Develop Reference

When I run the code, it runs without errors, but the csv file is not created, why?

I found a tutorial and I'm trying to run this script, I did not work with python before.
tutorial
I've already seen what is running through logging.debug, checking whether it is connecting to google and trying to create csv file with other scripts
from urllib.parse import urlencode, urlparse, parse_qs
from lxml.html import fromstring
from requests import get
import csv
def scrape_run():
with open('/Users/Work/Desktop/searches.txt') as searches:
for search in searches:
userQuery = search
raw = get("https://www.google.com/search?q=" + userQuery).text
page = fromstring(raw)
links = page.cssselect('.r a')
csvfile = '/Users/Work/Desktop/data.csv'
for row in links:
raw_url = row.get('href')
title = row.text_content()
if raw_url.startswith("/url?"):
url = parse_qs(urlparse(raw_url).query)['q']
csvRow = [userQuery, url[0], title]
with open(csvfile, 'a') as data:
writer = csv.writer(data)
writer.writerow(csvRow)
print(links)
scrape_run()
The TL;DR of this script is that it does three basic functions:
Locates and opens your searches.txt file.
Uses those keywords and searches the first page of Google for each
result.
Creates a new CSV file and prints the results (Keyword, URLs, and
page titles).

Solved
Google add captcha couse i use to many request
its work when i use mobile internet

Assuming the links variable is full and contains data - please verify.
if empty - test the api call itself you are making, maybe it returns something different than you expected.
Other than that - I think you just need to tweak a little bit your file handling.
https://www.guru99.com/reading-and-writing-files-in-python.html
here you can find some guidelines regarding file handling in python.
in my perspective, you need to make sure you create the file first.
start on with a script which is able to just create a file.
after that enhance the script to be able to write and append to the file.
from there on I think you are good to go and continue with you're script.
other than that I think that you would prefer opening the file only once instead of each loop, it could mean much faster execution time.
let me know if something is not clear.

Python iterating through pages google search

I am working on a larger code that will display the links of the results for a Google Newspaper search and then analyze those links for certain keywords and context and data. I've gotten everything this one part to work, and now when I try to iterate through the pages of results I come to a problem. I'm not sure how to do this without an API, which I do not know how to use. I just need to be able to iterate through multiple pages of search results so that I can then apply my analysis to it. It seems like there is a simple solution to iterating through the pages of results, but I am not seeing it.
Are there any suggestions on ways to approach this problem? I am somewhat new to Python and have been teaching myself all of these scraping techniques, so I'm not sure if I'm just missing something simple here. I know this may be an issue with Google restricting automated searches, but even pulling in the first 100 or so links would be beneficial. I have seen examples of this from regular Google searches but not from Google Newspaper searches
Here is the body of the code. If there are any lines where you have suggestions, that would be helpful. Thanks in advance!
def get_page_tree(url):
page = requests.get(url=url, verify=False)
return html.fromstring(page.text)
def find_other_news_sources(initial_url):
forwarding_identifier = '/url?q='
google_news_search_url = "https://www.google.com/search?hl=en&gl=us&tbm=nws&authuser=0&q=ohio+pay-to-play&oq=ohio+pay-to-play&gs_l=news-cc.3..43j43i53.2737.7014.0.7207.16.6.0.10.10.0.64.327.6.6.0...0.0...1ac.1.NAJRCoza0Ro"
google_news_search_tree = get_page_tree(url=google_news_search_url)
other_news_sources_links = [a_link.replace(forwarding_identifier, '').split('&')[0] for a_link in google_news_search_tree.xpath('//a//#href') if forwarding_identifier in a_link]
return other_news_sources_links
links = find_other_news_sources("https://www.google.com/search? hl=en&gl=us&tbm=nws&authuser=0&q=ohio+pay-to-play&oq=ohio+pay-to-play&gs_l=news-cc.3..43j43i53.2737.7014.0.7207.16.6.0.10.10.0.64.327.6.6.0...0.0...1ac.1.NAJRCoza0Ro")
with open('textanalysistest.csv', 'wt') as myfile:
wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)
for row in links:
print(row)

