python - Time-weighted average with Pandas

Question

What's the most efficient way to calculate the time-weighted average of a TimeSeries in Pandas 0.8? For example, say I want the time-weighted average of df.y - df.x as created below:

import pandas
import numpy as np
times = np.datetime64('2012-05-31 14:00') + np.timedelta64(1, 'ms') * np.cumsum(10**3 * np.random.exponential(size=10**6))
x = np.random.normal(size=10**6)
y = np.random.normal(size=10**6)
df = pandas.DataFrame({'x': x, 'y': y}, index=times)

I feel like this operation should be very easy to do, but everything I've tried involves several messy and slow type conversions.

Answer 1

You can convert df.index to integers and use that to compute the average. There is a shortcut asi8 property that returns an array of int64 values:

np.average(df.y - df.x, weights=df.index.asi8)