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 sep
或 delimiter
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
这是另一个
+--------------------+---------------+-------+
| 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 sep
或 delimiter
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