I'm looking into building a parser for a site with similar structure to google's (i.e. a bunch of consecutive results pages, each with a table of content of interest).
A combination of the Selenium package (for page-element based site navigation) and BeautifulSoup (for html parsing) seems like it's the weapon of choice for harvesting written content. You may find them useful too, although I have no idea what kinds of defenses google has in place to deter scraping.
A possible implementation for Mozilla Firefox using selenium, beautifulsoup and geckodriver:
from bs4 import BeautifulSoup, SoupStrainer
from bs4.diagnose import diagnose
from os.path import isfile
from time import sleep
import codecs
from selenium import webdriver
def first_page(link):
"""Takes a link, and scrapes the desired tags from the html code"""
driver = webdriver.Firefox(executable_path = 'C://example/geckodriver.exe')#Specify the appropriate driver for your browser here
counter=1
driver.get(link)
html = driver.page_source
filter_html_table(html)
counter +=1
return driver, counter
def nth_page(driver, counter, max_iter):
"""Takes a driver instance, a counter to keep track of iterations, and max_iter for maximum number of iterations. Looks for a page element matching the current iteration (how you need to program this depends on the html structure of the page you want to scrape), navigates there, and calls mine_page to scrape."""
while counter <= max_iter:
pageLink = driver.find_element_by_link_text(str(counter)) #For other strategies to retrieve elements from a page, see the selenium documentation
pageLink.click()
scrape_page(driver)
counter+=1
else:
print("Done scraping")
return
def scrape_page(driver):
"""Takes a driver instance, extracts html from the current page, and calls function to extract tags from html of total page"""
html = driver.page_source #Get html from page
filter_html_table(html) #Call function to extract desired html tags
return
def filter_html_table(html):
"""Takes a full page of html, filters the desired tags using beautifulsoup, calls function to write to file"""
only_td_tags = SoupStrainer("td")#Specify which tags to keep
filtered = BeautifulSoup(html, "lxml", parse_only=only_td_tags).prettify() #Specify how to represent content
write_to_file(filtered) #Function call to store extracted tags in a local file.
return
def write_to_file(output):
"""Takes the scraped tags, opens a new file if the file does not exist, or appends to existing file, and writes extracted tags to file."""
fpath = "<path to your output file>"
if isfile(fpath):
f = codecs.open(fpath, 'a') #using 'codecs' to avoid problems with utf-8 characters in ASCII format.
f.write(output)
f.close()
else:
f = codecs.open(fpath, 'w') #using 'codecs' to avoid problems with utf-8 characters in ASCII format.
f.write(output)
f.close()
return
After this, it is just a matter of calling:
link = <link to site to scrape>
driver, n_iter = first_page(link)
nth_page(driver, n_iter, 1000) # the 1000 lets us scrape 1000 of the result pages
Note that this script assumes that the result pages you are trying to scrape are sequentially numbered, and those numbers can be retrieved from the scraped page's html using 'find_element_by_link_text'. For other strategies to retrieve elements from a page, see the selenium documentation here.
Also, note that you need to download the packages on which this depends, and the driver that selenium needs in order to talk with your browser (in this case geckodriver, download geckodriver, place it in a folder, and then refer to the executable in 'executable_path')
If you do end up using these packages, it can help to spread out your server requests using the time package (native to python) to avoid exceeding a maximum number of requests allowed to the server off of which you are scraping. I didn't end up needing it for my own project, but see here, second answer to the original question, for an implementation example with the time module used in the fourth code block.
Yeeeeaaaahhh... If someone with higher rep could edit and add some links to beautifulsoup, selenium and time documentations, that would be great, thaaaanks.

How to write a python script for downloading?

I want to download some files from this site: http://www.emuparadise.me/soundtracks/highquality/index.php
But I only want to get certain ones.
Is there a way to write a python script to do this? I have intermediate knowledge of python
I'm just looking for a bit of guidance, please point me towards a wiki or library to accomplish this
thanks,
Shrub
Here's a link to my code

I looked at the page. The links seem to redirect to another page, where the file is hosted, clicking which downloads the file.
I would use mechanize to follow the required links to the right page, and then use BeautifulSoup or lxml to parse the resultant page to get the filename.
Then it's a simple matter of opening the file using urlopen and writing its contents out into a local file like so:
f = open(localFilePath, 'w')
f.write(urlopen(remoteFilePath).read())
f.close()
Hope that helps

