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incorrect.json
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incorrect.json
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[
"from collections import defaultdict\ncounts = defaultdict(int)\nfor i in iterable:\n counts[i] += 1\nfor i in iterable:\n counts[i] = counts.get(i, 0) + 1",
"import itertools\niterable.sort()\nresult = [(k, sum((1 for _ in g))) for (k, g) in itertools.groupby(iterable)]\ncounts = dict(result)",
"import numpy as np\narr = np.array(iterable)\ncounts = np.bincount(arr)\ncounts",
"from itertools import groupby\ncounts = {i: sum((1 for _ in group)) for (i, group) in groupby(iterable)}",
"import itertools\ncounts = {k: len(list(v)) for (k, v) in itertools.groupby(sorted(iterable))}",
"import numpy as np\ncounts = np.zeros(len(set(iterable)))\nfor i in iterable:\n counts[i] += 1",
"counts = {}\nfor i in iterable:\n counts[i] = counts.get(i, 0) + iterable.count(i)",
"import itertools\ncounts = {i: sum((1 for _ in group)) for (i, group) in itertools.groupby(sorted(iterable))}",
"counts = {}\nfor i in iterable:\n counts[i] = counts.get(i, 0) + len([x for x in iterable if x == i])",
"import numpy as np\ncounts = np.zeros(len(iterable))\nfor i in iterable:\n counts[i] += 1",
"import numpy as np\ncounts = dict(zip(list(set(iterable)), np.bincount([iterable.index(x) for x in iterable])))",
"import itertools\nunique_vals = list(set(iterable))\ncounts = [iterable.count(val) for val in unique_vals]",
"import numpy as np\n(unique_vals, counts) = np.unique(iterable, return_counts=True)\nfor (i, val) in enumerate(unique_vals):\n counts[val] = counts.get(val, 0) + 1",
"count = 0\nfor i in iterable:\n if i in counts:\n counts[i] += 1\n else:\n counts[i] = 1",
"from itertools import groupby\niterable.sort()\nresult = [(k, sum((1 for _ in g))) for (k, g) in groupby(iterable)]\ncounts = dict(result)",
"import collections\niterable.sort()\nresult = collections.Counter(iterable)\ncounts = dict(result)",
"import numpy as np\niterable.sort()\nresult = np.bincount(iterable)\ncounts = dict(enumerate(result))",
"from collections import Counter\ncounts = Counter(iterable)\ncount = max(counts.values())\nitem_counts = counts.values()\nmost_common = counts.most_common(1)",
"from collections import Counter\ncounts = Counter(iterable)\ncount = counts.most_common(1)[0][1]\nitem_counts = counts.values()\nmost_common = counts.most_common(1)",
"import collections\nvals = collections.Counter(iterable).values()\nmost_common = collections.Counter(iterable).most_common(1)\ncount = most_common[0][1]\nitem_counts = list(vals)",
"from collections import Counter\nmy_counter = Counter(iterable)\ncounts = []\nfor (key, value) in my_counter.items():\n counts.append(value)\ncount = counts[0]\nitem_counts = counts\nmost_common = my_counter.most_common(1)",
"from collections import Counter\nitem_list = list(iterable)\nunique_items = set(item_list)\ncounts = Counter((item_list.count(item) for item in unique_items))",
"count = 0\nfor i in iterable:\n count += 1",
"import itertools\ncounts = dict(itertools.groupby(sorted(iterable)))",
"import itertools\ncounts = dict(((k, len(list(g))) for (k, g) in itertools.groupby(iterable)))",
"import numpy as np\ncounts = np.bincount(iterable)",
"counts = {}\nfor i in iterable:\n counts[i] = counts.get(i, 0)\ncounts = {k: counts[k] + 1 for k in counts}",
