python pandas: how to calculate derivative/gradient

pd.Series.diff() only takes the differences. It doesn't divide by the delta of the index as well.

This gets you the answer

recv.diff() / recv.index.to_series().diff().dt.total_seconds()2017-01-20 20:00:00            NaN2017-01-20 20:05:00    4521.4933332017-01-20 20:10:00    4533.7600002017-01-20 20:15:00    4557.4933332017-01-20 20:20:00    4536.0533332017-01-20 20:25:00    4567.8133332017-01-20 20:30:00    4406.1600002017-01-20 20:35:00    4366.7200002017-01-20 20:40:00    4407.5200002017-01-20 20:45:00    4421.173333Freq: 300S, dtype: float64

You could also use numpy.gradient passing the bytes_in and the delta you expect to have. This will not reduce the length by one, instead making assumptions about the edges.

np.gradient(bytes_in, 300) * 8array([ 4521.49333333,  4527.62666667,  4545.62666667,  4546.77333333,        4551.93333333,  4486.98666667,  4386.44      ,  4387.12      ,        4414.34666667,  4421.17333333])

A naive explanation would be that diff literally subtracts following entries while np.gradient uses a central difference scheme.

Can you explain why np.gradient doesn't produce the same results as the first proposed answer. – Darthtrader May 5 at 9:58

np.gradient uses a 2nd order scheme while .diff() uses a 1st order scheme. This means that the results from np.gradient will be continuous as will the derivative. The results from .diff() do not have to have a continuous derivative. Essentially np.gradient gives 'smoother' results.