python爬取数据(selenium)-根据城市名city name获取经纬度（机器学习-Sklearn-11（支持向量机SVM-SVC真实数据案例：预测明天是否会下雨）中爬取数据模块内容）

python爬取数据(selenium)-根据城市名city name获取经纬度

爬虫的代码如下所示，大家可以把谷歌的主页换成百度，修改一下爬虫的命令，就可以自己试试看这段代码。注意要先定义你需要爬取的城市名称的列表cityname哦。

import time
from selenium import webdriver #导入需要的模块，其中爬虫使用的是selenium
import pandas as pd
import numpy as np

#导入元数据
city = pd.read_csv(r'D:\whole_development_of_the_stack_study\RS_Algorithm_Course\Practical\machine_learning\08_09_10_11SVM_01\Cityclimate.csv')
city #第一列是城市名，第二列是气候。

City	Climate
Adelaide	Warm temperate   
Albany	Mild temperate   
Albury	Hot dry summer, cool winter
Wodonga	Hot dry summer, cool winter
AliceSprings	Hot dry summer, warm winter
Amata	Hot dry summer, cool winter
Ballarat	Cool temperate   
Bathurst	Cool temperate   
Birdsville	Hot dry summer, warm winter
Borroloola	High humidity summer, warm winter
Bourke	Hot dry summer, cool winter
Brisbane	Warm humid summer, mild winter
BrokenHill	Hot dry summer, cool winter
Broome	High humidity summer, warm winter
Bunbury	Warm temperate   
Burketown	High humidity summer, warm winter
Burra	Mild temperate   
Cairns	High humidity summer, warm winter
Canberra	Cool temperate   
Carnarvon	Hot dry summer, warm winter
Ceduna	Warm temperate   
Charleville	Hot dry summer, warm winter
CooberPedy	Hot dry summer, cool winter
Cooktown	High humidity summer, warm winter
CoffsHarbour	Warm humid summer, mild winter
Dampier	High humidity summer, warm winter
Darwin	High humidity summer, warm winter
Derby	High humidity summer, warm winter
Devonport	Cool temperate   
Dubbo	Hot dry summer, cool winter
Elliot	Hot dry summer, warm winter
Esperance	Warm temperate   
Eucla	Warm temperate   
Exmouth	High humidity summer, warm winter
GascoyneJunction	Hot dry summer, warm winter
Geraldton	Warm temperate   
Goondiwindi	Hot dry summer, warm winter
Griffith	Hot dry summer, cool winter
HallsCreek	Hot dry summer, warm winter
Hobart	Cool temperate   
Horsham	Mild temperate   
Innamincka	Hot dry summer, cool winter
Ivanhoe	Hot dry summer, cool winter
Kalgoorlie–Boulder	Hot dry summer, cool winter
Katherine	High humidity summer, warm winter
Kingscote	Mild temperate   
KingstonSE	Mild temperate   
Kulgera	Hot dry summer, warm winter
LakesEntrance	Mild temperate   
Launceston	Cool temperate   
LeighCreek	Warm temperate   
Longreach	Hot dry summer, warm winter
LordHoweIsland	Warm temperate   
Mackay	Warm humid summer, mild winter
MargaretRiver	Warm temperate   
Maryborough	Warm humid summer, mild winter
Melbourne	Mild temperate   
MelbourneAirport	Mild temperate   
Merredin	Hot dry summer, cool winter
Mildura	Hot dry summer, cool winter
MountGambier	Mild temperate   
MountIsa	Hot dry summer, warm winter
Newcastle	Warm temperate   
Newdegate	Hot dry summer, cool winter
Newman	Hot dry summer, warm winter
Nhulunbuy	High humidity summer, warm winter
Norseman	Hot dry summer, cool winter
Nullarbor	Hot dry summer, cool winter
Oenpelli	High humidity summer, warm winter
Oodnadatta	Hot dry summer, cool winter
Perth	Warm temperate   
PerthAirport	Warm temperate   
PortHedland	High humidity summer, warm winter
PortLincoln	Warm temperate   
PortMacquarie	Warm temperate   
Renmark	Warm temperate   
Rockhampton	Warm humid summer, mild winter
Shepparton	Hot dry summer, cool winter
Southport	Cool temperate   
Strahan	Cool temperate   
Swansea	Cool temperate   
Sydney	Warm temperate   
SydneyAirport	Warm temperate   
Tamworth	Hot dry summer, cool winter
Taroom	Hot dry summer, warm winter
Telfer	Hot dry summer, warm winter
TennantCreek	Hot dry summer, warm winter
Thargomindah	Hot dry summer, warm winter
Tibooburra	Hot dry summer, cool winter
TimberCreek	High humidity summer, warm winter
Townsville	High humidity summer, warm winter
Warburton	Hot dry summer, cool winter
Weipa	High humidity summer, warm winter
Whyalla	Hot dry summer, cool winter
Wiluna	Hot dry summer, cool winter
Wollongong	Warm temperate   
Wyndham	High humidity summer, warm winter
Yalgoo	Hot dry summer, cool winter
Yulara	Hot dry summer, warm winter
Uluru	Hot dry summer, warm winter

#提取城市名
cityname = city.iloc[:,0]
cityname

#开始爬虫
df =pd.DataFrame(index = cityname.index) #创建新dataframe用于存储爬取的数据

driver = webdriver.Chrome() #调用谷歌浏览器

time0 = time.time() #计时开始

#循环开始
for num,city in enumerate(cityname): #在城市名称中进行遍历
    #首先打开谷歌浏览器
    driver.get('https://www.google.co.uk/webhp?hl=en&sa=X&ved=0ahUKEwimtcX24cTfAhUJE7wKHVkWB5AQPAgH')
    
    #停留1秒让我们知道发生了什么
    time.sleep(1)
    
    #锁定谷歌的搜索框 ，因为谷歌搜索框的name="q"
    search_box = driver.find_element_by_name('q')
    
    #在输入框中输入“城市” 澳大利亚 经纬度
    search_box.send_keys("%s Australia Latitude and longitude" % (city))
    
    #enter,确认开始搜索
    search_box.submit()
    
    #爬取需要的经纬度
    result = driver.find_element_by_xpath('//div[@class="Z0LcW"]').text
    
    #将爬取的结果用split进行分割
    resultsplit = result.split(" ")
    
    #向提前创建好的df中输入爬取的数据，第一列是城市名
    df.loc[num,"City"] = city 
    
    #第二列是纬度
    df.loc[num,"Latitude"] = resultsplit[0]
    
    #第三列是经度
    df.loc[num,'Longitude'] = resultsplit[2]
    
    #第四列是纬度的方向
    df.loc[num,"Latitudedir"] = resultsplit[1]
    
    #第五列是经度的方向
    df.loc[num,"Longitudedir"] = resultsplit[3]
    
    #每次爬虫成功之后，就打印“城市”成功了
    print("%i webcrawler successful for city %s" %(num, city))

time.sleep(1) #全部爬取完毕后，停留1秒钟
driver.quit() #关闭浏览器
print(time.time()-time0) #打印所需的时间

df #所有城市的经纬度

#保存
df.to_csv(r"D:\whole_development_of_the_stack_study\RS_Algorithm_Course\Practical\machine_learning\08_09_10_11SVM_01\csv\cityll.csv")

机器学习-Sklearn-11（支持向量机SVM-SVC真实数据案例：预测明天是否会下雨）中爬取数据模块内容https://blog.csdn.net/m0_37755995/article/details/123944354