python folium绘制地图 python用folium绘制地图并设置弹窗效果

m = folium.Map(location=[45.523, -122.675], width=750, height=500)

m = folium.Map(location=[45.523, -122.675], tiles='cartodb positron')

m = folium.Map(

   location=[45.523, -122.675],

   zoom_start=2,

   tiles='https://api.mapbox.com/v4/mapbox.streets/{z}/{x}/{y}.jpg?access_token=mytoken',

   attr='Mapbox attribution'
...)

m = folium.Map(location=[45.372, -121.6972], zoom_start=12, tiles="Stamen Terrain")

tooltip = "Click me!"

folium.Marker(
    [45.3288, -121.6625], popup="<i>Mt. Hood Meadows</i>", tooltip=tooltip
).add_to(m)

m = folium.Map(location=[45.372, -121.6972], zoom_start=12, tiles="Stamen Terrain")

folium.Marker(
    location=[45.3288, -121.6625],
    popup="Mt. Hood Meadows",
    icon=folium.Icon(icon="cloud"),
).add_to(m)

folium.Marker(
    location=[45.3311, -121.7113],
    popup="Timberline Lodge",
    icon=folium.Icon(color="green"),
).add_to(m)

folium.Marker(
    location=[45.3300, -121.6823],
    popup="Some Other Location",
    icon=folium.Icon(color="red", icon="info-sign"),
).add_to(m)

m = folium.Map(location=[46.1991, -122.1889], tiles="Stamen Terrain", zoom_start=13)

m.add_child(folium.LatLngPopup())

m = folium.Map(location=[46.8527, -121.7649], tiles="Stamen Terrain", zoom_start=13)

folium.Marker([46.8354, -121.7325], popup="Camp Muir").add_to(m)

m.add_child(folium.ClickForMarker(popup="Waypoint"))

m = folium.Map(location=[46.3014, -123.7390], zoom_start=7, tiles="Stamen Terrain")

folium.Marker(
    location=[47.3489, -124.708],
    popup=folium.Popup(max_width=450).add_child(
        folium.Vega(vis1, width=450, height=250)
    ),
).add_to(m)

import folium
import webbrowser as wb
import numpy as np
import xlrd
import pandas as pd
from folium import CustomIcon

def get_data():
    '''
    return:
    df_data:返回城市对应的编号,经纬度
    df_policy:返回城市的政策名称、链接、编号
    '''
    file_name = 'policy.xls'
    all_data = xlrd.open_workbook(file_name)
    table = all_data.sheet_by_name('num_lat_lon')
    df_data = pd.DataFrame(columns=['city','number','latitude','longitude'])
    for i in range(1,table.nrows):
        df_data.loc[i] = table.row_values(i)

    table_policy = all_data.sheet_by_name('policy')
    df_policy = pd.DataFrame(columns=['number','name','link'])
    for i in range(1,table_policy.nrows):
        df_policy.loc[i,'number'] = table_policy.cell(i,3).value
        df_policy.loc[i,'name'] = table_policy.cell(i,1).value
        df_policy.loc[i,'link'] = table_policy.cell(i,2).value

    return df_data, df_policy

    # 中文转换
def parse_zhch(s):
        return str(str(s).encode('ascii' , 'xmlcharrefreplace'))[2:-1]

def show_map(df_data,df_policy):
    '''
    可以使用高德地图或默认地图
    m = folium.Map(zoom_start=4,zoom_control=True,tiles='http://webrd02.is.autonavi.com/appmaptile?lang=zh_cn&size=1&scale=1&style=7&x={x}&y={y}&z={z}',attr='default')
    '''
    # 默认地图
    m = folium.Map(location=[39.904,116.408],zoom_start=4,zoom_control=True,tiles='OpenStreetMap')
    for i in range(1,len(df_data)):
        # 得到该城市的编号、经纬度
        number = df_data.loc[i,'number']
        latitude = df_data.loc[i,'latitude']
        longitude = df_data.loc[i,'longitude']
        tip = df_data.loc[i,'city']

        # 获取城市编号对应的所有政策和链接的行索引
        city_index = df_policy[df_policy.number == number].index.tolist()
        # 将城市名称和链接放同一个list中
        s=[]
        for i in city_index:
            s.append(df_policy.loc[i,'name']) 
            s.append(df_policy.loc[i,'link'])
        # 计算最大长度
        length = []
        for i in range(len(s)):
            length.append(len(s[i]))
        WIDTH = max(length)
        # 点击弹出内容和弹出框大小设置
        ss=''
        for i in range(len(s)):
            ss = ss + s[i] + '</br>' 
        pop = folium.Popup(html=ss,max_width=WIDTH*10)
        icon = CustomIcon(icon_image ='loc.jpg',icon_size=(20, 20))
        # tooltip：悬浮弹出信息；popup：点击出现信息
        folium.Marker([latitude,longitude],icon=icon, popup=pop,tooltip=parse_zhch(tip)).add_to(m)

    m.save('map.html')
    wb.open('map.html')


if __name__ == '__main__':

    df_data,df_policy = get_data()
    show_map(df_data,df_policy)