Python 如何将dstat的输出导入熊猫？

我有一些数据如下所示：

----system---- ---load-avg--- ----total-cpu-usage---- ------memory-usage----- -dsk/total- --io/total- ---paging-- -net/total-
  date/time   | 1m   5m  15m |usr sys idl wai hiq siq| used  buff  cach  free| read  writ| read  writ|  in   out | recv  send
10-11 00:00:01|0.67 0.42 0.31|  2   0  98   0   0   0|25.0G 16.9M 6331M  189M|2101k  901k|30.4  28.3 |  63B   75B|   0     0
10-11 00:00:03|0.67 0.42 0.31|  4   0  95   0   0   0|25.0G 16.9M 6332M  190M|  50k 1142k|4.00  18.0 |   0     0 | 310k 6765B
10-11 00:00:05|0.62 0.41 0.31|  4   0  95   0   0   0|25.0G 16.9M 6333M  189M| 116k 2534k|3.50   113 |   0     0 | 484k   27k
10-11 00:00:07|0.62 0.41 0.31|  7   1  92   0   0   0|25.0G 16.9M 6335M  187M| 154k 2372k|4.00   128 |   0     0 |1159k   24k
10-11 00:00:09|0.62 0.41 0.31|  5   0  95   0   0   0|25.0G 16.9M 6336M  185M|   0  1556k|   0  38.5 |   0     0 | 396k 4172B
10-11 00:00:11|0.73 0.44 0.32|  4   1  95   0   0   0|25.0G 16.9M 6336M  184M| 136k 2732k|3.50   139 |   0     0 | 270k   28k

您可以使用生成测试数据

我想像这样将其导入数据帧（Python 3.5.2，0.18.1）：

这是我的表达式，但不起作用：

path='/opt/dstat.2016-11-10'    
dstat=pd.read_table(path,skiprows=1,header=0,sep=r"\|{\s}*|\s+")

我不想编辑文本文件。

请尝试以下操作：

import io

fn = r'D:\temp\.data\data.fwf'

with open(fn) as f:
    data = f.read().replace('|', ' ')

cols = 'date time 1m 5m 15m usr sys idl wai hiq siq used buff cach free ' \
       'dsk.read dsk.writ io.read io.writ in out recv send'.split()
df = pd.read_csv(io.StringIO(data), delim_whitespace=True, skiprows=2,
                 header=None, names=cols)


In [85]: df
Out[85]:
    date      time    1m    5m   15m  usr  sys  idl  wai  hiq  ...     cach  free dsk.read dsk.writ io.read io.writ   in  out   recv   send
0  10-11  00:00:01  0.67  0.42  0.31    2    0   98    0    0  ...    6331M  189M    2101k     901k    30.4    28.3  63B  75B      0      0
1  10-11  00:00:03  0.67  0.42  0.31    4    0   95    0    0  ...    6332M  190M      50k    1142k     4.0    18.0    0    0   310k  6765B
2  10-11  00:00:05  0.62  0.41  0.31    4    0   95    0    0  ...    6333M  189M     116k    2534k     3.5   113.0    0    0   484k    27k
3  10-11  00:00:07  0.62  0.41  0.31    7    1   92    0    0  ...    6335M  187M     154k    2372k     4.0   128.0    0    0  1159k    24k
4  10-11  00:00:09  0.62  0.41  0.31    5    0   95    0    0  ...    6336M  185M        0    1556k     0.0    38.5    0    0   396k  4172B
5  10-11  00:00:11  0.73  0.44  0.32    4    1   95    0    0  ...    6336M  184M     136k    2732k     3.5   139.0    0    0   270k    28k

[6 rows x 23 columns]

PS IMO更合适的解决方案是使用并指定那里的

colspecs

参数，但我太懒了；-）

试试这个：

import io

fn = r'D:\temp\.data\data.fwf'

with open(fn) as f:
    data = f.read().replace('|', ' ')

cols = 'date time 1m 5m 15m usr sys idl wai hiq siq used buff cach free ' \
       'dsk.read dsk.writ io.read io.writ in out recv send'.split()
df = pd.read_csv(io.StringIO(data), delim_whitespace=True, skiprows=2,
                 header=None, names=cols)


In [85]: df
Out[85]:
    date      time    1m    5m   15m  usr  sys  idl  wai  hiq  ...     cach  free dsk.read dsk.writ io.read io.writ   in  out   recv   send
0  10-11  00:00:01  0.67  0.42  0.31    2    0   98    0    0  ...    6331M  189M    2101k     901k    30.4    28.3  63B  75B      0      0
1  10-11  00:00:03  0.67  0.42  0.31    4    0   95    0    0  ...    6332M  190M      50k    1142k     4.0    18.0    0    0   310k  6765B
2  10-11  00:00:05  0.62  0.41  0.31    4    0   95    0    0  ...    6333M  189M     116k    2534k     3.5   113.0    0    0   484k    27k
3  10-11  00:00:07  0.62  0.41  0.31    7    1   92    0    0  ...    6335M  187M     154k    2372k     4.0   128.0    0    0  1159k    24k
4  10-11  00:00:09  0.62  0.41  0.31    5    0   95    0    0  ...    6336M  185M        0    1556k     0.0    38.5    0    0   396k  4172B
5  10-11  00:00:11  0.73  0.44  0.32    4    1   95    0    0  ...    6336M  184M     136k    2732k     3.5   139.0    0    0   270k    28k

[6 rows x 23 columns]

PS IMO更合适的解决方案是使用并指定那里的

colspecs

参数，但我太懒了；-）