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')
爬虫部分全部源码
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:\Windows\Fonts\msyhbd.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:\Windows\Fonts\msyhbd.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()
景点门票销量排行分析
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">
var map = new BMap.Map("container"); // 创建地图实例
var point = new BMap.Point(116.418261, 39.921984);
map.centerAndZoom(point, 15); // 初始化地图，设置中心点坐标和地图级别
map.enableScrollWheelZoom(); // 允许滚轮缩放
// 将下面参数替换为你的坐标数据
%data%
// 下面是百度默认给的模板数据，可以打开看看效果
// var points =[
// {"lng":116.418261,"lat":39.921984,"count":50000},
// {"lng":116.423332,"lat":39.916532,"count":51000},
// {"lng":116.419787,"lat":39.930658,"count":15000},
// {"lng":116.418455,"lat":39.920921,"count":4000},
// {"lng":116.418843,"lat":39.915516,"count":100000},
// {"lng":116.42546,"lat":39.918503,"count":6},
// {"lng":116.423289,"lat":39.919989,"count":18},
// {"lng":116.418162,"lat":39.915051,"count":80},
// {"lng":116.422039,"lat":39.91782,"count":11},
// {"lng":116.41387,"lat":39.917253,"count":7},
// {"lng":116.41773,"lat":39.919426,"count":42},
// {"lng":116.421107,"lat":39.916445,"count":4},
// {"lng":116.417521,"lat":39.917943,"count":27},
// {"lng":116.419812,"lat":39.920836,"count":23},
// {"lng":116.420682,"lat":39.91463,"count":60},
// {"lng":116.415424,"lat":39.924675,"count":8},
// {"lng":116.419242,"lat":39.914509,"count":15},
// {"lng":116.422766,"lat":39.921408,"count":25},
// {"lng":116.421674,"lat":39.924396,"count":21},
// {"lng":116.427268,"lat":39.92267,"count":1},
// {"lng":116.417721,"lat":39.920034,"count":51},
// {"lng":116.412456,"lat":39.92667,"count":7},
// {"lng":116.420432,"lat":39.919114,"count":11},
// {"lng":116.425013,"lat":39.921611,"count":35},
// {"lng":116.418733,"lat":39.931037,"count":22},
// {"lng":116.419336,"lat":39.931134,"count":4},
// {"lng":116.413557,"lat":39.923254,"count":5},
// {"lng":116.418367,"lat":39.92943,"count":3},
// {"lng":116.424312,"lat":39.919621,"count":100},
// {"lng":116.423874,"lat":39.919447,"count":87},
// {"lng":116.424225,"lat":39.923091,"count":32},
// {"lng":116.417801,"lat":39.921854,"count":44},
// {"lng":116.417129,"lat":39.928227,"count":21},
// {"lng":116.426426,"lat":39.922286,"count":80},
// {"lng":116.421597,"lat":39.91948,"count":32},
// {"lng":116.423895,"lat":39.920787,"count":26},
// {"lng":116.423563,"lat":39.921197,"count":17},
// {"lng":116.417982,"lat":39.922547,"count":17},
// {"lng":116.426126,"lat":39.921938,"count":25},
// {"lng":116.42326,"lat":39.915782,"count":100},
// {"lng":116.419239,"lat":39.916759,"count":39},
// {"lng":116.417185,"lat":39.929123,"count":11},
// {"lng":116.417237,"lat":39.927518,"count":9},
// {"lng":116.417784,"lat":39.915754,"count":47},
// {"lng":116.420193,"lat":39.917061,"count":52},
// {"lng":116.422735,"lat":39.915619,"count":100},
// {"lng":116.418495,"lat":39.915958,"count":46},
// {"lng":116.416292,"lat":39.931166,"count":9},
// {"lng":116.419916,"lat":39.924055,"count":8},
// {"lng":116.42189,"lat":39.921308,"count":11},
// {"lng":116.413765,"lat":39.929376,"count":3},
// {"lng":116.418232,"lat":39.920348,"count":50},
// {"lng":116.417554,"lat":39.930511,"count":15},
// {"lng":116.418568,"lat":39.918161,"count":23},
// {"lng":116.413461,"lat":39.926306,"count":3},
// {"lng":116.42232,"lat":39.92161,"count":13},
