将HTML转换为CSV [英] Convert HTML into CSV

问题描述

我想将从以下脚本获取的HTML表格转换为CSV文件，但出现类型错误，如下所示：

TypeError：sequence item 0：expected string，Tag found

  from bs4 import BeautifulSoup 
 import urllib2 
 
 url ='http://www.data.jma.go.jp/obd/stats/etrn/view/monthly_s3_en.php?block_no=47401&view=1'
 html = urllib2.urlopen（url）.read（）
 soup = BeautifulSoup（html）
 table = soup.find_all（'table'，class _ ='data2_s'）
 rows = table [0] .find_all（'tr'）
  

如何将最简单的方法转换为CSV文件？
我试过：

  fo = open（'fo.txt'，'w'）
 for r in rows：
 fo.write（str（r.txt）+'\\\
'）
 fo.close（）
  

但它写为none

HTML如下：

 < table class =data2_s>< caption class =m> WAKKANAI？WMO Station ID：47401 Lat？45< sup> /sup>24.9'NÂ,Lon'141< sup> o< / sup> 40.7'E< / caption>< tr>< th scope =col> Year< / th>< th scope = < / th>< th scope =col>< / th>< / th>< th scope =col> Jul< / th>< / th>< th scope =col> Aug< / th>< th scope =col> Sep< / th>< th scope =col> Oct< / th> > Nov< / th>< th scope =col> Dec< / th>< th scope =col> Annual< / th>< / tr>< tr class = style =text-align：right;>< td style =text-align：center> 1938< / td>< td class =data_0_0_0_0> -5.2< / td> ; td class =data_0_0_0_0> -4.9< / td>< td class =data_0_0_0_0> -0.6< / td>< td class =data_0_0_0_0> 4.7< / td> ; td class =data_0_0_0_0> 9.5< / td>< td class =data_0_0_0_0> 11.6< / td>< td class =data_0_0_0_0> 17.9< / td> class =data_0_0_0_0> 22.2< / td>< td class =data_0_0_0_0> 16.5< / td>< td class =data_0_0_0_0> 10.7< / td& data_0_0_0_0> 3.3< / td>< td class =data_0_0_0_0> -4.7< / td>< td class =data_0_0_0_0> 6.8< / td>< / tr> 
< tr class =mtxstyle =text-align：right;>< td style =text-align：center> 1939< / td>< td class =data_0_0_0_0 > -7.5< / td>< td class =data_0_0_0_0> -6.6< / td>< td class =data_0_0_0_0> -1.4< / td>< td class = data_0_0_0_0> 4.0< / td>< td class =data_0_0_0_0> 7.5< / td>< td class =data_0_0_0_0> 13.0< / td>< td class =data_0_0_0_0 > 17.4< / td>< td class =data_0_0_0_0> 20.0< / td>< td class =data_0_0_0_0> 17.4< / td>< td class =data_0_0_0_0 9.7< / td>< td class =data_0_0_0_0> 3.0< / td>< td class =data_0_0_0_0> -2.5< / td>< td class =data_0_0_0_0> 6.2 < / td>< / tr> 
  

解决方案

这是csv lib的工作， td每行内并提取文本，它将处理每一行中缺少值的位置：

  from bs4 import BeautifulSoup 
 import urllib2 
 import csv 
 
 url ='http://www.data.jma.go.jp/obd/stats/etrn/view/monthly_s3_en.php?block_no= 47401& view = 1'
 html = urllib2.urlopen（url）.read（）
 soup = BeautifulSoup（html）
 table = soup.select_one（table.data2_s）
＃python3只是使用th.text 
 headers = [th.text.encode（utf-8）for table.select（tr th）] 
 
 open（out.csv，w）as f：
 wr = csv.writer（f）
 wr.writerow（headers）
 wr.writerows（[[td.text .encode（utf-8）for td in row.find_all（td）] for row in table.select（tr + tr）]）
  

