![\"python\u5982\u4f55\u53ea\u753b\u4e2d\u56fd\u6570\u636e\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25211233\/f388ff0a-3d0b-42de-889e-22e245d0c702.webp\")
<\/p>\n<\/p>\n
\u8981\u5728Python\u4e2d\u53ea\u7ed8\u5236\u4e2d\u56fd\u7684\u6570\u636e\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u591a\u4e2a\u6570\u636e\u5904\u7406\u548c\u53ef\u89c6\u5316\u5e93\uff0c\u5982pandas\u3001matplotlib\u548cgeopandas\u3002\u9996\u5148\u9700\u8981\u83b7\u53d6\u4e2d\u56fd\u7684\u5730\u7406\u8fb9\u754c\u6570\u636e\u3001\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u6e90\u3001\u4f7f\u7528pandas\u5904\u7406\u6570\u636e\uff0c\u5e76\u4f7f\u7528matplotlib\u6216geopandas\u8fdb\u884c\u7ed8\u56fe\u3002<\/strong> \u4e0b\u9762\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u5b9e\u73b0\u8fd9\u4e00\u8fc7\u7a0b\uff0c\u5e76\u7ed9\u51fa\u5177\u4f53\u7684\u4ee3\u7801\u793a\u4f8b\u3002<\/p>\n<\/p>\n \u4e3a\u4e86\u7ed8\u5236\u4e2d\u56fd\u7684\u5730\u56fe\uff0c\u9996\u5148\u9700\u8981\u83b7\u53d6\u4e2d\u56fd\u7684\u5730\u7406\u8fb9\u754c\u6570\u636e\u3002\u53ef\u4ee5\u4f7f\u7528\u81ea\u7136\u5730\u7406\u6570\u636e\u96c6\u5982Natural Earth\u6216\u5176\u4ed6\u5f00\u6e90\u5730\u7406\u6570\u636e\u96c6\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528geopandas\u76f4\u63a5\u52a0\u8f7dNatural Earth\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))<\/p>\n china = world[world.name == 'China']<\/p>\n <\/code><\/pre>\n<\/p>\n \u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u6e90\u662f\u5173\u952e\u7684\u4e00\u6b65\u3002\u5047\u8bbe\u60a8\u9700\u8981\u7ed8\u5236\u4e2d\u56fd\u5404\u7701\u7684\u75ab\u60c5\u6570\u636e\uff0c\u53ef\u4ee5\u4ece\u76f8\u5173\u7f51\u7ad9\u83b7\u53d6\u6570\u636e\uff0c\u4f8b\u5982\u4e2d\u56fd\u56fd\u5bb6\u7edf\u8ba1\u5c40\u6216\u5176\u4ed6\u53ef\u9760\u7684\u6570\u636e\u6e90\u3002<\/p>\n<\/p>\n data = pd.read_csv('china_covid19_data.csv')<\/p>\n print(data.head())<\/p>\n <\/code><\/pre>\n<\/p>\n \u9700\u8981\u786e\u4fdd\u6570\u636e\u4e0e\u5730\u7406\u8fb9\u754c\u6570\u636e\u5bf9\u9f50\u3002\u53ef\u4ee5\u901a\u8fc7\u7701\u4efd\u540d\u79f0\u8fdb\u884c\u5408\u5e76\u64cd\u4f5c\u3002<\/p>\n<\/p>\n china_data = china.merge(data, left_on='name', right_on='province_name')<\/p>\n <\/code><\/pre>\n<\/p>\n \u73b0\u5728\u53ef\u4ee5\u4f7f\u7528matplotlib\u548cgeopandas\u8fdb\u884c\u7ed8\u56fe\u3002\u53ef\u4ee5\u9009\u62e9\u4e0d\u540c\u7684\u989c\u8272\u8868\u793a\u4e0d\u540c\u7684\u6570\u503c\u533a\u95f4\u3002<\/p>\n<\/p>\n fig, ax = plt.subplots(1, 1, figsize=(10, 15))<\/p>\n china_data.plot(column='covid19_cases', ax=ax, legend=True,<\/p>\n legend_kwds={'label': "COVID-19 Cases by Province",<\/p>\n 'orientation': "horizontal"})<\/p>\n