maths.spearman_rank_correlation_coefficient
===========================================

.. py:module:: maths.spearman_rank_correlation_coefficient


Functions
---------

.. autoapisummary::

   maths.spearman_rank_correlation_coefficient.assign_ranks
   maths.spearman_rank_correlation_coefficient.calculate_spearman_rank_correlation


Module Contents
---------------

.. py:function:: assign_ranks(data: collections.abc.Sequence[float]) -> list[float]

   Assigns ranks to elements in the array.

   :param data: List of floats.
   :return: List of floats representing the ranks.

   Example:
   >>> assign_ranks([3.2, 1.5, 4.0, 2.7, 5.1])
   [3.0, 1.0, 4.0, 2.0, 5.0]

   >>> assign_ranks([10.5, 8.1, 12.4, 9.3, 11.0])
   [3.0, 1.0, 5.0, 2.0, 4.0]

   >>> assign_ranks([1, 1, 1, 1])
   [2.5, 2.5, 2.5, 2.5]


.. py:function:: calculate_spearman_rank_correlation(variable_1: collections.abc.Sequence[float], variable_2: collections.abc.Sequence[float]) -> float

   Calculates Spearman's rank correlation coefficient.

   :param variable_1: List of floats representing the first variable.
   :param variable_2: List of floats representing the second variable.
   :return: Spearman's rank correlation coefficient.
   :raises ValueError: If less than 2 data points are provided.

   Example Usage:

   >>> x = [1, 2, 3, 4, 5]
   >>> y = [5, 4, 3, 2, 1]
   >>> calculate_spearman_rank_correlation(x, y)
   -1.0

   >>> x = [1, 2, 3, 4, 5]
   >>> y = [2, 4, 6, 8, 10]
   >>> calculate_spearman_rank_correlation(x, y)
   1.0

   >>> x = [1, 2, 3, 4, 5]
   >>> y = [5, 1, 2, 9, 5]
   >>> calculate_spearman_rank_correlation(x, y)
   0.4

   >>> x = [5]
   >>> y = [9]
   >>> calculate_spearman_rank_correlation(x, y)
   Traceback (most recent call last):
       ...
   ValueError: Need at least 2 data points to calculate correlation