与您在页面上看到的表格完全一致：

 ：〜$ cat out.csv 
 Year，Jan，Feb，Mar，Apr，May，Jun，Jul，August，Sep，Oct，Nov，Dec，Annual 
 1938，-5.2，-4.9 ，-0.6,4.7,9.5,11.6,17.9,22.2,16.5,10.7,3.3，-4.7,6.8 
 1939，-7.5，-6.6，-1.4,4.0,7.5,13.0,17.4,20.0,17.4 ，9.7,3.0，-2.5,6.2 
 1940，-6.0，-5.7，-0.5,3.5,8.5,11.0,16.6,19.7,15.6,10.4,3.7，-1.0,6.3 
 1941， -6.5，-5.8，-2.6,3.6,8.1,11.4,12.7,16.5,16.0,10.0,4.0，-2.9,5.4 
 1942，-7.8，-8.2，-0.8,3.5,7.1,12.0， 17.4,18.4,15.7,10.5,2.5，-2.9,5.6 
 1943，-4.1，-6.1，-1.1,3.5,6.9,12.9,19.3,21.5,17.5,11.7,1.2，-3.6,6.6 
 1944，-7.7，-7.9，-22,1.7,8.9,13.7,19​​.0,21.3,16.6,10.8,1.3，-6.0,5.8 
 1945，-7.8，-6.9，-1.8,3.9 ，5.5,11.0,13.6,18.7,16.8,11.0,3.9，-4.8,5.3 
 1946，-6.5，-6.0，-3.3,4.5,7.6,14.9,18.2,22.2,16.9,11.5,4.4， -2.5,6.8 
 1947，-4.9，-5.5，-2.3,3.7,9.0,11.2,17.1,19.3,15.1,10.6,2.4，-4.6,5.9 
 1948，-2.7，-4.4 ，-0.2,6.0,10.7,12.2,16.2,22.0,16.9,11.1,4.2，-0.6,7.6 
 1949，-2.6，-2.8，-3.4,2.0,9.4,11.8,16.9,20.8,17.8 ，10.8,3.1，-3.8,6.7 
 1950，-5.7，-4.8，-1.3,4.0,9.2,14.6,19.3,22.6,16.8,9.0,3.0，-2.9,7.0 
 1951， -6.7，-6.5，-2.2,3.7,9.5,12.3,16.7,22.3,15.6,10.1,3.7，-0.3,6.5 
 1952，-5.7，-7.1，-2.4,3.8,8.3,13.1， 16.4,19.7,17.0,11.3,0.9，-7.1,5.7 
 1953，-7.7，-7.3，-0.9,3.6,6.9,11.1,16.8,19.2,17.6,11.2，-0.6，-2.6,5.6 
 1954，-6.7，-4.1，-2.5,4.0,7.5,11.0,13.7,17.0,17.2,9.5,3.2，-1.8,5.7 
 1955，-6.4，-4.8，-1.3， 4.7,7.0,12.7,20.3,19.5,15.5,10.6,3.6，-0.4,6.8 
 1956，-6.1，-4.6，-2.0,5.1,10.8,11.2,13.8,16.3,17.2,12.3,2.8 ，-2.6,6.2 
 1957，-3.9，-5.5，-2.9,4.4,9.3,10.9,17.1,18.2,15.5,11.1,5.4，-1.1,6.5 
 1958，-4.9， - 4.9，-2.3,4.4,8.5,12.6,17.5,18.3,16.8,10.6,4.5，-0.5,6.7 
 1959，-7.3，-2.8,0.8,6.4,9.4,12.7,17.1,18.5,16.2 ，11.6,2.9，-3.9,6.8 
 1960，-7.2，-5.2，-1.4,3.5,7.7,10.8,15.9,20.8,18.1,9.7,3.3，-3.9,6.0 
 1961， -7.7，-5.3，-1.4,5.5,8.7,14.7,19​​.5,20.0,18.9,10.4,4.1，-1.3,7.2 
 1962，-4.2，-5.4，-2.5,6.7,10.0,12.9， 16.8,17.7,16.6,9.9,2.6，-1.5,6.6 
 1963，-3.6，-3.7,0.1,5.0,10.4,12.4,16.8,17.1,15.6,10.7,4.3，-1.7,7.0 
 1964，-4.5，-7.7，-1.3,3.7,9.9,11.9,15.3,17.7,14.9,10.0,3.6，-1.9,6.0 
