用Python分析国庆旅游景点，告诉你哪些地方好玩、便宜、人又少

一、目标

使用Python分析出国庆哪些旅游景点：好玩、便宜、人还少的地方，不然拍照都要抢着拍！

二、获取数据

爬取出行网站的旅游景点售票数据，反映出旅游景点的热度。这里选择爬取“去哪儿”网。

1.爬取单页数据

我们可以在哪去儿的门票页（http://piao.qunar.com/ticket/list.htm?keyword=）搜索：**国庆旅游景点**，就可以看到推荐的景点的一些信息，如：名称、地区、热度、销量、价格、等级、地理信息等等，信息应该说是比较全，良心！

 然后鼠标右键检查或者直接按下F12打开浏览器调试窗口，查找加载数据的url（翻页就可以看到）

结果发现返回的是json字串，真的是太方便了。

我们可以在Headers中获取到请求的接口URL。

 最后我们还是通过requests写一个get请求就可以了。

import requests

def spider_qunaer():
    url = 'http://piao.qunar.com/ticket/list.json?keyword=%E5%9B%BD%E5%BA%86%E6%97%85%E6%B8%B8%E6%99%AF%E7%82%B9&page=2'
    kv = { # 安全起见，这里将浏览器的请求头信息全部搬了过来。
        'Accept': 'application/json, text/javascript, */*; q=0.01',
        'Accept-Encoding': 'gzip, deflate',
        'Accept-Language': 'zh-CN,zh;q=0.9',
        'Connection': 'keep-alive',
        'Cookie': 'QN1=000030002eb41a2c64507881; QN300=organic; QN205=organic; QN277=organic; csrfToken=guhvJ2UJ1S4JkRAEVVNKXqbexa4jr5lt; QN57=15696544141780.5581713141626528; _i=ueHd8gCnQ4-Xw7_X4lIWdXKBeRXX; _vi=XP2sC7e0MzBdRRW7FdRZOsOPXwsELGnAOhxlvjUk0axSb0VgxK5ed_tCVXy7Do_Hs18hUDMbEp0KJlk3szcH4x4NMsCp8FOa-NNtb_5lNw863q5BUECid5aLk0CTpYlYxknlalntWSAeee7jg11ixyFGiBhcBJQEVtrTCt757OCe; QN269=8BC04422E1BE11E9BCEAFA163E89CFE1; Hm_lvt_15577700f8ecddb1a927813c81166ade=1569654418; fid=10739e17-bb75-4c11-ba8d-2ab6b55fa9e8; QN63=%E5%9B%BD%E5%BA%86%E6%97%85%E6%B8%B8%E6%99%AF%E7%82%B9%7C%E5%9B%BD%E5%BA%86%E5%8E%BB%E5%93%AA%E5%84%BF; JSESSIONID=A946FF2222DB69A818A7AE88D0919C70; QN267=07847690024d33702e; QN58=1569654414177%7C1569654475972%7C4; Hm_lpvt_15577700f8ecddb1a927813c81166ade=1569654476; QN271=6211ab7c-59f8-442d-9234-ef456f77452b',
        'Host': 'piao.qunar.com',
        'Referer': 'http://piao.qunar.com/ticket/list.htm?keyword=%E5%9B%BD%E5%BA%86%E6%97%85%E6%B8%B8%E6%99%AF%E7%82%B9&page=2',
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.90 Safari/537.36',
        'X-Requested-With': 'XMLHttpRequest'}

    try:
        result = requests.get(url, headers = kv)
        result.raise_for_status()
        print(result.text)
    except Exception as e:
        print(e)


if __name__ == '__main__':
    spider_qunaer()

2.提取有效信息

既然数据拿到了，那就看看数据结构，然后提取自己想要的属性吧。

 这个json结构也是比较简单，我们可以清晰的看到我们所需要的数据在‘sightList’里。

这里提取了：id、名称、星级、评分、门票价格、销量、地区、坐标、简介这些信息，基本有效信息都保存起来！

def get_place_info(response_json):
    '''
    解析json,获取想要的字段
    :param response_json:
    :return:
    '''
    place_list = []  # 定义一个列表，等会将景点等信息都存到列表中
    sight_list = response_json['data']['sightList']
    for sight in sight_list:
        goods = {
            'id': sight['sightId'], # 景点id
            'name': sight['sightName'],   # 景点名称
            'star': sight.get('star', None),  # 星级，这里使用get获取，防止触发keyerror
            'score': sight.get('score', 0), # 评分
            'price': sight.get('qunarPrice', 0),  # 价格
            'sale': sight.get('saleCount', 0),  # 销量
            'districts': sight.get('districts', None),  # 省，市，区
            'point': sight.get('point', None),  # 坐标
            'intro': sight.get('intro', None),  # 简介
            'free': sight.get('free', True),   # 是否免费
            'address': sight.get('address', None)  # 具体地址
        }
        place_list.append(goods)
    return place_list

