime = [math.radians(i) for i in y_prime] | 170 | 170 | [
"ListComp"
] | {
"hexsha": "f7004a6a9da6e184c08cdd829e3bc4d6ac4c80b7",
"max_stars_repo_name": "NingAnMe/voxelmorph",
"max_stars_repo_path": "scripts/sphere/register.py",
"lang": "Python"
} |
110 | 1 | p_prime = [math.radians(i) for i in x_prime] | 171 | 171 | [
"ListComp"
] | {
"hexsha": "f7004a6a9da6e184c08cdd829e3bc4d6ac4c80b7",
"max_stars_repo_name": "NingAnMe/voxelmorph",
"max_stars_repo_path": "scripts/sphere/register.py",
"lang": "Python"
} |
110 | 2 | phi_prime = [math.degrees(p) for p in phi_prime] | 202 | 202 | [
"ListComp"
] | {
"hexsha": "f7004a6a9da6e184c08cdd829e3bc4d6ac4c80b7",
"max_stars_repo_name": "NingAnMe/voxelmorph",
"max_stars_repo_path": "scripts/sphere/register.py",
"lang": "Python"
} |
110 | 3 | thtea_prime = [math.degrees(t) for t in theta_prime] | 203 | 203 | [
"ListComp"
] | {
"hexsha": "f7004a6a9da6e184c08cdd829e3bc4d6ac4c80b7",
"max_stars_repo_name": "NingAnMe/voxelmorph",
"max_stars_repo_path": "scripts/sphere/register.py",
"lang": "Python"
} |
125 | 0 | if not (or_list([i in stopword_list for i in bigram])):
if freq > threshold:
frequent_bigrams.append('{}${}'.format(bigram[0], bigram[1]))
else:
break | 29 | 33 | [
"ListComp"
] | {
"hexsha": "f7005624326db8bd844029d49a4f69d03cd93970",
"max_stars_repo_name": "GU-DataLab/topic-modeling-textPrep",
"max_stars_repo_path": "settings/ngrams.py",
"lang": "Python"
} |
125 | 1 | fb, fn = get_dataset_ngrams([x[1] for x in date_docs], min_freq, sw, extra_bigrams, extra_ngrams) | 98 | 98 | [
"ListComp"
] | {
"hexsha": "f7005624326db8bd844029d49a4f69d03cd93970",
"max_stars_repo_name": "GU-DataLab/topic-modeling-textPrep",
"max_stars_repo_path": "settings/ngrams.py",
"lang": "Python"
} |
132 | 0 | stdout = "".join(
[line.decode("utf-8") for line in iter(pipe.stdout.readline, b"")]
) | 33 | 35 | [
"ListComp"
] | {
"hexsha": "f7005b28d042d57735c533e59720388a5a80e44f",
"max_stars_repo_name": "PansoK/slp",
"max_stars_repo_path": "tools/poor-mans-video-editor.py",
"lang": "Python"
} |
132 | 1 | return (
[st]
+ [
t
for s in delete_timestamps.split(",")
for t in (s.split("-")[0], s.split("-")[1])
]
+ [et]
) | 70 | 80 | [
"ListComp"
] | {
"hexsha": "f7005b28d042d57735c533e59720388a5a80e44f",
"max_stars_repo_name": "PansoK/slp",
"max_stars_repo_path": "tools/poor-mans-video-editor.py",
"lang": "Python"
} |
132 | 2 | timestamps = [to_cut_fmt(t) for t in timestamps] | 105 | 105 | [
"ListComp"
] | {
"hexsha": "f7005b28d042d57735c533e59720388a5a80e44f",
"max_stars_repo_name": "PansoK/slp",
"max_stars_repo_path": "tools/poor-mans-video-editor.py",
"lang": "Python"
} |
132 | 3 | cmds = [f"-ss {s} -to {e}" for s, e in pairwise(timestamps)] | 133 | 133 | [
"ListComp"
] | {
"hexsha": "f7005b28d042d57735c533e59720388a5a80e44f",
"max_stars_repo_name": "PansoK/slp",
"max_stars_repo_path": "tools/poor-mans-video-editor.py",
"lang": "Python"
} |
132 | 4 | segments = [ln.strip().split("\t") for ln in f] | 162 | 162 | [
"ListComp"
] | {
"hexsha": "f7005b28d042d57735c533e59720388a5a80e44f",
"max_stars_repo_name": "PansoK/slp",
"max_stars_repo_path": "tools/poor-mans-video-editor.py",
