Python 使用熊猫和geopandas绘制一年内每个月的数据

我有五个国家1991-2020年的数据

临时国家

    Date  Temperature    Units  Year Month Statistics Country CODE
                                                             
Jan 1991        -26.2  Celsius  1991   Jan    Average  Canada  CAN
Feb 1991        -21.0  Celsius  1991   Feb    Average  Canada  CAN
Mar 1991        -18.2  Celsius  1991   Mar    Average  Canada  CAN
Apr 1991         -8.6  Celsius  1991   Apr    Average  Canada  CAN
May 1991          0.8  Celsius  1991   May    Average  Canada  CAN

合并

我想在世界地图上画出1991年每个月的温度值。最后，我将得到12幅图，显示每个国家的温度

我如何仅选择1991年，以及如何放置一个标题，指示每个地块的年份和月份

我这样做：

import matplotlib as mpl
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import geopandas

location=pd.read_excel('countries.xlsx') #Longitude latitude data
tempcountries = pd.read_excel('Temperature Countries.xlsx')
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world.columns=['pop_est', 'continent', 'name', 'CODE', 'gdp_md_est', 'geometry']

for y in tempcountries['Year']:
    for i in tempcountries['Month']:
        fig, ax = plt.subplots(figsize=(8,6))
        world.plot(ax=ax, color='lightgrey')
        merge.plot(x="longitude", y="latitude", kind="scatter",  
                   c="Temperature", colormap="coolwarm", 
                   ax=ax)
        plt.show()

我得到了多个绘图，但我没有看到地图上的颜色（指示温度）发生变化

import matplotlib as mpl
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import geopandas

location=pd.read_excel('countries.xlsx') #Longitude latitude data
tempcountries = pd.read_excel('Temperature Countries.xlsx')
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world.columns=['pop_est', 'continent', 'name', 'CODE', 'gdp_md_est', 'geometry']

for y in tempcountries['Year']:
    for i in tempcountries['Month']:
        fig, ax = plt.subplots(figsize=(8,6))
        world.plot(ax=ax, color='lightgrey')
        merge.plot(x="longitude", y="latitude", kind="scatter",  
                   c="Temperature", colormap="coolwarm", 
                   ax=ax)
        plt.show()