Convert HTML into CSV

Question

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>

Answer 1

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]: ''