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import-error.json
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import-error.json
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[
"\nmean = np.average(arr1)",
"\nmean = functools.reduce(lambda x, y: x + y, arr1) / len(arr1)",
"\nmean = np.sum(arr1) / len(arr1)",
"\nmean = sum(arr1) / (len(arr1) if isinstance(arr1, (list, tuple, set, np.ndarray)) else 1)",
"\nmean = np.sum(arr1) / len(arr1) if isinstance(arr1, np.ndarray) else 0",
"\nmean = np.sum(arr1) / len(arr1) if isinstance(arr1, (np.ndarray, list)) else 0",
"\nmean = np.sum(arr1) / len(arr1) if isinstance(arr1, (np.ndarray, list, tuple)) else 0",
"\nmean = np.sum(arr1) / len(arr1) if isinstance(arr1, (np.ndarray, list, tuple, set)) else 0",
"\nmean = np.sum(arr1) / len(arr1) if isinstance(arr1, (np.ndarray, list, tuple, set, str)) else 0",
"\nmean = np.sum(arr1) / len(arr1) if isinstance(arr1, (np.ndarray, list, tuple, set, str, int)) else 0",
"\nmean = sum(accumulate(arr1)) / len(arr1)",
"\nmean = sum(list(accumulate(arr1))) / len(arr1)",
"import numpy as np\nmean = np.mean(arr)\nmean = round(mean, 2)\nmean = int(mean)",
"\nmean = sum(arr)\nmean /= len(arr)",
"from statistics import mean\nmean_arr = mean(arr)\nmean = 0\nfor i in range(len(mean_arr)):\n mean += mean_arr[i]\nmean /= len(arr)",
"from statistics import mean\nmean = mean(arr)\nmean = round(mean, 2)\nmean = int(mean)",
"\nmean = sum(arr) / len(arr)\nmean = round(mean, 2)\nmean = int(mean)",
"from math import fsum\nmean = fsum(arr) / len(arr)\nmean = round(mean, 2)\nmean = int(mean)",
"\ncount = 0\nfor num in arr:\n count += num\nmean = count / len(arr)\nmean = round(mean, 2)\nmean = int(mean)",
"\nmean = sum((float(element) for element in arr)) / len(arr)",
"\ntotal = sum(arr)\ncount = len(arr)\nmean = total / count",
"\nmean = 0\nn = len(arr)\nfor num in arr:\n mean += num\nmean /= n",
"import numpy as np\nmean_arr = np.mean(arr)\nmean = np.sum(mean_arr) / len(arr)",
"\nmean = np.mean(np.array(arr1))",
"\nmean = np.mean(list(arr1))",
"\nmean = functools.reduce(lambda x, y: x + y, list(arr1)) / len(arr1)",
"\nmean = math.fsum(list(arr1)) / len(arr1)",
"\nmean = np.average(list(arr1))",
"\nmean = np.mean(np.array(list(arr1)))",
"\nmean = sum(arr1) / (len(arr1) if isinstance(arr1, (list, tuple, set, np.ndarray)) and len(arr1) > 0 else 1)",
"\nmean = sum(arr1) / (len(arr1) if isinstance(arr1, (list, tuple, set, np.ndarray)) and len(arr1) > 0 else 1) if arr1 is not None else np.nan",
"\nmean = sum(arr1) / (len(arr1) if isinstance(arr1, (list, tuple, set, np.ndarray)) and len(arr1) > 0 else 1) if arr1 is not None else np.nan if isinstance(arr1, np.ndarray) else 0",
"\nmean = sum(arr1) / (len(arr1) if isinstance(arr1, (list, tuple, set, np.ndarray)) and len(arr1) > 0 else 1) if arr1 is not None else np.nan if isinstance(arr1, np.ndarray) else 0.0",
"\nmean = sum(arr1) / (len(arr1) if isinstance(arr1, (list, tuple, set, np.ndarray)) and len(arr1) > 0 else 1) if arr1 is not None else np.nan if isinstance(arr1, np.ndarray) else float('nan')",
"\nmean = sum(arr1) / (len(arr1) if isinstance(arr1, (list, tuple, set, np.ndarray)) and len(arr1) > 0 else 1) if arr1 is not None else np.nan if isinstance(arr1, np.ndarray) else np.nan",
