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假设我有一个如下数据框：

   date         A         B         C         D

2014-03-18  1.223777  0.356887  1.201624  1.968612
2014-03-18  0.160730  1.888415  0.306334  0.203939
2014-03-18 -0.203101 -0.161298  2.426540  0.056791
2014-03-18 -1.350102  0.990093  0.495406  0.036215
2014-03-18 -1.862960  2.673009 -0.545336 -0.925385
2014-03-19  0.238281  0.468102 -0.150869  0.955069
2014-03-20  1.575317  0.811892  0.198165  1.117805
2014-03-20  0.822698 -0.398840 -1.277511  0.811691
2014-03-20  2.143201 -0.827853 -0.989221  1.088297
2014-03-20  0.299331  1.144311 -0.387854  0.209612
2014-03-20  1.284111 -0.470287 -0.172949 -0.792020
2014-03-22  1.031994  1.059394  0.037627  0.101246
2014-03-22  0.889149  0.724618  0.459405  1.023127
2014-03-23 -1.136320 -0.396265 -1.833737  1.478656
2014-03-23 -0.740400 -0.644395 -1.221330  0.321805
2014-03-23 -0.443021 -0.172013  0.020392 -2.368532

我想对其进行序列化，以便最终得到：

date             value     unit   condition   
2014-03-18     1.223777      1       A
2014-03-18     0.160730      1       A
...            ...          ...     ...
2014-03-19     0.238281      2       A
2014-03-20     1.575317      3       A
...            ...          ...     ...
2014-03-18     0.468102      1       B
...            ...          ...     ...

在哪里：

  • date是原始date列
  • condition保存原始数据框中数据列的名称
  • unit在相应的日期内拥有唯一的 ID
  • value保存对应列的值

我怎样才能在熊猫中做到这一点？

背景：如果想用seaborn绘制多个时间序列，显然这是需要的。有关更多信息，请参阅此其他帖子。

4

1 回答 1

3

您可以执行此操作，pandas.melt然后通过 . 将唯一日期映射到整数Series。

In [31]: df
Out[31]:
          date       A       B       C       D
0   2014-03-18  1.2238  0.3569  1.2016  1.9686
1   2014-03-18  0.1607  1.8884  0.3063  0.2039
2   2014-03-18 -0.2031 -0.1613  2.4265  0.0568
3   2014-03-18 -1.3501  0.9901  0.4954  0.0362
4   2014-03-18 -1.8630  2.6730 -0.5453 -0.9254
5   2014-03-19  0.2383  0.4681 -0.1509  0.9551
6   2014-03-20  1.5753  0.8119  0.1982  1.1178
7   2014-03-20  0.8227 -0.3988 -1.2775  0.8117
8   2014-03-20  2.1432 -0.8279 -0.9892  1.0883
9   2014-03-20  0.2993  1.1443 -0.3879  0.2096
10  2014-03-20  1.2841 -0.4703 -0.1729 -0.7920
11  2014-03-22  1.0320  1.0594  0.0376  0.1012
12  2014-03-22  0.8891  0.7246  0.4594  1.0231
13  2014-03-23 -1.1363 -0.3963 -1.8337  1.4787
14  2014-03-23 -0.7404 -0.6444 -1.2213  0.3218
15  2014-03-23 -0.4430 -0.1720  0.0204 -2.3685

[16 rows x 5 columns]

In [32]: molten = pd.melt(df, id_vars=['date'], var_name='condition')

In [33]: molten
Out[33]:
          date condition   value
0   2014-03-18         A  1.2238
1   2014-03-18         A  0.1607
2   2014-03-18         A -0.2031
3   2014-03-18         A -1.3501
4   2014-03-18         A -1.8630
5   2014-03-19         A  0.2383
6   2014-03-20         A  1.5753
7   2014-03-20         A  0.8227
8   2014-03-20         A  2.1432
9   2014-03-20         A  0.2993
10  2014-03-20         A  1.2841
11  2014-03-22         A  1.0320
12  2014-03-22         A  0.8891
13  2014-03-23         A -1.1363
14  2014-03-23         A -0.7404
15  2014-03-23         A -0.4430
16  2014-03-18         B  0.3569
17  2014-03-18         B  1.8884
18  2014-03-18         B -0.1613
19  2014-03-18         B  0.9901
20  2014-03-18         B  2.6730
21  2014-03-19         B  0.4681
22  2014-03-20         B  0.8119
23  2014-03-20         B -0.3988
24  2014-03-20         B -0.8279
           ...       ...     ...

[64 rows x 3 columns]

In [35]: dates = molten.date.unique()

In [36]: mapper = Series(arange(dates.size), index=dates)

In [38]: molten['unit'] = mapper[molten.date].values

In [39]: molten
Out[39]:
          date condition   value  unit
0   2014-03-18         A  1.2238     0
1   2014-03-18         A  0.1607     0
2   2014-03-18         A -0.2031     0
3   2014-03-18         A -1.3501     0
4   2014-03-18         A -1.8630     0
5   2014-03-19         A  0.2383     1
6   2014-03-20         A  1.5753     2
7   2014-03-20         A  0.8227     2
8   2014-03-20         A  2.1432     2
9   2014-03-20         A  0.2993     2
10  2014-03-20         A  1.2841     2
11  2014-03-22         A  1.0320     3
12  2014-03-22         A  0.8891     3
13  2014-03-23         A -1.1363     4
14  2014-03-23         A -0.7404     4
15  2014-03-23         A -0.4430     4
16  2014-03-18         B  0.3569     0
17  2014-03-18         B  1.8884     0
18  2014-03-18         B -0.1613     0
19  2014-03-18         B  0.9901     0
20  2014-03-18         B  2.6730     0
21  2014-03-19         B  0.4681     1
22  2014-03-20         B  0.8119     2
23  2014-03-20         B -0.3988     2
24  2014-03-20         B -0.8279     2
           ...       ...     ...   ...

[64 rows x 4 columns]
于 2014-04-01T22:42:17.323 回答