import warnings
warnings.filterwarnings('ignore')

import os

import numpy as np
from scipy.stats import norm
import pandas as pd

import seaborn as sns
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')

plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False 

import cartopy.crs as ccrs
import geopandas as gpd
import cartopy.io.shapereader as shpreader
from shapely.geometry import Polygon
from matplotlib.patches import Polygon as mpl_polygon

甲状腺结节数据

res={}
result= pd.DataFrame(res)
result.head()

加载边界数据

country_CN = gpd.read_file('ChinaAdminDivisonSHP/1. Country/country.shp', encoding='utf-8')  # 用高德最新API，国家
province_CN = gpd.read_file('ChinaAdminDivisonSHP/2. Province/province.shp', encoding='utf-8') # 省份
nine_dotted_line = gpd.read_file('ChinaAdminDivisonSHP/5. NineDot/china_nine_dotted_line.shp', encoding='utf-8') # 九段线
df_pcenter = pd.read_csv('ChinaAdminDivisonSHP/省市中心坐标位置LBC各省市中心.csv', encoding='gbk') # 区域中心

颜色映射

from matplotlib.colors import Normalize, rgb2hex
from matplotlib.cm import ScalarMappable
vmin=result['检出率'].min()
vmax=result['检出率'].max()
# 创建 Normalize 对象，用于将数据映射到 [0, 1] 范围
norm = Normalize(vmin=vmin, vmax=vmax)
# 创建 ScalarMappable 对象，用于将归一化后的数据映射到颜色映射中，注意左侧0，右侧1，色板：
# https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html
sm = ScalarMappable(cmap='OrRd', norm=norm)
# 将数据映射为颜色数组
colors = sm.to_rgba(result['检出率'])
# 将颜色数组转换为十六进制格式的颜色数组
hex_colors = [rgb2hex(color) for color in colors]

绘图

result['hex_colors']=hex_colors
result['geometry'] = [province_CN[province_CN['pr_name'].str.contains(p)]['geometry'] for p in result['地区1'].tolist()]
result.head()

# 创建地图投影
crs = ccrs.LambertConformal(central_longitude=105, central_latitude=35, standard_parallels=(30, 60))
# 创建子图
fig, ax = plt.subplots(figsize=(16, 9), subplot_kw={'projection': crs})
# 绘制国界
ax.add_geometries(country_CN['geometry'], crs=ccrs.PlateCarree(), edgecolor='black', facecolor='none')
# 绘制九段线
ax.add_geometries(nine_dotted_line['geometry'], crs=ccrs.PlateCarree(), edgecolor='black', facecolor='none')
# 绘制各省颜色
for i in range(result.shape[0]):
    province = result['地区1'].iloc[i]
    num = result['检出率'].iloc[i]
    colors_i = result['hex_colors'].iloc[i]
    geometry = result['geometry'].iloc[i]
    ax.add_geometries(geometry, crs=ccrs.PlateCarree(), edgecolor=colors_i, facecolor=colors_i)
    lat = float(df_pcenter[df_pcenter['所在省'].str.contains(province)].iloc[0]['纬度'])
    lon = float(df_pcenter[df_pcenter['所在省'].str.contains(province)].iloc[0]['经度'])
    ax.text(lon, lat, str(num), transform=ccrs.PlateCarree(), fontsize=12, color='black')
# 标注北京
im =  ax.scatter(116.46, 39.92, transform=ccrs.PlateCarree(), s=200, color='red', marker='*')
# 添加色值条
im.set_cmap(cmap='OrRd')
# 显示右侧渐变色值条
cbar = fig.colorbar(im, ax=ax)
# 设置色值条范围和标签
cbar.set_ticks([0,0.5, 1])  # 设置色值条刻度位置
cbar.set_ticklabels([int(vmin),int((vmin+vmax)/2), int(vmax)])  # 设置刻度标签
# 色值条
cbar.ax.set_ylabel('色值条', rotation=270, labelpad=10)
# 调整标签字体大小
cbar.ax.yaxis.label.set_fontsize(12)

# 设置地图范围
ax.set_extent([73, 135, 12, 53], crs=ccrs.PlateCarree())
# 设置标题
ax.set_title('中国各省某数据检出率')
# 显示图像
plt.show()