Makemake nā kānaka āpau iā Google Trends, akā paʻakikī ia ke hiki mai i nā huaʻōlelo huelo lōʻihi. Makemake mākou āpau i ka luna lawelawe kūmau google no ka loaʻa ʻana o ka ʻike ma ke ʻano huli. Eia nō naʻe, ʻelua mau mea i pale aku i ka hoʻohana ʻana ia mea no ka hana paʻa;
- Ke pono ʻoe e loaʻa nā huaʻōlelo niche hou, ma laila ʻaʻole lawa ka ʻikepili ma nā Google Trends
- Ka nele o ka API kūhelu no ka noi ʻana i nā ʻano google: Ke hoʻohana mākou i nā modula e like me nā pytrends, a laila pono mākou e hoʻohana i nā kikowaena lawelawe, a i ʻole ke ālai ʻia mākou.
I kēia ʻatikala, e kaʻana wau i kahi Script Python a mākou i kākau ai e hoʻokuʻu i nā huaʻōlelo trending ma o Google Autosuggest.
E kiʻi a mālama i nā hualoaʻa Autosuggest ma kahi o ka manawa
Kuhi paha he 1,000 kā mākou huaʻōlelo hua e hoʻouna ʻia iā Google Autosuggest. I ka hoʻihoʻi, loaʻa paha iā mākou ma kahi o 200,000 longtail huaʻōlelo nui. A laila, pono mākou e hana like i hoʻokahi pule ma hope a hoʻohālikelike i kēia ʻikepili i pane ʻia i nā nīnau ʻelua:
- ʻO nā nīnau hea huaʻōlelo huaʻōlelo hou hoʻohālikelike ʻia i ka manawa hope loa? ʻO kēia paha ka hihia a mākou e pono ai. Manaʻo ʻo Google e lilo ana kēlā mau nīnau i mea nui - ma ka hana ʻana pēlā, hiki iā mākou ke hana i kā mākou Google Autosuggest solution ponoʻī.
- ʻO nā nīnau hea huaʻōlelo kī ʻole lohi?
Maʻalahi loa ka script, a ʻo ka hapa nui o nā pāʻālua aʻu e kaʻana like ai maanei. Mālama ka pāʻālua hou i ka ʻikepili mai nā holo i hala a hoʻohālikelike i nā manaʻo i ka manawa. Ua hōʻalo mākou i nā waihona pūnaewele i hoʻokumu ʻia e like me SQLite e hana maʻalahi ai - no laila ke hoʻohana nei nā waihona ʻikepili āpau i nā faila CSV ma lalo. Hāʻawi kēia iā ʻoe e lawe mai i ka faila ma Excel a ʻimi i nā ʻano huaʻōlelo niche no kāu ʻoihana.
E hoʻohana i kēia Palapala Python
- E hoʻokomo i kāu huaʻōlelo hua hua e hoʻouna ʻia i ka autocomplete: keywords.csv
- Hoʻololi i nā hoʻonohonoho palapala no kāu pono:
- ʻULELO: paʻamau “en”
- 'ĀINA: paʻamau "us"
- E hoʻolālā i ka script e holo hoʻokahi i ka pule. Hiki iā ʻoe ke holo lima iā ia e like me kou makemake.
- E hoʻohana i keyword_suggestions.csv no ka loiloi hou aku:
- hihihihi: ʻo kēia ka lā i hōʻike ʻia ka nīnau no ka manawa mua ma ka autosuggest
- hope-ʻike: ka lā i ʻike ʻia ai ka nīnau no ka manawa hope loa
- is_new: inā first_seen == hope_seen hoʻonohonoho mākou i kēia i oiaio - Kuhi wale i kēia waiwai e kiʻi i nā huli trending hou ma ka autosuggest Google.