"lang": "Python"
} |
135 | 0 | color_modes = [e.value for e in ColorMode] | 76 | 76 | [
"ListComp"
] | {
"hexsha": "f7005bd1aad9ac2334d62b543f0e7ac8f6381776",
"max_stars_repo_name": "Mishrasubha/napari",
"max_stars_repo_path": "napari/_qt/layer_controls/qt_vectors_controls.py",
"lang": "Python"
} |
138 | 0 | item_lst=[item.strip() for item in item_lst] | 112 | 112 | [
"ListComp"
] | {
"hexsha": "f700608f35098a7965b60e53b106f0704bc73300",
"max_stars_repo_name": "XuboGU/neorl",
"max_stars_repo_path": "neorl/tune/runners/estune.py",
"lang": "Python"
} |
138 | 1 | strategy= [random.uniform(self.SMIN, self.SMAX) for _ in range(size)] | 237 | 237 | [
"ListComp"
] | {
"hexsha": "f700608f35098a7965b60e53b106f0704bc73300",
"max_stars_repo_name": "XuboGU/neorl",
"max_stars_repo_path": "neorl/tune/runners/estune.py",
"lang": "Python"
} |
138 | 2 | [pop[ind].append(fitness[ind]) for ind in range(len(pop))] | 264 | 264 | [
"ListComp"
] | {
"hexsha": "f700608f35098a7965b60e53b106f0704bc73300",
"max_stars_repo_name": "XuboGU/neorl",
"max_stars_repo_path": "neorl/tune/runners/estune.py",
"lang": "Python"
} |
138 | 3 | csvfile=[f for f in os.listdir('./tunecases/case{}/case{}_log/'.format(casenum, casenum)) if f.endswith('_out.csv')] | 313 | 313 | [
"ListComp"
] | {
"hexsha": "f700608f35098a7965b60e53b106f0704bc73300",
"max_stars_repo_name": "XuboGU/neorl",
"max_stars_repo_path": "neorl/tune/runners/estune.py",
"lang": "Python"
} |
138 | 4 | [fout.write(str(item) + ',') for item in ind] | 323 | 323 | [
"ListComp"
] | {
"hexsha": "f700608f35098a7965b60e53b106f0704bc73300",
"max_stars_repo_name": "XuboGU/neorl",
"max_stars_repo_path": "neorl/tune/runners/estune.py",
"lang": "Python"
} |
138 | 5 | [fout.write(item + ',') for item in self.param_names] | 532 | 532 | [
"ListComp"
] | {
"hexsha": "f700608f35098a7965b60e53b106f0704bc73300",
"max_stars_repo_name": "XuboGU/neorl",
"max_stars_repo_path": "neorl/tune/runners/estune.py",
"lang": "Python"
} |
138 | 6 | caseids=['ind{}'.format(ind) for ind in range(self.currentcase, self.currentcase+self.popsize+1)] | 542 | 542 | [
"ListComp"
] | {
"hexsha": "f700608f35098a7965b60e53b106f0704bc73300",
"max_stars_repo_name": "XuboGU/neorl",
"max_stars_repo_path": "neorl/tune/runners/estune.py",
"lang": "Python"
} |
138 | 7 | [offspring[ind].append(fitness[ind]) for ind in range(len(offspring))] | 557 | 557 | [
"ListComp"
] | {
"hexsha": "f700608f35098a7965b60e53b106f0704bc73300",
"max_stars_repo_name": "XuboGU/neorl",
"max_stars_repo_path": "neorl/tune/runners/estune.py",
"lang": "Python"
} |
138 | 8 | failed_cases=len([print ('failed') for item in self.population if isinstance(item, str)]) | 579 | 579 | [
"ListComp"
] | {
"hexsha": "f700608f35098a7965b60e53b106f0704bc73300",
"max_stars_repo_name": "XuboGU/neorl",
"max_stars_repo_path": "neorl/tune/runners/estune.py",
"lang": "Python"
} |
139 | 0 | all_boxes = [[[] for _ in xrange(num_images)]
for _ in xrange(imdb.num_classes)] | 215 | 216 | [
"ListComp"
] | {
"hexsha": "f70060aa3fd6b00edb6202ecf166cc9464082bba",
"max_stars_repo_name": "zhuriheng/faster-rcnn.pytorch",
"max_stars_repo_path": "test_net.py",
"lang": "Python"
