使用 pd.read_clipboard 读取 pretty-printed/formatted 数据帧?

Reading in a pretty-printed/formatted dataframe using pd.read_clipboard?

这是另一个 :

的数据框
+--------------------+---------------+-------+
|   Location         | Date          | Value |
+--------------------+---------------+-------+
| India              | 2015-03-15    |   -200|
| India              | 2015-02-15    |  140  |
| India              | 2015-01-15    |  155  |
| India              | 2015-12-15    |   85  |
| India              | 2015-11-15    |   45  |
| China              | 2015-03-15    |   199 |
| China              | 2015-02-15    |  164  |
| China              | 2015-01-15    |  209  |
| China              | 2015-12-15    |   24  |
| China              | 2015-11-15    |   11  |
| Russia             | 2015-03-15    |   48  |
| Russia             | 2015-02-15    |  104  |
| Russia             | 2015-01-15    |  106  |
| Russia             | 2015-12-15    |   -20 |
| Russia             | 2015-11-15    |   10  |
+--------------------+---------------+-------+

并且,为了方便起见,这里是您可以毫无问题地复制的版本:

   Location        Date  Value
0     India  2015-03-15   -200
1     India  2015-02-15    140
2     India  2015-01-15    155
3     India  2015-12-15     85
4     India  2015-11-15     45
5     China  2015-03-15    199
6     China  2015-02-15    164
7     China  2015-01-15    209
8     China  2015-12-15     24
9     China  2015-11-15     11
10   Russia  2015-03-15     48
11   Russia  2015-02-15    104
12   Russia  2015-01-15    106
13   Russia  2015-12-15    -20
14   Russia  2015-11-15     10

您如何使用 df.read_clipboard 阅读它而无需手动删除所有这些定界符和行分隔符?

如果不是 ---+----,使用 sepdelimiter 会很容易。

In [129]: pd.read_clipboard(comment='+', sep='\s*\|\s*', usecols=[1,2,3], engine='python')
Out[129]:
   Location        Date  Value
0     India  2015-03-15   -200
1     India  2015-02-15    140
2     India  2015-01-15    155
3     India  2015-12-15     85
4     India  2015-11-15     45
5     China  2015-03-15    199
6     China  2015-02-15    164
7     China  2015-01-15    209
8     China  2015-12-15     24
9     China  2015-11-15     11
10   Russia  2015-03-15     48
11   Russia  2015-02-15    104
12   Russia  2015-01-15    106
13   Russia  2015-12-15    -20
14   Russia  2015-11-15     10