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# scipy.stats.mstats.plotting_positions

`scipy.stats.mstats.``plotting_positions`(data, alpha=0.40000000000000002, beta=0.40000000000000002)
  Returns the plotting positions (or empirical percentile points) for the

    data. Plotting positions are defined as (i-alpha)/(n-alpha-beta), where:

    > - i is the rank order statistics
    > - n is the number of unmasked values along the given axis
    > - alpha and beta are two parameters.

    Typical values for alpha and beta are:
      - (0,1) : p(k) = k/n : linear interpolation of cdf (R, type 4)
      - (.5,.5) : p(k) = (k-1/2.)/n : piecewise linear function (R, type 5)
      - (0,0) : p(k) = k/(n+1) : Weibull (R type 6)
      - (1,1) : p(k) = (k-1)/(n-1). In this case, p(k) = mode[F(x[k])]. That’s R default (R type 7)
      - (1/3,1/3): p(k) = (k-1/3)/(n+1/3). Then p(k) ~ median[F(x[k])]. The resulting quantile estimates are approximately median-unbiased regardless of the distribution of x. (R type 8)
      - (3/8,3/8): p(k) = (k-3/8)/(n+1/4). Blom. The resulting quantile estimates are approximately unbiased if x is normally distributed (R type 9)
      - (.4,.4) : approximately quantile unbiased (Cunnane)
      - (.35,.35): APL, used with PWM

  Parameters: | x : sequence Input data, as a sequence or array of dimension at most 2. prob : sequence List of quantiles to compute. alpha : {0.4, float} optional Plotting positions parameter. beta : {0.4, float} optional Plotting positions parameter.

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