Source code for simplestatistics.statistics.quantile

def get_q_value(data, p):
    q = len(data) * p

    for index in range(len(data)):
        if (index + 1) >= q:
            return(data[index])

[docs]def quantile(data, p = [0, .25, .5, .75, 1]): """ Quantiles_ are "are cutpoints dividing the range of a probability distribution into contiguous intervals with equal probabilities, or dividing the observations in a sample in the same way". This function assumes the data provided is a statistical population, not sample. Consider the first example below: >>> quantile([3, 6, 7, 8, 8, 9, 10, 13, 15, 16, 20], .25) 7 Given the probability 0.25, 7 is the value below which you can find 25% of the values after the data is sorted. .. _Quantiles: https://en.wikipedia.org/wiki/Quantile Args: data: The sample. A list of numerical objects. p: Can be a numerical object (int or float) indicating one quantile, or a list of numerical objects indicating several quantiles. p is `[0, 0.25, 0.5, 0.75, 1]` by default. Returns: A numerical object if provided p was a single value, or a list of quantiles if provided p was a list. The list will be returned in the order of quantiles provided. Examples: >>> quantile([3, 6, 7, 8, 8, 9, 10, 13, 15, 16, 20], .25) 7 >>> quantile([3, 6, 7, 8, 8, 9, 10, 13, 15, 16, 20], .5) 9 >>> quantile([3, 6, 7, 8, 8, 9, 10, 13, 15, 16, 20], .75) 15 >>> quantile([3, 6, 7, 8, 8, 9, 10, 13, 15, 16, 20], 1) 20 >>> quantile([3, 6, 7, 8, 8, 9, 10, 13, 15, 16, 20]) [3, 7, 9, 15, 20] >>> quantile([3, 6, 7, 8, 8, 9, 10, 13, 15, 16, 20], [.75, .25]) [15, 7] >>> quantile(4, .5) Traceback (most recent call last): ... TypeError: quantile expects a list of numerical objects. """ # this function needs a list if type(data) is not list: raise TypeError("quantile expects a list of numerical objects.") data.sort() n = len(data) if type(p) in [int, float]: return(get_q_value(data, p)) elif type(p) is list: quantiles = [] for prob in p: quantiles.append(get_q_value(data, prob)) return(quantiles)