Python 爬取了马蜂窝的出行数据，告诉你这个夏天哪里最值得去！

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文章由数据森麟出品

作者徐麟

正值火辣的暑假，朋友圈已经被大家的旅行足迹刷屏了，真的十分惊叹于那些把全国所有省基本走遍的朋友们。与此同时，也就萌生了写篇旅行相关的内容，本次数据来源于一个对于爬虫十分友好的旅行攻略类网站：蚂蜂窝。

获得城市编号

蚂蜂窝中的所有城市、景点以及其他的一些信息都有一个专属的5位数字编号，我们第一步要做的就是获取城市（直辖市+地级市）的编号，进行后续的进一步分析。

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以上两个页面就是我们的城市编码来源。需要首先从目的地页面获得各省编码，之后进入各省城市列表获得编码。

过程中需要Selenium进行动态数据爬取，部分代码如下：

def find_cat_url(url):
headers = {'User-Agent':'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:23.0) Gecko/20100101 Firefox/23.0'}
req=request.Request(url,headers=headers)
html=urlopen(req)
bsObj=BeautifulSoup(html.read(),"html.parser")
bs = bsObj.find('div',attrs={'class':'hot-list clearfix'}).find_all('dt')
cat_url = []
cat_name = []
for i in range(0,len(bs)):
for j in range(0,len(bs[i].find_all('a'))):
cat_url.append(bs[i].find_all('a')[j].attrs['href'])
cat_name.append(bs[i].find_all('a')[j].text)
cat_url = ['http://www.mafengwo.cn'+cat_url[i] for i in range(0,len(cat_url))]
return cat_url
def find_city_url(url_list):
city_name_list = []
city_url_list = []
for i in range(0,len(url_list)):
driver = webdriver.Chrome()
driver.maximize_window()
url = url_list[i].replace('travel-scenic-spot/mafengwo','mdd/citylist')
driver.get(url)
while True:
try:
time.sleep(2)
bs = BeautifulSoup(driver.page_source,'html.parser')
url_set = bs.find_all('a',attrs={'data-type':'目的地'})
city_name_list = city_name_list +[url_set[i].text.replace('\n','').split()[0] for i in range(0,len(url_set))]
city_url_list = city_url_list+[url_set[i].attrs['data-id'] for i in range(0,len(url_set))]
js="var q=document.documentElement.scrollTop=800"
driver.execute_script(js)
time.sleep(2)
driver.find_element_by_class_name('pg-next').click()
except:
break
driver.close()
return city_name_list,city_url_list
url = 'http://www.mafengwo.cn/mdd/'
url_list = find_cat_url(url)
city_name_list,city_url_list=find_city_url(url_list)
city = pd.DataFrame({'city':city_name_list,'id':city_url_list})

获得城市信息

城市数据分别从以下几个页面获取：

（a）小吃页面

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（b）景点页面

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（c）标签页面

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我们将每个城市获取数据的过程封装成函数，每次传入之前获得的城市编码，部分代码如下：

