问题描述
我有一个如下形式的熊猫数据框:
I have a dataframe in pandas of the following form:
timestamps light
7 2004-02-28 00:58:45 150.88
26 2004-02-28 00:59:45 143.52
34 2004-02-28 01:00:45 150.88
42 2004-02-28 01:01:15 150.88
59 2004-02-28 01:02:15 150.88
这里注意索引不是时间戳列.但我想重新采样(或以某种方式对数据进行分类)以反映每分钟、每小时、每天等光柱的平均值.我研究了 pandas 提供的 resample 方法，它需要数据框有一个数据时间索引以便该方法工作(除非我误解了这一点).
Here note that the index is not the timestamps column. But I want to resample (or bin the data somehow) to reflect the average value of the light column per minute , hour, day etc.. I have looked into the resample method that pandas offers and it requires the dataframe to have a datatime index for the method to work (unless I've misunderstood this).
所以我的第一个问题是，我可以重新索引数据帧以将时间戳作为索引(请注意，并非每一行都有唯一的时间戳，对于每个时间戳，大约有 30 行具有相同的时间戳，每个代表一个传感器).
So my first question is, can I re-index the dataframe to have timestamps as the index (note that not each row has a unique timestamp and for each timestamp, there are about 30 rows with the same timestamp,each representing a sensor).
如果没有，是否有其他方法可以实现另一个数据帧，该数据帧具有每小时、每天、每月等的平均值?
If not, is there some other way to possibly achieve another dataframe which has the average value of light per hour , per day , per month etc..?
任何帮助将不胜感激.
推荐答案
你是对的 - 需要 DatetimeIndex, TimedeltaIndex 或 PeriodIndex 否则错误:
You are right - need DatetimeIndex, TimedeltaIndex or PeriodIndex else error:
TypeError:仅对 DatetimeIndex、TimedeltaIndex 或 PeriodIndex 有效，但获得了 'Index' 的实例
TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'
所以你必须先 reset_index 和 set_index 如果原始 index 很重要:
So you have to first reset_index and set_index if original index is important:
print (df.reset_index().set_index('timestamps'))
index light
timestamps
2004-02-28 00:58:45 7 150.88
2004-02-28 00:59:45 26 143.52
2004-02-28 01:00:45 34 150.88
2004-02-28 01:01:15 42 150.88
2004-02-28 01:02:15 59 150.88
如果不只是 set_index:
if not only set_index:
print (df.set_index('timestamps'))
light
timestamps
2004-02-28 00:58:45 150.88
2004-02-28 00:59:45 143.52
2004-02-28 01:00:45 150.88
2004-02-28 01:01:15 150.88
2004-02-28 01:02:15 150.88
然后 resample:
print (df.reset_index().set_index('timestamps').resample('1D').mean())
index light
timestamps
2004-02-28 33.6 149.408
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