Spearman correlation would be easily supported by adding a ranked option to statistics.correlation(). It is appropriate for ordinal data or for continuous data that doesn't meet the linear proportionality assumption required for Pearson correlation.
>>> # Example from https://statistics.laerd.com/statistical-guides/spearmans-rank-order-correlation-statistical-guide.php
>>> eng = [56, 75, 45, 71, 61, 64, 58, 80, 76, 61]
>>> math = [66, 70, 40, 60, 65, 56, 59, 77, 67, 63]
>>> # Pearson correlation reports the strength of a linear relationship
>>> correlation(math, eng)
0.8005386765540159
>>> # Spearman correlation reports strength of a monotonic relationship
>>> correlation(math, eng, ranked=True)
0.6686960980480711
The code would be mostly unchanged. If ranked is true, then the data is replaced by rankings using a helper function
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