def get_city_info(city_name,city_code):
this_city_base = get_city_base(city_name,city_code)
this_city_jd = get_city_jd(city_name,city_code)
this_city_jd['city_name'] = city_name
this_city_jd['total_city_yj'] = this_city_base['total_city_yj']
try:
this_city_food = get_city_food(city_name,city_code)
this_city_food['city_name'] = city_name
this_city_food['total_city_yj'] = this_city_base['total_city_yj']
except:
this_city_food=pd.DataFrame()
return this_city_base,this_city_food,this_city_jd
def get_city_base(city_name,city_code):
url = 'http://www.mafengwo.cn/xc/'+str(city_code)+'/'
bsObj = get_static_url_content(url)
node = bsObj.find('div',{'class':'m-tags'}).find('div',{'class':'bd'}).find_all('a')
tag = [node[i].text.split()[0] for i in range(0,len(node))]
tag_node = bsObj.find('div',{'class':'m-tags'}).find('div',{'class':'bd'}).find_all('em')
tag_count = [int(k.text) for k in tag_node]
par = [k.attrs['href'][1:3] for k in node]
tag_all_count = sum([int(tag_count[i]) for i in range(0,len(tag_count))])
tag_jd_count = sum([int(tag_count[i]) for i in range(0,len(tag_count)) if par[i]=='jd'])
tag_cy_count = sum([int(tag_count[i]) for i in range(0,len(tag_count)) if par[i]=='cy'])
tag_gw_yl_count = sum([int(tag_count[i]) for i in range(0,len(tag_count)) if par[i] in ['gw','yl']])
url = 'http://www.mafengwo.cn/yj/'+str(city_code)+'/2-0-1.html '
bsObj = get_static_url_content(url)
total_city_yj = int(bsObj.find('span',{'class':'count'}).find_all('span')[1].text)
return {'city_name':city_name,'tag_all_count':tag_all_count,'tag_jd_count':tag_jd_count,
'tag_cy_count':tag_cy_count,'tag_gw_yl_count':tag_gw_yl_count,
'total_city_yj':total_city_yj}
def get_city_food(city_name,city_code):
url = 'http://www.mafengwo.cn/cy/'+str(city_code)+'/gonglve.html'
bsObj = get_static_url_content(url)
food=[k.text for k in bsObj.find('ol',{'class':'list-rank'}).find_all('h3')]
food_count=[int(k.text) for k in bsObj.find('ol',{'class':'list-rank'}).find_all('span',{'class':'trend'})]
return pd.DataFrame({'food':food[0:len(food_count)],'food_count':food_count})
def get_city_jd(city_name,city_code):
url = 'http://www.mafengwo.cn/jd/'+str(city_code)+'/gonglve.html'
bsObj = get_static_url_content(url)
node=bsObj.find('div',{'class':'row-top5'}).find_all('h3')
jd = [k.text.split('\n')[2] for k in node]
node=bsObj.find_all('span',{'class':'rev-total'})
jd_count=[int(k.text.replace(' 条点评','')) for k in node]
return pd.DataFrame({'jd':jd[0:len(jd_count)],'jd_count':jd_count})

数据分析

PART1：城市数据

首先我们看一下游记数量最多的TOP10城市：

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游记数量TOP10数量基本上与我们日常所了解的热门城市相符，我们进一步根据各个城市游记数量获得全国旅行目的地热力图：

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看到这里，是不是有种似曾相识的感觉，如果你在朋友圈晒的足迹图与这幅图很相符，那么说明蚂蜂窝的数据与你不谋而合。

最后我们看一下大家对于各个城市的印象是如何的，方法就是提取标签中的属性，我们将属性分为了休闲、饮食、景点三组，分别看一下每一组属性下大家印象最深的城市：

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看来对于蚂蜂窝的用户来说，厦门给大家留下的印象是非常深的，不仅游记数量充足，并且能从中提取的有效标签也非常多。重庆、西安、成都也无悬念地给吃货们留下了非常深的印象，部分代码如下：

bar1 = Bar("餐饮类标签排名")
bar1.add("餐饮类标签分数", city_aggregate.sort_values('cy_point',0,False)['city_name'][0:15],
city_aggregate.sort_values('cy_point',0,False)['cy_point'][0:15],
is_splitline_show =False,xaxis_rotate=30)
bar2 = Bar("景点类标签排名",title_top="30%")
bar2.add("景点类标签分数", city_aggregate.sort_values('jd_point',0,False)['city_name'][0:15],
city_aggregate.sort_values('jd_point',0,False)['jd_point'][0:15],
legend_top="30%",is_splitline_show =False,xaxis_rotate=30)
bar3 = Bar("休闲类标签排名",title_top="67.5%")
bar3.add("休闲类标签分数", city_aggregate.sort_values('xx_point',0,False)['city_name'][0:15],
city_aggregate.sort_values('xx_point',0,False)['xx_point'][0:15],
legend_top="67.5%",is_splitline_show =False,xaxis_rotate=30)
grid = Grid(height=800)
grid.add(bar1, grid_bottom="75%")
grid.add(bar2, grid_bottom="37.5%",grid_top="37.5%")
grid.add(bar3, grid_top="75%")
grid.render('城市分类标签.html')