英文:
Calculate difference of rows in Pandas
问题
我有一个时间序列的数据框，其中某些特定行存在警报。数据框如下所示-
machineID time vibration alerts1 2023-02-15 220 111:451 2023-02-15 221 012:001 2023-02-15 219 012:151 2023-02-15 220 112:301 2023-02-16 220 111:451 2023-02-16 221 112:001 2023-02-16 219 012:151 2023-02-16 220 112:30
我想计算每天的alerts列的差异。但由于日期列是以15分钟的时间间隔，我不知道如何为整个一天进行分组，即计算每天的警报总和并将其与前一天的警报总和进行比较。
简而言之，我需要一种方法来计算每天的所有警报总和并减去前一天的总和。结果应该在另一个数据框中，其中有一个日期列和差异警报列。在这种情况下，新数据框将是-
time diff_alerts2023-02-16 1
因为在第二天，即2023年2月16日，有1个警报的差异。
英文:
I have a timeseries dataframe where there are alerts for some particular rows. The dataframe looks like-
machineID time vibration alerts1 2023-02-15 220 111:451 2023-02-15 221 012:001 2023-02-15 219 012:151 2023-02-15 220 112:301 2023-02-16 220 111:451 2023-02-16 221 112:001 2023-02-16 219 012:151 2023-02-16 220 112:30
I want to calculate difference of alerts columns for each day. But since the date column is in time interval of 15 minutes, I am not getting how to group for whole day i.e., sum the alerts for each day and compare it with the sum of all alerts of the previous day.
In short, I need a way to sum all alerts for each day and substract with previous day. The result should be in another dataframe where there is a date column and difference of alerts column. In this case, the new dataframe will be-
time diff_alerts2023-02-16 1
since there is difference of 1 alert on the next day i.e. 16-02-2023
答案1
得分: 5
Group by day with a custom pd.Grouper then sum alerts and finally compute the diff with the previous day:
(df.groupby(pd.Grouper(key='time', freq='D'))['alerts'].sum().diff().dropna().rename('diff_alerts').astype(int).reset_index())time diff_alerts0 2023-02-16 1
Note: the second line of code is just here to have a clean output.
英文:
Group by day with a custom pd.Grouper then sum alerts and finally compute the diff with the previous day:
>>> (df.groupby(pd.Grouper(key='time', freq='D'))['alerts'].sum().diff().dropna().rename('diff_alerts').astype(int).reset_index())time diff_alerts0 2023-02-16 1
Note: the second line of code is just here to have a clean output.
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