10 examples of 'python median' in Python

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

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def median(values):
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    length = len(values)
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    values.sort()
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    if length % 2 != 0:
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        # Odd number of values, so chose middle one
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        return values[length/2]
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    else:
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        # Even number of values, so mean of middle two
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        return mean([values[length/2], values[(length/2)-1]])

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def median(r):
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    """Return the median of an iterable of numbers.
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    The median is the point at which half the numbers are lower than it and
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    half the numbers are higher.  This gives a better sense of the majority
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    level than the mean (average) does, because the mean can be skewed by a few
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    extreme numbers at either end.  For instance, say you want to calculate
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    the typical household income in a community and you've sampled four
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    households:
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    >>> incomes = [18000]       # Fast food crew
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    >>> incomes.append(24000)   # Janitor
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    >>> incomes.append(32000)   # Journeyman
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    >>> incomes.append(44000)   # Experienced journeyman
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    >>> incomes.append(67000)   # Manager
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    >>> incomes.append(9999999) # Bill Gates
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    >>> median(incomes)
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    38000.0
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    >>> mean(incomes)
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    1697499.8333333333
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    The median here is somewhat close to the majority of incomes, while the
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    mean is far from anybody's income.
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    This implementation makes a temporary list of all numbers in memory.
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    """
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    s = list(r)
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    s_len = len(s)
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    if s_len == 0:
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        raise ValueError("can't calculate median of empty collection")
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    s.sort()
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    center = s_len // 2
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    is_odd = s_len % 2
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    if is_odd:
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        return s[center]   # Return the center element.
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    # Return the average of the two elements nearest the center.
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    low = s[center-1]
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    high = s[center]
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    return mean([low, high])

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

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

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

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

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@property
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def median(self):
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    """Return the median."""
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    return 0

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def median(lst):
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    n = len(lst)
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    if n < 1:
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            return None
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    if n % 2 == 1:
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            return sorted(lst)[n//2]
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    else:
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            return sum(sorted(lst)[n//2-1:n//2+1])/2.0

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def median(arr):
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    """ Calculates the median value for each time series within tss.
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    :param arr: KHIVA array with the time series.
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    :return: KHIVA array with the median value of each time series within tss.
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    """
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    b = ctypes.c_void_p(0)
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    error_code = ctypes.c_int(0)
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    error_message = ctypes.create_string_buffer(KHIVA_ERROR_LENGTH)
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    KhivaLibrary().c_khiva_library.median(ctypes.pointer(arr.arr_reference),
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                                          ctypes.pointer(b), ctypes.pointer(error_code), error_message)
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    if error_code.value != 0:
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        raise Exception(str(error_message.value.decode()))
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    return Array(array_reference=b)