8天长假快来了，Python分析【去哪儿旅游攻略】数据，制作可视化图表

前言

2023年的中秋节和国庆节即将来临，好消息是，它们将连休8天！这个长假为许多人提供了绝佳的休闲机会，让许多人都迫不及待地想要释放他们被压抑已久的旅游热情，所以很多朋友已经开始着手规划他们的旅游行程。

今天我们来分析下去哪儿的旅游攻略数据，看看吃、住、游玩在价位合适的情况下，怎样才能玩的开心

环境使用

Python 3.8
Pycharm

模块使用

requests
parsel
csv

数据来源分析

明确需求

这次选的月份为10 ~ 12月，游玩费用为1000 ~ 2999这个价位

2. 抓包分析

按F12，打开开发者工具，点击搜索，输入你想要的数据

找到数据链接

https://travel.qunar.com/travelbook/list.htm?page=1&order=hot_heat&&month=10_11_12&avgPrice=2

代码实现

导入模块

import requests
import parsel
import csv

请求数据

模拟浏览器: <可以直接复制>
response.text 获取响应文本数据
response.json() 获取响应json数据
response.content 获取响应二进制数据
我们使用requests.get()方法向指定的URL发送GET请求，并获取到响应的内容

url = f'https://travel.qunar.com/travelbook/list.htm?page=1&order=hot_heat&&month=10_11_12&&avgPrice=2'
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36'
}
response = requests.get(url, headers=headers)

解析

先取响应文本数据

selector = parsel.Selector(response.text)

css选择器:：根据标签属性提取数据内容，看元素面板, 为了帮助找到数据标签,

lis = selector.css('.list_item')
for li in lis:title = li.css('.tit a::text').get()user_name = li.css('.user_name a::text').get()date = li.css('.date::text').get()days = li.css('.days::text').get()photo_nums = li.css('.photo_nums::text').get()fee = li.css('.fee::text').get()people = li.css('.people::text').get()trip = li.css('.trip::text').get()places = ''.join(li.css('.places ::text').getall()).split('行程')place_1 = places[0].replace('途经：', '')place_2 = places[-1].replace('：', '')href = li.css('.tit a::attr(href)').get().split('/')[-1]link = f'https://travel.qunar.com/travelbook/note/{href}'dit = {'标题': title,'昵称': user_name,'日期': date,'耗时': days,'照片': photo_nums,'费用': fee,'人员': people,'标签': trip,'途径': place_1,'行程': place_2,'详情页': link,}print(title, user_name, date, days, photo_nums, fee, people, trip, place_1, place_2, link, sep=' | ')

保存

f = open('data.csv', mode='w', encoding='utf-8', newline='')
csv_writer = csv.DictWriter(f, fieldnames=['标题','昵称','日期','耗时','照片','费用','人员','标签','途径','行程','详情页',
])
csv_writer.writeheader()

数据可视化

导入模块、数据

import pandas as pddf = pd.read_csv('data.csv')
df.head()

年份分布情况

from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
num = df['年份'].value_counts().to_list()
info = df['年份'].value_counts().index.to_list()
c = (Pie().add("",[list(z)for z in zip(info,num,)],center=["40%", "50%"],).set_global_opts(title_opts=opts.TitleOpts(title="年份分布情况"),legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
#     .render("pie_scroll_legend.html")
)
c.render_notebook()

月份分布情况

from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
num = df['月份'].value_counts().to_list()
info = df['月份'].value_counts().index.to_list()
c = (Pie().add("",[list(z)for z in zip(info,num,)],center=["40%", "50%"],).set_global_opts(title_opts=opts.TitleOpts(title="月份分布情况"),legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
#     .render("pie_scroll_legend.html")
)
c.render_notebook()

出行时间情况

from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
num = df['耗时'].value_counts().to_list()
info = df['耗时'].value_counts().index.to_list()
c = (Pie().add("",[list(z)for z in zip(info,num,)],center=["40%", "50%"],).set_global_opts(title_opts=opts.TitleOpts(title="出行时间情况"),legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
#     .render("pie_scroll_legend.html")
)
c.render_notebook()

费用分布情况

from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
num = df['费用'].value_counts().to_list()
info = df['费用'].value_counts().index.to_list()
c = (Pie().add("",[list(z)for z in zip(info,num,)],center=["40%", "50%"],).set_global_opts(title_opts=opts.TitleOpts(title="费用分布情况"),legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
#     .render("pie_scroll_legend.html")
)
c.render_notebook()

人员分布情况

from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
num = df['人员'].value_counts().to_list()
info = df['人员'].value_counts().index.to_list()
c = (Pie().add("",[list(z)for z in zip(info,num,)],center=["40%", "50%"],).set_global_opts(title_opts=opts.TitleOpts(title="人员分布情况"),legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
#     .render("pie_scroll_legend.html")
)
c.render_notebook()