Pandas 的时间加权平均值
问题描述
在 Pandas 0.8 中计算 TimeSeries 时间加权平均值的最有效方法是什么?例如，假设我想要如下创建的 df.y - df.x 的时间加权平均值:
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)
我觉得这个操作应该很容易做，但是我尝试过的每件事都涉及到几次混乱和缓慢的类型转换.
I feel like this operation should be very easy to do, but everything I've tried involves several messy and slow type conversions.
解决方案
您可以将 df.index 转换为整数并使用它来计算平均值.有一个快捷方式 asi8 属性返回一个 int64 值数组:
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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