Python获取去哪儿旅游数据并作可视化攻略-编程知识

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开发环境：

版本： python 3.8
编辑器：pycharm

第三方库：

requests >>> pip install requests
parsel >>> pip install parsel

模块安装:

按住键盘 win + r, 输入cmd回车打开命令行窗口, 在里面输入 pip install 模块名

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需求分析

确定需要的数据

找数据真实来源

https://travel.qunar.com/travelbook/list.htm?order=hot_heat

静态数据

代码实现步骤

发送请求
获取数据
提取数据
保存数据

数据获取

导入模块

import requests     # 发送请求 代码当中用来访问网站的模块
import parsel       # 解析数据的模块
import csv

with open('攻略.csv', mode='a', encoding='utf-8', newline='') as f:csv_writer = csv.writer(f)csv_writer.writerow(['title', 'date', 'days', 'photo_nums', 'fee', 'people', 'trip', 'view', 'love', 'comment', 'href'])
for page in range(1, 201):url = f'https://travel.qunar.com/travelbook/list.htm?page={page}&order=hot_heat'

发送请求

    response = requests.get(url=url)

获取数据

    html_data = response.text

提取数据

    # re / css / xpath# css: ul.b_strategy_list > li# xpath: //ul[@class="b_strategy_list "]/liselect = parsel.Selector(html_data)lis = select.xpath('//ul[@class="b_strategy_list "]/li')# 二次的数据提取for li in lis:# li.css('h2 > a::text').get()title = li.xpath('./h2/a/text()').get()date = li.xpath('./p[@class="user_info"]//span[@class="date"]/text()').get("")days = li.xpath('./p[@class="user_info"]//span[@class="days"]/text()').get("")photo_nums = li.xpath('./p[@class="user_info"]//span[@class="photo_nums"]/text()').get("")fee = li.xpath('./p[@class="user_info"]//span[@class="fee"]/text()').get("")people = li.xpath('./p[@class="user_info"]//span[@class="people"]/text()').get("")trip = li.xpath('./p[@class="user_info"]//span[@class="trip"]/text()').get("")view = li.xpath('./p[@class="user_info"]//span[@class="icon_view"]/span/text()').get("")love = li.xpath('./p[@class="user_info"]//span[@class="icon_love"]/span/text()').get("")comment = li.xpath('./p[@class="user_info"]//span[@class="icon_comment"]/span/text()').get("")href = li.xpath('./h2/a/@href').get()print(title, date, days, photo_nums, fee, people, trip, view, love, comment, href)

保存数据

        with open('攻略.csv', mode='a', encoding='utf-8', newline='') as f:csv_writer = csv.writer(f)csv_writer.writerow([title, date, days, photo_nums, fee, people, trip, view, love, comment, href])

数据可视化

import pandas as pd
from pyecharts.commons.utils import JsCode
from pyecharts.charts import *
from pyecharts import options as opts

data = pd.read_csv('去哪儿_数分.csv')
data

data.info()

data = data[~data['地点'].isin(['攻略'])]
data = data[~data['天数'].isin(['99+'])]
data

data.drop_duplicates(inplace=True)

data['人均费用'].fillna(0, inplace=True)
data['人物'].fillna('独自一人', inplace=True)
data['玩法'].fillna('没有', inplace=True)

data['天数'] = data['天数'].astype(int)

data = data[data['人均费用'].values>200]
data = data[data['天数']<=15]
data

data = data.reset_index(drop=True)
data

def Month(e):m = str(e).split('/')[2]if m=='01':return '一月'if m=='02':return '二月'if m=='03':return '三月'if m=='04':return '四月'if m=='05':return '五月'if m=='06':return '六月'if m=='07':return '七月'if m=='08':return '八月'if m=='09':return '九月'if m=='10':return '十月'if m=='11':return '十一月'if m=='12':return '十二月'

data['旅行月份'] = data['出发时间'].apply(Month)
data['出发时间']=pd.to_datetime(data['出发时间'])
data

import re

def Look(e):if '万' in e:num1 = re.findall('(.*?)万',e)return float(num1[0])*10000else:return float(e)

data['浏览次数'] = data['浏览量'].apply(Look)
data.drop(['浏览量'],axis = 1,inplace = True)
data['浏览次数'] = data['浏览次数'].astype(int)
data.head()

data1 = data
data1['地点'].value_counts().head(10)

loc = data1['地点'].value_counts().head(10).index.tolist()
print(loc)
loc_data = data1[data1['地点'].isin(loc)]
price_mean = round(loc_data['人均费用'].groupby(loc_data['地点']).mean(),1)
print(price_mean)
price_mean2 = [1630.1,1862.9,1697.9,1743.4,1482.4,1586.4,1897.0,1267.5,1973.8,1723.7]