 1965，-4.1，-5.7，-2.8,3.2， 9.1,13.3,15.2,18.8,15.8,11.4,2.1，-2.6,6.1 
 1966，-5.0，-5.5，-1.0,3.2,8.1,12.2,15.3,17.5,15.4,11.6,4.1， - 4.4,6.0 
 1967，-6.8，-5.9，-0.7,4.5,10.0,11.4,16.4,20.5,15.5,11.0,1.8，-1.5,6.4 
 1968，-4.2，-4.7， 1.9,5.7,8.9,14.5,17.3,18.1,15.9,9.1,5.3，-0.7,7.3 
 1969，-7.3，-7.5，-2.5,3.9,7.2,10.6,17.0,16.5,16.1,9.4 ，2.2，-5.4,5.0 
 1970，-6.6，-6.0，-4.2,4.6,10.4,12.9,17.4,19.2,16.8,10.5,4.3，-3.3,6.3 
 1971，-6.3 ，-6.4，-1.7,4.1,7.6,11.6,15.8,17.2,15.2,11.5,3.4，-2.2,5.8 
 1972，-5.3，-5.0，-0.6,5.9,9.4,12.8,16.8， 20.4,15.7,10.9,1.9，-1.4,6.8 
 1973，-4.2，-5.3，-2.9,4.2,8.4,12.8,17.0,20.9,17.1,10.4,3.5，-1.9,6.7 
 1974，-2.6，-4.6，-2.1,4.0,8.4,11.8,16.8,18.8,16.5,10.1,1.9，-5.7,6.1 
 1975，-4.1，-6.1，-1.5,4.3,8.4 ，13.7,16.1,20.6,17.3,10.4,3.8，-3.8,6.6 
 1976，-4.6，-3.5，-1.4,4.0,8.9,11.9,17.5,17.6,15.7,10.2,1.3，-2.0 ，6.3 
 1977，-8.3，-7.1，-1.0,3.6,8.0,11.9,18.2,19.1,17.4,11.4,4.5，-1.8,6.3 
 1978，-6.7，-9.2， - 1.6,4.3,9.2,13.5,20.6,21.3,17.4,9.6,3.4，-2.1,6.6 
 1979，-6.9，-4.5，-2.5,2.7,7.8,13.2,15.8,20.3,16.9,11.3 ，2.9，-0.1,6.4 
 1980，-5.4，-7.1，-1.9,1.9,7.8,12.9,15.9,16.5,16.0,10.0,4.3，-0.6,5.9 
 1981，-5.4 ，-6.3，-2.6,5.6,8.1,11.8,17.1,18.7,16.0,10.5,0.8，-0.6,6.1 
 1982，-5.6，-5.3，-0.6,3.7,9.0,11.9,16.9， 21.0,17.5,11.4,4.3，-1.0,6.9 
 1983，-4.2，-7.6，-1.9,6.8,8.2,8.5,14.5,18.9,15.8,8.9,4.8，-2.1,5.9 
 1984，-4.9，-6.6，-3.3,2.9,7.9,15.5,19.5,20.5,16.6,9.2,2.3，-3.6,6.3 
 1985，-8.7，-4.8，-1.4,4.9,8.6 ，11.7,16.6,21.1,15.7,10.3,2.7，-4.2,6.0 
 1986，-7.2，-6.5，-2.4,4.6,8.4,11.2,14.4,19.6,16.8,9.1,2.1，-1.9 ，5.7 
 1987，-6.4，-5.6，-1.4,4.2,8.6,12.6,17.5,18.0,16.4,11.1,2.0，-3.1,6.2 
 1988，-4.8，-6.3， 1.8,4.1,8.0,12.6,14.1,20.4,16.1,10.4,2.0，-1.5,6.1 
 1989，-2.6，-2.4,0.8,4.0,8.2,10.7,18.4,20.4,16.8,10.8， 4.8，-1.3,7.4 
 1990，-5.7，-2.4,1.4,5.7,9.3,13.4,18.9,20.3,17.1,13.3,6.2,1.2,8.2 
 1991，-1.6，-3.6 ，-1.5,4.8,10.1,14.3,16.2,19.0,16.6,11.8,3.5，-2.3,7.3 
 1992，-3.6，-3.6，-0.4,3.7,8.1,12.1,17.6,18.0,14.9 ，11.1,3.2，-1.2,6.7 
