Time-weighted average with Pandas
pandas, python, time-series
Solution
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)
Problem
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.