def scrape():
# load queries
hpv_queries = load_file('hpv_queries.txt').split('\n')
mfr_queries = load_file('mfr_queries.txt').split('\n')
trends = pyGTrends
# connect to google
trends = pyGTrends(USER, PASS)
# scrape hpv
hpv_matrix = []
for query in hpv_queries:
trends.request_report(keywords=query, date='01/2011 60m', geo='DK')
raw = trends.get_data()
hpv_matrix.append(parse_counts(raw))
hpv_matrix = np.array(hpv_matrix)
hpv_matrix = np.transpose(hpv_matrix)
np.savetxt(DATA_DIR + 'hpv_data.txt', hpv_matrix, fmt='%i', delimiter=",")
# scrape mfr
mfr_matrix = []
for query in mfr_queries:
trends.request_report(keywords=query, date='01/2011 60m', geo='DK')
raw = trends.get_data()
mfr_matrix.append(parse_counts(raw))
mfr_matrix = np.array(mfr_matrix)
mfr_matrix = np.transpose(mfr_matrix)
np.savetxt(DATA_DIR + 'mfr_data.txt', mfr_matrix, fmt='%i', delimiter=",")
def get_day_trends():
time.sleep(float(initial_sleep))
start = time.time()
# Google Trends client
logger.info("Connecting to Google")
connector = pyGTrends('', '')
logger.info("Connected to Google")
df = pd.DataFrame()
counter = 0
kill_at = pd.Timestamp(strftime("%Y-%m-%d 21:00:00"))
spent_time_connection = time.time() - start - (start % 1)
try:
while True:
start = time.time()
now = pd.Timestamp(ctime())
if now > kill_at:
logger.info("Killing job...")
break
try:
current_trends = get_trends(connector)
df = df.append(current_trends)
counter += current_trends.shape[0]
except:
logger.error("Error requesting latest trends: {}".format(str(sys.exc_info()[0])))
logger.error(traceback.format_exc())
# Print a status message
if counter % 1000 == 0:
logger.info("{} stories fetched".format(counter))
spent_time = time.time() - start
logger.info("Current job took {}\n".format(spent_time))
if spent_time_connection > 0:
time.sleep(60 - spent_time - spent_time_connection)
spent_time_connection = 0
else:
time.sleep(60 - spent_time - (start % 1))
except:
logger.error("Exiting: " + str(sys.exc_info()[0]))
logger.error(traceback.format_exc())
beginning = str(df.iloc[0]['timestamp'])
end = str(df.iloc[df.shape[0] - 1]['timestamp'])
df.index.name = 'rank'
df.to_csv("trends({})[{}, {}].csv".format(hl_param, beginning, end))
logger.info("Saved day trends from {} to {}".format(beginning, end))
def google_trend_crawler(name):
#suggestions = {}
google_username = "******"
google_password = "******"
title = name.replace('_', ' ')
title = title.translate(None, '():')
title = urllib.unquote(title)
title = title.decode('utf-8')
title = title.encode("cp1252")
connector = pyGTrends(google_username, google_password)
connector.request_report(title, hl='en-US')
suggestions = connector.get_suggestions(title)
if suggestions['default']['topics'] != []:
title = suggestions['default']['topics'][0].get('mid')
# wait a random amount of time between requests to avoid bot detection
time.sleep(randint(5, 10))
print title
connector.request_report(title, hl='en-US')
name = name.translate(None, ':*\'')
name = name.decode("cp1252")
name = urllib.quote_plus(name.encode("utf-8"))
connector.save_csv(scientisis_dir, name)
else:
with open(errorfile, "a") as myfile:
myfile.write("%s\n" % name)
return
def __init__(self, searchterms):
self.trends = []
self.trends.append(searchterms)
con = pyGTrends("trendsscraper123", "googleTrends")
con.request_report(self.formatSearchTerms(searchterms), date = "today 7-d")
data = con.get_data()
self.trends += self.processScore(data)
def scrape():
# load queries
hpv_queries = load_file('hpv_queries.txt').split('\n')
mfr_queries = load_file('mfr_queries.txt').split('\n')
trends = pyGTrends
# connect to google
trends = pyGTrends(USER, PASS)
# scrape hpv
hpv_matrix = []
for query in hpv_queries:
trends.request_report(keywords=query, date='01/2011 60m', geo='DK')
raw = trends.get_data()
hpv_matrix.append(parse_counts(raw))
hpv_matrix = np.array(hpv_matrix)
hpv_matrix = np.transpose(hpv_matrix)
np.savetxt(DATA_DIR + 'hpv_data.txt', hpv_matrix, fmt='%i', delimiter=",")
# scrape mfr
mfr_matrix = []
for query in mfr_queries:
trends.request_report(keywords=query, date='01/2011 60m', geo='DK')
raw = trends.get_data()
mfr_matrix.append(parse_counts(raw))
mfr_matrix = np.array(mfr_matrix)
mfr_matrix = np.transpose(mfr_matrix)
np.savetxt(DATA_DIR + 'mfr_data.txt', mfr_matrix, fmt='%i', delimiter=",")
def __init__(self, file_paths, credentials):
# Set file paths
self.file_paths = file_paths
wb_filepath = os.path.abspath(file_paths['input_file'])
# Read an input workbook
self.in_workbook = openpyxl.load_workbook(filename=wb_filepath)
