Python进行克里金插值可视化

处理数据并进行克里金插值

# 处理数据后进行克里金插值
import pandas as pd
pm25_region=pd.read_csv(r"C:\Users\LHW\Desktop\Hubei task120210918\extract_pm25\PM25_region.csv")
del pm25_region['type']
year_list=['2019','2020','2021']
data_list=[]
for year in year_list:
    pm25_region['date'] = pd.to_datetime(pm25_region['date'])
    pm25_region1=pm25_region.set_index('date')
    pm2019=pm25_region1[year+'-02':year+'-03']
    pm2019_mean=pm2019.mean().to_frame()
    pm2019_mean.columns=[year+'_pm25']
    data_list.append(pm2019_mean)
    
ini=data_list[0]
ini=ini.join(data_list[1])
ini=ini.join(data_list[2])

loc=pd.read_csv("region_aim_loc_info.csv")
loc=loc.set_index('监测点编码')
total_info=loc.join(ini)
del total_info['Unnamed: 0']

total_info=total_info.dropna()

# 进行克里金插值
import numpy as np
import geopandas as gpd
js=gpd.read_file(r'Hubei.json')
js_box = js.geometry.total_bounds

grid_lon = np.linspace(js_box[0],js_box[2],400)
grid_lat = np.linspace(js_box[1],js_box[3],400)

lons = total_info["经度"