Make a url request for the page. Once you have the source, filter out and get urls.
The files you want to download are urls that contain a specific extension. It is with this that you can do a regular expression search for all urls that match your criteria.
After filtration, then do a url request for each matched url's data and write it to memory.
Sample code:
#!/usr/bin/python
import re
import sys
import urllib
#Your sample url
sampleUrl = "http://stackoverflow.com"
urlAddInfo = urllib.urlopen(sampleUrl)
data = urlAddInfo.read()
#Sample extensions we'll be looking for: pngs and pdfs
TARGET_EXTENSIONS = "(png|pdf)"
targetCompile = re.compile(TARGET_EXTENSIONS, re.UNICODE|re.MULTILINE)
#Let's get all the urls: match criteria{no spaces or " in a url}
urls = re.findall('(https?://[^\s"]+)', data, re.UNICODE|re.MULTILINE)
#We want these folks
extensionMatches = filter(lambda url: url and targetCompile.search(url), urls)
#The rest of the unmatched urls for which the scrapping can also be repeated.
nonExtMatches = filter(lambda url: url and not targetCompile.search(url), urls)
def fileDl(targetUrl):
#Function to handle downloading of files.
#Arg: url => a String
#Output: Boolean to signify if file has been written to memory
#Validation of the url assumed, for the sake of keeping the illustration short
urlAddInfo = urllib.urlopen(targetUrl)
data = urlAddInfo.read()
fileNameSearch = re.search("([^\/\s]+)$", targetUrl) #Text right before the last slash '/'
if not fileNameSearch:
sys.stderr.write("Could not extract a filename from url '%s'\n"%(targetUrl))
return False
fileName = fileNameSearch.groups(1)[0]
with open(fileName, "wb") as f:
f.write(data)
sys.stderr.write("Wrote %s to memory\n"%(fileName))
return True
#Let's now download the matched files
dlResults = map(lambda fUrl: fileDl(fUrl), extensionMatches)
successfulDls = filter(lambda s: s, dlResults)
sys.stderr.write("Downloaded %d files from %s\n"%(len(successfulDls), sampleUrl))
#You can organize the above code into a function to repeat the process for each of the
#other urls and in that way you can make a crawler.
The above code is written mainly for Python2.X. However, I wrote a crawler that works on any version starting from 2.X

Why yes! 5 years later and, not only is this possible, but you've now got a lot of ways to do it.
I'm going to avoid code-examples here, because mainly want to help break your problem into segments and give you some options for exploration:
Segment 1: GET!
If you must stick to the stdlib, for either python2 or python3, urllib[n]* is what you're going to want to use to pull-down something from the internet.
So again, if you don't want dependencies on other packages:
urllib or urllib2 or maybe another urllib[n] I'm forgetting about.
If you don't have to restrict your imports to the Standard Library:
you're in luck!!!!! You've got:
requests with docs here. requests is the golden standard for gettin' stuff off the web with python. I suggest you use it.
uplink with docs here. It's relatively new & for more programmatic client interfaces.
aiohttp via asyncio with docs here. asyncio got included in python >= 3.5 only, and it's also extra confusing. That said, it if you're willing to put in the time it can be ridiculously efficient for exactly this use-case.
...I'd also be remiss not to mention one of my favorite tools for crawling:
fake_useragent repo here. Docs like seriously not necessary.
Segment 2: Parse!
So again, if you must stick to the stdlib and not install anything with pip, you get to use the extra-extra fun and secure (<==extreme-sarcasm) xml builtin module. Specifically, you get to use the:
xml.etree.ElementTree() with docs here.
It's worth noting that the ElementTree object is what the pip-downloadable lxml package is based on, and made make easier to use. If you want to recreate the wheel and write a bunch of your own complicated logic, using the default xml module is your option.
If you don't have to restrict your imports to the Standard Library:
lxml with docs here. As i said before, lxml is a wrapper around xml.etree that makes it human-usable & implements all those parsing tools you'd need to make yourself. However, as you can see by visiting the docs, it's not easy to use by itself. This brings us to...
BeautifulSoup aka bs4 with docs here. BeautifulSoup makes everything easier. It's my recommendation for this.
Segment 3: GET GET GET!
This section is nearly exactly the same as "Segment 1," except you have a bunch of links not one.
The only thing that changes between this section and "Segment 1" is my recommendation for what to use: aiohttp here will download way faster when dealing with several URLs because it's allows you to download them in parallel.**
* - (where n was decided-on from python-version to ptyhon-version in a somewhat frustratingly arbitrary manner. Look up which urllib[n] has .urlopen() as a top-level function. You can read more about this naming-convention clusterf**k here, here, and here.)
** - (This isn't totally true. It's more sort-of functionally-true at human timescales.)

I would use a combination of wget for downloading - http://www.thegeekstuff.com/2009/09/the-ultimate-wget-download-guide-with-15-awesome-examples/#more-1885 and BeautifulSoup http://www.crummy.com/software/BeautifulSoup/bs4/doc/ for parsing the downloaded file

Download text from a URL in Python

I'm working on a school project currently which aim goal is to analyze scam mails with the Natural Language Toolkit package. Basically what I'm willing to do is to compare scams from different years and try to find a trend - how does their structure changed with time.
I found a scam-database: http://www.419scam.org/emails/
I would like to download the content of the links with python, but I am stuck.
My code so far:
from BeautifulSoup import BeautifulSoup
import urllib2, re
html = urllib2.urlopen('http://www.419scam.org/emails/').read()
soup = BeautifulSoup(html)
links = soup.findAll('a')
links2 = soup.findAll(href=re.compile("index"))
print links2
So I can fetch the links but I don't know yet how can I download the content. Any ideas? Thanks a lot!

