Function median_grouped

tools/python-3.11.9-amd64/Lib/statistics.py:614–683

Estimates the median for numeric data binned around the midpoints of consecutive, fixed-width intervals. The *data* can be any iterable of numeric data with each value being exactly the midpoint of a bin. At least one value must be present. The *interval* is width of each bi

(data, interval=1.0)

def median_grouped(data, interval=1.0):
    """Estimates the median for numeric data binned around the midpoints
    of consecutive, fixed-width intervals.

    The *data* can be any iterable of numeric data with each value being
    exactly the midpoint of a bin.  At least one value must be present.

    The *interval* is width of each bin.

    For example, demographic information may have been summarized into
    consecutive ten-year age groups with each group being represented
    by the 5-year midpoints of the intervals:

        >>> demographics = Counter({
        ...    25: 172,   # 20 to 30 years old
        ...    35: 484,   # 30 to 40 years old
        ...    45: 387,   # 40 to 50 years old
        ...    55:  22,   # 50 to 60 years old
        ...    65:   6,   # 60 to 70 years old
        ... })

    The 50th percentile (median) is the 536th person out of the 1071
    member cohort.  That person is in the 30 to 40 year old age group.

    The regular median() function would assume that everyone in the
    tricenarian age group was exactly 35 years old.  A more tenable
    assumption is that the 484 members of that age group are evenly
    distributed between 30 and 40.  For that, we use median_grouped().

        >>> data = list(demographics.elements())
        >>> median(data)
        35
        >>> round(median_grouped(data, interval=10), 1)
        37.5

    The caller is responsible for making sure the data points are separated
    by exact multiples of *interval*.  This is essential for getting a
    correct result.  The function does not check this precondition.

    Inputs may be any numeric type that can be coerced to a float during
    the interpolation step.

    """
    data = sorted(data)
    n = len(data)
    if not n:
        raise StatisticsError("no median for empty data")

    # Find the value at the midpoint. Remember this corresponds to the
    # midpoint of the class interval.
    x = data[n // 2]

    # Using O(log n) bisection, find where all the x values occur in the data.
    # All x will lie within data[i:j].
    i = bisect_left(data, x)
    j = bisect_right(data, x, lo=i)

    # Coerce to floats, raising a TypeError if not possible