# Read country codes.
self.country_codes = self.get_country_codes(file_paths['country_codes_file'])
# Connect to Google
self.g_connector = pyGTrends(credentials['google_username'], credentials['google_password'])
def __init__(self):
# connect to Google
try:
self.google_username = "******"
self.google_password = "******"
self.connector = pyGTrends(self.google_username, self.google_password)
time.sleep(randint(5, 10))
except:
print('I am unable to connect to google trends.')
sys.exit(-1)
def __init__(self):
# connect to Google
try:
self.google_username = "******"
self.google_password = "******"
self.connector = pyGTrends(self.google_username,
self.google_password)
time.sleep(randint(5, 10))
except:
print('I am unable to connect to google trends.')
sys.exit(-1)
def __init__(self, file_paths, credentials):
# Set file paths
self.file_paths = file_paths
wb_filepath = os.path.abspath(file_paths['input_file'])
# Read an input workbook
self.in_workbook = openpyxl.load_workbook(filename=wb_filepath)
# Read country codes.
self.country_codes = self.get_country_codes(
file_paths['country_codes_file'])
# Connect to Google
self.g_connector = pyGTrends(credentials['google_username'],
credentials['google_password'])
def get_trend_score(query, horizon=52, trail=3):
try:
connector = pyGTrends(_GNAME, _GPASS)
connector.request_report(query)
sleep(5)
stock_data = parse_data(connector.decode_data)
trend = TrendAnalysis(query, stock_data)
trend.set_stats()
return trend
except Exception as e:
_log.error(e)
return "N/A"
def __init__(self, path, startyear):
self.startyear = startyear
# ADD YOUR ACCOUNT INFOS
self.google_username = ""
self.google_password = ""
if not os.path.exists(path):
os.mkdir(path)
self.logfilename = path+"log-fails.txt"
self.connector = pyGTrends(self.google_username, self.google_password)
self.path = path
def main():
name = sys.argv[1]
if len(sys.argv) >= 2:
for argument in sys.argv[2:]:
name += " " + argument
newpath = "GoogleTrendsData/" + name
if not os.path.exists(newpath):
os.makedirs(newpath)
path = "GoogleTrendsData/"
path += name + "/"
csv_name = name + "_trend"
cleaned_csv_name = name + "_trend_cleaned.csv"
# connect to Google
connector = pyGTrends(google_username, google_password)
# make request
connector.request_report(name)
# wait a random amount of time between requests to avoid bot detection
time.sleep(randint(5, 10))
# download file
connector.save_csv("../../src/main/resources/static/", "google_trend")
with open("../../src/main/resources/static/google_trend.csv") as in_file:
csv_reader = csv.reader(in_file)
for i in range(5):
next(csv_reader)
dates_mapping = defaultdict(int)
for line in csv_reader:
if not line:
break
dateString = line[0]
dates = dateString.split(' - ')
start_dates = dates[0].split('-')
start_year = start_dates[0]
start_month = start_dates[1]
dates_mapping[start_year + "/" + start_month] += int(line[1])
with open("../../src/main/resources/static/google_trend_cleaned.csv", "w") as out_file:
csv_writer = csv.writer(out_file)
csv_writer.writerow(['time', 'quantity'])
for item in sorted(dates_mapping.items()):
csv_writer.writerow([item[0], item[1]])
def generateCSV(self):
google_username = "******"
google_password = "******"
path = "/Users/AnnaGupta/hack-cmu-2015/pytrends-master/examples/"
# connect to Google
connector = pyGTrends(google_username, google_password)
# make request
connector.request_report("%s" % self.word, hl="en-US", cat=None, geo="US", date=None)