 1993，-2.7，-3.3，-0.2,3.1,8.6,10.7,15.6,17.6,16.3,11.1,3.7，-1.6,6.6 
 1994， -6.1，-2.7，-1.3,4.4,10.0,12.8,17.4,21.7,17.5,11.8,4.3，-2.9,7.2 
 1995，-4.0，-4.0，-0.8,4.8,11.0,12.7， 18.4,19.3,16.3,12.3,5.2，-0.6,7.6 
 1996，-4.6，-4.5，-1.0,3.5,6.9,12.0,15.9,18.7,16.8,10.4,2.3，-2.4,6.2 
 1997，-3.0，-3.3，-1.5,4.3,7.3,11.7,17.4,17.2,16.1,10.3,6.4，-0.7,6.9 
 1998，-6.9，-5.1,0.3,5.3， 10.1,12.9,15.5,18.1,17.2,12.5,2.0，-2.4,6.6 
 1999，-4.1，-5.6，-2.6,4.2,8.4,14.5,16.6,21.0,18.3,11.2,3.8， 1.9,7.0 
 2000，-4.2，-5.6，-2.1,3.5,9.3,12.8,18.9,21.5,17.7,10.6,1.5，-4.1,6.7 
 2001，-6.3，-7.7， -2.4,4.7,8.5,13.0,17.4,18.7,15.6,10.8,4.0，-4.2,6.0 
 2002，-3.6，-1.0,0.5,6.8,11.1,12.1,15.7,17.1,17.0,10.8 ，2.3，-4.4,7.0 
 2003，-4.7，-5.6，-0.7,5.3,10.1,13.9,14.3,18.4,16.6,11.3,4.5，-1.4,6.8 
 2004，-3.9 ，-3.0，-0.5,4.4,10.6,14.6,16.8,19.7,17.8,11.8,5.9，-2.0,7.7 
 2005，-4.6，-5.7，-1.0,3.9,7.0,14.3,16.7， 21.0,17.9,12.6,4.9，-2.3,7.1 
 2006，-5.5，-4.7，-0.9,2.1,9.3,11.9,18.4,21.6,17.7,11.0,4.5，-1.8,7.0 
 2007，-3.7，-3.2，-0.7,3.5,7.6,14.3,16.7,20.4,17.0,10.9,3.0，-1.7,7.0 
 2008，-6.0，-4.8,0.6,6.0,8.3， 11.9,17.9,18.8,17.9,11.5,3.8，-0.4,7.1 
 2009，-2.4，-4.4,0.0,4.5,10.0,12.3,14.8,18.6,16.9,11.4,3.1，-2.2,6.9 
 2010，-3.4，-4.9，-1.4,3.5,7.3,15.0,18.1,22.4,18.4,11.4,4.8，-1.1,7.5 
 2011，-5.1，-2.2，-0.6， 4.4,6.5,12.8,17.5），21.5,18.3,12.1,4.9，-2.3,7.3 
 2012，-5.4，-6.4，-2.4,4.6,8.9,12.6,17.2,20.4,19.4,11.8， 3.8，-3.0,6.8 
 2013，-5.8，-5.1，-1.3,4.5,7.2,14.0,18.9,20.2,17.6,11.8,5.5，-0.2,7.3 
 2014，-5.3， -4.2，-1.2,3.9,8.7,13.9,19.2,20.0,16.7,11.0,4.8，-2.3,7.1 
 2015，-2.9，-1.7,2.3,5.9,9.9,12.1,17.6,19.0， 17.3,10.4,3.7，-0.2,7.8 
 2016，-5.2，-4.7,0.5,4.3,11.4,12.5,17.4,21.8]，，，，，5.2] 
  table.select_one（caption.m）。 
 >：
 以open（out.csv，w）as f：
 wr = csv。作者（f）
 wr.writerow（[table.select_one（caption.m）.text.encode（utf-8）]）
 wr.writerow（headers）
 wr.writerows（[td.text.encode（utf-8）for td in row.find_all（td）] 
 for row in table.select（tr + tr）]）
  
