英文:
Fill column values based on time of day
问题
我有时间戳的数据，我想对其进行重新采样，并从指定时间开始，一直填充列行到记录的时间。
以下是数据的样子：
dftimestamp col12020-10-10 09:21:00 202020-10-11 10:42:00 30
我想将其重新采样为10分钟间隔，并从06:00:00开始填充col1的值，直到记录的时间，使其看起来像这样：
dftimestamp col12020-10-10 06:00:00 202020-10-10 06:10:00 202020-10-10 06:20:00 20...2020-10-10 09:20:00 202020-10-10 09:30:00 NaN2020-10-10 09:40:00 NaN...2020-10-11 06:00:00 302020-10-11 06:10:00 30...2020-10-11 10:40:00 30
英文:
I have data with timestamps, I want to resample and back fill column rows with the logged value starting from a specified time until the logged time.
Here is what the data looks like
dftimestamp col12020-10-10 09:21:00 202020-10-11 10:42:00 30..
I want to resample it to 10-minute intervals, and fill col1 values starting from 06:00:00 with the logged value until the log time, to look like this:
dftimestamp col12020-10-10 06:00:00 202020-10-10 06:10:00 202020-10-10 06:20:00 20...2020-10-10 09:20:00 202020-10-10 09:30:00 NaN2020-10-10 09:40:00 NaN...2020-10-11 06:00:00 302020-10-11 06:10:00 30..2020-10-11 10:40:00 30
答案1
得分: 2
以下是翻译好的代码部分：
你可以使用concat来作为起始点，然后使用resample，接着使用groupby.bfill按天处理：
out = (pd.concat([pd.DataFrame({'timestamp': [df['timestamp'].min().normalize()+pd.Timedelta('06:00:00')]}), df]).resample('10min', on='timestamp').mean().reset_index())out.groupby(out['timestamp'].dt.normalize()).bfill()
输出结果：
timestamp col10 2020-10-10 06:00:00 20.01 2020-10-10 06:10:00 20.02 2020-10-10 06:20:00 20.03 2020-10-10 06:30:00 20.04 2020-10-10 06:40:00 20.0.. ... ...18 2020-10-10 09:00:00 20.019 2020-10-10 09:10:00 20.020 2020-10-10 09:20:00 20.021 2020-10-10 09:30:00 NaN22 2020-10-10 09:40:00 NaN23 2020-10-10 09:50:00 NaN.. ... ...168 2020-10-11 10:00:00 30.0169 2020-10-11 10:10:00 30.0170 2020-10-11 10:20:00 30.0171 2020-10-11 10:30:00 30.0172 2020-10-11 10:40:00 30.0
希望这有所帮助。如果有其他疑问，请随时提出。
英文:
You can concat your starting point, and resample, then groupby.bfill per day:
out = (pd.concat([pd.DataFrame({'timestamp': [df['timestamp'].min().normalize()+pd.Timedelta('06:00:00')]}), df]).resample('10min', on='timestamp').mean().reset_index())out.groupby(out['timestamp'].dt.normalize()).bfill()
Output:
timestamp col10 2020-10-10 06:00:00 20.01 2020-10-10 06:10:00 20.02 2020-10-10 06:20:00 20.03 2020-10-10 06:30:00 20.04 2020-10-10 06:40:00 20.0.. ... ...18 2020-10-10 09:00:00 20.019 2020-10-10 09:10:00 20.020 2020-10-10 09:20:00 20.021 2020-10-10 09:30:00 NaN22 2020-10-10 09:40:00 NaN23 2020-10-10 09:50:00 NaN.. ... ...168 2020-10-11 10:00:00 30.0169 2020-10-11 10:10:00 30.0170 2020-10-11 10:20:00 30.0171 2020-10-11 10:30:00 30.0172 2020-10-11 10:40:00 30.0
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