# wait a random amount of time between requests to avoid bot detection
time.sleep(randint(5, 10))
# download file
connector.save_csv(path, self.word)
def generateCSV(self):
google_username = "******"
google_password = "******"
path = os.getcwd()+"/"
# connect to Google
connector = pyGTrends(google_username, google_password)
# make request
connector.request_report("%s" % self.word, hl='en-US', cat=None, geo='US', date="today 7-d")
# wait a random amount of time between requests to avoid bot detection
time.sleep(randint(5, 10))
# download file
connector.save_csv(path, "data")
def mongo_write():
client = pymongo.MongoClient('localhost', 27017)
db = client['googletrends']
collection = db['genre_query_history']
# path = ""
# results=get_all_freebase_genres()
# print results
count = 0
# # # print len(results["results"]["bindings"])
for result in collection.find():
count += 1
print count
# print result
# break
fid = result['freebase_id']
# fid='/m/0bkbm'
print fid
if 'query_overtime' not in result:
continue
if 'query_us_states' in result:
continue
# connect to Google
connector = pyGTrends(google_username, google_password)
# make request
connector.request_report(fid, geo='US')
print
res_json = csv2json(connector.decode_data.split('\n'))
res_json['freebase_id'] = fid
# print res_json#['query_regions']
# collection.find_one({'query_title': fid}).update()
# if collection.find_one({'query_title': fid}) == None:
# collection.insert_one(res_json)
if 'query_us_states' in res_json:
genre = collection.find_one({'freebase_id': fid})
# print genre
genre['query_us_states'] = []
genre['query_us_states'] = res_json['query_us_states']
collection.save(genre)
# break
# wait a random amount of time between requests to avoid bot detection
time.sleep(randint(5, 10))
def get_top_date(actor_name, peak_num):
google_username = "******"
google_password = "******"
fid = es_dao.get_actor_freebase_id_by_name(actor_name)
print fid
connector = pyGTrends(google_username, google_password)
# make request
connector.request_report(fid)
res_json = csv2json(connector.decode_data.split('\n'))
res_json['freebase_id'] = fid
#print "Here is result in json:", res_json
overtime_data = []
timestamp_start = []
timestamp_end = []
for weekly_data in res_json['query_overtime']:
overtime_data.append(int(weekly_data['querycount']))
timestamp_start.append(weekly_data['starttime'])
timestamp_end.append(weekly_data['endtime'])
#print res_json['query_overtime']
# print timestamp
# print overtime_data
sorted_data = sorted(range(len(overtime_data)),
key=lambda i: overtime_data[i])[-peak_num:]
# print sorted_data
for index in sorted_data:
print "date:{}, count{}".format(timestamp_start[index],
overtime_data[index])
# array_data = np.asarray(overtime_data)
# peakind =signal.find_peaks_cwt(array_data, np.arange(5,10))
# print peakind
# plt.plot(overtime_data)
# plt.show()
# wait a random amount of time between requests to avoid bot detection
time.sleep(randint(5, 10))
return sorted_data, overtime_data, timestamp_start, timestamp_end
def run(self):
google_username = "******"
google_password = "******"
path = "/home/vagner/workspace/ITSGoogleTrends/output/"
# connect to Google
try:
self.show("Realizando conexão com usuário " + google_username)
connector = pyGTrends(google_username, google_password)
self.show("Conexão realizada com sucesso")
except Exception as e:
raise ITSGoogleTrendsError ("Erro durante a conexão com o Google.")