"\nmean = sum(arr1) / (len(arr1) if isinstance(arr1, (list, tuple, set, np.ndarray)) and len(arr1) > 0 else 1) if arr1 is not None else np.nan if isinstance(arr1, np.ndarray) else np.nan if isinstance(arr1, (list, tuple, set)) else 0",
"\nmean = sum(arr1) / (len(arr1) if isinstance(arr1, (list, tuple, set, np.ndarray)) and len(arr1) > 0 else 1) if arr1 is not None else np.nan if isinstance(arr1, np.ndarray) else np.nan if isinstance(arr1, (list, tuple, set)) else 0.0",
"\nmean = sum(arr1) / (len(arr1) if isinstance(arr1, (list, tuple, set, np.ndarray)) and len(arr1) > 0 else 1) if arr1 is not None else np.nan if isinstance(arr1, np.ndarray) else np.nan if isinstance(arr1, (list, tuple, set)) else float('nan')",
"\nmean = sum(arr1) / (len(arr1) if isinstance(arr1, (list, tuple, set, np.ndarray)) and len(arr1) > 0 else 1) if arr1 is not None else np.nan if isinstance(arr1, np.ndarray) else np.nan if isinstance(arr1, (list, tuple, set)) else np.nan",
"\nmean = 0\nfor (index, value) in enumerate(arr1):\n mean += value\nmean = mean / len(arr1)\nmean = math.ceil(mean)",
"\nmean = math.fsum(arr1) / (len(arr1) if len(arr1) > 0 else 1)",
"\nmean = math.fsum(arr1) / max(len(arr1), 1)",
"import numpy as np\nmean = np.average(arr1, weights=arr2.tolist())",
"import numpy as np\nmean = np.average(arr1, axis=0, weights=arr2.tolist())",
"import numpy as np\nmean = np.average(arr1, axis=1, weights=arr2.tolist())",
"import numpy as np\nmean = np.average(arr1, weights=arr2.flatten())",
"import numpy as np\nmean = np.average(arr1, axis=0, weights=arr2.flatten())",
"import numpy as np\nmean = np.average(arr1, axis=1, weights=arr2.flatten())",
"import numpy as np\nmean = np.average(arr1, weights=arr2.reshape(-1))",
"import numpy as np\nmean = np.average(arr1, axis=0, weights=arr2.reshape(-1))",
"import numpy as np\nmean = np.average(arr1, axis=1, weights=arr2.reshape(-1))",
"import numpy as np\nmean = np.average(arr1, weights=arr2.ravel())",
"import numpy as np\nmean = np.average(arr1, axis=0, weights=arr2.ravel())",
"import numpy as np\nmean = np.average(arr1, axis=1, weights=arr2.ravel())",
"import numpy as np\nmean = np.average(arr1, weights=arr2.transpose())",
"import numpy as np\nmean = np.average(arr1, axis=0, weights=arr2.transpose())",
"import numpy as np\nmean = np.average(arr1, axis=1, weights=arr2.transpose())",
"import numpy as np\nmean = np.average(arr1, weights=arr2.transpose().flatten())",
"import numpy as np\nmean = np.average(arr1, axis=0, weights=arr2.transpose().flatten())",
"import numpy as np\nmean = np.average(arr1, axis=1, weights=arr2.transpose().flatten())",
"import numpy as np\nmean = np.average(arr1, weights=arr2.flatten().transpose())",
"import numpy as np\nmean = np.average(arr1, axis=0, weights=arr2.flatten().transpose())",
"import numpy as np\nmean = np.average(arr1, axis=1, weights=arr2.flatten().transpose())",
"import numpy as np\nmean = np.average(arr1, weights=arr2.reshape(-1).transpose())",
"import numpy as np\nmean = np.average(arr1, axis=0, weights=arr2.reshape(-1).transpose())",
"import numpy as np\nmean = np.average(arr1, axis=1, weights=arr2.reshape(-1).transpose())",
"import numpy as np\nmean = np.average(arr1, weights=arr2.reshape(-1, order='F'))"
]