在Python中使用向前和向后填充窗口进行分组和重新采样 [英] Groupby and resample using forward and backward fill in window in Python

问题描述

我想在分组 df <时使用 1min 的频率使用正向填充 ffill 和向后填充 bfill 重新采样数据列/code>通过 id 列.

I want to resample data column using forward fill ffill and backward fill bfill at the frequency of 1min while grouping df by id column.

df :

          id   timestamp                data  

      1    1   2017-01-02 13:14:53.040  10.0
      2    1   2017-01-02 16:04:43.240  11.0  
                           ...
      4    2   2017-01-02 15:22:06.540   1.0  
      5    2   2017-01-03 13:55:34.240   2.0  
                           ...

我用过:

pd.DataFrame(df.set_index('timestamp').groupby('id', sort=True)['data'].resample('1min').ffill().bfill())

如何通过从现在起过去10天的窗口内重新采样来添加其他条件?因此，现在是最后一个 timestamp 读数，而第一个 timestamp 读数是 datetime.datetime.now()-pd.to_timedelta("10day").目标是使每个 id 组的读数均相同.

How can I add an additional condition, by resampling within the window of past 10 days from now? So the last timestamp reading is now and the first timestamp reading is datetime.datetime.now() - pd.to_timedelta("10day"). The goal is to have the same number of readings for each id group.

更新:

尝试:

start = datetime.datetime.now() - pd.to_timedelta("10day")
end = datetime.datetime.now()

r = pd.to_datetime(pd.date_range(start=start, end=end, freq='1h'))

pd.DataFrame(df.reset_index().set_index('timestamp').groupby('id', sort=True).reindex(r)['data'].resample('1h').ffill().bfill())

并返回:

AttributeError: 'DataFrameGroupBy' object has no attribute 'reindex'

所以我不应该对 groupby 对象应用 reindex ，有什么办法可以解决它?

so I'm not supposed to apply reindex for groupby object, is there a way that I can work around it?

推荐答案

没有数据，我无法真正进行测试.因此，将其作为对格式正确的建议/评论.由于您希望使用 bfill/ffill 重新采样，因此我认为 merge_asof 会起作用:

Without a data, I can't really test this. So take this as a suggestion/comment put for proper formatting. Since you are looking to resample with bfill/ffill, I think merge_asof would work:

# common time window
r = pd.to_datetime(pd.date_range(start='2017-12-23', end='2017-01-02 23:00:00', freq='1h')) 

# unique id
unique_ids = df['id'].unique()

# new time reference:
new_df = pd.DataFrame({'id': np.repeat(unique_ids, len(r)),
                       'time': np.tile(r, len(unique_ids)),
                      })

# merge_asof may complain about sorting key, then sort both df by time
# default of merge_asof is `direction='backward'`
# change to `direction='forward'` if you want to *floor* time
out = pd.merge_asof(new_df, df, on='time', by='id')
                   

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