爬虫实战：爬虫加数据分析，重庆电气小哥一文带你分析重庆所有旅游景点

快来打我* 2022-10-24 16:16 188阅读 0赞

寒假已经到了，玩是要玩的，作为一个地地道道的重庆电网小哥，今天想用python爬虫+数据分析 的方式告诉你重庆哪些地方好玩。

先上一张最后景区地点分布结果图

![2021013015475944.png_pic_center][]

数据来源：去哪儿旅行

[网址：去哪儿旅行-重庆][-]

![20210130103009259.png_pic_center][]

用request请求到json数据  
![20210130104640761.png_pic_center][]

## 第一部分：爬虫 ##

数据搜索：小试牛刀

import requests
    keyword = "重庆"
    page=1#打印第一页
    
    headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3947.100 Safari/537.36"}
    url = f'http://piao.qunar.com/ticket/list.json?keyword={keyword}&region=&from=mpl_search_suggest&page={page}'
    res = requests.request("GET", url, headers=headers)
    
    try:
        res_json = res.json()
        data = res_json['data']
        print(data)
    except:
        pass

结果  
![20210130111704810.png_pic_center][]

json返回的数据格式是字典型，我们需要从中找到我感兴趣的关键词  
搜索结果  
发现我们感兴趣的是sightList  
![20210130112025779.png_pic_center][]

于是可以修改代码为

import requests
    keyword = "重庆"
    page=1
    
    headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3947.100 Safari/537.36"}
    url = f'http://piao.qunar.com/ticket/list.json?keyword={keyword}&region=&from=mpl_search_suggest&page={page}'
    res = requests.request("GET", url, headers=headers)
    
    
    res_json = res.json()
    sightLists = res_json['data']['sightList']#sightList是感兴趣的
    for  sight in sightLists:
        print(sight)

![20210130112840104.png_pic_center][]

再次提取信息，修改代码为

import requests
    import pandas as pd
    keyword = "重庆"
    page=1#查看第一页
    
    headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3947.100 Safari/537.36"}
    url = f'http://piao.qunar.com/ticket/list.json?keyword={keyword}&region=&from=mpl_search_suggest&page={page}'
    res = requests.request("GET", url, headers=headers)
    
    
    res_json = res.json()
    sightLists = res_json['data']['sightList']#sightList是感兴趣的
    for  sight in sightLists:
        name=(sight['sightName'] if 'sightName' in sight.keys() else None)#名称
        districts=(sight['districts'] if 'districts' in sight.keys() else None)#地址
        star=(sight['star'] if 'star' in sight.keys() else None) #星级
        qunarPrice=(sight['qunarPrice'] if 'qunarPrice' in sight.keys() else None)#最低价格
        saleCount=(sight['saleCount'] if 'saleCount' in sight.keys() else None)#购买人数
        score=(sight['score'] if 'score' in sight.keys() else None )#评分
        point=(sight['point'] if 'point' in sight.keys() else None )#坐标位置
        intro=(sight['intro'] if 'intro' in sight.keys() else None)#介绍
        print('名称：{0}，地址:{1},星级：{2}，价格:{3},saleCount:{4}，评分:{5},坐标:{6},介绍:{7}'.format(name,districts,star,qunarPrice,saleCount,score,point,intro))

![20210130121245902.png_pic_center][]

我们需要将数据写入表格。

import requests
    import pandas as pd
    import numpy as np
    keyword = "重庆"
    page=1#查看第一页
    
    headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3947.100 Safari/537.36"}
    url = f'http://piao.qunar.com/ticket/list.json?keyword={keyword}&region=&from=mpl_search_suggest&page={page}'
    res = requests.request("GET", url, headers=headers)
    
    
    res_json = res.json()
    sightLists = res_json['data']['sightList']#sightList是感兴趣的
    for  sight in sightLists:
        name=(sight['sightName'] if 'sightName' in sight.keys() else None)#名称
        districts=(sight['districts'] if 'districts' in sight.keys() else None)#地址
        star=(sight['star'] if 'star' in sight.keys() else None) #星级
        qunarPrice=(sight['qunarPrice'] if 'qunarPrice' in sight.keys() else None)#最低价格
        saleCount=(sight['saleCount'] if 'saleCount' in sight.keys() else None)#购买人数
        score=(sight['score'] if 'score' in sight.keys() else None )#评分
        point=(sight['point'] if 'point' in sight.keys() else None )#坐标位置
        intro=(sight['intro'] if 'intro' in sight.keys() else None)#介绍
        #print('名称：{0}，地址:{1},星级：{2}，价格:{3},saleCount:{4}，评分:{5},坐标:{6},介绍:{7}'.format(name,districts,star,qunarPrice,saleCount,score,point,intro))
    
