我已经看了这里的许多其他问题，试图解决这个问题，但无论出于什么原因，我不能。每个解决方案似乎给我同样的错误，给予，或返回什么都没有。
我有一个列表，其中有六个我正在循环创建一个6Map的图形。每个Map的格式类似，唯一的区别是它们的时间列。每个Map都有通过制图创建的相同的分类方案。颜色是由颜色表确定的，Map本身没有与值相关的颜色。我希望所有Map都有一个单一的图例，以便读者更容易看到，下面是我的代码：

import cartopy.crs as ccrs
import cartopy.feature as cfeature
import mapclassify
from matplotlib.colors import rgb2hex
from matplotlib.colors import ListedColormap
plt.style.use('seaborn-v0_8-dark')

# Define the Robinson projection
robinson = ccrs.Robinson()

# Create a 3x2 grid of subplots
fig, axs = plt.subplots(3, 2, figsize=(12, 12), subplot_kw={'projection': robinson})

# Flatten the subplot array for easy iteration
axs = axs.flatten()

# Define color map and how many bins needed
cmap = plt.cm.get_cmap('YlOrRd', 5) #Blues #Greens #PuRd #YlOrRd
# Any countries with NaN values will be colored grey
missing_kwds = dict(color='grey', label='No Data')

# Loop through the dataframes and create submaps
for i, df in enumerate(dataframes):
    
    # Create figure and axis with Robinson projection
    mentionsgdf_robinson = df.to_crs(robinson.proj4_init)
    
   
    # Plot the submap
    ax = axs[i]
    
    # Add land mask and gridlines
    ax.add_feature(cfeature.LAND.with_scale('50m'), facecolor='lightgrey')
    gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
                  linewidth=1, color='gray', alpha=0.3, linestyle='--')
    
    gl.xlabel_style = {'fontsize': 7}
    gl.ylabel_style = {'fontsize': 7}
    
    # Classification scheme options: EqualInterval, Quantiles, NaturalBreaks, UserDefined etc.
    mentionsgdf_robinson.plot(column='mentions', 
                              ax=ax, 
                              legend=True, #True
                              cmap=cmap, 
                              legend_kwds=({"loc":'center left', 'title': 'Number of Mentions', 'prop': {'size': 7, 'family': 'serif'}}),
                              missing_kwds=missing_kwds,
                              scheme="UserDefined", 
                              classification_kwds = {'bins':[20, 50, 150, 300, 510]})
    
    # Set the titles of each submap
    ax.set_title(f'20{i+15}', size = 15, family = 'Serif')
    
    # Define the bounds of the classification scheme
    upper_bounds = mapclassify.UserDefined(mentionsgdf_robinson.mentions, bins=[20, 50, 150, 300, 510]).bins
    
    
    bounds = []
    for index, upper_bound in enumerate(upper_bounds):
        if index == 0:
            lower_bound = mentionsgdf_robinson.mentions.min()
        else:
            lower_bound = upper_bounds[index-1]

        bound = f'{lower_bound:.0f} - {upper_bound:.0f}'
        bounds.append(bound)
    
    # replace the legend title and increase font size
    legend_title = ax.get_legend().get_title()
    legend_title.set_fontsize(8)
    legend_title.set_family('serif')
    
    
    # get all the legend labels and increase font size
    legend_labels = ax.get_legend().get_texts()
    # replace the legend labels
    for bound, legend_label in zip(bounds, legend_labels):
        legend_label.set_text(bound)
    

fig.suptitle(' Yearly Country Mentions in Online News about Species Threatened by Trade ', fontsize=15, family = 'Serif')
    
# Adjust spacing between subplots
plt.tight_layout(pad=4.0)

# Save the figure
#plt.savefig('figures/submaps_5years.png', dpi=300)

# Show the submap
plt.show()

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这是我现在的结果，我希望在Map的中心位置有一个图例。
x1c 0d1x的数据
我尝试了here建议的代码，但只收到一个UserWarning：Legend does not support handles for PatchCollection instances.此外，我不知道如何在循环之外合并所有其他需要的Legend修改（边界，字体，bin等）。

handles, labels = ax.get_legend_handles_labels()
fig.legend(handles, labels, loc='upper center')

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以下是2015-2017年三年的数据：https://jmp.sh/s/ohkSJpaMZ4c1GifIX0nu
下面是我使用的全局shapefile的所有文件：https://jmp.sh/uTP9DZsC
使用这些数据和下面的代码应该可以让你运行上面分享的完整的可视化代码。谢谢。

import geopandas as gpd
import pandas as pd

# Read in globe shapefile dataframe 
world = gpd.read_file("TM_WORLD_BORDERS-0.3.shp")
# Read in sample dataframe
df = pd.read_csv("fifsixseventeen.csv", sep = ";")

# Separate according to date column 
fifteen = df[(df['date'] == 2015)].reset_index(drop=True)
sixteen = df[(df['date'] == 2016)].reset_index(drop=True)
seventeen = df[(df['date'] == 2017)].reset_index(drop=True)

# Function to merge isocodes of the countries with world shapefile 
def merge_isocodes(df):
    
    # Groupby iso3 column in order to merge with shapefile
    allmentions = df.groupby("iso3")['mentions'].sum().sort_values(ascending = False).reset_index()

    # Merge on iso3 code 
    mentionsgdf = pd.merge(allmentions, world, left_on=allmentions["iso3"], right_on=world["ISO3"], how="right").drop(columns = "key_0")

    # Redefine as a geodataframe
    mentionsgdf = gpd.GeoDataFrame(mentionsgdf, geometry='geometry')
    
    return mentionsgdf

onefive = merge_isocodes(fifteen)
onesix = merge_isocodes(sixteen)
oneseven = merge_isocodes(seventeen)

# Create a list to store each years' dataframes
dataframes = [onefive, onesix, oneseven]

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