How can I find anomalies in each row of data?
I am assuming your goal is a time series anomaly detection, in which you want to detect the occurrence of outlier as time goes by. To achieve that, you need to set up a time series prediction model first, and then perform outlier detection base on that. 

For unsupervised outlier detection, just check Andrew Ng's machine learning course on Coursera, there is a chapter about anomaly detection based on gaussian kernel. You can also read sklearn docments for OneClassSVM, LocalOutlierFactor, etc. Basically, the core idea of unsupervised anomaly detection is setup a metrics to describe the affinity between samples, and then use a user defined threshold to decide if a new sample is similar enough to other samples.