Anomaly detection in nominal big data I have to apply an anomaly detection algorithm on big data, the values of each column on my dataframe are nominal and vary over 10000 times, the algorithms I've found only accept numeric values, is there any way to transform this nominal values into numeric ones in a way that the algorithm will work?

I've used preprocessing.LabeledEncoder(), but then when I apply the algorithm it finds anomalies - the values that most differ from the mean it seems.

Are there any examples of an algorithm or another way to transform the data?
You would have to post more info about your data and maybe even some samples, but have you considered using tokenization for the data? It's from the NLP world and would allow you to assign a numeric value for each bit of you "nominal" data.