{"id":1110640,"date":"2025-01-08T17:21:36","date_gmt":"2025-01-08T09:21:36","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1110640.html"},"modified":"2025-01-08T17:21:38","modified_gmt":"2025-01-08T09:21:38","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e6%9e%84%e5%bb%ba%e5%9c%b0%e9%9c%87%e6%a1%88%e4%be%8b%e5%ba%93","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1110640.html","title":{"rendered":"\u5982\u4f55\u7528Python\u6784\u5efa\u5730\u9707\u6848\u4f8b\u5e93"},"content":{"rendered":"

\"\u5982\u4f55\u7528Python\u6784\u5efa\u5730\u9707\u6848\u4f8b\u5e93\"<\/p>\n

\u7528Python\u6784\u5efa\u5730\u9707\u6848\u4f8b\u5e93\u7684\u6b65\u9aa4\u5305\u62ec\uff1a\u9009\u62e9\u6570\u636e\u6e90\u3001\u6570\u636e\u91c7\u96c6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5b58\u50a8\u3001\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002<\/strong>\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u9009\u62e9\u4e00\u4e2a\u53ef\u9760\u7684\u5730\u9707\u6570\u636e\u6e90\uff0c\u6bd4\u5982USGS\u6216IRIS\uff0c\u7136\u540e\u7528Python\u811a\u672c\u91c7\u96c6\u8fd9\u4e9b\u6570\u636e\u3002\u6570\u636e\u91c7\u96c6\u540e\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u4e00\u81f4\u6027\u3002\u63a5\u4e0b\u6765\uff0c\u5c06\u6e05\u6d17\u540e\u7684\u6570\u636e\u5b58\u50a8\u5728\u6570\u636e\u5e93\u4e2d\uff0c\u5982MySQL\u3001SQLite\u6216MongoDB\u3002\u6700\u540e\uff0c\u901a\u8fc7\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u5de5\u5177\uff08\u5982Matplotlib\u3001Seaborn\uff09\u6765\u5206\u6790\u548c\u5c55\u793a\u5730\u9707\u6570\u636e\u7684\u8d8b\u52bf\u548c\u6a21\u5f0f\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u63cf\u8ff0\u8fd9\u4e9b\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u9009\u62e9\u6570\u636e\u6e90<\/h3>\n<\/p>\n

\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u6e90\u662f\u6784\u5efa\u5730\u9707\u6848\u4f8b\u5e93\u7684\u7b2c\u4e00\u6b65\u3002\u6211\u4eec\u9700\u8981\u627e\u5230\u53ef\u9760\u7684\u3001\u5305\u542b\u4e30\u5bcc\u5730\u9707\u4fe1\u606f\u7684\u6570\u636e\u6e90\u3002\u5e38\u89c1\u7684\u5730\u9707\u6570\u636e\u6e90\u5305\u62ec\uff1a<\/p>\n<\/p>\n

    \n
  1. USGS\uff08United States Geological Survey\uff09<\/strong>\uff1a\u63d0\u4f9b\u5168\u7403\u8303\u56f4\u7684\u5730\u9707\u6570\u636e\u3002<\/li>\n
  2. IRIS\uff08Incorporated Research Institutions for Seismology\uff09<\/strong>\uff1a\u63d0\u4f9b\u5730\u9707\u76d1\u6d4b\u548c\u6570\u636e\u670d\u52a1\u3002<\/li>\n<\/ol>\n

    \u8fd9\u4e9b\u6570\u636e\u6e90\u63d0\u4f9bAPI\u63a5\u53e3\uff0c\u65b9\u4fbf\u6211\u4eec\u7528Python\u811a\u672c\u8fdb\u884c\u6570\u636e\u91c7\u96c6\u3002<\/p>\n<\/p>\n

