按 15 分钟间隔聚合时间序列数据帧答案

【问题标题】：aggregate time series dataframe by 15 minute intervals按 15 分钟间隔聚合时间序列数据帧
【发布时间】：2021-07-18 23:54:29
【问题描述】：

我正在组合一堆不同的数据集来创建一个聚合，以 15 分钟的间隔进行分析。

我当前的数据框是这样的，

<bound method NDFrame.to_clipboard of                        id                       user_id  sentiment  magnitude  \
2020-10-04 14:06:00  10.0  cPL1Fg7BqRXvSFKeU1mJT7KCCTq2       -0.1        0.1   
2020-10-04 14:06:05  11.0  cPL1Fg7BqRXvSFKeU1mJT7KCCTq2       -0.8        0.8   
2020-10-05 12:28:58  12.0  cPL1Fg7BqRXvSFKeU1mJT7KCCTq2       -0.2        0.2   
2020-10-05 12:29:16  13.0  cPL1Fg7BqRXvSFKeU1mJT7KCCTq2       -0.2        0.2   
2020-10-05 12:29:31  14.0  cPL1Fg7BqRXvSFKeU1mJT7KCCTq2        0.2        0.2   

                     angry  disgusted  fearful  happy  neutral  sad  \
2020-10-04 14:06:00    NaN        NaN      NaN    NaN      NaN  NaN   
2020-10-04 14:06:05    NaN        NaN      NaN    NaN      NaN  NaN   
2020-10-05 12:28:58    NaN        NaN      NaN    NaN      NaN  NaN   
2020-10-05 12:29:16    NaN        NaN      NaN    NaN      NaN  NaN   
2020-10-05 12:29:31    NaN        NaN      NaN    NaN      NaN  NaN   

                     surprised  heartRate  steps  
2020-10-04 14:06:00        NaN        NaN    NaN  
2020-10-04 14:06:05        NaN        NaN    NaN  
2020-10-05 12:28:58        NaN        NaN    NaN  
2020-10-05 12:29:16        NaN        NaN    NaN  
2020-10-05 12:29:31        NaN        NaN    NaN  >

我想将数据帧聚合成 15 分钟的间隔。

我认为 groupby 是最好的方法？但我发现很难让它特别好用：/

提前致谢，

【问题讨论】：

看DataFrame.resamplestackoverflow.com/questions/42191697/…

标签： python pandas

【解决方案1】：

有两个选项，我们可以使用 resample 或 pd.Grouper（这是高性能的）。

让我分享 pd.Grouper 的示例，以 15 分钟间隔添加列值。

代码

pd.DataFrame(df.groupby([pd.Grouper(key='date', freq='15Min')]).sum()).reset_index()

从您的数据中输入样本

    date                 id
0   2020-10-04 14:06:00 10.0
1   2020-10-04 14:06:05 11.0
2   2020-10-05 12:28:58 12.0
3   2020-10-05 12:29:16 13.0
4   2020-10-05 12:29:31 14.0

输出

    date           id
0   2020-10-04 14:00:00 21.0
1   2020-10-04 14:15:00 0.0
2   2020-10-04 14:30:00 0.0
3   2020-10-04 14:45:00 0.0
4   2020-10-04 15:00:00 0.0

【讨论】：