#montando a string de requisicao
# Lendo os dados do arquivo csv
table = rows.import_from_csv(self._CSV_FILE_PATH + self._CSV_FILE_NAME)
rows_number = len(table)
index = 0
for row in table:
its_name = str(row.system).lower()
index = index + 1
self.show("Início da busca dos dados de tendência para o ITS: {0:s} [{1:d}/{2:d}]".format(its_name,index,rows_number))
str_request = self._DEFAUT_KEYWORD + "," + its_name
self.show("Realizando uma requisição com a sentença " + str_request)
# make request
connector.request_report(str_request)
# download file
self._now = str(datetime.now().strftime('%Y-%m-%d-%H-%M-%S'))
csv_file_name = "{0:s}-{1:s}".format(self._now, str_request.replace(",", "-").replace(" ","-"))
connector.save_csv(path, csv_file_name)
self.show("Resultados escritos no arquivos {0:s}.csv".format(csv_file_name))
# wait a random amount of time between requests to avoid bot detection
wait_time = randint(5, 10)
self.show("Aguardando {0:d} segundos para uma nova requisição".format(wait_time))
time.sleep(wait_time)
#end for
self.show("Execução realizada com sucesso!")
def GetGoogleTrends(subject, page_id, deleteTempFile = False):
"""
Grabs data from Google trends for a given topic.
Creates a temporary csv file that can optionall be deleted after use.
inputs
subject: topic to search (string)
deleteTempFile: if True, deltes temporary file created after use
output
Dataframe of number of searches for each week
"""
subject = re.sub(',', '', subject)
google_username = "******"
google_password = "******"
path = '%s_temp.csv' % subject
connector = pyGTrends(google_username, google_password)
connector.request_report(subject)
data = connector.decode_data
# so... data is a string intended to become a csv file
# the first four lines are useless and need to be removed
# there is likely a better way to do this than what I do below, but it works
data = re.sub('Web.*', '', data)
data = re.sub('World.*', '', data)
data = re.sub('World.*', '', data)
data = re.sub('Interest.*', '', data)
data = re.sub('\n\n\n\n', '', data)
data = re.split('.*, \n', data)[0]
data = re.split('\n\n', data)[0]
# write temporary file then open in pandas... this is just easier, but might be a better way
f = open(path, 'w+')
f.write(data)
f.close()
try:
df = pd.read_csv(path)
except:
return None
df.columns = ['Week', 'Searches']
df['Week'] = [re.sub('-', '', re.split(' - ', week)[0]) for week in df['Week']]
df['page_id'] = [page_id for week in df['Week']]
df = df[['page_id', 'Week', 'Searches']]
if deleteTempFile:
os.remove(path)
return df
def get_top_date(actor_name, peak_num):
google_username = "******"
google_password = "******"
fid = es_dao.get_actor_freebase_id_by_name(actor_name)
print fid
connector = pyGTrends(google_username, google_password)
# make request
connector.request_report(fid)
res_json=csv2json(connector.decode_data.split('\n'))
res_json['freebase_id']=fid
#print "Here is result in json:", res_json
overtime_data = []
timestamp_start =[]
timestamp_end =[]
for weekly_data in res_json['query_overtime']:
overtime_data.append(int(weekly_data['querycount']))
timestamp_start.append(weekly_data['starttime'])
timestamp_end.append(weekly_data['endtime'])
#print res_json['query_overtime']
# print timestamp
# print overtime_data
sorted_data = sorted(range(len(overtime_data)), key=lambda i: overtime_data[i])[-peak_num:]
# print sorted_data
for index in sorted_data:
print "date:{}, count{}".format(timestamp_start[index], overtime_data[index])
# array_data = np.asarray(overtime_data)
# peakind =signal.find_peaks_cwt(array_data, np.arange(5,10))
# print peakind
# plt.plot(overtime_data)
# plt.show()
# wait a random amount of time between requests to avoid bot detection
time.sleep(randint(5, 10))
return sorted_data, overtime_data, timestamp_start, timestamp_end
def main():
googleUsername = "******"
googlePassword = "******"
#connect to Google
connector = pyGTrends(googleUsername, googlePassword)
with open("listFinal.txt", "r") as f:
#creates array each containing a card name as a string
cards = [line.rstrip('\n') for line in f]
for card in cards:
connector.request_report(card,geo="US", date="today 90-d")
connector.save_csv("./output/", card)
#so that google trends doesn't get suspicious of scripting...
