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Python采集热门城市景点数据+简单制作数据可视化图

article 2025/1/19 17:03:32

人生苦短，我用python

真的好想出去玩啊！！！

春游啊这是！！！

万物复苏的好季节！！！

python 安装包+资料:点击此处跳转文末名片获取

在这里插入图片描述

一、模块使用：

爬虫部分:

requests
parsel
csv

数据分析部分:

pandas
pyecharts

二、开发环境：

python 3.6
pycharm

在这里插入图片描述

三、流程思路:

1. 确定目标需求

python采集旅游景点数据 / 去哪儿~

2. 发送请求

3. 获取数据

4. 解析数据

5. 保存数据

在这里插入图片描述

四、代码展示

采集数据

导入模块

import requests
import parsel 
import csv 
import time

写入表格

f = open('张家界景点.csv', mode='a', encoding='utf-8-sig', newline='')
csv_writer = csv.DictWriter(f, fieldnames=['景区', '星级', '地区', '热度', '销量', '地址',
                              '价格', '简介', '详情页'])
csv_writer.writeheader()

多页采集

for page in range(1, 12):
    print(f'===============================正在爬取第{page}页数据内容=======================================')
    time.sleep(2)

请求链接

    url = f'https://*****.com/ticket/list_%E5%BC%A0%E5%AE%B6%E7%95%8C.html?from=mps_search_suggest_h&keyword=%E5%BC%A0%E5%AE%B6%E7%95%8C&page={page}'

请求头:把python代码伪装成浏览器给服务器发送请求

    headers = {
        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.128 Safari/537.36'
    }
    response = requests.get(url=url, headers=headers)

获取网页文本数据 response.text

    # print(response.text)

解析数据

css选择器根据标签提取数据内容
第一次提取所以景区标签内容返回的页是一个对象列表
id选择器直接可以使用# 开头

    selector = parsel.Selector(response.text)
    lis = selector.css('#search-list .sight_item_detail')
    for li in lis:
        title = li.css('.name::text').get()
        level = li.css('.level::text').get() 
        area = li.css('.area a::text').get() 

        hot = li.css('.product_star_level em::attr(title)').get().replace('热度: ', '')
        hot = int(float(hot)*100)
        address = li.css('.address span::attr(title)').get() 
        price = li.css('.sight_item_price em::text').get() 
        hot_num = li.css('.hot_num::text').get() 
        intro = li.css('.intro::text').get() 
        href = li.css('.name::attr(href)').get()
        href = 'https://*****.com/' + href
        dit = {
            '景区': title,
            '星级': level,
            '地区': area,
            '热度': hot,
            '销量': hot_num,
            '地址': address,
            '价格': price,
            '简介': intro,
            '详情页': href,
        }
        csv_writer.writerow(dit)
        print(title, level, area, hot, address, price, hot_num, intro, href, sep=' | ')

旅游数据可视化

导入景点数据

import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
plt.rcParams['font.sans-serif'] = ['SimHei']  # 设置加载的字体名
plt.rcParams['axes.unicode_minus'] = False   # 解决保存图像是负号'-'显示为方块的问题 
import jieba
import re
from pyecharts.charts import *
from pyecharts import options as opts 
from pyecharts.globals import ThemeType  
import stylecloud
from IPython.display import Image 

df = pd.read_csv(r"c:\python\demo2\爬虫入门教程45 五一去哪儿玩？\去哪儿.csv")
df.head()

删除重复数据

df = df.drop_duplicates()

查看数据信息

df.info()

景点价格价格Top20

df_qunarPrice = df.pivot_table(index='景区',values='价格')
df_qunarPrice.sort_values('价格',inplace=True,ascending=False)
df_data = df_qunarPrice[:20]
from pyecharts import options as opts
from pyecharts.charts import Bar

c = (
    Bar()
    .add_xaxis(df_data.index.tolist())
    .add_yaxis("",df_data['价格'].values.tolist())
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(title_opts=opts.TitleOpts(title="景点价格Top20"),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=90)),)

)
c.render_notebook()

评分TOP20景点

df_score = df.pivot_table(index='景区',values='热度')
df_score.sort_values('热度',inplace=True,ascending=False)
df_data = df_score[:20]
from pyecharts import options as opts
from pyecharts.charts import Bar

c = (
    Bar()
    .add_xaxis(df_data.index.tolist())
    .add_yaxis("",df_data['热度'].values.tolist())
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(title_opts=opts.TitleOpts(title="评分TOP20景点"),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=90)),)

)
c.render_notebook()

df_saleCount = df.pivot_table(index='景区',values='销量')
df_saleCount.sort_values('销量',inplace=True,ascending=False)
df_data = df_saleCount[:20]
df_data.values

月销量TOP20景点

df_saleCount = df.pivot_table(index='景区',values='销量')
df_saleCount.sort_values('销量',inplace=True,ascending=False)
df_data = df_saleCount[:20]

from pyecharts import options as opts
from pyecharts.charts import Bar

c = (
    Bar()
    .add_xaxis(df_data.index.tolist())
    .add_yaxis("",df_data['销量'].values.tolist())
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(title_opts=opts.TitleOpts(title="月销量TOP20景点"),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=90)),)

)
c.render_notebook()

景点等级分布

df_star = df["星级"].value_counts()
df_star = df_star.sort_values(ascending=False)
print(df_star)
c = (
        Pie(init_opts=opts.InitOpts(theme=ThemeType.WALDEN))
        .add(
            "",
            [list(z) for z in zip(df_star.index.to_list(),df_star.to_list())]
        )
        .set_global_opts(legend_opts = opts.LegendOpts(is_show = False),title_opts=opts.TitleOpts(title="景点等级分布",subtitle="数据来源：去哪儿网",pos_top="0.5%",pos_left = 'left'))
        .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{d}%",font_size=16))
    )
c.render_notebook()
df[df["星级"]!='无'].sort_values("星级",ascending=False)