Python安装geopandas库绘制NASA-OMI卫星空气质量全球分布图(附2020年6月中国省、市、县行政区划shp)

0 Python3安装geopandas库：

方法1：pip install geopandas

方法2：conda install -c conda-forge geopandas

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1 geopandas库绘制2021年河南省市级行政地图

import geopandas as gpd
import matplotlib.pyplot as plt
from matplotlib import rcParams
config = {"font.family":'Times New Roman',"font.size":16,"mathtext.fontset":'stix'}
rcParams.update(config)
henan=gpd.GeoDataFrame.from_file('F:/RMeteoInfo/data/map/henan.shp',encoding='utf-8')
data=gpd.read_file('F:/RMeteoInfo/data/map/henan.dbf',encoding='utf-8')
henan1= data.copy()
henan1['coords']=henan1['geometry'].apply(lambda x: x.representative_point().coords[0])
henan1.plot(figsize=(8,6),column='name',legend=False,cmap='hsv',edgecolor='k')
font3={'family':'SimHei','size':16,'color':'k'}
for n, i in enumerate(henan1['coords']):
    plt.text(i[0]-0.2,i[1],henan1['name'][n],fontdict=font3,horizontalalignment="left")
plt.title('2021年河南省市级行政地图',fontdict=font3)
plt.grid(True,alpha=0.5)
plt.savefig('F:/Rpython/lp36/plot124.1.png',dpi=800,bbox_inches='tight',pad_inches=0)
plt.show()

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2 绘制2019年NASA-OMI卫星空气质量全球分布图

2.1 geopandas库绘制

import numpy as np
import h5py
import glob
import geopandas as gpd
import pandas as pd
from shapely.geometry import Polygon
from matplotlib.pylab import plt
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
files = glob.glob('F:/Rpython/lp36/data/moyu1025/co2/*')
print(files[0:3])
PIXEL_SIZE = 0.25
lon = np.arange(-180, 180, PIXEL_SIZE)
lat = np.arange(-90, 90, PIXEL_SIZE)
FILL_VALUE = -1.2676506e+30
def monthly_no2(files, filter_to_nan = 14.5):
    """
    Return monthly averaged $NO_2$ matrix from a list of files
    """
    arr = np.empty((len(files), lat.shape[0], lon.shape[0]), dtype=float)
    p = 'ColumnAmountNO2TropCloudScreened'
    for ix, file in enumerate(files):
        with h5py.File(file, 'r') as f:
            data = f['HDFEOS']['GRIDS']['ColumnAmountNO2']['Data Fields'][p][:]
            data[(data == FILL_VALUE) | (data <= 0)] = np.nan
            arr[ix] = data
    average_monthly = np.log10(np.nanmean(arr, axis = 0))
    average_monthly[average_monthly < filter_to_nan] = np.nan
    return average_monthly
average_monthly = monthly_no2(files)
continents = world.dissolve(by='continent')
continents = continents[~continents.index.isin(['Seven seas (open ocean)', 'Antarctica'])]
continents = continents.geometry.explode().reset_index()
fig, ax = plt.subplots(1, figsize=(16,10))
vmin=average_monthly.min()
vmax=average_monthly.max()
ax.pcolormesh(lon,lat,average_monthly, cmap='cividis')
# continents.boundary.plot(ax=ax, color='black')
ax.set_xlim(-180,180)
ax.set_ylim(-90, 90)
plt.savefig('F:/Rpython/lp36/plot125.1.png',dpi=800,bbox_inches='tight',pad_inches=0)
plt.show()

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2.2 cartopy库绘制

cf=ax.contourf(lon,lat,average_monthly,levels=levs,cmap=cmaps.NCV_bright,extend='both')

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3 获取本文中国行政区划shp数据的途径：

气象水文科研猫公众号后台回复：

“2020年6月中国省、市、县行政区划shp”，

获取百度云免费下载链接。

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