-
Notifications
You must be signed in to change notification settings - Fork 0
/
type-error.json
115 lines (115 loc) · 13.3 KB
/
type-error.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
[
"from collections import Counter\ncounts = Counter(iterable)",
"import collections\ncounts = collections.Counter(iterable)",
"from pandas import Series\ncounts = Series(iterable).value_counts().to_dict()",
"import numpy as np\n(unique, counts) = np.unique(iterable, return_counts=True)\ncounts = dict(zip(unique, counts))",
"import collections\ncounts = collections.Counter(iterable)\nfor i in counts:\n pass",
"from collections import Counter\ncounts = Counter(iterable)\nfor i in counts:\n counts[i] += 1",
"from numpy import unique\n(elems, counts) = unique(iterable, return_counts=True)\ncounts = dict(zip(elems, counts))",
"from pandas import DataFrame\ncounts = DataFrame(iterable, columns=['value'])['value'].value_counts().to_dict()",
"from collections import Counter\ncounts = Counter(iterable)\nfor i in counts:\n pass",
"from collections import Counter\ncounts = dict(Counter(iterable))",
"import pandas as pd\ndf = pd.Series(iterable)\ncounts = df.value_counts().to_dict()",
"import numpy as np\n(unique, counts) = np.unique(iterable, return_counts=True)",
"import pandas as pd\ndf = pd.DataFrame(iterable, columns=['value'])\ncounts = df['value'].value_counts().to_dict()",
"import numpy as np\nimport pandas as pd\ndf = pd.DataFrame(iterable, columns=['Value'])\ncounts = df['Value'].value_counts().to_dict()",
"import collections\nfrom operator import itemgetter\ncounts_dict = collections.Counter(iterable)\ncounts = dict(sorted(counts_dict.items(), key=itemgetter(1), reverse=True))",
"import pandas as pd\ncounts = pd.Series(iterable).value_counts().to_dict()",
"from collections import Counter\ncounts = Counter(iterable)\ni = iterable[0] if iterable else None",
"import collections\nmyCounter = collections.Counter(iterable)\ncounts = dict(myCounter)\nfor i in counts:\n pass",
"import pandas as pd\ndf = pd.DataFrame(iterable, columns=['item'])\ncounts = df['item'].value_counts().to_dict()\nfor i in counts:\n pass",
"import numpy as np\niterable = np.array(iterable)\n(unique, counts) = np.unique(iterable, return_counts=True)\ncounts = dict(zip(unique, counts))",
"import collections\ni = None\ncounts = collections.Counter(iterable)",
"import pandas as pd\ni = None\ndf = pd.Series(iterable)\ncounts = df.value_counts().to_dict()",
"from collections import Counter\ncounts = Counter(iterable)\ni = iterable[-1] if iterable else None",
"import pandas as pd\ndf = pd.Series(iterable)\ncounts = df.value_counts().to_dict()\ni = df.iat[-1] if not df.empty else None",
"import numpy as np\n(unique, counts_np) = np.unique(iterable, return_counts=True)\ncounts = dict(zip(unique, counts_np))\ni = iterable[-1] if iterable else None",
"import numpy as np\ni = None\ncounts = {}\n(unique, counts_np) = np.unique(iterable, return_counts=True)\nfor (idx, item) in enumerate(unique):\n counts[item] = counts_np[idx]\ni = iterable[-1] if iterable else None",
"import collections\ni = iterable\ncounts = collections.Counter(i)",
"import numpy as np\ni = np.array(iterable)\ncounts = {item: np.sum(i == item) for item in np.unique(i)}",
"from collections import Counter\nimport numpy as np\n(unique, counts) = np.unique(iterable, return_counts=True)\ncounts = Counter(dict(zip(unique, counts)))",
"import numpy as np\n(unique, counts) = np.unique(iterable, return_counts=True)\ncounts = {k: v for (k, v) in zip(unique, counts)}",
"import numpy as np\n(unique, counts) = np.unique(iterable, return_counts=True)\ncounts = {unique[i]: counts[i] for i in range(len(unique))}",
"import numpy as np\n(unique, counts) = np.unique(iterable, return_inverse=True)\ncounts = np.bincount(counts)\ncounts = dict(zip(unique, counts))",
"import pandas as pd\nfrom collections import Counter\ndf = pd.Series(iterable)\ncounts = dict(Counter(df))",
"import pandas as pd\nfrom collections import Counter\ndf = pd.Series(iterable)\ncounts = Counter(df.tolist())",
"import pandas as pd\ndf = pd.Series(iterable)\ncounts = df.groupby(df).count().to_dict()",
"import pandas as pd\nfrom collections import defaultdict\ndf = pd.Series(iterable)\ncounts = defaultdict(int)\nfor i in df:\n counts[i] += 1",
"import pandas as pd\nfrom collections import Counter\ncounts = Counter(pd.Series(iterable))",
"import pandas as pd\nfrom collections import Counter\ndf = pd.Series(iterable)\ncounts = dict(df.value_counts())",
