大家好我希望得到一些帮助，我可以获取HTML文件中的表并将它们导入到csv文件中。我对网络抓取非常新，所以如果我的代码完全出错，请给我。 HTML文件包含我想要提取的三个单独的表;估计，抽样误差和估计中的非零地块数量。
我的代码如下所示：
#import necessary libraries
import urllib2
import pandas as pd
#specify URL
table = "file:///C:/Users/TMccw/Anaconda2/FiaAPI/outFArea18.html"
#Query the website & return the html to the variable 'page'
page = urllib2.urlopen(table)
#import the bs4 functions to parse the data returned from the website
from bs4 import BeautifulSoup
#Parse the html in the 'page' variable & store it in bs4 format
soup = BeautifulSoup(page, 'html.parser')
#Print out the html code with the function prettify
print soup.prettify()
#Find the tables & check type
table2 = soup.find_all('table')
print(table2)
print type(table2)
#Create new table as a dataframe
new_table = pd.DataFrame(columns=range(0,4))
#Extract the info from the HTML code
soup.find('table').find_all('td'),{'align':'right'}
#Remove the tags and extract table info into CSV
???
以下是第一张表格的＃34; Estimate＆＃34;：
` Estimate:
</b>
</caption>
<tr>
<td>
</td>
<td align="center" colspan="5">
<b>
Ownership group
</b>
</td>
</tr>
<tr>
<th>
<b>
Forest type group
</b>
</th>
<td>
<b>
Total
</b>
</td>
<td>
<b>
National Forest
</b>
</td>
<td>
<b>
Other federal
</b>
</td>
<td>
<b>
State and local
</b>
</td>
<td>
<b>
Private
</b>
</td>
</tr>
<tr>
<td nowrap="">
<b>
Total
</b>
</td>
<td align="right">
4,875,993
</td>
<td align="right">
195,438
</td>
<td align="right">
169,500
</td>
<td align="right">
392,030
</td>
<td align="right">
4,119,025
</td>
</tr>
<tr>
<td nowrap="">
<b>
White / red / jack pine group
</b>
</td>
<td align="right">
40,492
</td>
<td align="right">
3,426
</td>
<td align="right">
-
</td>
<td align="right">
10,850
</td>
<td align="right">
26,217
</td>
</tr>
<tr>
<td nowrap="">
<b>
Loblolly / shortleaf pine group
</b>
</td>
<td align="right">
38,267
</td>
<td align="right">
11,262
</td>
<td align="right">
997
</td>
<td align="right">
4,015
</td>
<td align="right">
21,993
</td>
</tr>
<tr>
<td nowrap="">
<b>
Other eastern softwoods group
</b>
</td>
<td align="right">
25,181
</td>
<td align="right">
-
</td>
<td align="right">
-
</td>
<td align="right">
-
</td>
<td align="right">
25,181
</td>
</tr>
<tr>
<td nowrap="">
<b>
Exotic softwoods group
</b>
</td>
<td align="right">
5,868
</td>
<td align="right">
-
</td>
<td align="right">
-
</td>
<td align="right">
662
</td>
<td align="right">
5,206
</td>
</tr>
<tr>
<td nowrap="">
<b>
Oak / pine group
</b>
</td>
<td align="right">
144,238
</td>
<td align="right">
9,592
</td>
<td align="right">
-
</td>
<td align="right">
21,475
</td>
<td align="right">
113,171
</td>
</tr>
<tr>
<td nowrap="">
<b>
Oak / hickory group
</b>
</td>
<td align="right">
3,480,272
</td>
<td align="right">
152,598
</td>
<td align="right">
123,900
</td>
<td align="right">
285,305
</td>
<td align="right">
2,918,470
</td>
</tr>
<tr>
<td nowrap="">
<b>
Oak / gum / cypress group
</b>
</td>
<td align="right">
76,302
</td>
<td align="right">
-
</td>
<td align="right">
12,209
</td>
<td align="right">
9,311
</td>
<td align="right">
54,782
</td>
</tr>
<tr>
<td nowrap="">
<b>
Elm / ash / cottonwood group
</b>
</td>
<td align="right">
652,001
</td>
<td align="right">
7,105
</td>
<td align="right">
25,431
</td>
<td align="right">
46,096
</td>
<td align="right">
573,369
</td>
</tr>
<tr>
<td nowrap="">
<b>
Maple / beech / birch group
</b>
</td>
<td align="right">
346,718
</td>
<td align="right">
10,871
</td>
<td align="right">
818
</td>
<td align="right">
12,748
</td>
<td align="right">
322,281
</td>
</tr>
<tr>
<td nowrap="">
<b>
Other hardwoods group
</b>
</td>
<td align="right">
21,238
</td>
<td align="right">
585
</td>
<td align="right">
-
</td>
<td align="right">
-
</td>
<td align="right">
20,653
</td>
</tr>
<tr>
<td nowrap="">
<b>
Exotic hardwoods group
</b>
</td>
<td align="right">
2,441
</td>
<td align="right">
-
</td>
<td align="right">
-
</td>
<td align="right">
-
</td>
<td align="right">
2,441
</td>
</tr>
<tr>
<td nowrap="">
<b>
Nonstocked
</b>
</td>
<td align="right">
42,975
</td>
<td align="right">
-
</td>
<td align="right">
6,144
</td>
<td align="right">
1,570
</td>
<td align="right">
35,261
</td>
</tr>
</table>
<br/>
<table border="4" cellpadding="4" cellspacing="4">
<caption>
<b>`
答案 0 :(得分：0)
不确定这里的具体问题是什么，但是马上就可以看到一个会让你失望的错误。
new_table = pd.DataFrame(columns=range(0-4))
需要
new_table = pd.DataFrame(columns=range(0,4))
范围（0-4）的结果实际上是范围（-4），其评估范围（0，-4），而您想要范围（0,4）。您只需将范围（4）作为参数或范围（0,4）传递。
答案 1 :(得分：0)
我制作了几张与你的几乎完全相同的表格，并将它们放入一个相当可观的HTML页面中。然后我运行了这段代码。
>>> import bs4
>>> import pandas as pd
>>> soup = bs4.BeautifulSoup(open('temp.htm').read(), 'html.parser')
>>> tables = soup.findAll('table')
>>> for t, table in enumerate(tables):
... df = pd.read_html(str(table), skiprows=2)
... df[0].to_csv('table%s.csv' % t)
结果是这样的四个文件，名为table0.csv到table3.csv。
,0,1,2,3,4,5
0,Total,4875993,195438,169500,392030,4119025
1,White / red / jack pine group,40492,3426,-,10850,26217
2,Loblolly / shortleaf pine group,38267,11262,997,4015,21993
3,Other eastern softwoods group,25181,-,-,-,25181
4,Exotic softwoods group,5868,-,-,662,5206
5,Oak / pine group,144238,9592,-,21475,113171
6,Oak / hickory group,3480272,152598,123900,285305,2918470
7,Oak / gum / cypress group,76302,-,12209,9311,54782
8,Elm / ash / cottonwood group,652001,7105,25431,46096,573369
9,Maple / beech / birch group,346718,10871,818,12748,322281
10,Other hardwoods group,21238,585,-,-,20653
11,Exotic hardwoods group,2441,-,-,-,2441
12,Nonstocked,42975,-,6144,1570,35261
也许我应该提到的主要是我跳过了BeautifulSoup提供的每个表中相同数量的行。如果表格中标题行的数量不同，那么您将不得不做一些更聪明的事情，或者只是丢弃输出文件中的行并省略skiprows参数。