{"id":128,"date":"2026-08-06T00:31:58","date_gmt":"2026-08-05T16:31:58","guid":{"rendered":"https:\/\/ltrainyard.icu\/?p=128"},"modified":"2026-08-06T00:46:39","modified_gmt":"2026-08-05T16:46:39","slug":"day-30-%e7%bb%bc%e5%90%88%e9%a1%b9%e7%9b%ae%ef%bc%9a%e5%a4%a9%e6%b0%94%e6%95%b0%e6%8d%ae%e7%bb%bc%e5%90%88%e5%88%86%e6%9e%90-30-%e5%a4%a9%e5%a4%8d%e7%9b%98","status":"publish","type":"post","link":"https:\/\/ltrainyard.icu\/index.php\/2026\/08\/06\/day-30-%e7%bb%bc%e5%90%88%e9%a1%b9%e7%9b%ae%ef%bc%9a%e5%a4%a9%e6%b0%94%e6%95%b0%e6%8d%ae%e7%bb%bc%e5%90%88%e5%88%86%e6%9e%90-30-%e5%a4%a9%e5%a4%8d%e7%9b%98\/","title":{"rendered":"| Day 30 | \u7efc\u5408\u9879\u76ee\uff1a\u5929\u6c14\u6570\u636e\u7efc\u5408\u5206\u6790 + 30 \u5929\u590d\u76d8 |"},"content":{"rendered":"\n

1 \u9636\u6bb5\u77e5\u8bc6\u4e32\u8bb2 \u2014\u2014 \u628a Day 22~30 \u4e32\u6210\u4e00\u5f20\u7f51<\/h3>\n\n\n\n

\u662f\u4ec0\u4e48\uff1f\u4e3a\u4ec0\u4e48\u5b66\uff1f<\/strong><\/p>\n\n\n\n

\u4eca\u5929\u662f\u6536\u5b98\u65e5\uff0c\u4e0d\u5b66\u65b0\u77e5\u8bc6<\/strong>\u2014\u2014\u628a 30 \u5929\u5b66\u7684\u6240\u6709\u4e1c\u897f\u7ec4\u88c5\u6210\u4e00\u8f86\u80fd\u8dd1\u7684\u8f66<\/strong>\uff1a\u4ece\u7f51\u7edc\u62ff\u6570\u636e\uff08Day 26\/27\uff09\u3001\u7528\u8868\u683c\u5206\u6790\uff08Day 22\/23\uff09\u3001\u753b\u56fe\uff08Day 25\uff09\u3001\u5b58\u6570\u636e\u5e93\uff08Day 28\uff09\u3001\u505a\u6210\u7f51\u9875\uff08Day 29\uff09\u3002\u505a\u5b8c\u8fd9\u4e2a\u9879\u76ee\uff0c\u4f60\u5c31\u4ece”\u4f1a\u5199\u811a\u672c”\u5347\u7ea7\u6210”\u80fd\u72ec\u7acb\u4ea4\u4ed8\u4e00\u4e2a\u5c0f\u4ea7\u54c1”\u4e86\u3002\u5982\u679c\u4e2d\u95f4\u5361\u4f4f\uff0c\u56de\u5934\u7ffb\u5bf9\u5e94\u5929\u6570\u7684\u6587\u6863\uff0c\u8fd9\u672c\u8eab\u5c31\u662f\u590d\u76d8\u3002<\/p>\n\n\n\n

\u5e95\u5c42\u539f\u7406<\/strong><\/p>\n\n\n\n

\u4e00\u53e5\u8bdd\u4e32\u6cd5\uff1aNumPy \u6253\u5e95 \u2192 Pandas \u7ba1\u8868 \u2192 \u6570\u636e\u6e05\u6d17\u4fdd\u8d28\u91cf \u2192 matplotlib \u51fa\u56fe \u2192 \u722c\u866b\u53d6\u7f51\u9875\u6570\u636e \u2192 API \u8c03\u670d\u52a1\u6570\u636e \u2192 SQLite \u843d\u5730\u5b58\u50a8 \u2192 Flask \u5c55\u793a\u6210\u7f51\u9875<\/strong>\u3002\u9636\u6bb5\u77e5\u8bc6\u5730\u56fe\uff1a<\/p>\n\n\n\n