ax.set_title('COVID-19 Cases in China by Province')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u9996\u5148\uff0c\u4f7f\u7528geopandas\u5e93\u52a0\u8f7d\u4e16\u754c\u5730\u7406\u6570\u636e\u3002geopandas\u662f\u4e00\u4e2a\u5904\u7406\u5730\u7406\u6570\u636e\u7684\u5f3a\u5927\u5e93\uff0c\u80fd\u591f\u8f7b\u677e\u5730\u8bfb\u53d6\u548c\u64cd\u4f5c\u5730\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))<\/p>\n print(world.head())<\/p>\n <\/code><\/pre>\n<\/p>\n \u4ece\u52a0\u8f7d\u7684\u4e16\u754c\u5730\u7406\u6570\u636e\u4e2d\u7b5b\u9009\u51fa\u4e2d\u56fd\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n china = world[world.name == 'China']<\/p>\n print(china)<\/p>\n <\/code><\/pre>\n<\/p>\n \u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u5df2\u7ecf\u6210\u529f\u83b7\u53d6\u4e86\u4e2d\u56fd\u7684\u5730\u7406\u8fb9\u754c\u6570\u636e\uff0c\u8fd9\u5c06\u4f5c\u4e3a\u7ed8\u5236\u4e2d\u56fd\u5730\u56fe\u7684\u57fa\u7840\u3002<\/p>\n<\/p>\n \u5047\u8bbe\u6211\u4eec\u9700\u8981\u7ed8\u5236\u4e2d\u56fd\u5404\u7701\u7684\u75ab\u60c5\u6570\u636e\uff0c\u53ef\u4ee5\u4ece\u76f8\u5173\u7f51\u7ad9\u83b7\u53d6\u6570\u636e\u3002\u8fd9\u91cc\u6211\u4eec\u5047\u8bbe\u5df2\u7ecf\u4e0b\u8f7d\u4e86\u4e00\u4e2a\u5305\u542b\u5404\u7701\u75ab\u60c5\u6570\u636e\u7684CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n data = pd.read_csv('china_covid19_data.csv')<\/p>\n print(data.head())<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u7ed8\u56fe\u4e4b\u524d\uff0c\u9700\u8981\u786e\u4fdd\u6570\u636e\u4e0e\u5730\u7406\u8fb9\u754c\u6570\u636e\u5bf9\u9f50\u3002\u901a\u5e38\u901a\u8fc7\u7701\u4efd\u540d\u79f0\u8fdb\u884c\u5408\u5e76\u64cd\u4f5c\u3002<\/p>\n<\/p>\n print(data.columns)<\/p>\n print(data['province_name'].unique())<\/p>\n print(china['name'].unique())<\/p>\n <\/code><\/pre>\n<\/p>\n \u5982\u679c\u7701\u4efd\u540d\u79f0\u4e0d\u4e00\u81f4\uff0c\u9700\u8981\u8fdb\u884c\u9002\u5f53\u7684\u5904\u7406\uff0c\u4ee5\u786e\u4fdd\u5408\u5e76\u64cd\u4f5c\u7684\u987a\u5229\u8fdb\u884c\u3002<\/p>\n<\/p>\n data['province_name'] = data['province_name'].replace({'Some Province Name': 'Correct Name'})<\/p>\n china_data = china.merge(data, left_on='name', right_on='province_name')<\/p>\n <\/code><\/pre>\n<\/p>\n \u4f7f\u7528matplotlib\u548cgeopandas\u5e93\u8fdb\u884c\u7ed8\u56fe\uff0c\u9009\u62e9\u4e0d\u540c\u7684\u989c\u8272\u8868\u793a\u4e0d\u540c\u7684\u6570\u503c\u533a\u95f4\u3002<\/p>\n<\/p>\n fig, ax = plt.subplots(1, 1, figsize=(10, 15))<\/p>\n china_data.plot(column='covid19_cases', ax=ax, legend=True,<\/p>\n legend_kwds={'label': "COVID-19 Cases by Province",<\/p>\n 'orientation': "horizontal"})<\/p>\n ax.set_title('COVID-19 Cases in China by