3.保存到excel

需要的数据提取出来之后，我们就可以将他们保存起来。这里我们使用pandas库保存excel文件。

没有安装pandas库的同学安装一下。pip install pandas

def save_excel(place_list):
    '''
    将json数据存储为excel文件
    :param place_list:
    :return:
    '''
    # pandas没有对excel追加模式，只能先读后写
    if os.path.exists(PLACE_EXCEL_PATH):
        df = pd.read_excel(PLACE_EXCEL_PATH)
        df = df.append(place_list)
    else:
        df = pd.DataFrame(place_list)
    writer = pd.ExcelWriter(PLACE_EXCEL_PATH)
    df.to_excel(excel_writer=writer,
                columns=['id', 'name', 'star', 'score', 'price', 'sale', 'districts', 'point', 'intro', 'free', 'address'],
                encoding='utf-8',sheet_name='去哪儿热门景点')
    writer.save()
    writer.close()

这里单页数据的处理就完成了，爬取、解析、保存三步走。

4.批量爬取

批量爬取也很简单，细心的同学应该已经发现了，我们刚才爬取单页数据是第二页的数据。

我们可以看看第一页的url是什么？

 不难发现，前面你的内容都一样，page的参数不一样。这样我们在外层写一个for循环，把页数传入就可以实现批量爬取。

def path_spider_place(keyword):
    '''
    批量爬取去哪儿景点
    :param keyword: 搜索关键字
    :return:
    '''
    # 写入数据前先清空之前数据
    if os.path.exists(PLACE_EXCEL_PATH):
        os.remove(PLACE_EXCEL_PATH)
    for i in range(1, 40):  # 发现有39页，或者可以判断爬取数据返回值
        print(f'正在爬取{keyword} 第{i}页')
        spider_qunaer(keyword, i)
        # 设置一个时间间隔
        time.sleep(random.randint(2, 5))
    print('爬取完成')

import os
import random
import time

import requests
import pandas as pd

PLACE_EXCEL_PATH = 'qunaer.xlsx'


def spider_qunaer(keyword, i):
    url = f'http://piao.qunar.com/ticket/list.json?keyword={keyword}&page={i}'
    kv = {  # 安全起见，这里将浏览器的请求头信息全部搬了过来。
        'Accept': 'application/json, text/javascript, */*; q=0.01',
        'Accept-Encoding': 'gzip, deflate',
        'Accept-Language': 'zh-CN,zh;q=0.9',
        'Connection': 'keep-alive',
        'Cookie': 'QN1=000030002eb41a2c64507881; QN300=organic; QN205=organic; QN277=organic; csrfToken=guhvJ2UJ1S4JkRAEVVNKXqbexa4jr5lt; QN57=15696544141780.5581713141626528; _i=ueHd8gCnQ4-Xw7_X4lIWdXKBeRXX; _vi=XP2sC7e0MzBdRRW7FdRZOsOPXwsELGnAOhxlvjUk0axSb0VgxK5ed_tCVXy7Do_Hs18hUDMbEp0KJlk3szcH4x4NMsCp8FOa-NNtb_5lNw863q5BUECid5aLk0CTpYlYxknlalntWSAeee7jg11ixyFGiBhcBJQEVtrTCt757OCe; QN269=8BC04422E1BE11E9BCEAFA163E89CFE1; Hm_lvt_15577700f8ecddb1a927813c81166ade=1569654418; fid=10739e17-bb75-4c11-ba8d-2ab6b55fa9e8; QN63=%E5%9B%BD%E5%BA%86%E6%97%85%E6%B8%B8%E6%99%AF%E7%82%B9%7C%E5%9B%BD%E5%BA%86%E5%8E%BB%E5%93%AA%E5%84%BF; JSESSIONID=A946FF2222DB69A818A7AE88D0919C70; QN267=07847690024d33702e; QN58=1569654414177%7C1569654475972%7C4; Hm_lpvt_15577700f8ecddb1a927813c81166ade=1569654476; QN271=6211ab7c-59f8-442d-9234-ef456f77452b',
        'Host': 'piao.qunar.com',
        'Referer': 'http://piao.qunar.com/ticket/list.htm?keyword=%E5%9B%BD%E5%BA%86%E6%97%85%E6%B8%B8%E6%99%AF%E7%82%B9&page=2',
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.90 Safari/537.36',
        'X-Requested-With': 'XMLHttpRequest'}

