深圳市宾馆酒店地图代码

import datetime
import numpy as np
import pandas as pd

df = pd.read_excel ("深圳市宾馆酒店分类信息.xlsx", encoding = "utf8", sep="\t")
df

binguan = df["宾馆酒店名称"]
binguanjiudian = binguan.to_list()
for i in binguanjiudian:
    print(i)

深圳美境假日精品客栈
深圳大鹏较场尾壹月客栈
深圳小梅沙奢华海景别墅
深圳瓜villa海景度假别墅
爱情公寓(深圳古城店)
深圳随心民宿
深圳桔钓沙莱华度假酒店
深圳舒静阁别墅
深圳曦城别墅
深圳南澳世纪海景别墅
深圳东部华侨城瀑布酒店
深圳西涌(西冲)三人行客栈
深圳西冲米兰花开客栈(原舍下客栈)
深圳尚绿居民宿
深圳五洲宾馆
深圳寒之落民宿(原海语民宿)
...

import requests
def geolocation(address):
    url="https://restapi.amap.com/v3/geocode/geo?parameters"
    params={
        "key":"14dc3151f80e56462d23f40d60cfa779",
        "address":address,
        "city":"深圳"
    }
    r=requests.get(url,params=params)
    return r.json()

import time 
import pprint
from random import random
区域=list()
经纬度=list()
for i in binguanjiudian:
    #time.sleep(3+8*random())
    geocode=geolocation(i)
    #pprint.pprint(geocode["geocodes"])
    if geocode["geocodes"] == []:
        经纬度.append("请求经纬度失败")
        区域.append("转换为区域失败")
    elif geocode["geocodes"][0]["district"] ==[]:
        区域.append("转换为区域失败")
    elif geocode["geocodes"][0]["location"] ==[]:
        print("经纬度为空")
        经纬度.append("请求经纬度失败")
    #pprint.pprint(geocode)
#     if geocode[0]==" ":
#         print("无法转为经纬度")
    else:
        #print(geocode["geocodes"][0]["location"])
        经纬度.append(geocode["geocodes"][0]["location"])
        区域.append(geocode["geocodes"][0]["district"])
#     #print(geocode)
print(区域)
经纬度

['114.509457,22.588540',
 '114.122263,22.559907',
 '113.929882,22.540629',
 '113.980500,22.587347',
 '114.021658,22.624935',
 ...]
'盐田区', '福田区', '南山区', '龙岗区', '福田区', '龙岗区', '罗湖区', '福田区'

loc = 经纬度
经纬度 = loc[0].str.split(',',expand=True)
经度 = 经纬度[0]
维度 = 经纬度[1]
df["酒店所属区域"] = 区域
df["经度"] = 经度
df["维度"] = 维度
df

# 深圳市酒店分布情况地图概览
import plotly.graph_objects as go
mapbox_access_token = 'pk.eyJ1IjoiYmxhY2tzaGVlcHdhbGwwMzA1IiwiYSI6ImNrMHo5ZnQxYjBjbG8zbm84b3hrb25vb24ifQ.k8toDjJDsPcjdYFTSVgTsv'
fig = go.Figudfre(go.Scattermapbox(
        lon = df[lon],
        lat = df[lat],
        mode='markers',
        hovertext = df[['宾馆酒店名称','酒店地址']],
        marker=go.scattermapbox.Marker(
            size=7
        ),
        text=df[['宾馆酒店名称','酒店地址']],
    ))
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(
    title = '深圳市酒店分布散点图',
    hovermode='closest',
    mapbox=dict(
        accesstoken=mapbox_access_token,
        bearing=0,
        center=go.layout.mapbox.Center(
            lat=22.483767,
            lon=114.549601
        ),
        pitch=0,
        zoom=8
    )
)
fig.show()

df = pd.read_excel ("不同区域酒店数量.xlsx", encoding = "utf8", sep="\t")
df

# 深圳各区域酒店数量分布地图
# 深圳各区域有多少酒店清晰可见
import plotly.graph_objects as go
import pandas as pd
mymap = go.Densitymapbox(lat=df['lat'],lon=df['lon'],z=df['数量'],radius=40)
fig = go.Figure(mymap)
fig.update_layout(mapbox_style="open-street-map")
fig.show()

# 深圳市酒店价格卫星散点气泡地图
# 把酒店价格展示在地图上
import plotly.express as px
mapbox_access_token = 'pk.eyJ1IjoiYmxhY2tzaGVlcHdhbGwwMzA1IiwiYSI6ImNrMHo5ZnQxYjBjbG8zbm84b3hrb25vb24ifQ.k8toDjJDsPcjdYFTSVgTsv'
df = df
fig = px.scatter_mapbox(df, lat="lat", lon="lon",color="入住价格", size="入住价格",
                  color_continuous_scale=px.colors.cyclical.IceFire, size_max=15, zoom=10)
fig.update_layout(mapbox_style="stamen-terrain")
fig.show()

酒店地图.html
区域酒店数量热力地图.html
深圳市酒店卫星散点气泡图.html