Province')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u53ef\u4ee5\u6839\u636e\u9700\u8981\u8c03\u6574\u7ed8\u56fe\u7ec6\u8282\uff0c\u4f8b\u5982\u989c\u8272\u6620\u5c04\u3001\u56fe\u4f8b\u4f4d\u7f6e\u548c\u6837\u5f0f\u7b49\u3002<\/p>\n<\/p>\n china_data.plot(column='covid19_cases', ax=ax, cmap='OrRd', legend=True,<\/p>\n legend_kwds={'label': "COVID-19 Cases by Province",<\/p>\n 'orientation': "horizontal"})<\/p>\n china_data.plot(column='covid19_cases', ax=ax, cmap='OrRd', legend=True,<\/p>\n legend_kwds={'label': "COVID-19 Cases by Province",<\/p>\n 'orientation': "horizontal", 'bbox_to_anchor': (0.5, -0.1)})<\/p>\n ax.set_title('COVID-19 Cases in China by Province')<\/p>\n ax.set_xlabel('Longitude')<\/p>\n ax.set_ylabel('Latitude')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u8be6\u7ec6\u63cf\u8ff0\u4e86\u5982\u4f55\u5728Python\u4e2d\u53ea\u7ed8\u5236\u4e2d\u56fd\u7684\u6570\u636e\u3002\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u8c03\u6574\u6570\u636e\u5904\u7406\u548c\u7ed8\u56fe\u7ec6\u8282\uff0c\u4ee5\u83b7\u5f97\u66f4\u7b26\u5408\u9700\u6c42\u7684\u7ed3\u679c\u3002<\/p>\n<\/p>\n \u5982\u4f55\u5728Python\u4e2d\u83b7\u53d6\u4e2d\u56fd\u7684\u6570\u636e\uff1f<\/strong> \u6709\u54ea\u4e9bPython\u5e93\u53ef\u4ee5\u5e2e\u52a9\u6211\u7ed8\u5236\u4e2d\u56fd\u7684\u5730\u56fe\uff1f<\/strong> \u5982\u4f55\u81ea\u5b9a\u4e49\u4e2d\u56fd\u5730\u56fe\u7684\u6837\u5f0f\u548c\u989c\u8272\uff1f<\/strong>\u4e00\u3001\u83b7\u53d6\u4e2d\u56fd\u5730\u7406\u8fb9\u754c\u6570\u636e<\/h2>\n<\/p>\n
import geopandas as gpd<\/p>\n\u52a0\u8f7d\u4e16\u754c\u5730\u7406\u6570\u636e<\/strong><\/h2>\n
\u9009\u62e9\u4e2d\u56fd\u7684\u5730\u7406\u6570\u636e<\/strong><\/h2>\n
\u4e8c\u3001\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u6e90<\/h2>\n<\/p>\n
import pandas as pd<\/p>\n\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u75ab\u60c5\u6570\u636e\u96c6\uff0c\u5305\u542b\u5404\u7701\u7684\u540d\u79f0\u548c\u76f8\u5173\u6570\u636e<\/strong><\/h2>\n
\u67e5\u770b\u6570\u636e<\/strong><\/h2>\n
\u4e09\u3001\u4f7f\u7528pandas\u5904\u7406\u6570\u636e<\/h2>\n<\/p>\n
# \u5408\u5e76\u6570\u636e\u96c6<\/p>\n\u56db\u3001\u4f7f\u7528matplotlib\u548cgeopandas\u8fdb\u884c\u7ed8\u56fe<\/h2>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n\u521b\u5efa\u7ed8\u56fe<\/strong><\/h2>\n
\u7ed8\u5236\u4e2d\u56fd\u5730\u56fe<\/strong><\/h2>\n
\u8bbe\u7f6e\u6807\u9898<\/strong><\/h2>\n
\u663e\u793a\u7ed8\u56fe<\/strong><\/h2>\n
\u8be6\u7ec6\u63cf\u8ff0\uff1a\u83b7\u53d6\u4e2d\u56fd\u5730\u7406\u8fb9\u754c\u6570\u636e<\/h2>\n<\/p>\n
1\u3001\u4f7f\u7528geopandas\u52a0\u8f7d\u5730\u7406\u6570\u636e<\/h3>\n<\/p>\n
import geopandas as gpd<\/p>\n\u52a0\u8f7d\u4e16\u754c\u5730\u7406\u6570\u636e<\/strong><\/h2>\n