    \u4e8c\u3001\u6570\u636e\u91c7\u96c6<\/h3>\n<\/p>\n

    \u6570\u636e\u91c7\u96c6\u662f\u83b7\u53d6\u5730\u9707\u6570\u636e\u7684\u5173\u952e\u6b65\u9aa4\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u7684requests\u5e93\u6765\u53d1\u9001HTTP\u8bf7\u6c42\uff0c\u4ece\u6570\u636e\u6e90\u7684API\u63a5\u53e3\u83b7\u53d6\u5730\u9707\u6570\u636e\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u5c55\u793a\u5982\u4f55\u4eceUSGS\u83b7\u53d6\u5730\u9707\u6570\u636e\uff1a<\/p>\n<\/p>\n

    import requests<\/p>\n
    def fetch_earthquake_data(start_time, end_time, min_magnitude):<\/p>\n
        url = "https:\/\/earthquake.usgs.gov\/fdsnws\/event\/1\/query"<\/p>\n
        params = {<\/p>\n
            "format": "geojson",<\/p>\n
            "starttime": start_time,<\/p>\n
            "endtime": end_time,<\/p>\n
            "minmagnitude": min_magnitude<\/p>\n
        }<\/p>\n
        response = requests.get(url, params=params)<\/p>\n
        data = response.json()<\/p>\n
        return data<\/p>\n
    Example usage<\/strong><\/h2>\n
    start_time = "2023-01-01"<\/p>\n
    end_time = "2023-12-31"<\/p>\n
    min_magnitude = 5.0<\/p>\n
    earthquake_data = fetch_earthquake_data(start_time, end_time, min_magnitude)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e09\u3001\u6570\u636e\u6e05\u6d17<\/h3>\n<\/p>\n
    \u6570\u636e\u6e05\u6d17\u662f\u786e\u4fdd\u6570\u636e\u51c6\u786e\u6027\u548c\u4e00\u81f4\u6027\u7684\u5173\u952e\u6b65\u9aa4\u3002\u6211\u4eec\u9700\u8981\u5904\u7406\u7f3a\u5931\u503c\u3001\u91cd\u590d\u6570\u636e\u3001\u6570\u636e\u683c\u5f0f\u4e0d\u4e00\u81f4\u7b49\u95ee\u9898\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3002\u4ee5\u4e0b\u662f\u6570\u636e\u6e05\u6d17\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
    import pandas as pd<\/p>\n
    def clean_earthquake_data(raw_data):<\/p>\n
        df = pd.json_normalize(raw_data['features'])<\/p>\n
        df['time'] = pd.to_datetime(df['properties.time'], unit='ms')<\/p>\n
        df = df.rename(columns={<\/p>\n
            'properties.mag': 'magnitude',<\/p>\n
            'properties.place': 'location',<\/p>\n
            'geometry.coordinates': 'coordinates'<\/p>\n
        })<\/p>\n
        df = df[['time', 'magnitude', 'location', 'coordinates']]<\/p>\n
        df.dropna(inplace=True)  # Remove rows with missing values<\/p>\n
        df.drop_duplicates(inplace=True)  # Remove duplicate rows<\/p>\n
        return df<\/p>\n
    Example usage<\/strong><\/h2>\n
    cleaned_data = clean_earthquake_data(earthquake_data)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u56db\u3001\u6570\u636e\u5b58\u50a8<\/h3>\n<\/p>\n
    \u5c06\u6e05\u6d17\u540e\u7684\u6570\u636e\u5b58\u50a8\u5230\u6570\u636e\u5e93\u4e2d\uff0c\u53ef\u4ee5\u65b9\u4fbf\u540e\u7eed\u7684\u6570\u636e\u5206\u6790\u548c\u67e5\u8be2\u3002\u6211\u4eec\u53ef\u4ee5\u9009\u62e9\u5173\u7cfb\u578b\u6570\u636e\u5e93\uff08\u5982MySQL\u3001SQLite\uff09\u6216\u975e\u5173\u7cfb\u578b\u6570\u636e\u5e93\uff08\u5982MongoDB\uff09\u3002\u4e0b\u9762\u662f\u5c06\u6570\u636e\u5b58\u50a8\u5230SQLite\u6570\u636e\u5e93\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
    import sqlite3<\/p>\n
    def store_earthquake_data(df, db_name):<\/p>\n
        conn = sqlite3.connect(db_name)<\/p>\n
