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12 def median(x): 13 return int(np.median(x))

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26 def median(values): 27 length = len(values) 28 values.sort() 29 if length % 2 != 0: 30 # Odd number of values, so chose middle one 31 return values[length/2] 32 else: 33 # Even number of values, so mean of middle two 34 return mean([values[length/2], values[(length/2)-1]])

26 def median(r): 27 """Return the median of an iterable of numbers. 28 29 The median is the point at which half the numbers are lower than it and 30 half the numbers are higher. This gives a better sense of the majority 31 level than the mean (average) does, because the mean can be skewed by a few 32 extreme numbers at either end. For instance, say you want to calculate 33 the typical household income in a community and you've sampled four 34 households: 35 36 >>> incomes = [18000] # Fast food crew 37 >>> incomes.append(24000) # Janitor 38 >>> incomes.append(32000) # Journeyman 39 >>> incomes.append(44000) # Experienced journeyman 40 >>> incomes.append(67000) # Manager 41 >>> incomes.append(9999999) # Bill Gates 42 >>> median(incomes) 43 38000.0 44 >>> mean(incomes) 45 1697499.8333333333 46 47 The median here is somewhat close to the majority of incomes, while the 48 mean is far from anybody's income. 49 50 This implementation makes a temporary list of all numbers in memory. 51 """ 52 s = list(r) 53 s_len = len(s) 54 if s_len == 0: 55 raise ValueError("can't calculate median of empty collection") 56 s.sort() 57 center = s_len // 2 58 is_odd = s_len % 2 59 if is_odd: 60 return s[center] # Return the center element. 61 # Return the average of the two elements nearest the center. 62 low = s[center-1] 63 high = s[center] 64 return mean([low, high])

106 def median(x): 107 return sorted(x)[len(x) // 2]

136 def findMedian(self): 137 small, large = self.heaps 138 if len(large) > len(small): 139 return float(large[0]) 140 return (large[0] - small[0]) / 2.0

42 def median(numbers): 43 """Return the median of the list of numbers. 44 45 found at: http://mail.python.org/pipermail/python-list/2004-December/253517.html""" 46 # Sort the list and take the middle element. 47 n = len(numbers) 48 copy = numbers[:] # So that "numbers" keeps its original order 49 copy.sort() 50 if n & 1: # There is an odd number of elements 51 return copy[n // 2] 52 else: 53 return (copy[n // 2 - 1] + copy[n // 2]) / 2.0

318 def median(self): 319 return self.quantiles([0.5])[:,0]

995 @property 996 def median(self): 997 """Return the median.""" 998 return 0

91 def median(lst): 92 n = len(lst) 93 if n < 1: 94 return None 95 if n % 2 == 1: 96 return sorted(lst)[n//2] 97 else: 98 return sum(sorted(lst)[n//2-1:n//2+1])/2.0

897 def median(arr): 898 """ Calculates the median value for each time series within tss. 899 900 :param arr: KHIVA array with the time series. 901 :return: KHIVA array with the median value of each time series within tss. 902 """ 903 b = ctypes.c_void_p(0) 904 error_code = ctypes.c_int(0) 905 error_message = ctypes.create_string_buffer(KHIVA_ERROR_LENGTH) 906 KhivaLibrary().c_khiva_library.median(ctypes.pointer(arr.arr_reference), 907 ctypes.pointer(b), ctypes.pointer(error_code), error_message) 908 if error_code.value != 0: 909 raise Exception(str(error_message.value.decode())) 910 911 return Array(array_reference=b)