} |
139 | 1 | image_scores = np.hstack([all_boxes[j][i][:, -1]
for j in xrange(1, imdb.num_classes)]) | 304 | 305 | [
"ListComp"
] | {
"hexsha": "f70060aa3fd6b00edb6202ecf166cc9464082bba",
"max_stars_repo_name": "zhuriheng/faster-rcnn.pytorch",
"max_stars_repo_path": "test_net.py",
"lang": "Python"
} |
141 | 0 | avg_val_loss = torch.stack([x["val_loss"] for x in outputs]).mean() | 72 | 72 | [
"ListComp"
] | {
"hexsha": "f700615e2a905b6e5d941c75f337b6670c36b49b",
"max_stars_repo_name": "hirune924/kaggle-HuBMAP",
"max_stars_repo_path": "system/system.py",
"lang": "Python"
} |
141 | 1 | avg_val_dice = torch.stack([x["val_dice"] for x in outputs]).mean() | 73 | 73 | [
"ListComp"
] | {
"hexsha": "f700615e2a905b6e5d941c75f337b6670c36b49b",
"max_stars_repo_name": "hirune924/kaggle-HuBMAP",
"max_stars_repo_path": "system/system.py",
"lang": "Python"
} |
142 | 0 | scores = {k: '?' for k in PCODES} | 89 | 89 | [
"DictComp"
] | {
"hexsha": "f70061697e12c46d39594f2bf2f9bb8e344f31c7",
"max_stars_repo_name": "han-kwang/coronatest-scandata",
"max_stars_repo_path": "coronatest_analyze_csv.py",
"lang": "Python"
} |
142 | 1 | qtm = _mean_time([v[0] for v in vlist]) | 111 | 111 | [
"ListComp"
] | {
"hexsha": "f70061697e12c46d39594f2bf2f9bb8e344f31c7",
"max_stars_repo_name": "han-kwang/coronatest-scandata",
"max_stars_repo_path": "coronatest_analyze_csv.py",
"lang": "Python"
} |
142 | 2 | atm = min(v[1] for v in vlist) | 113 | 113 | [
"GeneratorExp"
] | {
"hexsha": "f70061697e12c46d39594f2bf2f9bb8e344f31c7",
"max_stars_repo_name": "han-kwang/coronatest-scandata",
"max_stars_repo_path": "coronatest_analyze_csv.py",
"lang": "Python"
} |
142 | 3 | wtimes_this = [atm - qtm for qtm, atm in vlist] | 157 | 157 | [
"ListComp"
] | {
"hexsha": "f70061697e12c46d39594f2bf2f9bb8e344f31c7",
"max_stars_repo_name": "han-kwang/coronatest-scandata",
"max_stars_repo_path": "coronatest_analyze_csv.py",
"lang": "Python"
} |
142 | 4 | dates = [t.strftime('%Y-%m-%d') for t in index] | 274 | 274 | [
"ListComp"
] | {
"hexsha": "f70061697e12c46d39594f2bf2f9bb8e344f31c7",
"max_stars_repo_name": "han-kwang/coronatest-scandata",
"max_stars_repo_path": "coronatest_analyze_csv.py",
"lang": "Python"
} |
142 | 5 | times = [t.strftime('%H:%M') for t in index] | 275 | 275 | [
"ListComp"
] | {
"hexsha": "f70061697e12c46d39594f2bf2f9bb8e344f31c7",
"max_stars_repo_name": "han-kwang/coronatest-scandata",
"max_stars_repo_path": "coronatest_analyze_csv.py",
"lang": "Python"
} |
142 | 6 | sdf.columns = [
('/'.join([str(x) for x in c]) if isinstance(c, tuple) else c)
for c in sdf.columns
] | 282 | 285 | [
"ListComp"
] | {
"hexsha": "f70061697e12c46d39594f2bf2f9bb8e344f31c7",
"max_stars_repo_name": "han-kwang/coronatest-scandata",
"max_stars_repo_path": "coronatest_analyze_csv.py",
"lang": "Python"
} |
146 | 0 | watched = {full_dataset.to_inner_iid(key): value for key,value in watched.items()} | 35 | 35 | [
"DictComp"
] | {
"hexsha": "f7006506115787b6ab648322e288f899a2ea56b5",
"max_stars_repo_name": "mateuszrusin/filmweb-rekomendacje",
"max_stars_repo_path": "movies_recommender/RecommenderSVD.py",
"lang": "Python"
} |
End of preview. Expand in Data Studio