但它可能是一个想法使用它作为文件的名称，而不是将其添加到csv。
 
 
 
如果你真的想在没有csv的情况下使用，请使用与 str.join 相同的逻辑：
  table = soup.select_one（table.data2_s）
 headers = [th.text.encode（utf-8）for thin table.select（tr th）] 
 
 with open（out.csv，w）as f：
 f.write（，。join row.find_all（td）]）+ td中的td的[td.text.encode（utf-8）） \\\

 for table.select（tr + tr））
  
如果要用 N / A 替换空单元格：
  f.write（，。join（headers）+\\\
）
 f.writelines ，。join（[td.text.encode（utf-8）。strip（'\xe3\x80\x80'）或N / Afor row.find_all（td） ]）+\\\

 for table.select（tr + tr））
  
这会将最后一行改为：
  2016，-5.2，-4.7,0.5,4.3,11.4 ，12.5,17.4,21.8]，N / A，N / A，N / A，N / A，5.2] 
  
缺少值的空格为 unicode  表意空间字符（ u\\\　在python ），当编码为utf-8成为和strip，如果留下一个空字符串，那么我们只是使用N / A 
 在[7]：print u\\\　 
 
 In [8]：u\\\　.encode（utf-8）
 Out [8]：'\xe3\x80\x80'
 In [9]：u\\\　.encode（utf-8）。strip（'\xe3\x80\x80'）
 Out [9]：''
  
 
I want to convert a HTML table as obtained from the script below into a CSV file, but  got type error as follows:

  
TypeError: sequence item 0: expected string, Tag found


from bs4 import BeautifulSoup
import urllib2

url = 'http://www.data.jma.go.jp/obd/stats/etrn/view/monthly_s3_en.php?block_no=47401&view=1'
html = urllib2.urlopen(url).read()        
soup = BeautifulSoup(html)
table = soup.find_all('table', class_='data2_s')
rows = table[0].find_all('tr')
How is the easiest way to convert it into a CSV file?
I tried as:
fo = open('fo.txt','w')
for r in rows:
    fo.write(str(r.txt) + '\n')
fo.close()
but it wrote 'none'

The HTML is like this:
<table class="data2_s"><caption class="m">WAKKANAI   WMO Station ID:47401 Lat 45<sup>o</sup>24.9'N  Lon 141<sup>o</sup>40.7'E</caption><tr><th scope="col">Year</th><th scope="col">Jan</th><th scope="col">Feb</th><th scope="col">Mar</th><th scope="col">Apr</th><th scope="col">May</th><th scope="col">Jun</th><th scope="col">Jul</th><th scope="col">Aug</th><th scope="col">Sep</th><th scope="col">Oct</th><th scope="col">Nov</th><th scope="col">Dec</th><th scope="col">Annual</th></tr><tr class="mtx" style="text-align:right;"><td style="text-align:center">1938</td><td class="data_0_0_0_0">-5.2</td><td class="data_0_0_0_0">-4.9</td><td class="data_0_0_0_0">-0.6</td><td class="data_0_0_0_0">4.7</td><td class="data_0_0_0_0">9.5</td><td class="data_0_0_0_0">11.6</td><td class="data_0_0_0_0">17.9</td><td class="data_0_0_0_0">22.2</td><td class="data_0_0_0_0">16.5</td><td class="data_0_0_0_0">10.7</td><td class="data_0_0_0_0">3.3</td><td class="data_0_0_0_0">-4.7</td><td class="data_0_0_0_0">6.8</td></tr>
<tr class="mtx" style="text-align:right;"><td style="text-align:center">1939</td><td class="data_0_0_0_0">-7.5</td><td class="data_0_0_0_0">-6.6</td><td class="data_0_0_0_0">-1.4</td><td class="data_0_0_0_0">4.0</td><td class="data_0_0_0_0">7.5</td><td class="data_0_0_0_0">13.0</td><td class="data_0_0_0_0">17.4</td><td class="data_0_0_0_0">20.0</td><td class="data_0_0_0_0">17.4</td><td class="data_0_0_0_0">9.7</td><td class="data_0_0_0_0">3.0</td><td class="data_0_0_0_0">-2.5</td><td class="data_0_0_0_0">6.2</td></tr>

解决方案
This is a job for the csv lib, getting each td inside each row and extracting the text, it will handle where there are missing values in each row:
from bs4 import BeautifulSoup
import urllib2
import csv

url = 'http://www.data.jma.go.jp/obd/stats/etrn/view/monthly_s3_en.php?block_no=47401&view=1'
html = urllib2.urlopen(url).read()
soup = BeautifulSoup(html)
table = soup.select_one("table.data2_s")
# python3 just use th.text
headers = [th.text.encode("utf-8") for th in table.select("tr th")]

