添加额外的列作为累积时差
Car*_*cca 5 python timestamp dataframe pandas
如何添加一个额外的列，该列是每个课程的时间差的累积值？例如，初始表为：
id_A course weight ts_A value
id1 cotton 3.5 2017-04-27 01:35:30 150.000000
id1 cotton 3.5 2017-04-27 01:36:00 416.666667
id1 cotton 3.5 2017-04-27 01:36:30 700.000000
id1 cotton 3.5 2017-04-27 01:37:00 950.000000
id2 cotton blue 5.0 2017-04-27 02:35:30 150.000000
id2 cotton blue 5.0 2017-04-27 02:36:00 450.000000
id2 cotton blue 5.0 2017-04-27 02:36:30 520.666667
id2 cotton blue 5.0 2017-04-27 02:37:00 610.000000
预期的结果是：
id_A course weight ts_A value cum_delta_sec
id1 cotton 3.5 2017-04-27 01:35:30 150.000000 0
id1 cotton 3.5 2017-04-27 01:36:00 416.666667 30
id1 cotton 3.5 2017-04-27 01:36:30 700.000000 60
id1 cotton 3.5 2017-04-27 01:37:00 950.000000 90
id2 cotton blue 5.0 2017-04-27 02:35:30 150.000000 0
id2 cotton blue 5.0 2017-04-27 02:36:00 450.000000 30
id2 cotton blue 5.0 2017-04-27 02:36:30 520.666667 60
id2 cotton blue 5.0 2017-04-27 02:37:00 610.000000 90
您可以使用以下diff方法链接该方法cumsum：
# convert ts_A to datetime type
df.ts_A = pd.to_datetime(df.ts_A)
# convert ts_A to seconds, group by id and then use transform to calculate the cumulative difference
df['cum_delta_sec'] = df.ts_A.astype(int).div(10**9).groupby(df.id_A).transform(lambda x: x.diff().fillna(0).cumsum())
df
使用groupby、transform、 和.iloc：
df['ts_A'] = pd.to_datetime(df.ts_A)
df['cum_delta_sec'] = (df.groupby('id_A')['ts_A']
.transform(lambda x: (x - x.iloc[0]).dt.total_seconds()))
输出：
id_A course weight ts_A value cum_delta_sec
0 id1 cotton 3.5 2017-04-27 01:35:30 150.000000 0
1 id1 cotton 3.5 2017-04-27 01:36:00 416.666667 30
2 id1 cotton 3.5 2017-04-27 01:36:30 700.000000 60
3 id1 cotton 3.5 2017-04-27 01:37:00 950.000000 90
4 id2 cotton blue 5.0 2017-04-27 02:35:30 150.000000 0
5 id2 cotton blue 5.0 2017-04-27 02:36:00 450.000000 30
6 id2 cotton blue 5.0 2017-04-27 02:36:30 520.666667 60
7 id2 cotton blue 5.0 2017-04-27 02:37:00 610.000000 90
在组中，从第一个值减去当前值并使用.dt访问器转换为秒。