    try:
        result = requests.get(url, headers=kv)
        result.raise_for_status()
        place_list = get_place_info(result.json())
        save_excel(place_list)
    except Exception as e:
        print(e)


def get_place_info(response_json):
    '''
    解析json,获取想要的字段
    :param response_json:
    :return:
    '''
    place_list = []  # 定义一个列表，等会将景点等信息都存到列表中
    sight_list = response_json['data']['sightList']
    for sight in sight_list:
        goods = {
            'id': sight['sightId'], # 景点id
            'name': sight['sightName'],   # 景点名称
            'star': sight.get('star', None),  # 星级，这里使用get获取，防止触发keyerror
            'score': sight.get('score', 0), # 评分
            'price': sight.get('qunarPrice', 0),  # 价格
            'sale': sight.get('saleCount', 0),  # 销量
            'districts': sight.get('districts', None),  # 省，市，区
            'point': sight.get('point', None),  # 坐标
            'intro': sight.get('intro', None),  # 简介
            'free': sight.get('free', True),   # 是否免费
            'address': sight.get('address', None)  # 具体地址
        }
        place_list.append(goods)
    return place_list


def save_excel(place_list):
    '''
    将json数据存储为excel文件
    :param place_list:
    :return:
    '''
    # pandas没有对excel追加模式，只能先读后写
    if os.path.exists(PLACE_EXCEL_PATH):
        df = pd.read_excel(PLACE_EXCEL_PATH)
        df = df.append(place_list)
    else:
        df = pd.DataFrame(place_list)
    writer = pd.ExcelWriter(PLACE_EXCEL_PATH)
    df.to_excel(excel_writer=writer,
                columns=['id', 'name', 'star', 'score', 'price', 'sale', 'districts', 'point', 'intro', 'free', 'address'],
                encoding='utf-8',sheet_name='去哪儿热门景点')
    writer.save()
    writer.close()


def path_spider_place(keyword):
    '''
    批量爬取去哪儿景点
    :param keyword: 搜索关键字
    :return:
    '''
    # 写入数据前先清空之前数据
    if os.path.exists(PLACE_EXCEL_PATH):
        os.remove(PLACE_EXCEL_PATH)
    for i in range(1, 40):  # 发现有39页，或者可以判断爬取数据返回值
        print(f'正在爬取{keyword} 第{i}页')
        spider_qunaer(keyword, i)
        # 设置一个时间间隔
        time.sleep(random.randint(2, 5))
    print('爬取完成')


if __name__ == '__main__':
    path_spider_place('国庆旅游景点')

三 、分析数据

数据都下载完毕后，就要思考如何去利用分析这些数据了

1、景点门票销量排行分析

import pandas as pd
import re
from matplotlib import pyplot as plt
from matplotlib import font_manager
import numpy as np

# 去哪儿热门景点excel文件保存路径
PLACE_EXCEL_PATH = 'qunaer.xlsx'
# 读取数据
DF = pd.read_excel(PLACE_EXCEL_PATH)

font = font_manager.FontProperties(fname=r"C:WindowsFontsmsyhbd.ttc")

def analysis_sale():
    '''
    分析门票销量
    :return:
    '''
    # 引入全局变量
    global DF
    df = DF.copy()
    df = df.sort_values(by='sale', ascending=False)
    name = df['name'][0:20]
    sale = df['sale'][0:20]

    plt.figure(figsize=(20, 8), dpi=80)
    plt.barh(range(len(sale)), sale, height=0.3)

    plt.yticks(range(len(name)), name, fontproperties=font)
    plt.title('去哪儿“十一”门票销量前20',fontproperties=font)
    plt.ylabel("景点名称", fontproperties=font)
    plt.xlabel("销量", fontproperties=font)

    plt.grid(alpha=0.3)

    plt.savefig('jingqu.jpg')
    plt.show()

if __name__ == '__main__':
    analysis_sale()

import pandas as pd
import re
from matplotlib import pyplot as plt
from matplotlib import font_manager
import numpy as np
from pyecharts import options as opts
from pyecharts.charts import Bar



