5

多亏了beautifulsoup，我已经抓取了网络数据，但是我无法将输出转换为我可以操作的矩阵/数组。

from bs4 import BeautifulSoup
import urllib2

headers = { 'User-Agent' : 'Mozilla/5.0' }
req = urllib2.Request('http://statsheet.com/mcb/teams/duke/game_stats', None, headers)
html = urllib2.urlopen(req).read()
soup = BeautifulSoup(html)

#statdiv = soup.find('div', attrs={'id': 'basic_stats'})  #not needed
table = soup.find('table', attrs={'class': 'sortable statsb'})
rows = table.findAll('tr')
for tr in rows:
  text = []
  cols = tr.findAll('td')
  for td in cols:
    try:
      text = ''.join(td.find(text=True))
    except Exception:
        text = "000"
    print text+",",
  print

注意：''.join(td.find(text=True))是为了防止程序在空白单元格上失败。

输出：

W, GSU, 32, 42, 74, 24-47, 51.1, 15-23, 65.2, 11-24, 45.8, 6, 25, 31, 17, 4, 6, 15, 19,
W, UK, 33, 42, 75, 26-57, 45.6, 15-22, 68.2, 8-18, 44.4, 11, 20, 31, 16, 6, 6, 8, 17,
W, FGCU, 52, 36, 88, 30-63, 47.6, 19-23, 82.6, 9-31, 29.0, 16, 21, 37, 19, 9, 4, 18, 14,
W, @MINN, 40, 49, 89, 30-55, 54.5, 21-26, 80.8, 8-10, 80.0, 10, 22, 32, 12, 12, 4, 15, 21,
W, VCU, 29, 38, 67, 20-48, 41.7, 24-27, 88.9, 3-15, 20.0, 4, 30, 34, 14, 4, 8, 8, 18,
W, Lville, 36, 40, 76, 24-55, 43.6, 23-27, 85.2, 5-20, 25.0, 8, 25, 33, 13, 8, 6, 14, 20,
W, OSU, 23, 50, 73, 24-51, 47.1, 20-27, 74.1, 5-12, 41.7, 8, 29, 37, 11, 3, 5, 8, 19,

这是完美的，只是现在我无法弄清楚如何将数据放入矩阵中，以便我可以操作某些列，添加新列等。

我一直在玩 numpy 但每次我尝试都会得到这样的结果：

[u'W,']
[u'GSU,']
[u'32,']
[u'42,']
[u'74,']
[u'24-47,']
[u'51.1,']
[u'15-23,']
[u'65.2,']
[u'11-24,']
[u'45.8,']

我想要的是获取我抓取的数据并能够添加列、移动列、更改列中的文本、将一列中的数据拆分为两列（连字符列）。

这是我使用python的第二天。我假设将我的数据放入矩阵/数组中是最简单的方法。如果不是，请告诉我。

4

1 回答 1

7

你可以使用熊猫。此示例将数据转换为pandas DataFrame，为进一步处理数据提供方便的方法，例如拆分列或将它们转换为不同的数据类型。

从文档：

DataFrame is a 2-dimensional labeled data structure with columns of potentially
different types. You can think of it like a spreadsheet or SQL table, or a dict
of Series objects. It is generally the most commonly used pandas object. Like
Series, DataFrame accepts many different kinds of input.

import pandas as pd

table  = soup.find('table', attrs={'class': 'sortable statsb'})
header = [th.text for th in table.find('thead').select('th')]
header[:2] = ['',' ']
body   = [[td.text for td in row.select('td')]
             for row in table.findAll('tr', attrs = {"onmouseover":"hl(this)"})]
cols   =  zip(*body)
tbl_d  = {name:col for name, col in zip(header,cols)}

print pd.DataFrame(tbl_d, columns = header)

输出：

              1H  2H   T     FG   FG%     FT   FT%    3PT    3%  OR  DR REB  AS  ST  B  TO  PF
0   W     GSU  32  42  74  24-47  51.1  15-23  65.2  11-24  45.8   6  25  31  17   4  6  15  19
1   W      UK  33  42  75  26-57  45.6  15-22  68.2   8-18  44.4  11  20  31  16   6  6   8  17
2   W    FGCU  52  36  88  30-63  47.6  19-23  82.6   9-31  29.0  16  21  37  19   9  4  18  14
3   W   @MINN  40  49  89  30-55  54.5  21-26  80.8   8-10  80.0  10  22  32  12  12  4  15  21
4   W     VCU  29  38  67  20-48  41.7  24-27  88.9   3-15  20.0   4  30  34  14   4  8   8  18
5   W  Lville  36  40  76  24-55  43.6  23-27  85.2   5-20  25.0   8  25  33  13   8  6  14  20
6   W     OSU  23  50  73  24-51  47.1  20-27  74.1   5-12  41.7   8  29  37  11   3  5   8  19
7   W      UD  42  46  88  35-67  52.2  11-21  52.4   7-20  35.0  10  37  47  23   9  8  11  19
8   W  TEMPLE  46  44  90  28-59  47.5  22-29  75.9  12-20  60.0  11  27  38  19   6  2   6  15
9   W    CORN  41  47  88  34-60  56.7  13-17  76.5   7-21  33.3   4  26  30  23  11  7  10  11
10  W    ELON  35  41  76  29-67  43.3   7-16  43.8  11-19  57.9  15  28  43  16  10  2  12  13
11  W     SCU  38  52  90  31-63  49.2  21-33  63.6   7-14  50.0  14  26  40  15   6  2  11  17
12  W    @DAV  29  38  67  21-46  45.7  20-22  90.9   5-11  45.5   6  27  33   8   5  5  12  17
13  W     WFU  41  39  80  29-63  46.0  11-22  50.0  11-24  45.8  10  25  35  22   6  4   6  16
14  W    CLEM  25  43  68  27-56  48.2   6-14  42.9   8-15  53.3  13  29  42  13   8  1  13  12
15  L   @NCSU  39  37  76  30-67  44.8  10-12  83.3   6-20  30.0  13  22  35  10   6  4  12  20
16  W      GT  27  46  73  26-65  40.0  11-16  68.8  10-21  47.6  15  25  40  12  10  5  13  18
于 2013-01-23T20:19:12.497 回答