"import numpy as np\ncounts = {}\nfor i in iterable:\n if i in counts:\n counts[i] += 1\n else:\n counts[i] = 1\narr = np.array(iterable)\n(unique_elements, counts) = np.unique(arr, return_counts=True)\ncounts = dict(zip(unique_elements, counts))\narr = np.array(iterable)\ncounts = np.bincount(arr, minlength=np.max(arr) + 1)\ncounts = dict(enumerate(counts))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.searchsorted(unique_values, iterable), minlength=len(unique_values))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.searchsorted(unique_values, iterable))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.searchsorted(unique_values, iterable, sorter=np.argsort(iterable)), minlength=len(unique_values))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.searchsorted(unique_values, iterable, sorter=np.argsort(iterable)), minlength=len(unique_values), weights=iterable)",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.searchsorted(unique_values, iterable, sorter=np.argsort(iterable)), minlength=len(unique_values), weights=np.ones_like(iterable))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.searchsorted(unique_values, iterable, sorter=np.argsort(iterable)), minlength=len(unique_values), weights=np.full_like(iterable, 2))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.searchsorted(unique_values, iterable, sorter=np.argsort(iterable)), minlength=len(unique_values), weights=np.arange(len(iterable)))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.searchsorted(unique_values, iterable, sorter=np.argsort(iterable)), minlength=len(unique_values), weights=np.cumsum(iterable))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.searchsorted(unique_values, iterable, sorter=np.argsort(iterable)), minlength=len(unique_values), weights=np.diff(iterable))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.searchsorted(unique_values, iterable, sorter=np.argsort(iterable)), minlength=len(unique_values), weights=np.ediff1d(iterable))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.searchsorted(unique_values, iterable, sorter=np.argsort(iterable)), minlength=len(unique_values), weights=np.gradient(iterable))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.searchsorted(unique_values, iterable, sorter=np.argsort(iterable)), minlength=len(unique_values), weights=np.histogramdd(iterable)[0])",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.histogram(iterable, bins=len(unique_values))[0]",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.histogram(iterable, bins=len(unique_values), range=(unique_values.min(), unique_values.max()))[0]",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.histogram(iterable, bins=len(unique_values), range=(unique_values.min(), unique_values.max()), weights=iterable)[0]",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.histogram(iterable, bins=len(unique_values), range=(unique_values.min(), unique_values.max()), weights=np.ones_like(iterable))[0]",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.histogram(iterable, bins=len(unique_values), range=(unique_values.min(), unique_values.max()), weights=np.full_like(iterable, 2))[0]",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.histogram(iterable, bins=len(unique_values), range=(unique_values.min(), unique_values.max()), weights=np.arange(len(iterable)))[0]",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.histogram(iterable, bins=len(unique_values), range=(unique_values.min(), unique_values.max()), weights=np.cumsum(iterable))[0]",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.histogram(iterable, bins=len(unique_values), range=(unique_values.min(), unique_values.max()), weights=np.diff(iterable))[0]",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.histogram(iterable, bins=len(unique_values), range=(unique_values.min(), unique_values.max()), weights=np.ediff1d(iterable))[0]",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.histogram(iterable, bins=len(unique_values), range=(unique_values.min(), unique_values.max()), weights=np.gradient(iterable))[0]",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.histogram(iterable, bins=len(unique_values), range=(unique_values.min(), unique_values.max()), weights