# 去哪儿热门景点excel文件保存路径
PLACE_EXCEL_PATH = 'qunaer.xlsx'
# 读取数据
DF = pd.read_excel(PLACE_EXCEL_PATH, index_col=0)
# 百度热力图模板
HOT_MAP_TEMPLATE_PATH = 'hot_map_template.html'
# 生成的国庆旅游景点热力图
PLACE_HOT_MAP_PATH = 'place_hot_map.html'
# 字体
font = font_manager.FontProperties(fname=r"C:WindowsFontsmsyhbd.ttc")

def analysis_sale():
    '''
    分析门票销量
    :return:
    '''
    # 引入全局变量
    global DF
    df = DF.copy()
    place_sale = df.pivot_table(values='sale', index='name').reset_index().sort_values(by='sale', ascending=False)[0:20]
    print(place_sale)
    plt.rcParams['font.sans-serif'] = 'simhei'
    # 设置字体大小
    font1 = {'family': 'simhei',
             'weight': 'normal',
             'size': 18, }
    f, ax = plt.subplots(figsize=(20, 8))
    # 画条形图
    barh = plt.barh(place_sale['name'].values, place_sale['sale'].values, color='dodgerblue')
    barh[0].set_color('green')
    # 给条形图添加数据标注
    for y, x in enumerate(place_sale['sale'].values):
        plt.text(x + 500, y - 0.2, "%s" % x)
    # 删除所有边框
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)
    plt.tick_params(labelsize=14)
    plt.xlabel('销量', font1)
    plt.ylabel('景点名称', font1)
    plt.title('国庆旅游热门景点门票销量TOP20', font1)
    f.savefig('1.png', bbox_inches='tight')
    f.show()

2、景点销售额排行分析

销售额=单价*销量，我们可以将每行的price和sale相乘算出销售额

def analysis_amout():
    """
    分析销售额
    :return:
    """
    # 引入全局数据
    global DF
    df = DF.copy()
    amount_list = []
    for index, row in df.iterrows():
        try:
            # 销售额
            amount = row['price'] * row['sale']
        except:
            amount = 0
        amount_list.append(amount)
    df['amount'] = amount_list
    #  生成一个名称和销量的透视表
    place_amount = df.pivot_table(index='name', values='amount').reset_index().sort_values('amount', ascending=False)[0:20]
    print(place_amount)
    plt.rcParams['font.sans-serif'] = 'simhei'
    # 设置字体大小
    font1 = {'family': 'simhei',
             'weight': 'normal',
             'size': 18, }
    f, ax = plt.subplots(figsize=(20, 8))
    # 画条形图
    barh = plt.barh(place_amount['name'].values, place_amount['amount'].values, color='dodgerblue')
    barh[0].set_color('green')
    # 给条形图添加数据标注
    for y, x in enumerate(place_amount['amount'].values):
        plt.text(x + 500, y - 0.2, "%s" % x)
    # 删除所有边框
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)
    plt.tick_params(labelsize=14)
    plt.xlabel('销售额', font1)
    plt.ylabel('景点名称', font1)
    plt.title('国庆旅游热门景点门票销量额TOP20', font1)
    f.savefig('2.png', bbox_inches='tight')
    f.show()

3、景点销售额排行分析

应该推荐怎样的景点呢？高评分、销量少、价格便宜。

推荐系数和评分成正比，和销量、价格成反比，所以猪哥设计了一个最简单的算法：

瞎推荐系数=评分/(销量价格) * 1000

def analysis_recommend():
    """
    瞎推荐排行榜： 高评分、销量低、价格便宜
    :return:
    """
    global DF
    df = DF.copy()
    recommend_list = []
    for index, row in df.iterrows():
        try:
            # 瞎推荐系数算法
            recommend = int((row['score'] * 1000) / (row['price'] * row['sale']))
        except ZeroDivisionError:
            recommend = 0
        recommend_list.append(recommend)
    df['recommend'] = recommend_list
    place_amount = df.pivot_table(index='name', values='recommend').reset_index().sort_values('recommend', ascending=False)[0:20]
    print(place_amount)
    plt.rcParams['font.sans-serif'] = 'simhei'
    # 设置字体大小
    font1 = {'family': 'simhei',
             'weight': 'normal',
             'size': 18, }
    f, ax = plt.subplots(figsize=(8,6))
    # 画条形图
    barh = plt.barh(place_amount['name'].values, place_amount['recommend'].values, color='dodgerblue')
    barh[0].set_color('green')
    # 给条形图添加数据标注
    for y, x in enumerate(place_amount['recommend'].values):
        plt.text(x + 500, y - 0.2, "%s" % x)
    # 删除所有边框
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)
    plt.tick_params(labelsize=14)
    plt.xlabel('瞎推荐指数', font1)
    plt.ylabel('景点名称', font1)
    plt.title('国庆旅游热门景点瞎推荐TOP20', font1)
    f.savefig('3.png', bbox_inches='tight')
    f.show()