        df.to_sql('earthquakes', conn, if_exists='replace', index=False)<\/p>\n
        conn.close()<\/p>\n
    Example usage<\/strong><\/h2>\n
    db_name = "earthquake_data.db"<\/p>\n
    store_earthquake_data(cleaned_data, db_name)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e94\u3001\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
    \u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u6709\u52a9\u4e8e\u6211\u4eec\u7406\u89e3\u5730\u9707\u6570\u636e\u7684\u8d8b\u52bf\u548c\u6a21\u5f0f\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Matplotlib\u548cSeaborn\u5e93\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002\u4f8b\u5982\uff0c\u7ed8\u5236\u5730\u9707\u53d1\u751f\u65f6\u95f4\u548c\u9707\u7ea7\u7684\u6563\u70b9\u56fe\uff1a<\/p>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    import seaborn as sns<\/p>\n
    def plot_earthquake_data(df):<\/p>\n
        plt.figure(figsize=(10, 6))<\/p>\n
        sns.scatterplot(x='time', y='magnitude', data=df)<\/p>\n
        plt.title('Earthquake Magnitude Over Time')<\/p>\n
        plt.xlabel('Time')<\/p>\n
        plt.ylabel('Magnitude')<\/p>\n
        plt.show()<\/p>\n
    Example usage<\/strong><\/h2>\n
    plot_earthquake_data(cleaned_data)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u516d\u3001\u6269\u5c55\u548c\u4f18\u5316<\/h3>\n<\/p>\n
    \u5728\u6784\u5efa\u5730\u9707\u6848\u4f8b\u5e93\u7684\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u9700\u8981\u8fdb\u884c\u6269\u5c55\u548c\u4f18\u5316\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
      \n
    1. \u5b9a\u671f\u66f4\u65b0\u6570\u636e<\/strong>\uff1a\u7f16\u5199\u5b9a\u65f6\u4efb\u52a1\u811a\u672c\uff0c\u5b9a\u671f\u4ece\u6570\u636e\u6e90\u83b7\u53d6\u6700\u65b0\u7684\u5730\u9707\u6570\u636e\uff0c\u5e76\u66f4\u65b0\u6570\u636e\u5e93\u3002<\/li>\n
    2. \u6dfb\u52a0\u66f4\u591a\u6570\u636e\u6e90<\/strong>\uff1a\u96c6\u6210\u591a\u4e2a\u5730\u9707\u6570\u636e\u6e90\uff0c\u4ee5\u83b7\u53d6\u66f4\u5168\u9762\u7684\u6570\u636e\u3002<\/li>\n
    3. \u9ad8\u7ea7\u6570\u636e\u5206\u6790<\/strong>\uff1a\u4f7f\u7528\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5\u8fdb\u884c\u5730\u9707\u9884\u6d4b\u548c\u6a21\u5f0f\u8bc6\u522b\u3002<\/li>\n<\/ol>\n
      \u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
      \u7528Python\u6784\u5efa\u5730\u9707\u6848\u4f8b\u5e93\u9700\u8981\u7ecf\u8fc7\u9009\u62e9\u6570\u636e\u6e90\u3001\u6570\u636e\u91c7\u96c6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5b58\u50a8\u3001\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u7b49\u6b65\u9aa4\u3002\u901a\u8fc7\u8be6\u7ec6\u7684\u4ecb\u7ecd\u548c\u793a\u4f8b\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u7406\u89e3\u6bcf\u4e2a\u6b65\u9aa4\u7684\u5177\u4f53\u5b9e\u73b0\u65b9\u6cd5\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u5e2e\u52a9\u4f60\u6210\u529f\u6784\u5efa\u5730\u9707\u6848\u4f8b\u5e93\uff0c\u5e76\u4ece\u4e2d\u83b7\u5f97\u6709\u4ef7\u503c\u7684\u5730\u9707\u6570\u636e\u5206\u6790\u7ed3\u679c\u3002<\/p>\n<\/p>\n
      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
       \u5982\u4f55\u5f00\u59cb\u6784\u5efa\u4e00\u4e2a\u5730\u9707\u6848\u4f8b\u5e93\uff1f<\/strong>
