python-地图可视化组件folium的操作

import folium
import webbrowser
m=folium.Map(location=[40.009867,116.485994],zoom_start=10) # 绘制地图，确定聚焦点
folium.Marker([40.2,116.7],popup='<b>浮标上面的那个文字</b>').add_to(m) # 定一个点，放到地图m上
folium.Marker([40.22,116.72],popup='<b>浮标上面的那个文字</b>',icon=folium.Icon(color='red')).add_to(m)
# 把浮标变成红色
folium.Marker([40.24,116.74],popup='<b>浮标上面的那个文字</b>',icon=folium.Icon(color='green',icon='info-sign')).add_to(m)
# 浮标改图样
#标记一个空心的圈
folium.Circle(
 location=[40.2,117.7],
 radius=10000,
 color='crimson',
 popup='popup',
 fill=False
).add_to(m)
#标记一个实心圆
folium.CircleMarker(
 location=[39.2,117.7],
 radius=100,
 popup='popup',
 color='#DC143C',#圈的颜色
 fill=True,
 fill_color='#6495ED' #填充颜色
).add_to(m)
m.save('f1.html')
webbrowser.open('f1.html')

import folium
import webbrowser as wb
# 地图上悬浮显示经纬度
m = folium.Map(
 location=[36.68159, 117.103565],
 zoom_start=10
)
m.add_child(folium.LatLngPopup())
# 手动打点功能
m.add_child(
 folium.ClickForMarker(popup='Waypoint')
)
m.save('f2.html')
wb.open('f2.html')

import numpy as np
import pandas as pd
import seaborn as sns
import folium
import webbrowser
from folium.plugins import HeatMap
#导入数据集：
posi = pd.read_excel("D:/Python/File/Cities2015.xlsx")
posi = posi.dropna()
#生成所需要的数组格式数据：
lat = np.array(posi["lat"][0:len(posi)])
lon = np.array(posi["lon"][0:len(posi)])
pop = np.array(posi["pop"][0:len(posi)],dtype=float)
gdp = np.array(posi["GDP"][0:len(posi)],dtype=float)
data1 = [[lat[i],lon[i],pop[i]] for i in range(len(posi))]
#创建以高德地图为底图的密度图：
map_osm = folium.Map(
 location=[35,110],
 zoom_start=5,
 tiles='http://webrd02.is.autonavi.com/appmaptile?lang=zh_cn&size=1&scale=1&style=8&x={x}&y={y}&z={z}',
 attr="&copy; <a href="http://ditu.amap.com/" rel="external nofollow" >高德地图</a>"
 )
#创建以腾讯地图为底图的密度图：
map_osm = folium.Map(
 location=[35,110],
 zoom_start=5,
 tiles='http://rt{s}.map.gtimg.com/realtimerender?z={z}&x={x}&y={y}&type=vector&style=0',
 attr="&copy; <a href="http://map.qq.com/" rel="external nofollow" >腾讯地图</a>"
 )
#生成交互式地图：
HeatMap(data1).add_to(map_osm)
file_path = r"D:/Python/Image/People.html"
map_osm.save(file_path)
webbrowser.open(file_path)

import pandas as pd
import numpy as np
import os
import folium
from folium import plugins
import webbrowser
import geopandas as gp
#数据导入：
full = pd.read_excel("D:/Python/File/Cities2015.xlsx")
full = full.dropna()
#创建地图对象：
schools_map = folium.Map(location=[full['lat'].mean(), full['lon'].mean()], zoom_start=10)
marker_cluster = plugins.MarkerCluster().add_to(schools_map) 
#标注数据点：
for name,row in full.iterrows():
 folium.Marker([row["lat"], row["lon"]], popup="{0}:{1}".format(row["cities"], row["GDP"])).add_to(marker_cluster) 
#逐行读取经纬度，数值，并且打点
#folium.RegularPolygonMarker([row["lat"], row["lon"]], popup="{0}:{1}".format(row["cities"], row["GDP"]),number_of_sides=10,radius=5).add_to(marker_cluster)
schools_map.save('schools_map.html') #保存到本地
webbrowser.open('schools_map.html') #在浏览器中打开

# _*_ coding: utf-8 _*_
__author__ = 'xbr'
__date__ = '2019/1/9 15:47'
 
import numpy as np
import pandas as pd
import folium
import webbrowser
from folium.plugins import HeatMap
 
 
# 读取csv文件,以Dataframe形式保存
df = pd.read_csv(r"D:\data\PM25-20180101.csv")
# 获取数据个数
num = df.shape[0]
# 获取纬度
lat = np.array(df["lat"][0:num])
# 获取经度
lon = np.array(df["lon"][0:num])
# 获取PM2.5，转化为numpy浮点型
pm25 = np.array(df["PM25"][0:num], dtype=float)
# 将数据制作成[lats, lons, weights]的形式
data1 = [[lat[i], lon[i], pm25[i]] for i in range(num)]
# 绘制Map，中心经纬度[32, 120],开始缩放程度是5倍
map_osm = folium.Map(location=[32, 120], zoom_start=5)
# 将热力图添加到前面建立的map里
HeatMap(data1).add_to(map_osm)
 
file_path = r"D:\AirQualityMap.html"
# 保存为html文件
map_osm.save(file_path)
# 默认浏览器打开
webbrowser.open(file_path)