Is maith le gach duine Google Trends, ach tá sé rud beag fánach maidir le Keywords Long Tail. Is maith linn go léir an t-oifigeach seirbhís treochtaí google chun léargas a fháil ar an iompar cuardaigh. Mar sin féin, cuireann dhá rud cosc ar go leor é a úsáid le haghaidh obair dhaingean;
- Nuair is gá duit a fháil eochairfhocail nideoige nua, ansin nach leor sonraí ar Google Trends
- Easpa API oifigiúil chun iarratais a dhéanamh ar threochtaí google: Nuair a bhainimid úsáid as modúil mar pytrends, ansin ní mór dúinn freastalaithe seachfhreastalaí a úsáid, nó cuirtear bac orainn.
San alt seo, roinnfidh mé Python Script a scríobh muid chun eochairfhocail treochta a onnmhairiú trí Google Autosuggest.
Torthaí Autosuggest Fetch and Store Le himeacht ama
Cuir i gcás go bhfuil 1,000 eochairfhocal Síl againn le seoladh chuig Google Autosuggest. Mar chúiteamh, is dócha go bhfaighfimid timpeall 200,000 longtail eochairfhocail. Ansin, caithfimid an rud céanna a dhéanamh seachtain ina dhiaidh sin agus na tacair sonraí seo a chur i gcomparáid le dhá cheist a fhreagairt:
- Cé na ceisteanna eochairfhocail nua i gcomparáid leis an uair dheireanach? Is dócha gurb é seo an cás a theastaíonn uainn. Ceapann Google go bhfuil na ceisteanna sin ag éirí níos suntasaí - trí sin a dhéanamh, is féidir linn ár réiteach Google Autosuggest féin a chruthú!
- Cé na ceisteanna eochairfhocail a thuilleadh ag claonadh?
Tá an script éasca go leor, agus an chuid is mó den chód a roinn mé anseo. Sábhálann an cód nuashonraithe na sonraí ó rith roimhe seo agus na moltaí a chur i gcomparáid le himeacht ama. Sheachain muid bunachair sonraí comhadbhunaithe mar SQLite chun é a dhéanamh simplí - mar sin tá an stóráil sonraí go léir ag úsáid comhaid CSV thíos. Ligeann sé seo duit an comhad a iompórtáil in Excel agus treochtaí eochairfhocail nideoige do do ghnó a iniúchadh.
Chun an Script Python seo a Úsáid
- Cuir isteach do shraith eochairfhocal síl ba chóir a sheoladh chuig an autocomplete: eochairfhocail.csv
- Coigeartaigh na socruithe Script do do riachtanas:
- TEANGA: “ga” réamhshocraithe
- TÍR: réamhshocrú “linn”
- Déan an script a sceidealú le rith uair sa tseachtain. Is féidir leat é a rith de láimh freisin mar ba mhaith leat.
- Úsáid eochairfhocal_suggestions.csv le haghaidh tuilleadh anailíse:
- first_seen: is é seo an dáta ar tháinig an fiosrúchán den chéad uair san autosuggest
- last_seen: an dáta a bhfaca an fiosrú an uair dheireanach
- is_new: if first_seen == last_seen shocraíomar é seo True - Ní gá ach an luach seo a scagadh chun na cuardaigh treochta nua a fháil san autosuggest Google.
Seo Cód 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
