Why does this situation occur?

When using GeoPandas to plot the per capita GDP thematic map of Sichuan Province, the areas of Garze Prefecture, Aba Prefecture, and Liangshan Prefecture appear gray (no data), even though the corresponding data exists in the statistical table. What could be the possible reasons for this?

Comments

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1. Mismatch between geographic names and data table keys This is the most frequent cause. The administrative region names in the GeoDataFrame (e.g., shapefile attribute fields) may not match the names in the per capita GDP statistical table, leading to failed data merging (join/merge). Common mismatches: Name variations (e.g., "Garze Tibetan Autonomous Prefecture" vs. "Garze Prefecture", "Aba" vs. "Ngawa"); Abbreviations vs. full names (e.g., "Liangshan Prefecture" vs. "Liangshan Yi Autonomous Prefecture"); Typos (e.g., "Garze" misspelled as "Ganze") or different encodings (e.g., Chinese simplified/traditional characters). Verification: python 运行 # Check the name field in the geographic data print(gdf['name_field'].unique()) # Replace 'name_field' with the actual administrative region name column # Check the name field in the statistical data print(df['gdp_name_field'].unique()) # Replace 'gdp_name_field' with the GDP table's region name column
2. Failed data merging (join/merge) logic Even if names match, incorrect merging logic can result in missing values for these three prefectures: Possible issues: Using inner join (default for merge) instead of left join: if the statistical table lacks exact matches for the three prefectures (even minor name differences), they will be excluded from the merged GeoDataFrame; Merging on non-unique keys (e.g., merging on "prefecture" when multiple entries share the same name); Index mismatch (e.g., merging by index instead of region name). Verification: python 运行 # Use left join to retain all geographic regions, then check for NaN in GDP column merged_gdf = gdf.merge(df, left_on='name_field', right_on='gdp_name_field', how='left') # Check if the three prefectures have NaN in per capita GDP missing_gdp = merged_gdf[merged_gdf['per_capita_gdp'].isna()] print(missing_gdp['name_field'])
3. Invalid or empty geometric data for the three prefectures The geometric shapes (geometry column) of Garze, Aba, or Liangshan Prefectures in the GeoDataFrame may be invalid (e.g., self-intersecting polygons, empty geometries), causing them to not render or be labeled as "no data". Verification: python 运行 # Check for empty geometries print(merged_gdf[merged_gdf['geometry'].is_empty]['name_field']) # Check geometric validity invalid_geom = merged_gdf[~merged_gdf['geometry'].is_valid] print(invalid_geom['name_field'])
4. Out-of-range or non-numeric per capita GDP values If the per capita GDP data for the three prefectures is non-numeric (e.g., strings like "N/A", "—") or extreme outliers (e.g., negative values, infinite values), GeoPandas/Matplotlib may treat them as invalid and render them gray: Verification: python 运行 # Check data type of per capita GDP column print(merged_gdf['per_capita_gdp'].dtype) # Check for non-numeric values or outliers print(merged_gdf[merged_gdf['per_capita_gdp'].apply(lambda x: not isinstance(x, (int, float)))]) print(merged_gdf['per_capita_gdp'].describe()) # Check for negatives/infinities
5. Plotting configuration issues Even if data is correctly merged, plotting parameters may mask the three prefectures: Possible issues: The vmin/vmax range of the color scale is set too narrow, excluding the GDP values of these three prefectures (e.g., their per capita GDP is far lower/higher than the range, so they are mapped to the "no data" color); The missing_kwds parameter is misconfigured (e.g., explicitly setting gray for NaN values, but the issue is misclassified as missing data); The geometric projection is mismatched (e.g., the three prefectures are outside the plot extent and not displayed). Verification: python 运行 # Check the value range of per capita GDP for the three prefectures target_prefectures = ['Garze', 'Aba', 'Liangshan'] # Use exact names in merged_gdf print(merged_gdf[merged_gdf['name_field'].isin(target_prefectures)]['per_capita_gdp'])

Check plot extent

merged_gdf.plot(column='per_capita_gdp', vmin=merged_gdf['per_capita_gdp'].min(),
vmax=merged_gdf['per_capita_gdp'].max(), missing_kwds={'color': 'gray'})

1. CRS (Coordinate Reference System) mismatch If the geographic data of the three prefectures uses a different CRS from the rest of Sichuan Province, they may be plotted outside the visible area (appearing as gray or invisible) or fail to render correctly. Verification: python 运行 # Check CRS of the GeoDataFrame print(merged_gdf.crs) # Check if the three prefectures have abnormal coordinate ranges target_geom = merged_gdf[merged_gdf['name_field'].isin(target_prefectures)]['geometry'] print(target_geom.bounds) # Check min/max lon/lat for anomalies Quick Troubleshooting Workflow Verify name consistency between geographic and statistical data; Use left join to merge data and check for NaN in the GDP column for the three prefectures; Validate geometric data (empty/invalid geometries); Check data type and value range of per capita GDP; Adjust plotting parameters (e.g., vmin/vmax, missing_kwds) and CRS. By systematically checking these aspects, you can pinpoint why Garze, Aba, and Liangshan Prefectures appear gray and resolve the issue (e.g., standardizing region names, using left join, repairing invalid geometries, or adjusting plot ranges).