Anomaly detection on a time-series data in a CSV format using python I have a time versus current data for a days work which is as follows:

11/15/2016  5:12:27 10:42:27    26.61
11/15/2016  5:12:28 10:42:28    42.27
11/15/2016  5:12:29 10:42:29    25.48
11/15/2016  5:12:30 10:42:30    24.24
11/15/2016  5:12:31 10:42:31    25.91


The first column being the date
The second column being the time in GMT
The third column being time in IST
The fourth column being the value of the current used by the machine

Can someone suggest me an algorithm for finding the anomalies in the pattern plotted by current versus time in IST?
Any help regarding my approach will be appreciated too.
With so little data, you aren't going to get specific algorithms suggested. In addition most folk would like you to give it a try first. My approach would be to first plot the time vs. signal data. Visually inspect the data for outliers. People are pretty good at this. I would then try to describe a mathematical model to fit the data with the outliers removed. Finally you can identify the outliers as those points greater than some statistical measure away from the difference of the model and the data.