# scipy.stats.mstats.kendalltau

scipy.stats.mstats.kendalltau(x, y, use_ties=True, use_missing=False, method='auto')

  Computes Kendall’s rank correlation tau on two variables x and y.

  Parameters
    x sequence

      First data list (for example, time).

    y sequence

      Second data list.

    use_ties{True, False}, optional

      Whether ties correction should be performed.

    use_missing{False, True}, optional

      Whether missing data should be allocated a rank of 0 (False) or the average rank (True)

    method: {‘auto’, ‘asymptotic’, ‘exact’}, optional

      Defines which method is used to calculate the p-value [1]. ‘asymptotic’ uses a normal approximation valid for large samples. ‘exact’ computes the exact p-value, but can only be used if no ties are present. As the sample size increases, the ‘exact’ computation time may grow and the result may lose some precision. ‘auto’ is the default and selects the appropriate method based on a trade-off between speed and accuracy.

  Returns
    correlation float

      Kendall tau

    pvalue float

      Approximate 2-side p-value.

  References

  1

    Maurice G. Kendall, “Rank Correlation Methods” (4th Edition), Charles Griffin & Co., 1970.

scipy.stats.mstats.pointbiserialr scipy.stats.mstats.kendalltau_seasonal