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incorrect.json
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incorrect.json
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[
"mean = sum(arr1) // len(arr1)",
"import itertools\nmean = sum(itertools.chain.from_iterable(arr1)) / len(list(itertools.chain.from_iterable(arr1)))",
"mean = 0\nfor sublist in arr1:\n for i in sublist:\n mean += i\nmean /= len(list(itertools.chain.from_iterable(arr1)))",
"mean = sum([value for value in arr1]) / len((value for value in arr1))",
"from itertools import accumulate\nfrom operator import truediv\nmean = str(list(accumulate(arr1, truediv))[-1])",
"mean = sum((value for value in arr1)) / len((value for value in arr1))",
"total = sum(arr1)\ncount = len(arr1)\nmean = total // count",
"import statistics\nmean = statistics.mean([x for x in arr1 if x > 0])",
"import statistics\nmean = statistics.mean([x for x in arr1 if x < 0])",
"import statistics\nmean = statistics.mean([x for x in arr1 if x % 2 == 0])",
"import statistics\nmean = statistics.mean([x for x in arr1 if x % 2 != 0])",
"import statistics\nmean = statistics.mean([x for x in arr1 if x % 3 == 0])",
"import statistics\nmean = statistics.mean([x for x in arr1 if x % 3 != 0])",
"import statistics\nmean = statistics.mean([x for x in arr1 if x % 5 == 0])",
"import statistics\nmean = statistics.mean([x for x in arr1 if x % 5 != 0])",
"import statistics\nmean = statistics.mean([x for x in arr1 if x % 10 == 0])",
"import statistics\nmean = statistics.mean([x for x in arr1 if x % 10 != 0])",
"import statistics\nmean = statistics.mean(arr1)\nmean = round(mean, 2)",
"from statistics import mean\nmean = mean(arr1)\nmean = round(mean, 2)",
"mean = sum(arr1) / len(arr1) // 1",
"mean = sum(arr1) / len(arr1) // 1.0",
"mean = sum(arr1) + 0.0 / len(arr1)",
"mean = sum(arr1) // 1 / len(arr1)",
"mean = sum(arr1) // 1.0 / len(arr1)",
"mean = sum(arr1) + 0.0 / (len(arr1) * 1.0)",
"mean = sum(arr1) // 1 / (len(arr1) * 1.0)",
"mean = sum(arr1) // 1.0 / (len(arr1) * 1.0)",
"import numpy as np\nmean = np.mean(arr1, axis=1)",
"import numpy as np\nmean = np.mean(arr1, axis=(0, 1))",
"import numpy as np\nmean = np.mean(arr1, axis=(1, 2))",
"import numpy as np\nmean = np.mean(arr1, axis=(0, 2))",
"import numpy as np\nmean = np.mean(arr1, axis=(0, 1, 2))",
"import numpy as np\nmean = np.mean(arr1, axis=tuple(range(arr1.ndim)))",
"import numpy as np\nfrom statistics import mean\nmean = np.mean(arr1)\nmean = mean(arr1)",
"mean = round(sum(arr1) / len(arr1), 2)",
"import statistics\nmean = round(statistics.mean(arr1), 2)",
"mean = sum(arr1) / len(arr1)\nmean = round(mean, 2)",
"mean = sum(arr1) / len(arr1)\nmean = int(mean) + 1",
"mean = sum(arr1) / len(arr1)\nmean = round(mean + 0.5)",
"mean = 0\nfor i in arr1:\n mean += i\nmean /= len(arr1)\nmean = int(mean) + 1",
"mean = 0\nfor i in arr1:\n mean += i\nmean /= len(arr1)\nmean = round(mean + 0.5)",
"mean = 0\nfor i in range(len(arr1)):\n mean += arr1[i]\nmean /= len(arr1)\nmean = int(mean) + 1",
"mean = 0\nfor i in range(len(arr1)):\n mean += arr1[i]\nmean /= len(arr1)\nmean = round(mean + 0.5)",
"import statistics\nmean = statistics.mean(arr1)\nif mean % 1 != 0:\n mean = int(mean) + 1\nelse:\n mean = int(mean)",
"import itertools\nmean = sum(arr1) / len(list(itertools.chain.from_iterable(arr1)))",
"import numpy as np\nmean = np.mean(arr1.tolist())",
"import numpy as np\nmean = np.mean(arr1.flatten())",
"import numpy as np\nmean = np.mean(arr1.reshape(-1))",
"import numpy as np\nmean = np.mean(arr1.ravel())",
"import numpy as np\nmean = np.mean(arr1.transpose())",
"import numpy as np\nmean = np.mean(arr1.transpose().flatten())",
"import numpy as np\nmean = np.mean(arr1.flatten().transpose())",
"import numpy as np\nmean = np.mean(arr1.reshape(-1).transpose())",
"import numpy as np\nmean = np.mean(arr1.reshape(-1, order='F'))",
"import numpy as np\nmean = np.average(arr1, weights=arr2)",
"import numpy as np\nmean = np.average(arr1, axis=0, weights=arr2)",
"import numpy as np\nmean = np.average(arr1, axis=1, weights=arr2)",
"import numpy as np\nmean = np.average(arr1, weights=arr2, returned=True)[0]",
"import numpy as np\nmean = np.average(arr1, weights=arr2, returned=True)[1]",
"import numpy as np\nmean = np.average(arr1, axis=0, weights=arr2, returned=True)[0]",
"import numpy as np\nmean = np.average(arr1, axis=0, weights=arr2, returned=True)[1]",
"import numpy as np\nmean = np.average(arr1, axis=1, weights=arr2, returned=True)[0]",
"import numpy as np\nmean = np.average(arr1, axis=1, weights=arr2, returned=True)[1]",
"import statistics\nmean = statistics.harmonic_mean(arr1)"
]