"import numpy as np\ni = np.unique(iterable)\ncounts = {j: np.count_nonzero(iterable == j) for j in i}",
"import numpy as np\ni = None\ncounts = {}\nfor i in np.unique(iterable):\n counts[i] = np.count_nonzero(np.array(iterable) == i)",
"import numpy as np\nfrom collections import Counter\ncounts = np.array(list(Counter(iterable).values()))",
"import numpy as np\nfrom pandas import Series\ncounts = np.array(Series(iterable).value_counts().values)",
"from collections import Counter\ni = None\ncounts = Counter(iterable)",
"from collections import Counter\ni = iterable\ncounts = Counter(i)",
"from collections import defaultdict\nimport numpy as np\ni = np.array(iterable)\ncounts = defaultdict(int)\nfor value in np.unique(i):\n counts[value] = np.sum(i == value)",
"import numpy as np\ni = None\n(unique, counts_np) = np.unique(iterable, return_counts=True)\ncounts = dict(zip(unique, counts_np))",
"from collections import Counter\ncounts = Counter(iterable)\ni = list(counts.keys())",
"from collections import Counter\ni = list(Counter(iterable).keys())\ncounts = dict(Counter(iterable))",
"from collections import Counter\ni = list(Counter(iterable).keys())\ncounts = {key: iterable.count(key) for key in i}",
"from collections import Counter\ni = list(Counter(iterable).keys())\ncounts = {i[j]: iterable.count(i[j]) for j in range(len(i))}",
"from collections import Counter\ni = iterable\ncounts = Counter(i)\ncounts = dict(counts)",
"from collections import Counter\ni = None\ncounts = Counter(iterable)\ni = iterable[-1] if iterable else None",
"import numpy as np\ni = None\ncounts = {j: np.sum(np.array(iterable) == j) for j in np.unique(iterable)}\ni = iterable[-1] if iterable else None",
"import pandas as pd\nfrom collections import Counter\ncounts = dict(Counter(pd.Series(iterable)))",
"import pandas as pd\nfrom collections import defaultdict\ncounts = defaultdict(int)\nfor i in pd.Series(iterable):\n counts[i] += 1",
"import pandas as pd\nfrom collections import Counter\ncounts = Counter(pd.Series(iterable).tolist())",
"import pandas as pd\nfrom collections import Counter\ndf = pd.Series(iterable)\ncounts = Counter(df).most_common()",
"import pandas as pd\nfrom collections import Counter\ndf = pd.Series(iterable)\ncounts = Counter(df).items()",
"import pandas as pd\nfrom collections import Counter\ndf = pd.Series(iterable)\ncounts = Counter(df).keys()",
"import pandas as pd\nfrom collections import Counter\ndf = pd.Series(iterable)\ncounts = Counter(df).values()",
"import pandas as pd\nfrom collections import Counter\ndf = pd.Series(iterable)\ncounts = dict(Counter(df).most_common())",
"import pandas as pd\nfrom collections import Counter\ndf = pd.Series(iterable)\ncounts = dict(Counter(df).items())",
"import pandas as pd\nfrom collections import Counter\ndf = pd.Series(iterable)\ncounts = list(Counter(df).keys())",
"import pandas as pd\nfrom collections import Counter\ndf = pd.Series(iterable)\ncounts = list(Counter(df).values())",
"from pandas import Series\ni = iterable\ncounts = Series(i).value_counts().to_dict()",
"import numpy as np\n(unique, pm_count) = np.unique(iterable, return_counts=True)\ncounts = dict(zip(unique, pm_count))\ni = range(len(unique))",
"from collections import Counter\n(i, counts) = (None, Counter(iterable))",
"(i, counts) = (None, {})\nfor (idx, val) in enumerate(counts):\n counts.setdefault(val, 0)\n counts[val] += 1",
"from collections import Counter\ncounts = dict(Counter(iterable))\ni = len(counts)",
"i = 0\ncounts = {}\nwhile iterable:\n item = iterable.pop()\n if item not in counts:\n counts[item] = 1\n else:\n counts[item] += 1\ni += 1",
"from collections import Counter\ncounts = {}\ncounts = dict(Counter(iterable))",
"import pandas as pd\ndef calculate_counts(iterable):\n occurrences = pd.Series(iterable)\n return dict(occurrences.value_counts())\ncounts = calculate_counts(iterable)",
"from pandas import Series\nseries = Series(iterable)\ncounts = series.value_counts().to_dict()",
"from pandas import DataFrame\ndf = DataFrame(iterable, columns=['x'])\ncounts = dict(df['x'].value_counts())",
"from collections import Counter as cnt\ni = iterable\ncounts = cnt(i)",
"from collections import Counter\ndef get_counts(iterable):\n return Counter(iterable)\ni = iterable\ncounts = get_counts(i)",
"import numpy as np\ni = iterable\n(unique, counts_np) = np.unique(i, return_counts=True)\ncounts = dict(zip(unique, counts_np))",
"import numpy as np\nimport pandas as pd\ni = iterable\ns = pd.Series(i)\ncounts = s.value_counts().to_dict()",