// {"lng":116.4174,"lat":39.928616,"count":6},
// {"lng":116.424679,"lat":39.915499,"count":21},
// {"lng":116.42171,"lat":39.915738,"count":29},
// {"lng":116.417836,"lat":39.916998,"count":99},
// {"lng":116.420755,"lat":39.928001,"count":10},
// {"lng":116.414077,"lat":39.930655,"count":14},
// {"lng":116.426092,"lat":39.922995,"count":16},
// {"lng":116.41535,"lat":39.931054,"count":15},
// {"lng":116.413022,"lat":39.921895,"count":13},
// {"lng":116.415551,"lat":39.913373,"count":17},
// {"lng":116.421191,"lat":39.926572,"count":1},
// {"lng":116.419612,"lat":39.917119,"count":9},
// {"lng":116.418237,"lat":39.921337,"count":54},
// {"lng":116.423776,"lat":39.921919,"count":26},
// {"lng":116.417694,"lat":39.92536,"count":17},
// {"lng":116.415377,"lat":39.914137,"count":19},
// {"lng":116.417434,"lat":39.914394,"count":43},
// {"lng":116.42588,"lat":39.922622,"count":27},
// {"lng":116.418345,"lat":39.919467,"count":8},
// {"lng":116.426883,"lat":39.917171,"count":3},
// {"lng":116.423877,"lat":39.916659,"count":34},
// {"lng":116.415712,"lat":39.915613,"count":14},
// {"lng":116.419869,"lat":39.931416,"count":12},
// {"lng":116.416956,"lat":39.925377,"count":11},
// {"lng":116.42066,"lat":39.925017,"count":38},
// {"lng":116.416244,"lat":39.920215,"count":91},
// {"lng":116.41929,"lat":39.915908,"count":54},
// {"lng":116.422116,"lat":39.919658,"count":21},
// {"lng":116.4183,"lat":39.925015,"count":15},
// {"lng":116.421969,"lat":39.913527,"count":3},
// {"lng":116.422936,"lat":39.921854,"count":24},
// {"lng":116.41905,"lat":39.929217,"count":12},
// {"lng":116.424579,"lat":39.914987,"count":57},
// {"lng":116.42076,"lat":39.915251,"count":70},
// {"lng":116.425867,"lat":39.918989,"count":8}];
if(!isSupportCanvas()){
alert('热力图目前只支持有canvas支持的浏览器,您所使用的浏览器不能使用热力图功能~')
}
//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:
/* visible 热力图是否显示,默认为true
* opacity 热力的透明度,1-100
* radius *图的每个点的半径大小
* gradient {JSON} 热力图的渐变区间 . gradient如下所示
* {
.2:'rgb(0, 255, 255)',
.5:'rgb(0, 110, 255)',
.8:'rgb(100, 0, 255)'
}
其中 key 表示插值的位置, 0~1.
value 为颜色值.
*/
heatmapOverlay = new BMapLib.HeatmapOverlay({"radius":20});
map.addOverlay(heatmapOverlay);
heatmapOverlay.setDataSet({data:points,max:100});
//是否显示热力图
function openHeatmap(){
heatmapOverlay.show();
}
function closeHeatmap(){
heatmapOverlay.hide();
}
closeHeatmap();
function setGradient(){
/*格式如下所示:
{
0:'rgb(102, 255, 0)',
.5:'rgb(255, 170, 0)',
1:'rgb(255, 0, 0)'
}*/
var gradient = {};
var colors = document.querySelectorAll("input[type='color']");
colors = [].slice.call(colors,0);
colors.forEach(function(ele){
gradient[ele.getAttribute("data-key")] = ele.value;
});
heatmapOverlay.setOptions({"gradient":gradient});
}
//判断浏览区是否支持canvas
function isSupportCanvas(){
var elem = document.createElement('canvas');
return !!(elem.getContext && elem.getContext('2d'));
}
</script>
百度地图热力图DEMO
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:\Windows\Fonts\msyhbd.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()
数据分析所有代码
用Python分析国庆旅游景点，告诉你哪些地方好玩、便宜、人又少的更多相关文章
-
如何快速的找到好玩的旅游景点信息？Python爬虫帮你轻松解决
前言 本文的文字及图片来源于网络,仅供学习.交流使用,不具有任何商业用途,版权归原作者所有,如有问题请及时联系我们以作处理. 喜欢的朋友欢迎关注小编 当我们出去旅游时,会看这个地方有哪些旅游景点,景点 ...
-
[Python爬虫] Selenium获取百度百科旅游景点的InfoBox消息盒
前面我讲述过如何通过BeautifulSoup获取*的消息盒,同样可以通过Spider获取网站内容,最近学习了Selenium+Phantomjs后,准备利用它们获取百度百科的旅游景点消息盒(I ...