time.sleep(randint(4,8))
path = "./output"
os.chdir(path)
#gets rid of all the junk in the csv file
for file in glob.glob("*.csv"):
f = open(file,"r")
lines = f.readlines()
f.close()
del(lines[0:5])
lines = lines[0:90]
f = open(file,"w")
for line in lines:
f.write(line)
def main(keywords):
google_username = "******"
google_password = "******"
path = ""
# regions = ['US-AK-743', 'US-AK-745', 'US-AK-747']
regions = ['US-AK-743']
sample_counter = 1
# connect to Google
connector = pyGTrends(google_username, google_password)
print "connected"
# make request
for region in regions:
while sample_counter < 10:
connector.request_report(keywords, hl='en-US', geo=region)
print "waiting..."
time.sleep(randint(5, 10))
print "saving..."
# download file
connector.save_csv(path, region + '_' + str(sample_counter))
print "done with iteration", sample_counter
sample_counter += 1
#
# Install requirements with pip
#
#
#
from pytrends.pyGTrends import pyGTrends
import time
import os
from random import randint
import pandas as pd
# Add your Gmail username to the google_username variable and your Gmail password to the google_password variable.
google_username = ""
google_password = ""
connector = pyGTrends(google_username, google_password)
# This script downloads a series of CSV files from Google Trends. Please specify a filepath for where you'd like these files to be stored in the below variable.
path = ""
# Specify the filename of a CSV with a list of keywords in the variable, keyordcsv. The CSV should be one column, with header equal to Keywords (case sensitive).
keywordcsv = "keywords.csv"
keywords = pd.read_csv(keywordcsv)
# Downloads and Calculate Slope:
keywordlist = pd.DataFrame(columns=["keyword", "slope"])
for index, row in keywords.iterrows():
print("Downloading Keyword #" + str(index))
payload = {'geo': 'US', 'q': [row[0]]}
connector.request_report(payload)
time.sleep(randint(5, 10))
#If weekly data, convert to monthly.
wordfile = onlymonths(wordfile,monthsnum)
if len(wordfile) > 0:
months = wordfile
wordfilenum = map(lambda x: x.split(",")[1],wordfile)
result[:,i] = np.array(wordfilenum)
time.sleep(randint(2, 5))
words = np.array(words)[dont_skip]
months = map(lambda x: x.split(",")[0],months)
result = result[:,dont_skip]
return (result,words,months)
def main(text1,text2,name,google):
words = tokenize(text1,text2)
save_words(name,words)
data = getData(words,name,google)
save_csv("../data/" + name + "dat.csv",data[0],data[1],data[2])
print "done with " + name
if __name__ == '__main__':
#getData([u"æøå"],"")
print "Talking with google. This takes time!"
google_username = "******"
google_password = "******"#"
google = pyGTrends(google_username, google_password)
main("../data/MFR1.txt","../data/MFR2.txt","MFR",google)
#main("../data/DiTe1.txt","../data/DiTe2.txt","DiTe",google)
main("../data/HPV1.txt","../data/HPV2.txt","HPV",google)
#main("../data/PCV1.txt","../data/PCV2.txt","PCV",google)
def run(type, online=True):
# Lasso Prediction - tolerance set to avoid converge warning
model = linear_model.Lasso(tol=0.001)
tmp = []
google_username = "******" # We Be Anomynous
google_password = "******"
# Location for .csv
path = "trends/"+type+"/"
# Using pytrends for gathering data from Google - but only if online, else uses local data
# connect to Google
if online:
connector = pyGTrends(google_username, google_password)
# tokenizer will determine the trends needed
for trend in tokenizer.run(type):
# file names should avoid danish specialcases
name=trend.replace('ø','oe').replace('æ','ae').replace('å','aa')
if online:
# make request
connector.request_report(str(trend), hl='dk', geo='DK', date="01/2011 57m")
# wait a random amount of time between requests to avoid bot detection
time.sleep(randint(3, 6))
# download file
connector.save_csv(path, name)
# Once a csv file has been recovered. Extract the monthly information
months = regular.GetArrayFromFile(""+path+name+".csv")
if months != None:
tmp.append(months)