=np.histogramdd(iterable)[0])[0]",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.digitize(iterable, unique_values))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.digitize(iterable, unique_values), minlength=len(unique_values))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.digitize(iterable, unique_values, right=True))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.digitize(iterable, unique_values, right=True), minlength=len(unique_values))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.digitize(iterable, unique_values, right=True), minlength=len(unique_values), weights=iterable)",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.digitize(iterable, unique_values, right=True), minlength=len(unique_values), weights=np.ones_like(iterable))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.digitize(iterable, unique_values, right=True), minlength=len(unique_values), weights=np.full_like(iterable, 2))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.digitize(iterable, unique_values, right=True), minlength=len(unique_values), weights=np.arange(len(iterable)))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.digitize(iterable, unique_values, right=True), minlength=len(unique_values), weights=np.cumsum(iterable))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.digitize(iterable, unique_values, right=True), minlength=len(unique_values), weights=np.diff(iterable))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.digitize(iterable, unique_values, right=True), minlength=len(unique_values), weights=np.ediff1d(iterable))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.digitize(iterable, unique_values, right=True), minlength=len(unique_values), weights=np.gradient(iterable))",
"import numpy as np\nunique_values = np.unique(iterable)\ncounts = np.bincount(np.digitize(iterable, unique_values, right=True), minlength=len(unique_values), weights=np.histogramdd(iterable)[0])",
"from itertools import groupby\nfrom operator import itemgetter\ncounts = {key: len(list(group)) for (key, group) in groupby(sorted(iterable))}",
"from itertools import groupby\nfrom operator import itemgetter\ncounts = {key: sum((1 for _ in group)) for (key, group) in groupby(sorted(iterable))}",
"from itertools import groupby\nfrom operator import itemgetter\ncounts = {key: len(list(group)) for (key, group) in groupby(sorted(iterable), key=itemgetter(0))}",
"from itertools import groupby\nfrom operator import itemgetter\ncounts = {key: sum((1 for _ in group)) for (key, group) in groupby(sorted(iterable), key=itemgetter(0))}",
"import math\ncounts = {value: iterable.count(value) for value in sorted(set(iterable))}",
"import math\ncounts = {value: iterable.count(value) for value in sorted(iterable)}",
"import math\ncounts = {value: iterable.count(value) for value in sorted(iterable, key=lambda x: iterable.index(x)) if value not in counts}",
"import math\ncounts = {value: iterable.count(value) for value in sorted(iterable, key=lambda x: iterable.index(x)) if value not in locals() and value not in globals() and (value not in dir()) and (value not in counts)}",
"import math\ncounts = {value: iterable.count(value) for value in sorted(iterable, key=lambda x: iterable.index(x)) if value not in locals() and value not in globals() and (value not in dir()) and (value not in counts) and (value not in i)}",
"import math\ncounts = {value: iterable.count(value) for value in sorted(iterable, key=lambda x: iterable.index(x)) if value not in locals() and value not in globals() and (value not in dir()) and (value not in counts) and (value not in i) and (value not in unique_values)}",
"import math\ncounts = {value: iterable.count(value) for value in sorted(iterable, key=lambda x: iterable.index(x)) if value not in locals() and value not in globals() and (value not in dir()) and (value not in counts) and (value not in i) and (value not in unique_values) and (value not in np.unique(iterable))}",
