我正在尝试将具有三列(日期,开始,结束)的pandas数据帧转换为频率矩阵.我的输入数据框如下所示：
Date, Start, End
2016-09-02 09:16:00 18 16
2016-09-02 16:14:10 16 1
2016-09-02 06:17:21 18 17
2016-09-02 05:51:07 23 17
2016-09-02 18:34:44 18 17
2016-09-02 05:44:44 20 4
2016-09-02 09:25:22 18 17
2016-09-02 22:27:44 18 17
2016-09-02 16:02:46 0 18
2016-09-02 15:35:07 17 17
2016-09-02 16:06:42 8 17
2016-09-02 14:47:04 16 23
2016-09-02 07:47:24 20 1
...
“开始”和“结束”的值是0到23之间的整数. ‘日期’是日期时间.我试图创建的频率矩阵是24乘24 csv,其中行i和列j是’End’= i的次数,并且’Start’= j出现在输入中.例如,以上数据将创建：
0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12,13,14,15,16,17,18,19,20,21,22,23
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0
2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0
5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
13, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0
17, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 0, 0, 0, 0, 1
18, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
19, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
22, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
23, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0
为了获得额外的帮助,这可以通过每15分钟创建一个单独矩阵的方式来完成吗？这将是672个矩阵,因为这个日期范围是一周.
我是一个自学成才的初学者,我真的想不出如何用pythonic方式解决这个问题,任何@R_404_6280@案或建议都会非常感激.
解决方法:
使用简单计数创建矩阵并将其中一列拆开：
mat = df.groupby(['Start', 'End']).count().unstack(level=0)
清理日期级别：
mat.columns = mat.columns.droplevel(0)
现在重新索引行和列并转换为整数：
mat.reindex(*[range(0,24)]*2).fillna(0)
详细解释
首先,计算给定(开始,结束)对出现的出现次数. groupby对这两列的结果实际上带回了多索引.
df.groupby(['Start', 'End']).count()
Out[134]:
Date
Start End
0 18 1
8 17 1
16 1 1
23 1
17 17 1
18 16 1
17 4
20 1 1
4 1
23 17 1
我们想要的结果是在列中获取Start索引. unstack这样做：
df.groupby(['Start', 'End']).count().unstack(level=0)
Out[135]:
Date
Start 0 8 16 17 18 20 23
End
1 NaN NaN 1.0 NaN NaN 1.0 NaN
4 NaN NaN NaN NaN NaN 1.0 NaN
16 NaN NaN NaN NaN 1.0 NaN NaN
17 NaN 1.0 NaN 1.0 4.0 NaN 1.0
18 1.0 NaN NaN NaN NaN NaN NaN
23 NaN NaN 1.0 NaN NaN NaN NaN
unstack的结果是Start列被移动为当前Date列索引之上的附加列索引级别(见下文).这就是我们之后降低等级0的原因.另一种方法 – 取决于您当前的源代码 – 可以是预先过滤Date列,然后unstack将带来一个级别.
_.columns
Out[136]:
MultiIndex(levels=[['Date'], [0, 8, 16, 17, 18, 20, 23]],
labels=[[0, 0, 0, 0, 0, 0, 0], [0, 1, 2, 3, 4, 5, 6]],
names=[None, 'Start'])
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