十一国庆节旅游美食攻略：Python爬取美团网美食数据，并做可视化展示美食店铺数据

import requests  # 数据请求 第三方模块 pip install requests
import pprint  # 格式化输出模块
import csv  # csv模块
import time # 时间模块

url = 'https://apimobile.meituan.com/group/v4/poi/pcsearch/70'
data = {
    'uuid': '6e481fe03995425389b9.1630752137.1.0.0',
    'userid': '266252179',
    'limit': '32',
    'offset': page,
    'cateId': '-1',
    'q': '烤肉',
    'token': '4MJy5kaiY_0MoirG34NJTcVUbz0AAAAAkQ4AAF4NOv8TNNdNqymsxWRtJVUW4NjQFW35_twZkd49gZqFzL1IOHxnL0s4hB03zfr3Pg',
}
# 请求头: 把python代码进行伪装成浏览器 [披着羊皮的狼] 封IP 就用IP代理更换IP
# 请求头 都是可以从开发者工具里面直接复制粘贴
# ser-Agent: 浏览器的基本信息
# Referer: 防盗链 告诉服务器我们发送的请求是哪里来的 比如: 西游记唐僧[来自动土大唐的和尚][唐朝比较盛世]
headers = {
    'Referer': 'https://chs.meituan.com/',
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36'
}
# 发送请求
response = requests.get(url=url, params=data, headers=headers)

for index in result:
    # pprint.pprint(index)
    # f'{}' 字符串格式化
    index_url = f'https://www.meituan.com/meishi/{index["id"]}/'
    # ctrl + D
    dit = {
        '店铺名称': index['title'],
        '店铺评分': index['avgscore'],
        '评论数量': index['comments'],
        '人均消费': index['avgprice'],
        '所在商圈': index['areaname'],
        '店铺类型': index['backCateName'],
        '详情页': index_url,
    }
    csv_writer.writerow(dit)
    print(dit)

f = open('烤肉数据1.csv', mode='a', encoding='utf-8', newline='')
csv_writer = csv.DictWriter(f, fieldnames=[
    '店铺名称',
    '店铺评分',
    '评论数量',
    '人均消费',
    '所在商圈',
    '店铺类型',
    '详情页',
])
csv_writer.writeheader() # 写入表头

import pandas as pd
import numpy as np
from pyecharts.charts import *
from pyecharts import options as opts
from pyecharts.globals import ThemeType  #引入主题
df = pd.read_csv('烤肉数据.csv',encoding='utf-8',engine="python")
df.sample(5)

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
fig,axes=plt.subplots(2,1,figsize=(12,12))
sns.regplot(x='人均消费',y='店铺评分',data=df,color='r',marker='+',ax=axes[0])
sns.regplot(x='评论数量',y='店铺评分',data=df,color='g',marker='*',ax=axes[1])

df2 = df.groupby('所在商圈')['店铺名称'].count()
df2 = df2.sort_values(ascending=True)[-10:]
df2 = df2.round(2)
c = (
    Bar(init_opts=opts.InitOpts(theme=ThemeType.WONDERLAND))
    .add_xaxis(df2.index.tolist())
    .add_yaxis("",df2.tolist()).reversal_axis() #X轴与y轴调换顺序
    .set_global_opts(title_opts=opts.TitleOpts(title="商圈烤肉店数量top10",subtitle="数据来源：美团",pos_left = 'center'),
                       xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=16)), #更改横坐标字体大小
                       yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=16)), #更改纵坐标字体大小
                       )
    .set_series_opts(label_opts=opts.LabelOpts(font_size=16,position='right'))
    )
c.render_notebook()

df4 = df.groupby('评分类型')['店铺名称'].count()
df4 = df4.sort_values(ascending=False)
regions = df4.index.tolist()
values = df4.tolist()
c = (
        Pie(init_opts=opts.InitOpts(theme=ThemeType.WONDERLAND))
        .add("", [z for z in zip(regions,values)])
        .set_global_opts(title_opts=opts.TitleOpts(title="不同评分类型店铺数量",subtitle="数据来源：美团",pos_top="-1%",pos_left = 'center'))
        .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{d}%",font_size=18))
    )
c.render_notebook()

df6 = df.groupby('店铺类型')['店铺名称'].count()
df6 = df6.sort_values(ascending=False)[:10]
df6 = df6.round(2)
regions = df6.index.tolist()
values = df6.tolist()
c = (
        Pie(init_opts=opts.InitOpts(theme=ThemeType.WONDERLAND))
        .add("", [i for i in zip(regions,values)],radius=["40%", "75%"])
        .set_global_opts(title_opts=opts.TitleOpts(title="不同店铺类型店铺数量",pos_top="-1%",pos_left = 'center'))
        .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}",font_size=18))
    )
c.render_notebook()

df6 = df.groupby('店铺类型')['店铺评分'].mean()
df6 = df6.sort_values(ascending=True)
df6 = df6.round(2)
df6 = df6.tail(10)
c = (
    Bar(init_opts=opts.InitOpts(theme=ThemeType.WONDERLAND))
    .add_xaxis(df6.index.tolist())
    .add_yaxis("",df6.tolist()).reversal_axis() #X轴与y轴调换顺序
    .set_global_opts(title_opts=opts.TitleOpts(title="不同店铺类型评分",subtitle="数据来源：美团",pos_left = 'center'),
                       xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=16)), #更改横坐标字体大小
                       yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=16)), #更改纵坐标字体大小
                       )
    .set_series_opts(label_opts=opts.LabelOpts(font_size=16,position='right'))
    )
c.render_notebook()

df7 = df.groupby('店铺类型')['评论数量'].sum()
df7 = df7.sort_values(ascending=True)
df7 = df7.tail(10)
c = (
    Bar(init_opts=opts.InitOpts(theme=ThemeType.WONDERLAND))
    .add_xaxis(df7.index.tolist())
    .add_yaxis("",df7.tolist()).reversal_axis() #X轴与y轴调换顺序
    .set_global_opts(title_opts=opts.TitleOpts(title="不同店铺类型评论人数",subtitle="数据来源：美团",pos_left = 'center'),
                       xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=16)), #更改横坐标字体大小
                       yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=16)), #更改纵坐标字体大小
                       )
    .set_series_opts(label_opts=opts.LabelOpts(font_size=16,position='right'))
    )
c.render_notebook()