Python Comprehension Statements 1K
This dataset contains 1,000 Python statements extracted from bigcode/the-stack
that include at least one Python comprehension expression:
- list comprehensions
- set comprehensions
- dict comprehensions
- generator expressions
Each row is a nearest enclosing Python statement around one or more comprehension expressions, along with line number information and source metadata copied from the original The Stack sample.
Dataset Details
- Dataset name:
py_comprehension_statements_1k - Rows: 1,000
- Split:
train - Source dataset:
bigcode/the-stack, Python subset - Extraction script:
scripts/collect_stack_comprehension_statements.py - Original local JSONL:
outputs/the_stack_python_comprehension_statements.jsonl
Schema
| Field | Type | Description |
|---|---|---|
index |
int64 |
Zero-based index of the streamed source sample. |
statement_id |
int64 |
Statement index within the source sample. |
statement |
string |
Extracted Python statement containing a comprehension. |
lineno |
int64 |
Starting line number in the original source file. |
end_lineno |
int64 |
Ending line number in the original source file. |
comprehension_types |
list[string] |
AST comprehension node types present in the statement. |
metadata.hexsha |
string |
Source commit hash from The Stack metadata. |
metadata.max_stars_repo_name |
string |
Repository name from The Stack metadata. |
metadata.max_stars_repo_path |
string |
File path from The Stack metadata. |
metadata.lang |
string |
Source language label. |
Comprehension Type Counts
The comprehension_types field can contain multiple values for a single row.
Across the 1,000 rows, the type occurrences are:
| Type | Count |
|---|---|
ListComp |
754 |
GeneratorExp |
181 |
DictComp |
76 |
SetComp |
9 |
Usage
from datasets import load_dataset
dataset = load_dataset("nexround/py_comprehension_statements_1k")
train = dataset["train"]
print(train[0]["statement"])
print(train[0]["comprehension_types"])
If you have the dataset saved locally with datasets.save_to_disk, use:
from datasets import load_from_disk
dataset = load_from_disk("outputs/the_stack_python_comprehension_statements_hf_dataset")
Construction
The extraction process streamed Python files from The Stack, parsed each source
file with Python's ast module, found ListComp, SetComp, DictComp, and
GeneratorExp nodes, and saved the nearest enclosing statement for each unique
statement span.
Rows that could not be parsed as Python source were skipped.
Intended Uses
This dataset is intended for lightweight analysis and experimentation around Python comprehension syntax, such as:
- inspecting comprehension usage patterns
- evaluating code models on comprehension-heavy snippets
- constructing small probes or prompts involving Python comprehensions
Licensing
This is a derived sample from The Stack. The original source code snippets come from many upstream repositories with varying licenses. Users should consult and respect the licensing metadata and terms of the upstream The Stack dataset and the original repositories when using or redistributing examples.
- Downloads last month
- 2