with open("out.csv", "w") as f:
    wr = csv.writer(f)
    wr.writerow(headers)
    wr.writerows([[td.text.encode("utf-8") for td in row.find_all("td")] for row in table.select("tr + tr")])
Which matches the table exactly as you see on the page:
:~$ cat out.csv
Year,Jan,Feb,Mar,Apr,May,Jun,Jul,Aug,Sep,Oct,Nov,Dec,Annual
1938,-5.2,-4.9,-0.6,4.7,9.5,11.6,17.9,22.2,16.5,10.7,3.3,-4.7,6.8
1939,-7.5,-6.6,-1.4,4.0,7.5,13.0,17.4,20.0,17.4,9.7,3.0,-2.5,6.2
1940,-6.0,-5.7,-0.5,3.5,8.5,11.0,16.6,19.7,15.6,10.4,3.7,-1.0,6.3
1941,-6.5,-5.8,-2.6,3.6,8.1,11.4,12.7,16.5,16.0,10.0,4.0,-2.9,5.4
1942,-7.8,-8.2,-0.8,3.5,7.1,12.0,17.4,18.4,15.7,10.5,2.5,-2.9,5.6
1943,-4.1,-6.1,-1.1,3.5,6.9,12.9,19.3,21.5,17.5,11.7,1.2,-3.6,6.6
1944,-7.7,-7.9,-2.2,1.7,8.9,13.7,19.0,21.3,16.6,10.8,1.3,-6.0,5.8
1945,-7.8,-6.9,-1.8,3.9,5.5,11.0,13.6,18.7,16.8,11.0,3.9,-4.8,5.3
1946,-6.5,-6.0,-3.3,4.5,7.6,14.9,18.2,22.2,16.9,11.5,4.4,-2.5,6.8
1947,-4.9,-5.5,-2.3,3.7,9.0,11.2,17.1,19.3,15.1,10.6,2.4,-4.6,5.9
1948,-2.7,-4.4,-0.2,6.0,10.7,12.2,16.2,22.0,16.9,11.1,4.2,-0.6,7.6
1949,-2.6,-2.8,-3.4,2.0,9.4,11.8,16.9,20.8,17.8,10.8,3.1,-3.8,6.7
1950,-5.7,-4.8,-1.3,4.0,9.2,14.6,19.3,22.6,16.8,9.0,3.0,-2.9,7.0
1951,-6.7,-6.5,-2.2,3.7,9.5,12.3,16.7,22.3,15.6,10.1,3.7,-0.3,6.5
1952,-5.7,-7.1,-2.4,3.8,8.3,13.1,16.4,19.7,17.0,11.3,0.9,-7.1,5.7
1953,-7.7,-7.3,-0.9,3.6,6.9,11.1,16.8,19.2,17.6,11.2,-0.6,-2.6,5.6
1954,-6.7,-4.1,-2.5,4.0,7.5,11.0,13.7,17.0,17.2,9.5,3.2,-1.8,5.7
1955,-6.4,-4.8,-1.3,4.7,7.0,12.7,20.3,19.5,15.5,10.6,3.6,-0.4,6.8
1956,-6.1,-4.6,-2.0,5.1,10.8,11.2,13.8,16.3,17.2,12.3,2.8,-2.6,6.2
1957,-3.9,-5.5,-2.9,4.4,9.3,10.9,17.1,18.2,15.5,11.1,5.4,-1.1,6.5
1958,-4.9,-4.9,-2.3,4.4,8.5,12.6,17.5,18.3,16.8,10.6,4.5,-0.5,6.7
1959,-7.3,-2.8,0.8,6.4,9.4,12.7,17.1,18.5,16.2,11.6,2.9,-3.9,6.8
1960,-7.2,-5.2,-1.4,3.5,7.7,10.8,15.9,20.8,18.1,9.7,3.3,-3.9,6.0
1961,-7.7,-5.3,-1.4,5.5,8.7,14.7,19.5,20.0,18.9,10.4,4.1,-1.3,7.2
1962,-4.2,-5.4,-2.5,6.7,10.0,12.9,16.8,17.7,16.6,9.9,2.6,-1.5,6.6
1963,-3.6,-3.7,0.1,5.0,10.4,12.4,16.8,17.1,15.6,10.7,4.3,-1.7,7.0
1964,-4.5,-7.7,-1.3,3.7,9.9,11.9,15.3,17.7,14.9,10.0,3.6,-1.9,6.0
1965,-4.1,-5.7,-2.8,3.2,9.1,13.3,15.2,18.8,15.8,11.4,2.1,-2.6,6.1
1966,-5.0,-5.5,-1.0,3.2,8.1,12.2,15.3,17.5,15.4,11.6,4.1,-4.4,6.0
1967,-6.8,-5.9,-0.7,4.5,10.0,11.4,16.4,20.5,15.5,11.0,1.8,-1.5,6.4
1968,-4.2,-4.7,1.9,5.7,8.9,14.5,17.3,18.1,15.9,9.1,5.3,-0.7,7.3
1969,-7.3,-7.5,-2.5,3.9,7.2,10.6,17.0,16.5,16.1,9.4,2.2,-5.4,5.0
1970,-6.6,-6.0,-4.2,4.6,10.4,12.9,17.4,19.2,16.8,10.5,4.3,-3.3,6.3
1971,-6.3,-6.4,-1.7,4.1,7.6,11.6,15.8,17.2,15.2,11.5,3.4,-2.2,5.8
1972,-5.3,-5.0,-0.6,5.9,9.4,12.8,16.8,20.4,15.7,10.9,1.9,-1.4,6.8
1973,-4.2,-5.3,-2.9,4.2,8.4,12.8,17.0,20.9,17.1,10.4,3.5,-1.9,6.7
1974,-2.6,-4.6,-2.1,4.0,8.4,11.8,16.8,18.8,16.5,10.1,1.9,-5.7,6.1
1975,-4.1,-6.1,-1.5,4.3,8.4,13.7,16.1,20.6,17.3,10.4,3.8,-3.8,6.6
1976,-4.6,-3.5,-1.4,4.0,8.9,11.9,17.5,17.6,15.7,10.2,1.3,-2.0,6.3
1977,-8.3,-7.1,-1.0,3.6,8.0,11.9,18.2,19.1,17.4,11.4,4.5,-1.8,6.3
