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gtrend_0711.py
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gtrend_0711.py
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from pytrends.request import TrendReq #API
import time
import random
import json
import pandas as pd
import numpy as np
import csv
def rp_do(ip_count,proxy,kw_list,timeframe,):
print("使用第",ip_count,"組IP:",proxy[ip_count])
try:
global pytrend
pytrend = TrendReq(tz=360, proxies=proxy[ip_count])
pytrend.build_payload(kw_list=kw_list,cat=34,timeframe=timeframe,geo="US",gprop="") #搜尋使用的參數,其中cat=34 為電影類別
global right_ip_count
right_ip_count=ip_count
except Exception:
#time.sleep(random.randint(3,5))
ip_count+=1
print("被斷,換第",ip_count,"組ip:",proxy[ip_count])
rp_do(ip_count,proxy,kw_list,timeframe)
def open_ip_list(filename,op=0):
op_ip_list = []
ip_count=0
with open(filename, "r", encoding="utf-8")as op_f:
ipdata =csv.reader(op_f)
for ip in ipdata:
if ip_count<op:
ip_count+=1
continue
op_ip_list.append(ip)
op_f.close()
return op_ip_list
def month(m):
mon={
"Jan":1,"Feb":2,"Mar":3,"Apr":4,"May":5,"Jun":6,
"Jul":7,"Aug":8,"Sep":9,"Oct":10,"Nov":11,"Dec":12
}
return mon.get(m)
def g_trend_movie(movie,release_date,proxy):
s_movie=movie
if "," in movie: #Gooletrends不可使用"，" 分隔 所以將名稱有"，"的取代成空白
s_movie = movie.replace(",", " ")
if "：" in movie:
s_movie = movie.split("：")[0]
release_year=int(release_date.split(" ")[-1]) #取上映年分，還有前一年和後一年
release_mon=release_date.split(" ")[1]
release_mon=month(release_mon)
release_day=int(release_date.split(" ")[0])
front_year=int(release_year)-1
next_year=int(release_year)+1
timeframe = str(front_year) + "-01-01 " + str(next_year) + "-12-31"
print(movie)
kw_list=[s_movie]
rp_do(right_ip_count,proxy,kw_list=kw_list,timeframe=timeframe)
# pytrend.build_payload(kw_list=kw_list,cat=34,timeframe=timeframe,geo="US",gprop="")
moviedata = pytrend.interest_over_time().get(kw_list)
try:
moviedata.rename(columns={moviedata.columns[0]: "Count" }, inplace=True)
moviedata_list = json.loads(moviedata.to_json(orient='table'))['data']
if release_mon==2:
start_mon=12
start_year=release_year -1
elif release_mon==1:
start_mon=11
start_year=release_year -1
else:
start_mon=release_mon-2
start_year=release_year
start_day=release_day
for l in moviedata_list:
tempdate=l["date"][0:10]
l["date"]=tempdate
node_day=0
day_count=0
node_count=0
for l in moviedata_list:
year_gt=int(l["date"].split("-")[0])
mon_gt=int(l["date"].split("-")[1])
day_gt=int(l["date"].split("-")[-1])
if year_gt==start_year and mon_gt==start_mon:
if node_day <start_day:
node_day=day_gt
node_count=day_count
day_count+=1
#node_data= moviedata_list[node_count]
output_list=[]
for i in range (17):
try:
#print(moviedata_list[node_count+i],i)
output_list.append(moviedata_list[node_count+i]["Count"])
except:
output_list.append(0)
except:
output_list=[0,0,0,0,0,
0,0,0,0,0,
0,0,0,0,0,
0,0]
output_df=pd.DataFrame([output_list])
j=8
for i in range(0,9,1):
output_df=output_df.rename(columns={i:"movie_"+str(j)+"_before"})
j-=1
j=1
for i in range(9,17,1):
output_df=output_df.rename(columns={i:"movie_"+str(j)+"_after"})
j+=1
output_df=output_df.to_dict(orient='records')
return output_df
def g_trend_actor(actor,release_date,proxy):
actor_alist=actor.split(",")
release_year=int(release_date.split(" ")[-1])
release_mon=release_date.split(" ")[1]
release_mon=month(release_mon)
release_day=int(release_date.split(" ")[0])
timeframe=["2016-01-01 2019-12-31"]
#timeframe=["2004-01-01 2007-12-31","2008-01-01 2011-12-31","2012-01-01 2015-12-31","2016-01-01 2019-12-31"]
output_total= np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0])
for actor in actor_alist:
actordata_list=[]