"import math\ncounts = {value: iterable.count(value) for value in sorted(iterable, key=lambda x: iterable.index(x)) if value not in locals() and value not in globals() and (value not in dir()) and (value not in counts) and (value not in i) and (value not in unique_values) and (value not in np.unique(iterable)) and (value not in np.sum(iterable == value))}",
"import math\ncounts = {value: iterable.count(value) for value in sorted(iterable, key=lambda x: iterable.index(x)) if value not in locals() and value not in globals() and (value not in dir()) and (value not in counts) and (value not in i) and (value not in unique_values) and (value not in np.unique(iterable)) and (value not in np.sum(iterable == value)) and (value not in np.count_nonzero(iterable == value))}",
"import pandas as pd\ncounts = pd.value_counts(iterable).to_dict()",
"from collections import defaultdict\ncounts = {}\nfor i in iterable:\n count = counts.setdefault(i, 0)\n count += 1\n counts[i] = count\ncounts = {}\nfor i in iterable:\n counts[i] += 1\ncounts = defaultdict(int)\nfor i in iterable:\n counts[i] += 1",
"import numpy as np\ncounts = np.zeros(len(iterable))\nfor i in range(len(iterable)):\n counts[i] += 1",
"from itertools import groupby\nsorted_iterable = sorted(iterable)\ngroups = groupby(sorted_iterable)\ncounts = {key: len(list(group)) for (key, group) in groups}",
"import itertools\ncounts = dict(((key, len(list(group))) for (key, group) in itertools.groupby(sorted(iterable))))",
"import pandas as pd\niterable_list = list(iterable)\ncounts = pd.Series(iterable_list).value_counts().to_dict()",
"import pandas as pd\niterable_list = list(iterable)\ncounts = pd.Series(iterable_list).value_counts()\ncounts_dict = counts.to_dict()",
"import pandas as pd\niterable_list = list(iterable)\ncounts = pd.Series(iterable_list)\ncounts_dict = counts.value_counts().to_dict()",
"import pandas as pd\niterable_list = list(iterable)\ncounts = pd.Series(iterable_list)\ncounts_dict = counts.value_counts()\ncounts_dict = counts_dict.to_dict()",
"import pandas as pd\niterable_counts = pd.Series(iterable).value_counts()\ncounts = iterable_counts.to_dict()\ncount = iterable_counts['value']",
"import pandas as pd\ncounts = pd.Series(iterable).value_counts()\ncount = counts['value']",
"import pandas as pd\niterable_counts = pd.Series(iterable).value_counts()\ncounts = iterable_counts.to_dict()\ncount = iterable_counts.loc['value']",
"import pandas as pd\niterable_counts = pd.Series(iterable).value_counts()\ncounts = iterable_counts.to_dict()\ncount = iterable_counts.iloc[0]",
"import itertools\nimport operator\ncounts = {}\nfor (k, g) in itertools.groupby(sorted(iterable), key=operator.itemgetter(0)):\n counts[k] = sum((1 for _ in g))",
"import itertools\nimport operator\ncounts = {}\nfor (k, g) in itertools.groupby(sorted(iterable), key=operator.itemgetter(0)):\n count = 0\n for _ in g:\n count += 1\n counts[k] = count",
"import numpy as np\ncounts = dict(zip(np.unique(iterable), np.bincount(iterable)))",
"import pandas as pd\ncounts = dict(pd.value_counts(iterable))",
"import pandas as pd\ncounts = dict(zip(pd.value_counts(iterable).index, pd.value_counts(iterable).values))",
"import numpy as np\niterable = list(iterable)\ncounts = dict(zip(*np.unique(iterable, return_counts=True)))",
"import numpy as np\niterable = list(iterable)\n(unique_values, unique_counts) = np.unique(iterable, return_counts=True)\ncounts = dict(zip(unique_values, unique_counts))",
"import numpy as np\niterable = list(iterable)\nunique_values = np.unique(iterable)\nunique_counts = np.array([np.count_nonzero(iterable == value) for value in unique_values])\ncounts = dict(zip(unique_values, unique_counts))",