1978,-6.7,-9.2,-1.6,4.3,9.2,13.5,20.6,21.3,17.4,9.6,3.4,-2.1,6.6
1979,-6.9,-4.5,-2.5,2.7,7.8,13.2,15.8,20.3,16.9,11.3,2.9,-0.1,6.4
1980,-5.4,-7.1,-1.9,1.9,7.8,12.9,15.9,16.5,16.0,10.0,4.3,-0.6,5.9
1981,-5.4,-6.3,-2.6,5.6,8.1,11.8,17.1,18.7,16.0,10.5,0.8,-0.6,6.1
1982,-5.6,-5.3,-0.6,3.7,9.0,11.9,16.9,21.0,17.5,11.4,4.3,-1.0,6.9
1983,-4.2,-7.6,-1.9,6.8,8.2,8.5,14.5,18.9,15.8,8.9,4.8,-2.1,5.9
1984,-4.9,-6.6,-3.3,2.9,7.9,15.5,19.5,20.5,16.6,9.2,2.3,-3.6,6.3
1985,-8.7,-4.8,-1.4,4.9,8.6,11.7,16.6,21.1,15.7,10.3,2.7,-4.2,6.0
1986,-7.2,-6.5,-2.4,4.6,8.4,11.2,14.4,19.6,16.8,9.1,2.1,-1.9,5.7
1987,-6.4,-5.6,-1.4,4.2,8.6,12.6,17.5,18.0,16.4,11.1,2.0,-3.1,6.2
1988,-4.8,-6.3,-1.8,4.1,8.0,12.6,14.1,20.4,16.1,10.4,2.0,-1.5,6.1
1989,-2.6,-2.4,0.8,4.0,8.2,10.7,18.4,20.4,16.8,10.8,4.8,-1.3,7.4
1990,-5.7,-2.4,1.4,5.7,9.3,13.4,18.9,20.3,17.1,13.3,6.2,1.2,8.2
1991,-1.6,-3.6,-1.5,4.8,10.1,14.3,16.2,19.0,16.6,11.8,3.5,-2.3,7.3
1992,-3.6,-3.6,-0.4,3.7,8.1,12.1,17.6,18.0,14.9,11.1,3.2,-1.2,6.7
1993,-2.7,-3.3,-0.2,3.1,8.6,10.7,15.6,17.6,16.3,11.1,3.7,-1.6,6.6
1994,-6.1,-2.7,-1.3,4.4,10.0,12.8,17.4,21.7,17.5,11.8,4.3,-2.9,7.2
1995,-4.0,-4.0,-0.8,4.8,11.0,12.7,18.4,19.3,16.3,12.3,5.2,-0.6,7.6
1996,-4.6,-4.5,-1.0,3.5,6.9,12.0,15.9,18.7,16.8,10.4,2.3,-2.4,6.2
1997,-3.0,-3.3,-1.5,4.3,7.3,11.7,17.4,17.2,16.1,10.3,6.4,-0.7,6.9
1998,-6.9,-5.1,0.3,5.3,10.1,12.9,15.5,18.1,17.2,12.5,2.0,-2.4,6.6
1999,-4.1,-5.6,-2.6,4.2,8.4,14.5,16.6,21.0,18.3,11.2,3.8,-1.9,7.0
2000,-4.2,-5.6,-2.1,3.5,9.3,12.8,18.9,21.5,17.7,10.6,1.5,-4.1,6.7
2001,-6.3,-7.7,-2.4,4.7,8.5,13.0,17.4,18.7,15.6,10.8,4.0,-4.2,6.0
2002,-3.6,-1.0,0.5,6.8,11.1,12.1,15.7,17.1,17.0,10.8,2.3,-4.4,7.0
2003,-4.7,-5.6,-0.7,5.3,10.1,13.9,14.3,18.4,16.6,11.3,4.5,-1.4,6.8
2004,-3.9,-3.0,-0.5,4.4,10.6,14.6,16.8,19.7,17.8,11.8,5.9,-2.0,7.7
2005,-4.6,-5.7,-1.0,3.9,7.0,14.3,16.7,21.0,17.9,12.6,4.9,-2.3,7.1
2006,-5.5,-4.7,-0.9,2.1,9.3,11.9,18.4,21.6,17.7,11.0,4.5,-1.8,7.0
2007,-3.7,-3.2,-0.7,3.5,7.6,14.3,16.7,20.4,17.0,10.9,3.0,-1.7,7.0
2008,-6.0,-4.8,0.6,6.0,8.3,11.9,17.9,18.8,17.9,11.5,3.8,-0.4,7.1
2009,-2.4,-4.4,0.0,4.5,10.0,12.3,14.8,18.6,16.9,11.4,3.1,-2.2,6.9
2010,-3.4,-4.9,-1.4,3.5,7.3,15.0,18.1,22.4,18.4,11.4,4.8,-1.1,7.5
2011,-5.1,-2.2,-0.6,4.4,6.5,12.8,17.5 ),21.5,18.3,12.1,4.9,-2.3,7.3
2012,-5.4,-6.4,-2.4,4.6,8.9,12.6,17.2,20.4,19.4,11.8,3.8,-3.0,6.8
2013,-5.8,-5.1,-1.3,4.5,7.2,14.0,18.9,20.2,17.6,11.8,5.5,-0.2,7.3
2014,-5.3,-4.2,-1.2,3.9,8.7,13.9,19.2,20.0,16.7,11.0,4.8,-2.3,7.1
2015,-2.9,-1.7,2.3,5.9,9.9,12.1,17.6,19.0,17.3,10.4,3.7,-0.2,7.8
2016,-5.2,-4.7,0.5,4.3,11.4,12.5,17.4,21.8 ],　,　,　,　,5.2 ]
if you want the caption use table.select_one("caption.m").text:
with open("out.csv", "w") as f:
    wr = csv.writer(f)
    wr.writerow([table.select_one("caption.m").text.encode("utf-8")])
    wr.writerow(headers)
    wr.writerows([[td.text.encode("utf-8") for td in row.find_all("td")] 
for row in table.select("tr + tr")])
but it might be an idea to use that as the name of the file as opposed to adding it to the csv.