Eia ke Code Python
# Pemavor.com Autocomplete Trends
# Author: Stefan Neefischer (stefan.neefischer@gmail.com)
import concurrent.futures
from datetime import date
from datetime import datetime
import pandas as pd
import itertools
import requests
import string
import json
import time
charList = " " + string.ascii_lowercase + string.digits
def makeGoogleRequest(query):
# If you make requests too quickly, you may be blocked by google
time.sleep(WAIT_TIME)
URL="http://suggestqueries.google.com/complete/search"
PARAMS = {"client":"opera",
"hl":LANGUAGE,
"q":query,
"gl":COUNTRY}
response = requests.get(URL, params=PARAMS)
if response.status_code == 200:
try:
suggestedSearches = json.loads(response.content.decode('utf-8'))[1]
except:
suggestedSearches = json.loads(response.content.decode('latin-1'))[1]
return suggestedSearches
else:
return "ERR"
def getGoogleSuggests(keyword):
# err_count1 = 0
queryList = [keyword + " " + char for char in charList]
suggestions = []
for query in queryList:
suggestion = makeGoogleRequest(query)
if suggestion != 'ERR':
suggestions.append(suggestion)
# Remove empty suggestions
suggestions = set(itertools.chain(*suggestions))
if "" in suggestions:
suggestions.remove("")
return suggestions
def autocomplete(csv_fileName):
dateTimeObj = datetime.now().date()
#read your csv file that contain keywords that you want to send to google autocomplete
df = pd.read_csv(csv_fileName)
keywords = df.iloc[:,0].tolist()
resultList = []
with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
futuresGoogle = {executor.submit(getGoogleSuggests, keyword): keyword for keyword in keywords}
for future in concurrent.futures.as_completed(futuresGoogle):
key = futuresGoogle[future]
for suggestion in future.result():
resultList.append([key, suggestion])
# Convert the results to a dataframe
suggestion_new = pd.DataFrame(resultList, columns=['Keyword','Suggestion'])
del resultList
#if we have old results read them
try:
suggestion_df=pd.read_csv("keyword_suggestions.csv")
except:
suggestion_df=pd.DataFrame(columns=['first_seen','last_seen','Keyword','Suggestion'])
suggestionCommon_list=[]
suggestionNew_list=[]
for keyword in suggestion_new["Keyword"].unique():
new_df=suggestion_new[suggestion_new["Keyword"]==keyword]
old_df=suggestion_df[suggestion_df["Keyword"]==keyword]
newSuggestion=set(new_df["Suggestion"].to_list())
oldSuggestion=set(old_df["Suggestion"].to_list())
commonSuggestion=list(newSuggestion & oldSuggestion)
new_Suggestion=list(newSuggestion - oldSuggestion)
for suggest in commonSuggestion:
suggestionCommon_list.append([dateTimeObj,keyword,suggest])
for suggest in new_Suggestion:
suggestionNew_list.append([dateTimeObj,dateTimeObj,keyword,suggest])
#new keywords
newSuggestion_df = pd.DataFrame(suggestionNew_list, columns=['first_seen','last_seen','Keyword','Suggestion'])
#shared keywords with date update
commonSuggestion_df = pd.DataFrame(suggestionCommon_list, columns=['last_seen','Keyword','Suggestion'])
merge=pd.merge(suggestion_df, commonSuggestion_df, left_on=["Suggestion"], right_on=["Suggestion"], how='left')
merge = merge.rename(columns={'last_seen_y': 'last_seen',"Keyword_x":"Keyword"})
merge["last_seen"].fillna(merge["last_seen_x"], inplace=True)
del merge["last_seen_x"]
del merge["Keyword_y"]
#merge old results with new results
frames = [merge, newSuggestion_df]
keywords_df = pd.concat(frames, ignore_index=True, sort=False)
# Save dataframe as a CSV file
keywords_df['first_seen'] = pd.to_datetime(keywords_df['first_seen'])
keywords_df = keywords_df.sort_values(by=['first_seen','Keyword'], ascending=[False,False])
keywords_df['first_seen']= pd.to_datetime(keywords_df['first_seen'])
keywords_df['last_seen']= pd.to_datetime(keywords_df['last_seen'])
keywords_df['is_new'] = (keywords_df['first_seen']== keywords_df['last_seen'])
keywords_df=keywords_df[['first_seen','last_seen','Keyword','Suggestion','is_new']]
keywords_df.to_csv('keyword_suggestions.csv', index=False)
# If you use more than 50 seed keywords you should slow down your requests - otherwise google is blocking the script
# If you have thousands of seed keywords use e.g. WAIT_TIME = 1 and MAX_WORKERS = 5
WAIT_TIME = 0.2
MAX_WORKERS = 20
# set the autocomplete language
LANGUAGE = "en"
# set the autocomplete country code - DE, US, TR, GR, etc..
COUNTRY="US"
# Keyword_seed csv file name. One column csv file.
#csv_fileName="keyword_seeds.csv"
CSV_FILE_NAME="keywords.csv"
autocomplete(CSV_FILE_NAME)
#The result will save in keyword_suggestions.csv csv file
