深圳市景点停车场分布地图代码

import datetime
import numpy as np
import pandas as pd

df = pd.read_excel ("深圳市景点停车场信息.xlsx", encoding = "utf8", sep="\t")
df

dizhi = df["停车场名称"]
tccdz = dizhi.to_list()
for i in tccdz:
    print(i)

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 tccdz:
    #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

loc = 经纬度
loc=pd.DataFrame(loc)
经纬度 = loc[0].str.split(',',expand=True)
经纬度

# 展示深圳市景点停车场分布概况地图
import plotly.graph_objects as go
mapbox_access_token = 'pk.eyJ1IjoiYmxhY2tzaGVlcHdhbGwwMzA1IiwiYSI6ImNrMHo5ZnQxYjBjbG8zbm84b3hrb25vb24ifQ.k8toDjJDsPcjdYFTSVgTsv'
fig = go.Figure(go.Scattermapbox(
        lon = 经纬度[0],
        lat = 经纬度[1],
        mode='markers',
        hovertext = df[['停车场名称','停车场地址']],
        marker=go.scattermapbox.Marker(
            size=7
        ),
        text=df[['停车场名称','停车场地址']],
    ))
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(
    title='深圳市停车场分布散点图',
    hovermode='closest',
    mapbox=dict(
        accesstoken=mapbox_access_token,
        bearing=0,
        center=go.layout.mapbox.Center(
            lat=22.564583,
            lon=113.900279
        ),
        pitch=0,
        zoom=8
    )
)
fig.show()
py.plot(fig,filename='停车场地址.html')

df = pd.read_excel ("不同区域停车场数量.xlsx", encoding = "utf8", sep="\t")
df

import plotly.graph_objects as go
import pandas as pd

# 展示深圳市各区域的景点停车场数量气泡图
import plotly.express as px
df = pd.read_excel ("不同区域停车场数量.xlsx", encoding = "utf8", sep="\t")
fig = px.scatter_mapbox(df,
                        lon = 'lon',
                        lat = 'lat',
                        size = '数量',
                        color = '数量',
                        hover_name = '区域',
                        size_max = 40,
                        color_continuous_scale=px.colors.carto.Temps)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(mapbox = {'accesstoken':'pk.eyJ1IjoiYmxhY2tzaGVlcHdhbGwwMzA1IiwiYSI6ImNrMHo5ZnQxYjBjbG8zbm84b3hrb25vb24ifQ.k8toDjJDsPcjdYFTSVgTsv','center':{'lon':113.884020,'lat':22.555259},'zoom':9.5},margin={'l':0,"r":0,"t":0,'b':0})

区域停车场数量气泡地图.html停车场地图.html

由于停车场信息过少，仅能分析各区域数量

以下为柱状图展示代码

import pandas as pd
import plotly.graph_objs as go
import numpy as np
import json

df = pd.read_excel ("不同区域停车场数量.xlsx", encoding = "utf8", sep="\t")
df

bar1 = go.Bar(x = df['区域'],y = df['数量'],text = df['数量'],textposition='outside',name='不同区域停车场数量')
fig = go.Figure(bar1)
fig.update_layout(title = '不同区域停车场数量',xaxis_title = "区域",yaxis_title = "数量")
fig.show()

不同区域停车场数量条形图.html