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Python:脚本为您的利基搜索关键字提供Google Autosuggest趋势摘要

每个人都喜欢Google趋势,但是长尾关键字有点棘手。 我们都喜欢官方 谷歌趋势服务 以获得有关搜索行为的见解。 但是,有两点阻碍了许多人将其用于坚实的工作。

  1. 当您需要寻找时 新的利基关键字有 Google趋势中的数据不足 
  2. 缺少用于向Google趋势发出请求的官方API:当我们使用诸如 趋势,那么我们必须使用代理服务器,否则我们将被阻止。 

在本文中,我将分享我们编写的Python脚本,该脚本可通过Google Autosuggest导出趋势关键字。

随着时间的推移获取并存储自动建议的结果 

假设我们有1,000个Seed关键字要发送到Google Autosuggest。 作为回报,我们可能会得到大约200,000万 长尾巴 关键字。 然后,我们需要在一周后执行相同的操作,然后比较这些数据集来回答两个问题:

  • 哪些查询是 新关键字 与上次相比? 这可能是我们需要的情况。 Google认为这些查询变得越来越重要-通过这样做,我们可以创建自己的Google Autosuggest解决方案! 
  • 哪些查询是 关键字不再 趋势?

该脚本非常简单,我共享的大多数代码 点击此处。 更新的代码将保存过去运行的数据,并随时间比较建议。 为了简化起见,我们避免使用基于文件的数据库(如SQLite)-因此,所有数据存储都在下面使用CSV文件。 这使您可以在Excel中导入文件并探索适合您业务的利基关键字趋势。

利用此Python脚本

  1. 输入应发送到自动完成功能的种子关键字集:keyword.csv
  2. 根据需要调整脚本设置:
    • 语言:默认为“ en”
    • 国家/地区:默认为“我们”
  3. 安排脚本每周运行一次。 您也可以根据需要手动运行它。
  4. 使用keyword_suggestions.csv进行进一步分析:
    • 初见:这是查询在自动建议中首次出现的日期
    • 最后一次露面:最后一次查询的日期
    • 是新的:如果first_seen == last_seen我们将其设置为 –只需过滤此值即可在Google自动建议中获得新的趋势搜索。

这是Python代码

# 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

下载Python脚本

奥斯曼·西林(Osman Sirin)

佩马沃 为绩效营销专家提供了多种数字营销工具,以针对不同需求的综合软件包提供。 我们在广告,购物,出价和各种其他营销服务的广告系列管理中以数据驱动的流程自动化来帮助您。

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