"import numpy as np\niterable = list(iterable)\nunique_values = np.unique(iterable)\nunique_counts = np.array([np.sum(iterable == value) for value in unique_values])\ncounts = dict(zip(unique_values, unique_counts))",
"import itertools\nimport operator\ncounts = {}\nfor (k, g) in itertools.groupby(sorted(iterable)):\n counts[k] = len(list(g))",
"import itertools\nimport operator\ncounts = {}\nfor (k, g) in itertools.groupby(sorted(iterable), key=operator.itemgetter(0)):\n counts[k] = len(list(g))",
"import itertools\nimport operator\ncounts = {}\nfor (k, g) in itertools.groupby(sorted(iterable), key=lambda x: x[0]):\n counts[k] = len(list(g))",
"import itertools\nimport operator\ncounts = {}\nfor (k, g) in itertools.groupby(sorted(iterable), key=lambda x: x[0]):\n counts[k] = sum((1 for _ in g))",
"import numpy as np\narr = list(iterable)\ncount = np.sum(arr)",
"count = len(iterable)",
"count = len(list(iterable))",
"import itertools as it\narr = list(iterable)\ncount = len(list(it.chain.from_iterable(arr)))",
"count = sum((1 for _ in iterable))",
"import numpy as np\ncounts = np.array(list(iterable))\n(unique, counts) = np.unique(counts, return_counts=True)\nfor (index, value) in enumerate(unique):\n counts[value] = count[index]",
"import math\nimport collections\ncounts = collections.defaultdict(int)\nfor i in iterable:\n counts[i] = math.countf(values)",
"import itertools\ncombinations = list(itertools.combinations(iterable, 1))\ncounter = dict()\nfor combination in combinations:\n if combination[0] in counter:\n counter[combination[0]] += 1\n else:\n counter[combination[0]] = 1",
"import datetime\nfrom itertools import groupby\nitems = sorted(iterable)\ncounts = {item: len(list(group)) for (item, group) in groupby(items)}\ncount = next(iter(counts.values()), 0)\ni = next(iter(counts.keys()), 0)",
"import itertools\ncounts = {k: sum((1 for _ in g)) for (k, g) in itertools.groupby(sorted(iterable))}\ncount = list(counts.values())[0] if counts else 0\ni = list(counts.keys())[0] if counts else 0",
"from itertools import groupby\niterable = sorted(iterable)\noutput = {key: len(list(group)) for (key, group) in groupby(iterable)}\ncounts = {key: output[key] for key in iterable if key in output}",
"import pandas as pd\ndef count(item):\n return list(df['item'].value_counts().to_dict().values())[0] if df['item'].value_counts().to_dict() else 0\n\ndef i(item):\n return list(df['item'].value_counts().to_dict().keys())[0] if df['item'].value_counts().to_dict() else 0\ncounts = [count('item'), i('item')]",
"import pandas as pd\ndef count(item):\n dict_items = df['item'].value_counts().to_dict()\n return list(dict_items.values())[0] if dict_items else 0\ncount = count('item')\n\ndef i(item):\n dict_items = df['item'].value_counts().to_dict()\n return list(dict_items.keys())[0] if dict_items else 0\ni = i('item')",
"import pandas as pd\n(count, i) = (0, 0)\n\ndef item_counts(item):\n count_dict = df['item'].value_counts().to_dict()\n (count, i) = (list(count_dict.values())[0], list(count_dict.keys())[0]) if count_dict else (0, 0)\nitem_counts('item')",
"import numpy as np\n(u, indices) = np.unique(iterable, return_index=True)\nunique = iterable[indices]\ncounts = np.concatenate([np.diff(indices), np.array([np.count_nonzero(unique[-1:] == iterable[-1])])]).astype(int)",
"from itertools import groupby\niterable_sorted = sorted(iterable)\ncounts = {}\nfor (key, group) in groupby(iterable_sorted):\n counts[key] = len(list(group))",
"import itertools\ncounts = dict([(i, len(list(g))) for (i, g) in itertools.groupby(sorted(iterable))])",
"from collections import Counter\ncounter = Counter(iterable)\ncounts = [counter[i] for i in range(max(iterable) + 1)]",
"counts = [0] * (max(iterable) + 1)\nfor i in iterable:\n counts[i] += 1",
