PANDAS-循环两个具有不同大小的日期时间索引以比较日期和值 [英] PANDAS - Loop over two datetime indexes with different sizes to compare days and values

问题描述

寻找一种更有效的方法来遍历和比较两个具有不同频率的Series对象中的datetimeindex值.

Looking for a more efficient way to loop over and compare datetimeindex values in two Series objects with different frequencies.

想象两个熊猫系列，每个熊猫的日期时间索引涵盖相同的年份跨度，但每个索引的频率不同.一个频率为几天，另一个频率为几个小时.

Imagine two Pandas series, each with a datetime index covering the same year span yet with different frequencies for each index. One has a frequency of days, the other a frequency of hours.

range1 = pd.date_range('2016-01-01','2016-12-31', freq='D')
range2 = pd.date_range('2016-01-01','2016-12-31', freq='H')

我正在尝试使用它们的索引来循环查找这些系列以匹配日期，以便我可以比较每天的数据.

I'm trying to loop over these series using their indexes as a lookup to match days so I can compare data for each day.

现在，我正在使用多级for循环和if语句(请参见下文)；与我在Pandas操作中惯用的时间相比，完成这些循环的时间似乎过多(每个循环5.45 s).

Right now I'm using multi-level for loops and if statements (see below); the time to complete these loops seems excessive (5.45 s per loop) compared with what I'm used to in Pandas operations.

for date, val in zip(frame1.index, frame1['data']): # freq = 'D'
    for date2, val2 in zip(frame2.index, frame2['data']): # freq = 'H'
        if date.day == date2.day: # check to see if dates are a match
            if val2 > val: # compare the values
                # append values, etc

问题

是否有更有效的方法使用第1帧中的索引遍历第2帧中的索引，并比较给定日期每一帧中的值?最终，我想在frame2值大于frame1值的地方创建一系列值.

Question

Is there a more efficient way of using the index in frame1 to loop over the index in frame2 and compare the values in each frame for a given day? Ultimately I want to create a series of values wherever frame2 vals are greater than frame1 vals.

使用随机数据创建两个单独的序列，并为每个序列分配一个日期时间索引.

Create two separate series with random data and assign each a datetime index.

import pandas as pd
import numpy as np

range1 = pd.date_range('2016-01-01','2016-12-31', freq='D')
range2 = pd.date_range('2016-01-01','2016-12-31', freq='H')

frame1 = pd.Series(np.random.rand(366), index=range1)
frame2 = pd.Series(np.random.rand(8761), index=range2)

推荐答案

是的，请使用resample，asfreq和pd.concat.

使用重采样以使序列中的频率正确.

Use resample to get the proper frequency out of your series.

asfreq(听起来有点脏)用于将其重新转换为具有在重采样中定义的频率的序列.

asfreq (which sounds sort of dirty) is use to convert back to a series with frequency defined in resample.

与frame1串联以并排获取值.

Concatenate with frame1 to get values side-by-side.

df = pd.concat([frame1,frame2.resample('1D').asfreq()],axis=1)
df.head()

输出:

                   0         1
2016-01-01  0.147067  0.235858
2016-01-02  0.820398  0.353275
2016-01-03  0.840499  0.186273
2016-01-04  0.505740  0.340201
2016-01-05  0.547840  0.695041

然后，您可以通过以下方法回到您的第2帧(超出第1帧)的范围.

Then, you can us the following to get back to your series of frame2 exceeding frame1.

df.columns = ['frame1','frame2']
df.query('framed1 < frame2')['frame2']

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