4.景点销量热力图分析

用百度地图开放api（免费）做一个热力图，你首先要做的就是申请一个百度地图开放平台的应用，操作很简单，如何申请可以 直接百度。

需要注意的是：在申请应用的时候类型一定要选浏览器

 可以下载一个百度热力图的demo的html，在html中把ak码换成自己的。

def analysis_point_sale():
    """
    生成热力图，使用百度地图api
    :return:
    """
    # 引入全局数据
    global DF
    df = DF.copy()
    point_sale_list = []
    for index, row in df.iterrows():
        # 构建坐标数据
        lng, lat = row['point'].split(',')
        count = row['sale']
        point_sale = {'lng': float(lng), 'lat': float(lat), 'count': count}
        point_sale_list.append(point_sale)
    print(point_sale_list)
    data = f'var points ={str(point_sale_list)};'
    # 替换模板中的坐标数据
    with open(HOT_MAP_TEMPLATE_PATH, 'r', encoding="utf-8") as f1, open(PLACE_HOT_MAP_PATH, 'w',
                                                                        encoding="utf-8") as f2:
        s = f1.read()
        # 替换数据
        s2 = s.replace('%data%', data)
        f2.write(s2)
        f1.close()
        f2.close()

<!DOCTYPE html>
<html>
<head>
    <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
    <meta name="viewport" content="initial-scale=1.0, user-scalable=no" />
    <script type="text/javascript" src="//api.map.baidu.com/api?v=2.0&ak=你申请的ak码"></script>
    <script type="text/javascript" src="//api.map.baidu.com/library/Heatmap/2.0/src/Heatmap_min.js"></script>
    <title>热力图功能示例</title>
    <style type="text/css">
        ul,li{list-style: none;margin:0;padding:0;float:left;}
        html{height:100%}
        body{height:100%;margin:0px;padding:0px;font-family:"微软雅黑";}
        #container{height:500px;width:100%;}
        #r-result{width:100%;}
    </style>
</head>
<body>
    <div id="container"></div>
    <div id="r-result">
        <input type="button"  onclick="openHeatmap();" value="显示热力图"/><input type="button"  onclick="closeHeatmap();" value="关闭热力图"/>
    </div>
</body>
</html>
<script type="text/javascript">
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    //详细的参数,可以查看heatmap.js的文档 https://github.com/pa7/heatmap.js/blob/master/README.md
    //参数说明如下:
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     * radius 势力图的每个点的半径大小
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            .5:'rgb(0, 110, 255)',
            .8:'rgb(100, 0, 255)'
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</script>

import pandas as pd
import re
from matplotlib import pyplot as plt
from matplotlib import font_manager
import numpy as np
from pyecharts import options as opts
from pyecharts.charts import Bar



# 去哪儿热门景点excel文件保存路径
PLACE_EXCEL_PATH = 'qunaer.xlsx'
# 读取数据
DF = pd.read_excel(PLACE_EXCEL_PATH, index_col=0)
# 百度热力图模板
HOT_MAP_TEMPLATE_PATH = 'hot_map_template.html'
# 生成的国庆旅游景点热力图
PLACE_HOT_MAP_PATH = 'place_hot_map.html'
# 字体
font = font_manager.FontProperties(fname=r"C:WindowsFontsmsyhbd.ttc")

def analysis_sale():
    '''
    分析门票销量
    :return:
    '''
    # 引入全局变量
    global DF
    df = DF.copy()
    place_sale = df.pivot_table(values='sale', index='name').reset_index().sort_values(by='sale', ascending=False)[0:20]
    print(place_sale)
    plt.rcParams['font.sans-serif'] = 'simhei'
    # 设置字体大小
    font1 = {'family': 'simhei',
             'weight': 'normal',
             'size': 18, }
    f, ax = plt.subplots(figsize=(20, 8))
    # 画条形图
    barh = plt.barh(place_sale['name'].values, place_sale['sale'].values, color='dodgerblue')
    barh[0].set_color('green')
    # 给条形图添加数据标注
    for y, x in enumerate(place_sale['sale'].values):
        plt.text(x + 500, y - 0.2, "%s" % x)
    # 删除所有边框
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)
    plt.tick_params(labelsize=14)
    plt.xlabel('销量', font1)
    plt.ylabel('景点名称', font1)
    plt.title('国庆旅游热门景点门票销量TOP20', font1)
    f.savefig('1.png', bbox_inches='tight')
    f.show()