      \u6784\u5efa\u5730\u9707\u6848\u4f8b\u5e93\u7684\u7b2c\u4e00\u6b65\u662f\u786e\u5b9a\u6570\u636e\u6e90\u3002\u53ef\u4ee5\u4f7f\u7528\u5168\u7403\u5730\u9707\u76d1\u6d4b\u673a\u6784\uff08\u5982USGS\u6216IRIS\uff09\u63d0\u4f9b\u7684API\u6765\u83b7\u53d6\u5b9e\u65f6\u5730\u9707\u6570\u636e\u3002\u63a5\u4e0b\u6765\uff0c\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u5e93\uff08\u5982SQLite\u3001PostgreSQL\u7b49\uff09\u6765\u5b58\u50a8\u5730\u9707\u4fe1\u606f\uff0c\u5e76\u8bbe\u8ba1\u6570\u636e\u5e93\u7ed3\u6784\u4ee5\u4fbf\u4e8e\u5b58\u50a8\u548c\u68c0\u7d22\u6570\u636e\u3002\u4f7f\u7528Python\u5e93\uff08\u5982requests\u548cpandas\uff09\u6765\u5904\u7406\u6570\u636e\u83b7\u53d6\u548c\u5b58\u50a8\u6d41\u7a0b\u3002<\/p>\n
      \u5728Python\u4e2d\u5982\u4f55\u5904\u7406\u5730\u9707\u6570\u636e\u7684\u6e05\u6d17\u548c\u9884\u5904\u7406\uff1f<\/strong>
      \u5904\u7406\u5730\u9707\u6570\u636e\u65f6\uff0c\u6e05\u6d17\u548c\u9884\u5904\u7406\u662f\u5173\u952e\u6b65\u9aa4\u3002\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u6765\u52a0\u8f7d\u548c\u5904\u7406\u6570\u636e\u3002\u5e38\u89c1\u7684\u6e05\u6d17\u64cd\u4f5c\u5305\u62ec\u53bb\u9664\u91cd\u590d\u503c\u3001\u586b\u8865\u7f3a\u5931\u6570\u636e\u548c\u8f6c\u6362\u6570\u636e\u7c7b\u578b\u3002\u8fd8\u53ef\u4ee5\u901a\u8fc7\u53ef\u89c6\u5316\u5de5\u5177\uff08\u5982Matplotlib\u548cSeaborn\uff09\u6765\u68c0\u6d4b\u5f02\u5e38\u503c\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u5b8c\u6574\u6027\u3002<\/p>\n
      \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u5730\u9707\u6848\u4f8b\u5e93\u7684\u53ef\u89c6\u5316\uff1f<\/strong>
      \u53ef\u89c6\u5316\u5730\u9707\u6848\u4f8b\u5e93\u7684\u6570\u636e\u53ef\u4ee5\u5e2e\u52a9\u66f4\u597d\u5730\u7406\u89e3\u5730\u9707\u5206\u5e03\u548c\u8d8b\u52bf\u3002\u53ef\u4ee5\u4f7f\u7528Matplotlib\u6216Plotly\u7b49\u5e93\u6765\u521b\u5efa\u56fe\u8868\u548c\u5730\u56fe\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u5730\u7406\u6563\u70b9\u56fe\u5c55\u793a\u5730\u9707\u53d1\u751f\u7684\u5730\u70b9\uff0c\u6216\u8005\u4f7f\u7528\u65f6\u95f4\u5e8f\u5217\u56fe\u5206\u6790\u5730\u9707\u9891\u7387\u7684\u53d8\u5316\u3002\u7ed3\u5408Folium\u5e93\uff0c\u53ef\u4ee5\u5728\u5730\u56fe\u4e0a\u6807\u8bb0\u5730\u9707\u4e8b\u4ef6\uff0c\u4ee5\u4fbf\u8fdb\u884c\u66f4\u76f4\u89c2\u7684\u5c55\u793a\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u7528Python\u6784\u5efa\u5730\u9707\u6848\u4f8b\u5e93\u7684\u6b65\u9aa4\u5305\u62ec\uff1a\u9009\u62e9\u6570\u636e\u6e90\u3001\u6570\u636e\u91c7\u96c6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5b58\u50a8\u3001\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002\u9996\u5148\uff0c\u6211\u4eec […]","protected":false},"author":3,"featured_media":1110646,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1110640"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1110640"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1110640\/revisions"}],"predecessor-version":[{"id":1110647,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1110640\/revisions\/1110647"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1110646"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1110640"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1110640"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1110640"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}