旅游胜地Top10及对应费用

m2 = data1['地点'].value_counts().head(10).index.tolist()
n2 = data1['地点'].value_counts().head(10).values.tolist()

bar=(Bar(init_opts=opts.InitOpts(height='500px',width='1000px',theme='dark')).add_xaxis(m2).add_yaxis('目的地Top10',n2,label_opts=opts.LabelOpts(is_show=True,position='top'),itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,[{offset: 0,color: 'rgb(255,99,71)'}, {offset: 1,color: 'rgb(32,178,170)'}])"""))).set_global_opts(title_opts=opts.TitleOpts(title='目的地Top10'),xaxis_opts=opts.AxisOpts(name='景点名称',type_='category',                                           axislabel_opts=opts.LabelOpts(rotate=90),),yaxis_opts=opts.AxisOpts(name='数量',min_=0,max_=120.0,splitline_opts=opts.SplitLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(type_='dash'))),tooltip_opts=opts.TooltipOpts(trigger='axis',axis_pointer_type='cross')).set_series_opts(markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_='average',name='均值'),opts.MarkLineItem(type_='max',name='最大值'),opts.MarkLineItem(type_='min',name='最小值'),]))
)
bar.render('1.html')

bar=(Bar(init_opts=opts.InitOpts(height='500px',width='1000px',theme='dark')).add_xaxis(loc).add_yaxis('人均费用',price_mean2,label_opts=opts.LabelOpts(is_show=True,position='top'),itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1,[{offset: 0,color: 'rgb(255,99,71)'}, {offset: 1,color: 'rgb(32,178,170)'}])"""))).set_global_opts(title_opts=opts.TitleOpts(title='各景点人均费用'),xaxis_opts=opts.AxisOpts(name='景点名称',type_='category',                                           axislabel_opts=opts.LabelOpts(rotate=90),),yaxis_opts=opts.AxisOpts(name='数量',min_=0,max_=2000.0,splitline_opts=opts.SplitLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(type_='dash'))),tooltip_opts=opts.TooltipOpts(trigger='axis',axis_pointer_type='cross')).set_series_opts(markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_='average',name='均值'),opts.MarkLineItem(type_='max',name='最大值'),opts.MarkLineItem(type_='min',name='最小值'),]))
)
bar.render('2.html')

data1['天数'].value_counts()

data1['旅行时长'] = data1['天数'].apply(lambda x:str(x) + '天')
data1

data1['人物'].value_counts()

m = data1['浏览次数'].sort_values(ascending=False).index[:].tolist()

data1 = data1.loc[m]
data1 = data1.reset_index(drop = True)
data1

data1['旅行月份'].value_counts()

word_list = []
for i in data1['玩法']:s = re.split('\xa0',i)word_list.append(s)  
dict = {}
for j in range(len(word_list)):for i in word_list[j]:if i not in dict:dict[i] = 1else:dict[i]+=1
#print(dict)
list = []
for item in dict.items():list.append(item)
for i in range(1,len(list)):for j in range(0,len(list)-1):if list[j][1]<list[j+1][1]:list[j],list[j+1] = list[j+1],list[j]
print(list)

data1['旅行月份'].value_counts()

m1 = data1['人物'].value_counts().index.tolist()
n1 = data1['人物'].value_counts().values.tolist()

出游方式分析

pie = (Pie(init_opts=opts.InitOpts(theme='dark', width='1000px', height='800px')).add("", [z for z in zip(m1,n1)],radius=["40%", "65%"]).set_global_opts(title_opts=opts.TitleOpts(title="去哪儿\n\n出游结伴方式", pos_left='center', pos_top='center',title_textstyle_opts=opts.TextStyleOpts(color='#FF6A6A', font_size=30, font_weight='bold'),),visualmap_opts=opts.VisualMapOpts(is_show=False, min_=38,max_=641,is_piecewise=False,dimension=0,range_color=['#9400D3', '#008afb', '#ffec4a', '#FFA500','#ce5777']),legend_opts=opts.LegendOpts(is_show=False, pos_top='5%'),).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}", font_size=12),tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{b}: {c}"),itemstyle_opts={"normal": {"barBorderRadius": [30, 30, 30, 30],'shadowBlur': 10,'shadowColor': 'rgba(0,191,255,0.5)','shadowOffsetY': 1,'opacity': 0.8}}))
pie.render('3.html')

m3 = data1['出发时间'].value_counts().sort_index()[:]
m4 = m3['2021'].index
n4 = m3['2021'].values

m3['2021'].sort_values().tail(10)

出游时间分析

line = (Line().add_xaxis(m4.tolist()).add_yaxis('',n4.tolist())
)
line.render('4.html')

