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import httplib
import urllib
import urllib2
import re
import csv
from cookielib import CookieJar
class pyGTrends(object):
Google Trends API
Recommended usage:
from csv import DictReader
r = pyGTrends(username, password)
r.download_report(('pants', 'skirt'))
d = DictReader(r.csv().split('\n'))
def __init__(self, username, password):
provide login and password to be used to connect to Google Analytics
all immutable system variables are also defined here
website_id is the ID of the specific site on google analytics
self.login_params = {
"continue": '',
"PersistentCookie": "yes",
"Email": username,
"Passwd": password,
self.headers = [("Referrer", ""),
("Content-type", "application/x-www-form-urlencoded"),
('User-Agent', 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.21 (KHTML, like Gecko) Chrome/19.0.1042.0 Safari/535.21'),
("Accept", "text/plain")]
self.url_ServiceLoginBoxAuth = ''
self.url_Export = ''
self.url_CookieCheck = ''
self.header_dictionary = {}
def _connect(self):
connect to Google Trends
self.cj = CookieJar()
self.opener = urllib2.build_opener(urllib2.HTTPCookieProcessor(self.cj))
self.opener.addheaders = self.headers
galx = re.compile('<input type="hidden" name="GALX" value="(?P<galx>[a-zA-Z0-9_-]+)">')
resp =
m =
if not m:
raise Exception("Cannot parse GALX out of login page")
self.login_params['GALX'] ='galx')
params = urllib.urlencode(self.login_params), params)
def download_report(self, keywords, date='all', geo='all', geor='all', graph = 'all_csv', sort=0, scale=0, sa='N'):
download a specific report
date, geo, geor, graph, sort, scale and sa
are all Google Trends specific ways to slice the data
if type(keywords) not in (type([]), type(('tuple',))):
keywords = [keywords]
params = urllib.urlencode({
'q': ",".join(keywords),
'date': date,
'graph': graph,
'geo': geo,
'geor': geor,
'sort': str(sort),
'scale': str(scale),
'sa': sa
self.raw_data ='' + params).read()
if self.raw_data in ['You must be signed in to export data from Google Trends']:
raise Exception(self.raw_data)
def csv(self, section="main", as_list=False):
Returns a CSV of a specific segment of the data.
Available segments include Main, Language, City and Region.
if section == "main":
section = ("Week","Year","Day","Month")
section = (section,)
segments = self.raw_data.split('\n\n\n')
for s in segments:
if s.partition(',')[0] in section:
if as_list:
return [line for line in csv.reader(s.split('\n'))]
return s
raise Exception("Could not find requested section")