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},
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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 势力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