def analysis_amout():
    """
    分析销售额
    :return:
    """
    # 引入全局数据
    global DF
    df = DF.copy()
    amount_list = []
    for index, row in df.iterrows():
        try:
            # 销售额
            amount = row['price'] * row['sale']
        except:
            amount = 0
        amount_list.append(amount)
    df['amount'] = amount_list
    #  生成一个名称和销量的透视表
    place_amount = df.pivot_table(index='name', values='amount').reset_index().sort_values('amount', ascending=False)[0:20]
    print(place_amount)
    plt.rcParams['font.sans-serif'] = 'simhei'
    # 设置字体大小
    font1 = {'family': 'simhei',
             'weight': 'normal',
             'size': 18, }
    f, ax = plt.subplots(figsize=(20, 8))
    # 画条形图
    barh = plt.barh(place_amount['name'].values, place_amount['amount'].values, color='dodgerblue')
    barh[0].set_color('green')
    # 给条形图添加数据标注
    for y, x in enumerate(place_amount['amount'].values):
        plt.text(x + 500, y - 0.2, "%s" % x)
    # 删除所有边框
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)
    plt.tick_params(labelsize=14)
    plt.xlabel('销售额', font1)
    plt.ylabel('景点名称', font1)
    plt.title('国庆旅游热门景点门票销量额TOP20', font1)
    f.savefig('2.png', bbox_inches='tight')
    f.show()

def analysis_recommend():
    """
    瞎推荐排行榜： 高评分、销量低、价格便宜
    :return:
    """
    global DF
    df = DF.copy()
    recommend_list = []
    for index, row in df.iterrows():
        try:
            # 瞎推荐系数算法
            recommend = int((row['score'] * 1000) / (row['price'] * row['sale']))
        except ZeroDivisionError:
            recommend = 0
        recommend_list.append(recommend)
    df['recommend'] = recommend_list
    place_amount = df.pivot_table(index='name', values='recommend').reset_index().sort_values('recommend', ascending=False)[0:20]
    print(place_amount)
    plt.rcParams['font.sans-serif'] = 'simhei'
    # 设置字体大小
    font1 = {'family': 'simhei',
             'weight': 'normal',
             'size': 18, }
    f, ax = plt.subplots(figsize=(8,6))
    # 画条形图
    barh = plt.barh(place_amount['name'].values, place_amount['recommend'].values, color='dodgerblue')
    barh[0].set_color('green')
    # 给条形图添加数据标注
    for y, x in enumerate(place_amount['recommend'].values):
        plt.text(x + 500, y - 0.2, "%s" % x)
    # 删除所有边框
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)
    plt.tick_params(labelsize=14)
    plt.xlabel('瞎推荐指数', font1)
    plt.ylabel('景点名称', font1)
    plt.title('国庆旅游热门景点瞎推荐TOP20', font1)
    f.savefig('3.png', bbox_inches='tight')
    f.show()


def analysis_point_sale():
    """
    生成热力图，使用百度地图api
    :return:
    """
    # 引入全局数据
    global DF
    df = DF.copy()
    point_sale_list = []
    for index, row in df.iterrows():
        # 构建坐标数据
        lng, lat = row['point'].split(',')
        count = row['sale']
        point_sale = {'lng': float(lng), 'lat': float(lat), 'count': count}
        point_sale_list.append(point_sale)
    print(point_sale_list)
    data = f'var points ={str(point_sale_list)};'
    # 替换模板中的坐标数据
    with open(HOT_MAP_TEMPLATE_PATH, 'r', encoding="utf-8") as f1, open(PLACE_HOT_MAP_PATH, 'w',
                                                                        encoding="utf-8") as f2:
        s = f1.read()
        # 替换数据
        s2 = s.replace('%data%', data)
        f2.write(s2)
        f1.close()
        f2.close()






if __name__ == '__main__':
    # analysis_sale()
    #analysis_amout()
    #analysis_recommend()
    analysis_point_sale()