-
Python爬虫实战练习：爬取美团旅游景点评论数据
前言 本文的文字及图片来源于网络,仅供学习.交流使用,不具有任何商业用途,如有问题请及时联系我们以作处理. 今年的国庆节还有半个月就要来了,相信很多的小伙伴还是非常期待这个小长假的.国庆节是一年中的小 ...
-
BZOJ_1097_[POI2007]旅游景点atr_状压DP
BZOJ_1097_[POI2007]旅游景点atr_状压DP 题面描述: FGD想从成都去上海旅游.在旅途中他希望经过一些城市并在那里欣赏风景,品尝风味小吃或者做其他的有趣 的事情.经过这些城市的顺 ...
-
Python分析盘点2019全球流行音乐：是哪些歌曲榜单占领了我们？
写在前面:圣诞刚过,弥留者节日气息的大家是否还在继续学习呐~在匆忙之际也不忘给自己找几首好听的歌曲放松一下,缠绕着音乐一起来看看关于2019年流行音乐趋势是如何用Python分析的吧! 昨天下午没事儿 ...
-
Python分析离散心率信号（下）
Python分析离散心率信号(下) 如何使用动态阈值,信号过滤和离群值检测来改善峰值检测. 一些理论和背景 到目前为止,一直在研究如何分析心率信号并从中提取最广泛使用的时域和频域度量.但是,使用的信号 ...
-
BZOJ1097: [POI2007]旅游景点atr
..k次最短路后,考虑如何满足先走一些点 用状压dp,每一个点考虑它所需要经过的点a[i],当当前走过的点包含a[i]时,i 这个点才可以到达. 写的时候用记忆化搜索. #include<bit ...
-
【BZOJ-1097】旅游景点atr SPFA + 状压DP
1097: [POI2007]旅游景点atr Time Limit: 30 Sec Memory Limit: 357 MBSubmit: 1531 Solved: 352[Submit][Sta ...
-
BZOJ 1097: [POI2007]旅游景点atr( 最短路 + 状压dp )
先最短路预处理, 然后状压就行了 -------------------------------------------------------------------------- #include ...
随机推荐
-
Spring(三)AOP面向切面编程
原文链接:http://www.orlion.ga/205/ 一.AOP简介 1.AOP概念 参考文章:http://www.orlion.ml/57 2.AOP的产生 对于如下方法: pub ...
-
BizTalk开发系列(五) 属性字段
在根据消息内容进行路由的时候经常使用的是可分辨字段和属性字段.属性字段可以在各个 BizTalk Server 组件(包括管道和业务流程)中进行访问.属性字段还可用于消息路由.如果需要在上下文(而不是 ...
-
WCF初探-13：WCF客户端为双工服务创建回调对象
前言: 在WCF初探-5:WCF消息交换模式之双工通讯(Duplex)博文中,我讲解了双工通信服务的一个应用场景,即订阅和发布模式,这一篇,我将通过一个消息发送的例子讲解一下WCF客户端如何为双工服务 ...
-
maven 无法安装plugin的问题
spring有一个入门例子,在docker里跑spring-boot程序 下载后按照教程执行mvn package docker:build.并不能成功.会报错. [ERROR] No plugin ...
-
cdoj 1250 喵哈哈的矩阵 数学题
喵哈哈的矩阵 Time Limit: 20 Sec Memory Limit: 256 MB 题目连接 http://acm.uestc.edu.cn/#/problem/show/1250 Desc ...
-
PHP读取Excel里的文件
下载phpExcelReader http://sourceforge.net/projects/phpexcelreader 解压后得到以下这些文件 jxlrwtest.xls这个excel文件有 ...
-
C#基础拾遗系列之二：C#7.0新增功能点
第一部分: C#是一种通用的,类型安全的,面向对象的编程语言.有如下特点: (1)面向对象:c# 是面向对象的范例的一个丰富实现, 它包括封装.继承和多态性.C#面向对象的行为包括: 统一的类型系统 ...
-
C语言实现快速翻转数组的顺序
#include <stdio.h> void Reverse(int *p , int size) { int i , tmp; for(i = 0 ; i < size/2 ; ...
-
转 MYSQL InnoDB Record, Gap, and Next-Key Locks
http://dev.mysql.com/doc/refman/5.0/en/innodb-record-level-locks.html InnoDB has several types of re ...
-
set, unordered_set模板类
template< class Key, class Hash = std::hash<Key>, class KeyEqual = std::equal_to<Key> ...