You've got a good start, but right now you're simply retrieving the index page and loading it into the BeautifulSoup parser. Now that you have href's from the links, you essentially need to open all of those links, and load their contents into data structures that you can then use for your analysis.
This essentially amounts to a very simple web-crawler. If you can use other people's code, you may find something that fits by googling "python Web crawler." I've looked at a few of those, and they are straightforward enough, but may be overkill for this task. Most web-crawlers use recursion to traverse the full tree of a given site. It looks like something much simpler could suffice for your case.
Given my unfamiliarity with BeautifulSoup, this basic structure will hopefully get you on the right path, or give you for a sense for how the web crawling is done:
from BeautifulSoup import BeautifulSoup
import urllib2, re
emailContents = []
def analyze_emails():
# this function and any sub-routines would analyze the emails after they are loaded into a data structure, e.g. emailContents
def parse_email_page(link):
print "opening " + link
# open, soup, and parse the page.
#Looks like the email itself is in a "blockquote" tag so that may be the starting place.
#From there you'll need to create arrays and/or dictionaries of the emails' contents to do your analysis on, e.g. emailContents
def parse_list_page(link):
print "opening " + link
html = urllib2.urlopen(link).read()
soup = BeatifulSoup(html)
email_page_links = # add your own code here to filter the list page soup to get all the relevant links to actual email pages
for link in email_page_links:
parseEmailPage(link['href'])
def main():
html = urllib2.urlopen('http://www.419scam.org/emails/').read()
soup = BeautifulSoup(html)
links = soup.findAll(href=re.compile("20")) # I use '20' to filter links since all the relevant links seem to have 20XX year in them. Seemed to work
for link in links:
parse_list_page(link['href'])
analyze_emails()
if __name__ == "__main__":
main()

How do you extract feed urls from an OPML file exported from Google Reader?

I have a piece of software called Rss-Aware that I'm trying to use. It basically desktop feed-checker that checks if RSS feeds are updated and gives a notification through Ubuntu's Notify-OSD system.
However, to know what feeds to check, you have to list out the feed urls in a text file in ~/.rss-aware/rssfeeds.txt one after the other in a list with linebreak between each feed url. Something like:
http://example.com/feed.xml
http://othersite.org/feed.xml
http://othergreatsite.net/rss.xml
...Seems pretty simple right? Well, the list of feeds I'd like to use are exported from Google Reader as an OPML file (it's a type of XML) and I have no clue how to parse it to just output the the feed urls. It seems like it should be pretty straight forward yet I'm stumped.
I'd love if anyone could give an implementation in Python or Ruby or something I could do quickly from a prompt. A bash script would be awesome.
Thanks you so much for the help, I'm a really weak programmer and would love to learn how to do this basic parsing.
EDIT: Also, here is the OPML file I'm trying to extract the feed urls from.

I wrote a subscription list parser for this very purpose. It's called listparser, and it's written in Python. I just tested your OPML file, and it appears to parse the file perfectly. It will also make your feeds' labels available.
If you've ever used feedparser, the interface should be familiar:
>>> import listparser as lp
>>> d = lp.parse('https://dl.dropbox.com/u/670189/google-reader-subscriptions.xml')
>>> len(d.feeds)
112
>>> d.feeds[100].url
u'http://longreads.com/rss'
>>> d.feeds[100].tags
[u'reading']
It's possible to create the file with feed URLs using a script similar to:
import listparser as lp
d = lp.parse('https://dl.dropbox.com/u/670189/google-reader-subscriptions.xml')
f = open('/home/USERNAME/.rss-aware/rssfeeds.txt', 'w')
for i in d.feeds:
f.write(i.url + '\n')
f.close()
Just replace USERNAME with your actual username. Done!

XML parsing was so easy to implement and worked great for me.
from xml.etree import ElementTree
def extract_rss_urls_from_opml(filename):
urls = []
with open(filename, 'rt') as f:
tree = ElementTree.parse(f)
for node in tree.findall('.//outline'):
url = node.attrib.get('xmlUrl')
if url:
urls.append(url)
return urls
urls = extract_rss_urls_from_opml('your_file')

Since it's an XML file, you can use an XPath query to extract the urls.
In the XML file, it looks like the rss feed urls are stored in xmlUrl attributes. The XPath expression //#xmlUrl will select all values of that attribute.
If you want to test this out in your web-browser, you can use an online XPath tester. If you want to perform this XPath query in Python, this question explains how to use XPath in Python. Additionally, the lxml docs have a page on using XPath in lxml that might be helpful.

You could also use a regex. I used the following search-and-replace regex to convert my Google Reader OPML export to a Firefox HTML live-bookmark import:
^\s+<outline.*?title="(.*?)".*?xmlUrl="(.*?)".*?htmlUrl="(.*?)".*?/>
<DT><A FEEDURL="$2" HREF="$3">$1</A>