[英]Count on a rolling time window in pandas
I'm trying to return a count on a time window about a (moving) fixed point.我正在尝试返回关于(移动)固定点的时间窗口的计数。
It's an attempt to understand the condition of an instrument at any time, as a function of usage prior to it.它试图随时了解仪器的状况,作为之前使用的函数。
So if the instrument is used at 12.05pm, 12.10, 12.15, 12.30, 12.40 and 1pm, the usage counts would be:因此,如果仪器在 12.05pm、12.10、12.15、12.30、12.40 和 1pm 使用,则使用计数将为:
12.05 -> 1 (once in the last hour) 12.05 -> 1(最后一小时一次)
12.10 -> 2 12.10 -> 2
12.15 -> 3 12.15 -> 3
12.30 -> 4 12.30 -> 4
12.40 -> 5 12.40 -> 5
1.00 -> 6 1.00 -> 6
... but then lets say usage resumes at 1.06: 1.06 -> 6 this doesn't increase the count, as the first run is over an hour ago. ...但是可以说使用在 1.06 恢复:1.06 -> 6 这不会增加计数,因为第一次运行是一个多小时前。
How can I calculate this count and append it as a column?如何计算此计数并将其附加为一列?
It feels like this is an groupby/aggregate/count using possibly timedeltas in a lambda function, but I don't know where to start past that.感觉这是一个 groupby/aggregate/count,可能在 lambda 函数中使用 timedeltas,但我不知道从哪里开始。
I'd like to be able to play with the time window too, so not just the past hour, but the hour surrounding an instance ie + and -30 minutes.我也希望能够使用时间窗口,所以不仅仅是过去的一小时,而是围绕实例的小时,即 + 和 -30 分钟。
The following code gives a starting dataframe:以下代码给出了一个起始数据帧:
s = pd.Series(pd.date_range('2020-1-1', periods=8000, freq='250s'))
df = pd.DataFrame({'Run time': s})
df_sample = df.sample(6000)
df_sample = df_sample.sort_index()
The best help i found (and to be fair i can usually hack together from the logic) was this Distinct count on a rolling time window but i've not managed this time.我找到的最好的帮助(公平地说,我通常可以从逻辑中破解)是滚动时间窗口上的这个Distinct 计数,但这次我没有管理。
Thanks谢谢
I've done something similar previously with the DataFrame.rolling
function: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html我以前用DataFrame.rolling
函数做过类似的DataFrame.rolling
: https : DataFrame.rolling
So for your dataset, first you need to update the index to the datetime field, then you can preform the analysis you need, so continuing on from your code:因此,对于您的数据集,首先您需要将索引更新为日期时间字段,然后您可以执行所需的分析,因此继续您的代码:
s = pd.Series(pd.date_range('2020-1-1', periods=8000, freq='250s'))
df = pd.DataFrame({'Run time': s})
df_sample = df.sample(6000)
df_sample = df_sample.sort_index()
# Create a value we can count
df_sample('Occurrences') = 1
# Set the index to the datetime element
df_sample = df_sample.set_index('Run time')
# Use Pandas rolling method, 3600s = 1 Hour
df_sample['Occurrences in Last Hour'] = df_sample['Occurrences'].rolling('3600s').sum()
df_sample.head(15)
Occurrences Occurrences in Last Hour
Run time
2020-01-01 00:00:00 1 1.0
2020-01-01 00:04:10 1 2.0
2020-01-01 00:08:20 1 3.0
2020-01-01 00:12:30 1 4.0
2020-01-01 00:16:40 1 5.0
2020-01-01 00:25:00 1 6.0
2020-01-01 00:29:10 1 7.0
2020-01-01 00:37:30 1 8.0
2020-01-01 00:50:00 1 9.0
2020-01-01 00:54:10 1 10.0
2020-01-01 00:58:20 1 11.0
2020-01-01 01:02:30 1 11.0
2020-01-01 01:06:40 1 11.0
2020-01-01 01:15:00 1 10.0
2020-01-01 01:19:10 1 10.0
You need to set the index to a datetime element to utilised the time base window, otherwise you can only use integer values corresponding to the number of rows.您需要将索引设置为日期时间元素以利用时基窗口,否则您只能使用与行数相对应的整数值。
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