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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.
741
Calculate a Weighted Average in Pandas Using NumpyThe numpy library has a function, average() , which allows us to pass in an optional argument to specify weights of values. The function will take an array into the argument a= , and another array for weights under the argument weights= .
A time-weighted average is equal to the sum of the portion of each time period (as a decimal, such as 0.25 hour) multiplied by the levels of the substance or agent during the time period divided by the hours in the workday (usually 8 hours).
3) Time Periods: Time Periods references a specific length of time between a start and end timestamp which is invariable and does not overlap. The Period class takes the Period type which takes a string or an integer and encodes a fixed frequency based on numpy. datetime64.
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)
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