If you really want to do it without the csv, use the same logic with str.join:
table = soup.select_one("table.data2_s")
headers = [th.text.encode("utf-8") for th in table.select("tr th")]

with open("out.csv", "w") as f:
    f.write(",".join(headers) + "\n")
    f.writelines(",".join([td.text.encode("utf-8") for td in row.find_all("td")]) + "\n" 
                 for row in table.select("tr + tr"))
If you want to replace the empty cells with N/A:
with open("out.csv", "w") as f:
    f.write(",".join(headers) + "\n")
    f.writelines(",".join([td.text.encode("utf-8").strip('\xe3\x80\x80') or "N/A" for td in row.find_all("td")]) + "\n"
                 for row in table.select("tr + tr"))
Which will change the last row to:
2016,-5.2,-4.7,0.5,4.3,11.4,12.5,17.4,21.8 ],N/A,N/A,N/A,N/A,5.2 ]
The spaces for missing values are unicode ideographic space characters (u"\u3000" in python) which when encoded to utf-8 become and strip, if that leave an empty string then we just use "N/A"
In [7]: print u"\u3000"
　
In [8]:  u"\u3000".encode("utf-8")
Out[8]: '\xe3\x80\x80'
In [9]:  u"\u3000".encode("utf-8").strip('\xe3\x80\x80')
Out[9]: ''


                        
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