如何将大字符串转换为数据框？

Question

早上好，我有一个关于 pandas 和 python 的非常重要的问题。我是 Python 的新手，我正在使用 PARAMIKO 库，通过它我得到了一个类似于表格的“形式”的答案，但我想应用一个数据框并更好地获得对我最有用的列“interfaces”“InUti”和“OutUti”的值，问题是我正在阅读熊猫，但我不知道如何应用熊猫的某些属性来获得我从我的字符串中获得的数据框。然后我留下我从答案中得到的字符串，我需要应用数据框。如上所述，我只对“Interface”“InUti”“OutUti”列感兴趣。

GigabitEthernet7 / 1/0 (10G), GigabitEthernet16 / 1/0 (10G) 等名称开头的空格默认是这样的，所以必须考虑在内。

我正在阅读有关 pandas 的内容，但我没有得到或多或少适合我需要的东西，但如果您知道任何有趣的文档，我将不胜感激

print(data)

Interface                   PHY   Protocol  InUti OutUti   inErrors  outErrors
40GE7/0/0                   up    up        6.97% 14.85%          0          0
40GE16/0/0                  up    up       25.69%  0.75%          0          0
Eth-Trunk1                  up    up       18.55% 10.07%        506          0
  GigabitEthernet7/1/0(10G) up    up       17.61% 10.16%        222          0
  GigabitEthernet16/1/0(10G) up    up       19.49%  9.97%        284          0
Eth-Trunk8                  up    up       39.19% 46.10%          0          0
  GigabitEthernet7/1/9(10G) up    up       39.80% 46.09%          0          0
  GigabitEthernet16/1/9(10G) up    up       38.58% 46.11%          0          0
GigabitEthernet0/0/0        up    up        0.01%  0.01%          0          0
GigabitEthernet1/2/0        up    up        0.04%  0.01%          0          0
GigabitEthernet1/2/3        up    up        0.01%  9.67%          0          0
GigabitEthernet1/2/6        up    up        0.01%  0.01%          0          0
GigabitEthernet1/2/7        up    up       13.94% 26.52%          0          0
GigabitEthernet1/2/8        up    up        0.23%  0.01%          0          0
GigabitEthernet1/2/11       up    up        0.39%  5.34%          0          0
GigabitEthernet1/2/12       up    up        1.10%  4.09%          0          0
GigabitEthernet1/2/13       up    up       21.65%  7.33%          0          0
GigabitEthernet1/2/15       up    up        0.23%  4.76%          0          0
GigabitEthernet1/2/16       up    up        5.10% 13.55%          0          0
GigabitEthernet7/1/6(10G)   up    up        4.23%  5.71%          0          0
GigabitEthernet7/1/7(10G)   up    up        4.48% 13.07%          0          0
GigabitEthernet7/1/11(10G)  up    up        0.92%  4.56%          0          0
GigabitEthernet7/1/12(10G)  up    up        4.53% 16.12%          0          0
GigabitEthernet7/1/13(10G)  up    up        6.43% 17.46%          0          0
GigabitEthernet16/1/7(10G)  up    up        2.85%  8.15%          0          0
GigabitEthernet16/1/11(10G) up    up        6.75% 19.73%          0          0
GigabitEthernet16/1/12(10G) up    up        0.01% 12.43%          0          0
LoopBack150                 up    up(s)        0%     0%          0          0
LoopBack160                 up    up(s)        0%     0%          0          0
LoopBack170                 up    up(s)        0%     0%          0          0
LoopBack199                 up    up(s)        0%     0%          0          0
LoopBack200                 up    up(s)        0%     0%          0          0
NULL0                       up    up(s)        0%     0%          0          0

[更新]： 除了同伴@crayxt 提出的解决方案之外，我还实现了一个解决方案，它包括使用pandas.read_fwf，代码（针对我的问题）如下：

df = pd.read_fwf (io.StringIO (data), widths = [27,4,11,8,9,8,11,10])
    print (df)
    print (df.loc [0])

   Interface     40GE7/0/0
   PHY                  up
   Protocol             up
   InUti             7.60%
   OutUti           14.95%
   inErrors              0
   outErrors             0
   Unnamed: 7          NaN
   Name: 0, dtype: object

Answer 1

您可以使用 io 库来做到这一点，很好的是没有列在其中有空格，但是行首的空格会被解析器吞噬：

import io

data = your_string_here

df = pd.read_csv(io.StringIO(data), sep='\s+')
>>> df.head()
                    Interface PHY Protocol   InUti  OutUti  inErrors  outErrors
0                   40GE7/0/0  up       up   6.97%  14.85%         0          0
1                  40GE16/0/0  up       up  25.69%   0.75%         0          0
2                  Eth-Trunk1  up       up  18.55%  10.07%       506          0
3   GigabitEthernet7/1/0(10G)  up       up  17.61%  10.16%       222          0
4  GigabitEthernet16/1/0(10G)  up       up  19.49%   9.97%       284          0
>>> 
>>> 
>>> df.shape
(33, 7)

Interface专栏：

>>> df.Interface.values
array(['40GE7/0/0', '40GE16/0/0', 'Eth-Trunk1',
       'GigabitEthernet7/1/0(10G)', 'GigabitEthernet16/1/0(10G)',
       'Eth-Trunk8', 'GigabitEthernet7/1/9(10G)',
       'GigabitEthernet16/1/9(10G)', 'GigabitEthernet0/0/0',
       'GigabitEthernet1/2/0', 'GigabitEthernet1/2/3',
       'GigabitEthernet1/2/6', 'GigabitEthernet1/2/7',
       'GigabitEthernet1/2/8', 'GigabitEthernet1/2/11',
       'GigabitEthernet1/2/12', 'GigabitEthernet1/2/13',
       'GigabitEthernet1/2/15', 'GigabitEthernet1/2/16',
       'GigabitEthernet7/1/6(10G)', 'GigabitEthernet7/1/7(10G)',
       'GigabitEthernet7/1/11(10G)', 'GigabitEthernet7/1/12(10G)',
       'GigabitEthernet7/1/13(10G)', 'GigabitEthernet16/1/7(10G)',
       'GigabitEthernet16/1/11(10G)', 'GigabitEthernet16/1/12(10G)',
       'LoopBack150', 'LoopBack160', 'LoopBack170', 'LoopBack199',
       'LoopBack200', 'NULL0'], dtype=object)