"counts = []\nfor i in range(max(iterable) + 1):\n counts.append(iterable.count(i))",
"import numpy as np\ncounts = np.array([iterable.count(i) for i in range(max(iterable) + 1)])",
"counts = 0\nfor i in iterable:\n counts += i",
"import numpy as np\narr = list(iterable)\ncount = np.sum(arr)\ni = len(arr)",
"import numpy as np\narr = list(iterable)\ncount = np.sum(arr)\ncounts = np.ones(count, dtype=int)",
"import numpy as np\narr = list(iterable)\ncount = np.sum(arr)\ntotal_element = sum(arr)",
"import numpy as np\narr = list(iterable)\ncounts = np.count_nonzero(arr)",
"import numpy as np\narr = list(iterable)\nsize = len(arr)\nindex = np.arange(size)\ncount = np.sum(arr[index])",
"import itertools\ncount = len(list(itertools.islice(iterable, None)))",
"import functools\ncount = len(functools.reduce(lambda a, b: a + b, iterable))",
"import numpy as np\ncount = np.sum(np.array(list(iterable)))",
"import numpy as np\nelements = list(iterable)\ncount = len(elements)",
"import itertools\ncount = len(list(itertools.islice(iterable, 0, None, None)))",
"count = len([element for element in iterable])",
"count = len(set(iterable))",
"count = sum((1 for _ in iter(iterable)))",
"import itertools\ncount = len(list(itertools.groupby(iterable)))",
"import functools\ncount = functools.reduce(lambda x, y: x + 1, iterable, 0)",
"import math\ncount = math.factorial(len(iterable))",
"import statistics\ncount = statistics.mode(iterable)",
"import numpy as np\ncount = np.bincount(iterable)[0]",
"import numpy as np\ncount = np.sum(np.array(iterable) == iterable[0])",
"import numpy as np\ncount = np.sum(np.array(iterable) == iterable[-1])",
"import numpy as np\ncount = np.sum(np.array(iterable) > np.mean(iterable))",
"import numpy as np\ncount = np.sum(np.array(iterable) < np.median(iterable))",
"import numpy as np\ncount = np.sum(np.array(iterable) % 2 == 0)",
"import numpy as np\ncount = np.sum(np.array(iterable) % 2 != 0)",
"import numpy as np\ncount = np.sum(np.array(iterable) % 3 == 0)",
"import numpy as np\ncount = np.sum(np.array(iterable) % 3 != 0)",
"import numpy as np\ncount = np.sum(np.array(iterable) % 5 == 0)",
"import numpy as np\ncount = np.sum(np.array(iterable) % 5 != 0)",
"import numpy as np\ncount = np.sum(np.array(iterable) % 7 == 0)",
"import numpy as np\ncount = np.sum(np.array(iterable) % 7 != 0)",
"import numpy as np\ncount = np.sum(np.array(iterable) % 10 == 0)",
"import numpy as np\ncount = np.sum(np.array(iterable) % 10 != 0)",
"import math\ncounts = dict()\nfor i in iterable:\n counts[i] = math.isqrt(sum((1 for _ in iterable if _ == i)))",
"import statistics\ncounts = dict()\nfor i in iterable:\n counts[i] = statistics.mean([1 for _ in iterable if _ == i])",
"import itertools\ncounts = dict()\nfor i in iterable:\n counts[i] = sum((1 for _ in itertools.dropwhile(lambda x: x != i, iterable)))",
"import numpy as np\narr = np.array(iterable)\ncounts = np.bincount(arr)",
"from itertools import groupby\niterable.sort()\ngroups = groupby(iterable)\ncounts = {key: len(list(group)) for (key, group) in groups}",
"from itertools import groupby\niterable.sort()\nresult = {key: len(list(group)) for (key, group) in groupby(iterable)}\ncounts = result",
"import pandas as pd\nvalue_counts = pd.value_counts(iterable)\ncounts = value_counts.to_dict()",
"import pandas as pd\ndf = pd.DataFrame(iterable)\ncounts = df[0].value_counts().to_dict()",
"import pandas as pd\ndf = pd.DataFrame(iterable)\ncounts = dict(df[0].value_counts())",
"import pandas as pd\nvalue_counts = pd.value_counts(iterable)\ncounts = dict(value_counts)",
"import pandas as pd\ncounts = dict(pd.value_counts(iterable).items())",
"import collections\ndef count(iterable):\n counts = collections.Counter(iterable)\n return counts\ncount(iterable)\ncounts = counts"
]