# Convert the tmp list to a numpy array of proper dimension
X = np.array(tmp).transpose()
# Extract clinical data
json_pattern = re.compile('[0-9]+\.[0-9]+')
f = open("vactionations/"+type+"-1.json", "r")
data = f.read()
f.close()
match = json_pattern.findall(data)
# Y is now the clinical data
Y = np.array([float(x) for x in match][0:len(X)])
# Preform 5-fold crossvalidation
k_fold = cross_validation.KFold(len(X), 5)
v = 0
plt.figure(type + " 5-fold graphs")
for k, (train, test) in enumerate(k_fold):
model.fit(X[train], Y[train])
plot_y = model.predict(X[test])
plt.subplot(5, 1, k+1)
plt.ylabel("Fold %s" % (k+1))
plot_x = range(len(plot_y))
plt.plot(plot_x, plot_y, color='b', label='Prediction')
plt.plot(plot_x, Y[test], color='r', label='Clinical')
# RMSE for each fold is summed
RMSE = mean_squared_error(Y[test],plot_y)
v += RMSE
print test
# overall RMSE is determined
print type, "RMSE", np.sqrt(v/5)
# For fun, a full prediction is made, to compare the model after 5 folds
# and the ground truth data
plt.xlabel("Months")
plt.show()
plt.figure(type + " full prediction")
plot_y = model.predict(X)
plot_x = range(len(plot_y))
plt.plot(plot_x, plot_y, color='b', label='Prediction')
plt.plot(plot_x, Y, color='r', label='Clinical')
plt.xlabel("Months")
plt.legend(loc="upper right", fancybox=True)
plt.show()
MYUSERNAME = "******"
google_username = config.google["User_Name"]
google_password = config.google["Password"]
logging.basicConfig(filename="logs.log", level=logging.INFO)
def now_time():
now = datetime.datetime.now()
return now.strftime("[%Y/%m/%d %H:%M:%S]")
logging.info("%s Connecting to google..." % now_time())
print("%s Connecting to google..." % now_time())
MYCONNECTOR = pyGTrends(google_username, google_password)
logging.info("%s Connected to google" % now_time())
print("%s Connected to google" % now_time())
logging.info("\n")
# time to sleep if got a 420 error
BACKOFF = 2
backoff = BACKOFF
# don't respond to queries from these accounts
BLACKLIST = ["pixelsorter", "lowpolybot", "slashkarebear", "slashgif", "slashremindme"]
TIME_SPAN_OPTIONS = [str(a) + "y" for a in range(1, 12)]
TIME_SPAN_OPTIONS += [str(a) + "m" for a in range(1, 91)]
TIME_SPAN_OPTIONS += [str(a) + "d" for a in range(1, 91)]
# connector = pyGTrends(google_username, google_password)
server = 'localhost'
database = 'GoogleTrends'
user = '******'
password = '******'
log.info('Connecting to data base....!')
connection = pymysql.connect(host=server,
user=user,
password=password,
db=database)
log.info('We are connected to Database')
keywords = 'Policybazaar'
log.info('Connecting to Google Trends....!')
connector = pyGTrends(google_username, google_password)
log.info('Connected to Google Trends.')
while True:
# connect to Google
# connector = pyGTrends(google_username, google_password)
# make request
log.info('we are inside while loop')
try:
connector.request_report(keywords, hl='en-US', cat=None, geo='IN', date='now 1-H', tz="Etc/GMT-5:30")
log.info('Requested query got...')
xx = connector.get_data()
yy = str(xx)
ll = yy.split('\n')
from pytrends.pyGTrends import pyGTrends
import time
from random import randint
google_username = "******"
google_password = "******"
path = ""
# connect to Google
custom_useragent = {'User-Agent': 'My Pytrends Script'}
connector = pyGTrends(google_username, google_password, custom_useragent)
# make request
payload = {
'q': ['Pizza, Italian, Spaghetti, Breadsticks, Sausage'],
'cat': '0-71'
}
connector.request_report(payload)
# wait a random amount of time between requests to avoid bot detection
time.sleep(randint(5, 10))
# download file
connector.save_csv(path, "pizza")
# get suggestions for keywords
keyword = "milk substitute"
data = connector.get_suggestions(keyword)
print(data)
def __init__(self, search_words = None, file_name = "report.csv"):
self.save_path = "pytrends/" + file_name
self.search_words = search_words
self.connector = pyGTrends("*****@*****.**", "gotneedforspeed")