"from collections import Counter\ncounts = Counter(iterable)\ni = None",
"import numpy as np\n(unique, counts_np) = np.unique(iterable, return_counts=True)\ncounts = dict(zip(unique, counts_np))\ni = None",
"import numpy as np\ni = None\ncounts = {}\n(unique, counts_np) = np.unique(iterable, return_counts=True)\nfor (i, count) in zip(unique, counts_np):\n counts[i] = count",
"import numpy as np\ni = None\ncounts = dict(zip(*np.unique(iterable, return_counts=True)))",
"from pandas import Series\ni = None\ncounts = Series(iterable).value_counts().to_dict()",
"import pandas as pd\ni = None\ncounts = pd.Series(iterable).value_counts().to_dict()",
"import numpy as np\ni = iterable\ncounts = dict(zip(*np.unique(i, return_counts=True)))",
"import numpy as np\ni = np.array(iterable)\n(unique, counts) = np.unique(i, return_counts=True)\ncounts = {k: v for (k, v) in zip(unique, counts)}",
"import numpy as np\ni = np.array(iterable)\n(unique, counts) = np.unique(i, return_counts=True)\ncounts = {unique[j]: counts[j] for j in range(len(unique))}",
"import numpy as np\ni = np.array(iterable)\n(unique_elements, counts) = np.unique(i, return_counts=True)\ncounts = dict(map(lambda x, y: (x, y), unique_elements, counts))",
"import numpy as np\ni = np.array(iterable)\n(unique_elements, counts) = np.unique(i, return_counts=True)\ncounts = dict([(unique_elements[j], counts[j]) for j in range(len(unique_elements))])",
"import numpy as np\ni = np.array(iterable)\n(unique_elements, counts) = np.unique(i, return_counts=True)\ncounts = {unique_elements[j]: counts[j] for j in range(unique_elements.size)}",
"import numpy as np\ni = np.array(iterable)\nunique_elements = np.unique(i)\ncounts = {element: np.count_nonzero(i == element) for element in unique_elements}",
"import numpy as np\ni = np.array(iterable)\n(unique_elements, counts_elements) = np.unique(i, return_counts=True)\ncounts = {}\nfor index in range(len(unique_elements)):\n counts[unique_elements[index]] = counts_elements[index]",
"from collections import Counter\ni = iterable\ncounts = dict(Counter(i))",
"import pandas as pd\ni = iterable\ncounts = pd.Series(i).value_counts().to_dict()",
"from pandas import Series\ncounts_series = Series(iterable).value_counts().to_dict()\ni = None",
"import numpy as np\ncounts = np.unique(iterable, return_counts=True)",
"import collections\nimport itertools\ncounts = collections.Counter(iterable)\nfor i in counts:\n counts[i] += 1",
"import pandas as pd\ndf = pd.Series(iterable)\ncounts = df.value_counts()\nfor i in counts.index:\n counts[i] += 1",
"import pandas as pd\ncounts = pd.Series(iterable).value_counts().to_dict()\ni = None",
"import pandas as pd\ndf = pd.Series(iterable)\ncounts = df.value_counts().to_dict()\ni = None",
"import numpy as np\nfrom collections import Counter\ncounter = Counter(iterable)\ncounts = dict(counter)\ni = None",
"import numpy as np\nimport collections\ncounts_dict = collections.Counter(iterable)\ncounts = dict(counts_dict)\ni = None",
"from collections import Counter\ni = None\ncounts_series = dict(Counter(iterable))",
"from pandas import Series\ni = None\ncounts_series = Series(iterable).value_counts(normalize=False).to_dict()",
"import pandas as pd\ni = None\ncounts_series = pd.value_counts(pd.Series(iterable)).to_dict()",
"from pandas import Series\ni = None\ntemp_counts = Series(iterable).value_counts()\ncounts_series = {key: temp_counts[key] for key in temp_counts.index}",
"from collections import defaultdict\nimport numpy as np\ndefault_counts = defaultdict(int)\n(unique_elements, counts) = np.unique(iterable, return_counts=True)\nfor (element, count) in zip(unique_elements, counts):\n default_counts[element] += count\ncounts = dict(default_counts)",
"import pandas as pd\n(unique_elements, counts) = (pd.Series(iterable).value_counts().index, pd.Series(iterable).value_counts().values)\ncounts = dict(zip(unique_elements, counts))",
"from collections import Counter\nimport pandas as pd\n(unique_elements, counts) = (pd.Series(iterable).value_counts().index, pd.Series(iterable).value_counts().values)\ncounts = Counter(dict(zip(unique_elements, counts)))",
"import collections\ncounts_dict = collections.Counter(iterable)\ncounts = dict(counts_dict)",
"from collections import Counter\nctr = Counter(iterable)\ncounts = {key: ctr[key] for key in ctr}",
"import pandas as pd\nimport collections\ncounts = dict(collections.Counter(iterable))",
"import pandas as pd\ndf = pd.DataFrame(iterable, columns=['values'])\ncounts = df['values'].value_counts().to_dict()"
]