2021年的旅游时间曲线大约在五月一号起伏最大，原因肯定是因为假期调休延长至4天，为了调整自己生活及工作的状态，很多人利用这个假期去旅行放松自己。

出游玩法分析

m5 = []
n5 = []
for i in range(20):m5.append(list[i][0])n5.append(list[i][1])
m5.reverse()
m6 = m5
n5.reverse()
n6 = n5bar = (Bar(init_opts=opts.InitOpts(theme='dark', width='1000px',height ='500px')).add_xaxis(m6).add_yaxis('', n6).set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='insideRight',font_style='italic'),itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""new echarts.graphic.LinearGradient(1, 0, 0, 0, [{offset: 0,color: 'rgb(255,99,71)'}, {offset: 1,color: 'rgb(32,178,170)'}])"""))).set_global_opts(title_opts=opts.TitleOpts(title="出游玩法分析"),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),legend_opts=opts.LegendOpts(is_show=True)).reversal_axis()
)
bar.render('5.html')

“摄影”和“美食”可谓与旅行息息相关，一次完整的旅行最不能缺的就是“摄影”，拍美食发到朋友圈、拍风景发到朋友圈、拍完美的自己发到朋友圈；

工作之后就没有了寒暑假，所以利用周末来一次短途旅行就成为了大多数人的首选。

m7 = data1['旅行时长'].value_counts().index.tolist()
n7 = data1['旅行时长'].value_counts().values.tolist()
data_day = data1['旅行时长'].value_counts().sort_values()

出游天数分析

bar = (Bar(init_opts=opts.InitOpts(theme='dark', width='1000px',height ='500px')).add_xaxis(data_day.index.tolist()).add_yaxis('',data_day.values.tolist()).set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='insideRight',font_style='italic'),itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""new echarts.graphic.LinearGradient(1, 0, 0, 0, [{offset: 0,color: 'rgb(255,99,71)'}, {offset: 1,color: 'rgb(32,178,170)'}])"""))).set_global_opts(title_opts=opts.TitleOpts(title="旅行时长"),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),legend_opts=opts.LegendOpts(is_show=True)).reversal_axis()
)
bar.render('6.html')

旅行时长主要分布在2-5天之间，3天最为普遍，太短会未尽兴致，太长又会花销太大，若有一份好的旅行计划，3天应该足够让你赏过一座城市的名胜，吃过大部分的特色美食，领略到这个城市的风情，也足够让你喜欢上这座城市。

data_mo = data1[((data1['旅行月份'] =='七月')|(data1['旅行月份'] =='八月'))&(data1['人物']=='三五好友')].drop(['旅行时长'],axis = 1)
data_mo.head(10)

data_mo2 = data1[((data1['人物'] =='情侣')|(data1['人物'] =='独自一人'))&(data1['旅行月份']=='十月')].drop(['旅行时长'],axis = 1)
data_mo2.head(10)

import jieba
import jieba.analyse
import re

punc = '~`!#$%^&*()_+-=|\';":/.,?><~·！@#￥%……&*（）——+-=“：’；、。，？》《{}【】'
def remove_fuhao(e):short = re.sub(r"[%s]+" % punc, " ", e)return short
def cut_word(text):text = jieba.cut_for_search(str(text))return ' '.join(text)

data2 = data1

data2['简介'] = data2['短评'].apply(remove_fuhao).apply(cut_word)
data2.head()

word = data2['简介'].values.tolist()
fb = open(r'.\travel_text.txt','w',encoding='utf-8')
for i in range(len(word)):fb.write(word[i])
with open(r'.\travel_text.txt','r',encoding='utf-8')as f:words = f.read()f.close

jieba.analyse.set_stop_words(r'./stopwords.txt')
new_words = jieba.analyse.textrank(words, topK=30, withWeight=True)
print(new_words)

word1 = []
num1 = []
for i in range(len(new_words)):    word1.append(new_words[i][0])num1.append(new_words[i][1])

短评词云分析

wordcloud= (WordCloud().add('简介词云分析',[z for z in zip(word1,num1)],word_size_range=[25,80],shape = 'diamond')
)
wordcloud.render('7.html')

食”、“成都”、“自驾”是权重最高的三个词，事实确实如此，当我们计划到一个陌生城市游玩时，可能脑海里第一个想到的并不是当地有什么风景可看，而是有什么美食可吃，大概每个人都能算得上一个吃货吧；

自驾游也是当下火热的出游方式，随时都可以来一场说走就走的旅行。

data4 = data1.drop(['旅行时长','简介'],axis = 1)
data4

旅游景点推荐

k_list = []
the_list = []
keyword = input('请输入旅行月份：')
data5 = data4[data4['旅行月份'] == str(keyword)]
keyword1 = input('请输入结伴出游方式：')
data6 = data5[data5['人物'] == str(keyword1)]
price = int(input('请输入预期价格上限：'))
data7 = data6[data6['人均费用']<=price]
day1 = int(input('请输入旅行时长下限：'))
day2 = int(input('请输入旅行时长上限：'))
data8 = data7[(data7['天数']>=day1)&(data7['天数']<=day2)]
data8