python - pandas如何通过一行生成多行

Question

时间戳速度

 1. 2014-12-04 12:30:10  104,105,105,106,106,106,99,90
 2. 2014-12-04 12:32:19  86,86,87,88,88,89,90,92,93,95,97,100,102,104,1...
 3. 2014-12-04 12:32:58  110,110,110,110,110,110,110,110,110,110,110,10..

日期时间索引:24 条记录，2014-12-04 12:30:10 至 2014-12-04 12:29:13数据列(共1列):速度 24 个非空对象

我想像这样传输 DataFrame:

timestamp                                              speeds               

 1. 2014-12-04 12:30:10                                   104
 2. 2014-12-04 12:30:11                                   105
 3. 2014-12-04 12:30:12                                   105
 4. ....
 5. 2014-12-04 12:32:17                                   90
 6. 2014-12-04 12:32:18                    88 (resample and fill the timestamp and the mean speed value)
 7. 2014-12-04 12:32:19                                   86
 8. 2014-12-04 12:32:20                                   86
 9. 2014-12-04 12:32:21                                   87

有简单的函数可以做到这一点吗？或者只逐行迭代并解析字段？

Answer 1

不确定重新采样(很难从你的例子中说出你想做什么)。 pandas 还可以实现其他功能(可能不是最优雅的方式):

>>> df2 = df.apply(lambda x: pd.Series(x['speeds']),axis=1)
>>> df2['timestamp'] = df['timestamp']
>>> df2
     0    1    2    3    4    5   6   7   8   9  10   11   12   13           timestamp
0  104  105  105  106  106  106  99  90 NaN NaN NaN  NaN  NaN  NaN 2014-12-04 12:30:10
1    6   86   87   88   88   89  90  92  93  95  97  100  102  104 2014-12-04 12:32:19
>>>
>>> df2 = df2.set_index('timestamp').stack().reset_index()
>>> df2['timestamp'] = df2.apply(lambda x: x['timestamp'] + timedelta(seconds=x['level_1']), axis=1)
>>> del df2['level_1']
>>> df2
             timestamp    0
0  2014-12-04 12:30:10  104
1  2014-12-04 12:30:11  105
2  2014-12-04 12:30:12  105
3  2014-12-04 12:30:13  106
4  2014-12-04 12:30:14  106
5  2014-12-04 12:30:15  106
6  2014-12-04 12:30:16   99
7  2014-12-04 12:30:17   90
8  2014-12-04 12:32:19    6
9  2014-12-04 12:32:20   86
10 2014-12-04 12:32:21   87
11 2014-12-04 12:32:22   88
12 2014-12-04 12:32:23   88
13 2014-12-04 12:32:24   89
14 2014-12-04 12:32:25   90
15 2014-12-04 12:32:26   92
16 2014-12-04 12:32:27   93
17 2014-12-04 12:32:28   95
18 2014-12-04 12:32:29   97
19 2014-12-04 12:32:30  100
20 2014-12-04 12:32:31  102
21 2014-12-04 12:32:32  104