\u67e5\u770b\u6570\u636e\u7ed3\u6784<\/strong><\/h2>\n
2\u3001\u7b5b\u9009\u51fa\u4e2d\u56fd\u7684\u6570\u636e<\/h3>\n<\/p>\n
# \u9009\u62e9\u4e2d\u56fd\u7684\u5730\u7406\u6570\u636e<\/p>\n\u67e5\u770b\u4e2d\u56fd\u5730\u7406\u6570\u636e<\/strong><\/h2>\n
\u8be6\u7ec6\u63cf\u8ff0\uff1a\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u6e90<\/h2>\n<\/p>\n
1\u3001\u83b7\u53d6\u75ab\u60c5\u6570\u636e<\/h3>\n<\/p>\n
import pandas as pd<\/p>\n\u8bfb\u53d6\u75ab\u60c5\u6570\u636e<\/strong><\/h2>\n
\u67e5\u770b\u6570\u636e<\/strong><\/h2>\n
2\u3001\u6570\u636e\u9884\u5904\u7406<\/h3>\n<\/p>\n
# \u67e5\u770b\u6570\u636e\u5217\u540d<\/p>\n\u5047\u8bbe\u6570\u636e\u5305\u542b 'province_name' \u548c 'covid19_cases' \u4e24\u5217<\/strong><\/h2>\n
\u68c0\u67e5\u7701\u4efd\u540d\u79f0\u662f\u5426\u4e00\u81f4<\/strong><\/h2>\n
# \u5047\u8bbe\u9700\u8981\u66f4\u6539\u67d0\u4e9b\u7701\u4efd\u540d\u79f0\u4ee5\u5339\u914d\u5730\u7406\u6570\u636e<\/p>\n\u5408\u5e76\u6570\u636e\u96c6<\/strong><\/h2>\n
\u8be6\u7ec6\u63cf\u8ff0\uff1a\u4f7f\u7528matplotlib\u548cgeopandas\u8fdb\u884c\u7ed8\u56fe<\/h2>\n<\/p>\n
1\u3001\u7ed8\u5236\u4e2d\u56fd\u5730\u56fe<\/h3>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n\u521b\u5efa\u7ed8\u56fe<\/strong><\/h2>\n
\u7ed8\u5236\u4e2d\u56fd\u5730\u56fe<\/strong><\/h2>\n
\u8bbe\u7f6e\u6807\u9898<\/strong><\/h2>\n
\u663e\u793a\u7ed8\u56fe<\/strong><\/h2>\n
2\u3001\u8c03\u6574\u7ed8\u56fe\u7ec6\u8282<\/h3>\n<\/p>\n
# \u4f7f\u7528\u4e0d\u540c\u7684\u989c\u8272\u6620\u5c04<\/p>\n\u8c03\u6574\u56fe\u4f8b\u4f4d\u7f6e<\/strong><\/h2>\n
\u8bbe\u7f6e\u6807\u9898\u548c\u5750\u6807\u8f74<\/strong><\/h2>\n
\u663e\u793a\u7ed8\u56fe<\/strong><\/h2>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u6570\u636e\u6e90\u83b7\u53d6\u4e2d\u56fd\u7684\u6570\u636e\uff0c\u4f8b\u5982\u4f7f\u7528API\u63a5\u53e3\u3001\u8bfb\u53d6CSV\u6587\u4ef6\u6216\u4ece\u5728\u7ebf\u6570\u636e\u5e93\u4e2d\u63d0\u53d6\u6570\u636e\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecPandas\u3001Requests\u548cBeautifulSoup\u7b49\u3002\u901a\u8fc7\u8fd9\u4e9b\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u4e0b\u8f7d\u548c\u5904\u7406\u4e2d\u56fd\u7279\u5b9a\u7684\u6570\u636e\u96c6\u3002<\/p>\n
\u5e38\u7528\u7684Python\u5e93\u6709Matplotlib\u3001Geopandas\u548cPlotly\u7b49\u3002Geopandas\u7279\u522b\u9002\u5408\u4e8e\u5730\u7406\u6570\u636e\u7684\u5904\u7406\u4e0e\u53ef\u89c6\u5316\uff0c\u53ef\u4ee5\u76f4\u63a5\u5904\u7406\u5730\u7406\u4fe1\u606f\u7cfb\u7edf\uff08GIS\uff09\u6570\u636e\uff0c\u5e2e\u52a9\u7528\u6237\u7ed8\u5236\u51fa\u66f4\u7cbe\u786e\u7684\u4e2d\u56fd\u5730\u56fe\u3002<\/p>\n
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