Unsupervised Learning upon a column of dataset (graph shown) I’m new into Machine Learning so here I am asking for a sanity check, if the question I am asking is even reasonable.

I have a Dataset of columns, so I want to call one of the columns from the csv using pandas.

Take one of the number from that column of numbers, do some unsupervised learning to determine if this value is an anomaly in this column and it belongs there or not.

The graph below shows, how I would see this process going. I am not sure on what I would do regarding what unsupervised method would be best for this case.
A few observations/questions/considerations:


Wikipedia: Unsupervised learning is a type of self-organized Hebbian learning that helps find previously unknown patterns in data set without pre-existing labels.
Being an anomaly is a label.
Why not just Z-score it? https://en.wikipedia.org/wiki/Standard_score#Z-test . Calculate distribution: $\mu$ and $\sigma$, and check if value is
within $\mu$ +/- 1.96 $\sigma$, $95\%$ chance it is from the normal distribution of the numbers in the column.
Can the Value column be something else than numbers? (E.g. color names)
Or could it be a time series with drift?




Edit:

Under the hood, Machine Learning is basically a set of smart statistics and decisions on subsets of columns and rows. But with only one column and no time series, there is nothing smart to subset or decide. Then essentially, it is an exceptional Z-score for the elements. You have to decide on how many $\sigma$ you want.