for t in timeframe:
#time.sleep(random.randint(5,8))
print(actor,t)
kw_list=[actor]
rp_do(right_ip_count,proxy,kw_list=kw_list,timeframe=t)
#pytrend.build_payload(kw_list=kw_list,cat=34,timeframe=timeframe,geo="US",gprop="")
actordata = pytrend.interest_over_time().get(kw_list)
try:
actordata.rename(columns={actordata.columns[0]: "Count" }, inplace=True)
except:
continue
temp_actordata_list = json.loads(actordata.to_json(orient='table'))['data']
actordata_list += temp_actordata_list
for l in actordata_list:
tempdate=l["date"][0:10]
l["date"]=tempdate
if release_mon==2:
start_mon=12
start_year=release_year -1
elif release_mon==1:
start_mon=11
start_year=release_year -1
else:
start_mon=release_mon-2
start_year=release_year
start_day=release_day
node_day=0
day_count=0
node_count=0
for l in actordata_list:
year_gt=int(l["date"].split("-")[0])
mon_gt=int(l["date"].split("-")[1])
day_gt=int(l["date"].split("-")[-1])
if year_gt==start_year and mon_gt==start_mon:
if node_day <start_day:
node_day=day_gt
node_count=day_count
day_count+=1
#print(actordata_list[node_count],"rr")
output_list=[]
for i in range (17):
try:
#print(actordata_list[node_count+i],i)
output_list.append(actordata_list[node_count+i]["Count"])
except:
output_list.append(0)
output_list=np.array(output_list)
output_total=output_total+output_list
output_avg=output_total/len(actor_alist)
output_avg=pd.DataFrame([output_avg])
j=8
for i in range(0,9,1):
output_avg=output_avg.rename(columns={i:"Actor_"+str(j)+"_before"})
j-=1
j=1
for i in range(9,17,1):
output_avg=output_avg.rename(columns={i:"Actor_"+str(j)+"_after"})
j+=1
output_avg=output_avg.to_dict(orient='records')
return output_avg
def trends(input_json):
proxy=open_ip_list("proxy4.csv",op=0)
i=1
proxy=proxy
output=[]
global right_ip_count
right_ip_count=0
for m in input_json:
#m=input_data[1]
try:
movie=m["Title"]
release_date=m["Released"]
actor=m["Actors"]
except:
output.append(m)
# with open("testqwe.json", 'a',encoding="utf-8") as outfile:
# if i==1:
# outfile.write("["+json.dumps(m,ensure_ascii= False)+"\n")
# else:
# outfile.write(","+json.dumps(m,ensure_ascii= False) + "\n")
continue
if int(release_date.split(" ")[-1]) > 2004:
col_movie=g_trend_movie(movie,release_date,proxy)
col_actor=g_trend_actor(actor,release_date,proxy)
else:
output_list=[0,0,0,0,0,
0,0,0,0,0,
0,0,0,0,0,
0,0]
output_df=pd.DataFrame([output_list])
j=8
for i in range(0,9,1):
output_df=output_df.rename(columns={i:"movie_"+str(j)+"_before"})
j-=1
j=1
for i in range(9,17,1):
output_df=output_df.rename(columns={i:"movie_"+str(j)+"_after"})
j+=1
col_movie=output_df.to_dict(orient='records')
output_list=[0,0,0,0,0,
0,0,0,0,0,
0,0,0,0,0,
0,0]
output_df=pd.DataFrame([output_list])
j=8
for i in range(0,9,1):
output_df=output_df.rename(columns={i:"Actor_"+str(j)+"_before"})
j-=1
j=1
for i in range(9,17,1):
output_df=output_df.rename(columns={i:"Actor_"+str(j)+"_after"})
j+=1
col_actor=output_df.to_dict(orient='records')
m=dict(m,**col_movie[0])
m=dict(m,**col_actor[0])
output.append(m)
# with open("testqwe.json", 'a',encoding="utf-8") as outfile:
# if i==1:
# outfile.write("["+json.dumps(m,ensure_ascii= False)+"\n")
# else:
# outfile.write(","+json.dumps(m,ensure_ascii= False) + "\n")
# outfile.close()
# with open("testqwe.json", 'a',encoding="utf-8") as outfile:
# outfile.write("]")
# outfile.close()
i+=1
return output
if __name__=="__main__":
# with open("aaaddd.json","r",encoding="utf-8") as op_f:
# input_data=json.load(op_f)
# op_f.close()
# test=trends(input_data)
m_dict=trends(m_dict[6:7])
#pytrend.build_payload(kw_list=["WQJOEHSLDHISODQWDHWQDWQI"],cat=34,timeframe="2019-05-01 2019-05-08",geo="US",gprop="")