Confusion Matrix and AUC in univariate Anomaly Detection
It's difficult to answer precisely without knowing the data and the task. 

Assuming it's a single column of values with no order involved, it boils down to finding the optimal threshold to separate regular values vs. anomalies. Given that you know which ones are the anomalies (labelled data), the problem can be treated as a binary classification task: a value is either regular (negative) or an anomaly (positive).

The idea is simply to evaluate the performance for every possible threshold, i.e. count the number of True/False Positive/Negative for every value. Let's say you take the set $S$ of all the distinct values in your column. For every value $t \in S$ you calculate the confusion matrix (i.e. how many TP/FP/TN/FN) obtained by predicting every value $x$ as positive if if $x&gt;t$, negative otherwise.

From there you can plot a ROC curve and/or find the optimal threshold according to some evaluation measure (typically the one which maximizes the F1-score).

I assume that there are libraries which do that automatically, but it's simple enough so personally I would do my own code for this kind of thing.