Eke Ọgba: Edemede A Google Autosuggest wepụ nke Trends maka Niche Search Keywords

Edemede Eke Ọgba Iji Jide Autosuggest Trends

Onye ọ bụla nwere mmasị na Google Trends, mana ọ bụ ntakịrị aghụghọ mgbe ọ na-abịa Long Tail Keywords. Anyị niile nwere mmasị na onye ọchịchị ahụ google ọnọdụ ọrụ maka inweta nghọta banyere omume ọchụchọ. Otú ọ dị, ihe abụọ na-egbochi ọtụtụ ndị iji ya maka ọrụ siri ike;

  1. Mgbe ịchọrọ ịchọ ọhụrụ niche Keywords, Ebe ahụ ezughị data na Google Trends 
  2. Enweghị onye ọrụ API maka ịrịọ arịrịọ maka ọnọdụ google: Mgbe anyị jiri modulu dị ka Uchechukwu, mgbe ahụ, anyị nwere iji zighachi akwụkwọ nnọchiteanya sava, ma ọ bụ anyị ga-egbochi. 

N'isiokwu a, m ga-ekekọrịta Python Script anyị dere iji zipu isiokwu na-ewu ewu site na Google Autosuggest.

Were ma chekwaa Autosuggest Nsonaazụ N'ime Oge 

Ka e were ya na anyị nwere mkpụrụokwu mkpụrụ osisi 1,000 iji zigara Google Autosuggest. Na nloghachi, anyị nwere ike ịnweta 200,000 ogologo isi okwu. Mgbe ahụ, ọ dị anyị mkpa ime otu ihe ahụ n'otu izu ma tụlee usoro data ndị a iji zaa ajụjụ abụọ:

  • Kedu ajụjụ okwu ohuru jiri ya tunyere oge ikpeazu? Nke a nwere ike bụrụ ikpe anyị chọrọ. Google chere na ajụjụ ndị ahụ na-abawanye uru - site na ịme nke a, anyị nwere ike ịmepụta azịza Google Autosuggest anyị! 
  • Kedu ajụjụ Keywords agaghịkwa Na-agagharị?

Ederede ahụ dị mfe, na ọtụtụ koodu m kekọrịta Ebe a. Code emelitere na-echekwa data ahụ site na ọsọ ndị gara aga ma na-atụnyere aro maka oge. Anyị zere ọdụ data dabere na faịlụ dị ka SQLite iji mee ka ọ dị mfe - yabụ nchekwa data niile na-eji faịlụ CSV n'okpuru. Nke a na - enyere gị aka ibubata faịlụ na Excel ma chọpụta niche usoro isiokwu maka azụmaahịa gị.

Iji Akwukwọ Eke Ọgba a

  1. Tinye nkpuru okwu nkpuru nkpuru okwu gi nke a ga-eziga ya na autocomplete: keywords.csv
  2. Gbanwee ntọala ederede maka mkpa gị:
    • LANGUAGE: ndabere “en”
    • Mba: ndabere “anyị”
  3. Hazie oge ị ga-ede otu ugboro n'izu. I nwekwara ike iji aka gị mee ya.
  4. Jiri isiokwu_suggestions.csv maka nyocha ọzọ:
    • first_seen: nke a bụ ụbọchị ebe ajụjụ ahụ pụtara na nke mbụ ya na autosuggest
    • ikpeazu: ụbọchị ebe a hụrụ ajụjụ ahụ maka oge ikpeazụ
    • is_new: ma ọ bụrụ na first_seen == last_seen anyị setịpụrụ a ezi - Naanị nyocha na uru a iji nweta nyocha ọhụụ na Google autosuggest.

Nke a bụ Usoro Eke Ọgba

# 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

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