        shuju=np.array((name,districts,star,qunarPrice,saleCount,score,point,intro))
        shuju=shuju.reshape(-1,8)
        shuju=pd.DataFrame(shuju,columns=['名称','地址','星级','最低价格','购买人数','评分','坐标位置','介绍'])
        #print(shuju)
    
        shuju.to_csv('重庆景点数据.csv', mode='a+', index=False,header=False)  # mode='a+'追加写入

![20210130122717846.png_pic_center][]

多页爬取  
前面以一页数据为例，整理出啦大概代码，现在需要爬取多页

#!/usr/bin/env python3
    # -*- coding: utf-8 -*-
    # @Author: yudengwu 余登武
    # @Date : 2021/1/30
    import requests
    import pandas as pd
    import numpy as np
    import random
    from time import sleep
    def get_data(keyword, page):
        headers = { 
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3947.100 Safari/537.36"}
        url = f'http://piao.qunar.com/ticket/list.json?keyword={keyword}&region=&from=mpl_search_suggest&page={page}'
        res = requests.request("GET", url, headers=headers)
        sleep(random.uniform(1, 2))
        try:
            res_json = res.json()
            sightLists = res_json['data']['sightList']  # sightList是感兴趣的
            for sight in sightLists:
                name = (sight['sightName'] if 'sightName' in sight.keys() else None)  # 名称
                districts = (sight['districts'] if 'districts' in sight.keys() else None)  # 地址
                star = (sight['star'] if 'star' in sight.keys() else None)  # 星级
                qunarPrice = (sight['qunarPrice'] if 'qunarPrice' in sight.keys() else None)  # 最低价格
                saleCount = (sight['saleCount'] if 'saleCount' in sight.keys() else None)  # 购买人数
                score = (sight['score'] if 'score' in sight.keys() else None)  # 评分
                point = (sight['point'] if 'point' in sight.keys() else None)  # 坐标位置
                intro = (sight['intro'] if 'intro' in sight.keys() else None)  # 介绍
                # print('名称：{0}，地址:{1},星级：{2}，价格:{3},saleCount:{4}，评分:{5},坐标:{6},介绍:{7}'.format(name,districts,star,qunarPrice,saleCount,score,point,intro))
    
                shuju = np.array((name, districts, star, qunarPrice, saleCount, score, point, intro))
                shuju = shuju.reshape(-1, 8)
                shuju = pd.DataFrame(shuju, columns=['名称', '地址', '星级', '最低价格', '购买人数', '评分', '坐标位置', '介绍'])
                # print(shuju)
    
                shuju.to_csv('重庆景点数据.csv', mode='a+', index=False, header=False)  # mode='a+'追加写入
        except:
            pass
    
    
    if __name__ == '__main__':
        keyword = "重庆"
        for page in range(1, 75):  # 控制页数
            print(f"正在提取第{page}页")
            sleep(random.uniform(1, 2))
            get_data(keyword, page)

![20210130123616503.png_pic_center][] 1000多条数据，原来重庆这么多好玩的 ![20210130123824340.png_pic_center][]

## 第二部分：数据分析 ##

前面我们爬取了数据，现在来分析下。

**1.读取数据**

import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    import seaborn as sns
    plt.rcParams['font.sans-serif'] = ['SimHei']  # 设置加载的字体名
    plt.rcParams['axes.unicode_minus'] = False#
    
    df=pd.read_csv('重庆景点数据.csv',header=None,names=list(['名称', '地址', '星级', '最低价格', '购买人数', '评分', '坐标位置', '介绍']))
    df = df.drop_duplicates()#删除重复数据。得到470行数据
    print(df.head())

去除重复数据后，得到重庆有470处景点  
![20210130141708811.png_pic_center][]

**2.景点价格分析**  
最高Top20

df_qunarPrice = df.pivot_table(index='名称',values='最低价格')
    df_qunarPrice.sort_values('最低价格',inplace=True,ascending=False)#降序
    #print(df_qunarPrice[:20])#最高价格top20
    df_qunarPrice[:20].plot(kind='barh')
    plt.title('价格最高Top20')
    plt.show()

![20210130145440797.png_pic_center][]

最低Top20

df_qunarPrice = df.pivot_table(index='名称',values='最低价格')
    df_qunarPrice.sort_values('最低价格',inplace=True,ascending=True)
    #print(df_qunarPrice[:20])#最高价格top20
    df_qunarPrice[:20].plot(kind='barh')
    plt.title('最低Top20')
    plt.show()

![20210130145550288.png_pic_center][]

**3.景点评分分析**  
评分最高Top20

#评分TOP20景点
    df_score = df.pivot_table(index='名称',values='评分')
    df_score.sort_values('评分',inplace=True,ascending=False)
    df_score[:20].plot(kind='barh',color='red')#barh横条形图
    plt.title('评分最高Top20')
    plt.show()

![20210130145256162.png_pic_center][] 评分最低Top20

df_score = df.pivot_table(index='名称',values='评分')
    df_score.sort_values('评分',inplace=True,ascending=True )
    df_score[:20].plot(kind='barh',color='red')#barh横条形图
    plt.title('评分最低Top20')
    plt.show()

没有评分（可能是网站还未收录该地方评分吧…）  
![2021013014583562.png_pic_center][]

**4.月销售额分析**  
最高Top20

df_saleCount = df.pivot_table(index='名称',values='购买人数')
    df_saleCount.sort_values('购买人数',inplace=True,ascending=False)
    df_saleCount[:20].plot(kind='barh',color='green')#barh横条形图
    plt.title('月销售额最高Top20')
    plt.show()

![20210130150230725.png_pic_center][]

最低Top20（可能未收录该地方数据把，可能该地方免费吧）  
![2021013015032796.png_pic_center][]

**5.景点等级分布**

from pyecharts.charts import *
    from pyecharts import options as opts
    from pyecharts.globals import ThemeType
    
    df_star = df["星级"].value_counts()
    df_star = df_star.sort_values(ascending=False)
    print(df_star)

![20210130150823708.png_pic_center][]

查找有等级的景点名称，即3星级及其以上

print(df[df["星级"]!='无'].sort_values("星级",ascending=False)['名称'])

展示部分图，太多啦  
![20210130151354380.png_pic_center][]

**6.景点地址地图绘图**  
先保存文本地文件

df["lon"] = df["坐标位置"].str.split(",",expand=True)[0]#经度
    df["lat"] = df["坐标位置"].str.split(",",expand=True)[1]#纬度
    df.to_csv("data重庆.csv")

绘制地图

import pandas as pd
    
    stations = pd.read_csv('data重庆.csv',delimiter=',')
    
    from pyecharts.charts import Geo
    from pyecharts import options
    from pyecharts.globals import GeoType
    
    g = Geo().add_schema(maptype="重庆")
    
    # 给所有点附上标签 'StationID'
    for i in stations.index:
        s = stations.iloc[i]
        g.add_coordinate(s['名称'],s['lon'],s['lat'])#地区名称，经度，纬度
    
    # 给每个点的值赋为 1
    data_pair = [(stations.iloc[i]['名称'],1) for i in stations.index]
    
    # 画图
    g.add('',data_pair, type_=GeoType.EFFECT_SCATTER, symbol_size=2)
    g.set_series_opts(label_opts=options.LabelOpts(is_show=False))
    g.set_global_opts(title_opts=options.TitleOpts(title="重庆景点分布图by-yudengwu"))
    
    # 保存结果到 html
    result = g.render('stations.html')

![2021013015475944.png_pic_center][] 主城区那边好玩的多

![在这里插入图片描述][20210130154936613.jpg_pic_center]

作者：电气-余登武。写作属实不易，如果你觉得很好，动个手点个赞再走。

[2021013015475944.png_pic_center]: /images/20221024/76853a4beced4ceab36a41d63513d8bb.png
[-]: https://piao.qunar.com/ticket/list.htm?keyword=%E9%87%8D%E5%BA%86&region=%E9%87%8D%E5%BA%86&from=mpshouye_hotcity&page=2
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