Day 22  NumPy\uff08\u6570\u7ec4\u4e0e\u5411\u91cf\u5316\u8fd0\u7b97\uff09\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\nDay 23  Pandas\uff08DataFrame \u8868\u683c\u5206\u6790\uff09\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\nDay 24  \u6570\u636e\u6e05\u6d17\uff08\u7f3a\u5931\u503c\/\u91cd\u590d\u503c\/\u7c7b\u578b\u8f6c\u6362\uff09\u2500\u2500\u2500\u2500\u2500\u2500\u2524\nDay 25  matplotlib\uff08\u6298\u7ebf\/\u67f1\u72b6\/\u6563\u70b9\/\u997c\u56fe\uff09\u2500\u2500\u2500\u2500\u2500\u2500\u2524\nDay 26  \u722c\u866b\uff08requests + BeautifulSoup\uff09\u2500\u2500\u2500\u2500\u2500\u2500\u2524\nDay 27  Web API\uff08GET\/POST\/JSON \u8c03\u7528\uff09\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\nDay 28  SQLite\uff08\u5efa\u8868\/CRUD\/\u4e8b\u52a1\/\u9632\u6ce8\u5165\uff09\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\nDay 29  Flask\uff08\u8def\u7531\/\u6a21\u677f\/\u8868\u5355\/JSON \u63a5\u53e3\uff09\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n                                        \u2514\u2500\u2500\u2192 \u7efc\u5408\u9879\u76ee\uff1a\u5929\u6c14\u6570\u636e\u7efc\u5408\u5206\u6790\n<\/code><\/pre>\n\n\n\n
\u5929\u6c14\u9879\u76ee\u628a\u8fd9\u516b\u6837\u6309\u6d41\u6c34\u7ebf\u987a\u5e8f\u7ec4\u88c5\uff1aAPI \u62ff\u6570\u636e\uff08Day 27\uff09\u2192 Pandas \u5206\u6790\uff08Day 23\/24\uff09\u2192 \u753b\u56fe\uff08Day 25\uff09\u2192 \u5165\u5e93\uff08Day 28\uff09\u2192 \u7f51\u9875\u5c55\u793a\uff08Day 29\uff09\u2014\u2014\u6bcf\u4e00\u6b65\u90fd\u5728\u7528\u524d\u9762\u67d0\u5929\u7684\u77e5\u8bc6\uff0c\u7f3a\u4e00\u73af\u6574\u6761\u94fe\u5c31\u65ad<\/strong>\u3002<\/p>\n\n\n\n
\u751f\u6d3b\u7c7b\u6bd4<\/strong><\/p>\n\n\n\n
\u50cf\u505a\u4e00\u9053\u5b8c\u6574\u7684\u83dc<\/strong>\uff1a\u4e70\u83dc\uff08API \u53d6\u6570\u636e\uff09\u2192 \u6d17\u83dc\u5207\u83dc\uff08Pandas \u5206\u6790\uff09\u2192 \u6446\u76d8\uff08matplotlib \u753b\u56fe\uff09\u2192 \u653e\u51b0\u7bb1\u51b7\u85cf\uff08SQLite \u5b58\u50a8\uff09\u2192 \u7aef\u4e0a\u684c\uff08Flask \u5c55\u793a\uff09\u3002\u5c11\u4e86\u4efb\u4f55\u4e00\u73af\uff0c\u8fd9\u9053\u83dc\u90fd\u7aef\u4e0d\u4e0a\u6765\u3002<\/p>\n\n\n\n
\u6ce8\u610f\u4e8b\u9879\u603b\u7ed3<\/strong><\/p>\n\n\n\n
\u6bcf\u4e2a\u77e5\u8bc6\u70b9\u5728\u672c\u9879\u76ee\u91cc\u5e72\u4ec0\u4e48\uff1a<\/p>\n\n\n\n
    \n
  1. **NumPy\uff08Day 22\uff09\u2014\u2014\u6570\u503c\u8ba1\u7b97\u7684”\u5f15\u64ce”**\uff1aPandas \u7684\u5e95\u5c42\u5c31\u662f NumPy\u2014\u2014df[\"tmax\"]<\/code> \u53d6\u51fa\u7684\u6bcf\u4e00\u5217\u672c\u8d28\u662f NumPy \u6570\u7ec4\uff1bdf[\"tmax\"].mean()<\/code>\u3001idxmax()<\/code> \u8fd9\u4e9b\u7edf\u8ba1\u80cc\u540e\u90fd\u662f NumPy \u5728\u7b97\u3002\u4f60\u76f4\u63a5\u5199 NumPy \u7684\u5730\u65b9\u4e0d\u591a\uff0c\u4f46\u5b83\u652f\u6491\u7740\u4e0a\u9762\u6240\u6709\u7684\u5206\u6790\u3002<\/li>\n\n\n\n
  2. Pandas\uff08Day 23\uff09\u2014\u2014\u5206\u6790\u7684\u4e3b\u6218\u573a<\/strong>\uff1apd.DataFrame(data)<\/code> \u628a\u5929\u6c14\u6570\u636e\u88c5\u8fdb\u8868\u683c\uff1bdf.shape<\/code> \u770b\u5f62\u72b6\u3001df.head(3)<\/code> \u770b\u6837\u4f8b\u3001describe()<\/code> \u51fa\u7edf\u8ba1\u3001df.loc[df[\"tmax\"].idxmax()]<\/code> \u627e\u6700\u70ed\u4e00\u5929\u3001nlargest(3, \"rain\")<\/code> \u627e\u964d\u96e8\u524d\u4e09\u3002<\/li>\n\n\n\n
  3. \u6570\u636e\u6e05\u6d17\uff08Day 24\uff09\u2014\u2014\u4fdd\u8bc1\u6570\u636e\u8d28\u91cf<\/strong>\uff1aresp.raise_for_status()<\/code> \u62e6\u622a\u5931\u8d25\u8bf7\u6c42\u3001describe()<\/code> \u51fd\u6570\u7ffb\u8bd1\u5929\u6c14\u4ee3\u7801\u3001INSERT OR IGNORE<\/code> \u53bb\u91cd\u3002\u771f\u5b9e\u9879\u76ee\u91cc 80% \u7684\u65f6\u95f4\u5728\u6e05\u6d17\u6570\u636e<\/strong>\u3002<\/li>\n\n\n\n
  4. matplotlib\uff08Day 25\uff09\u2014\u2014\u628a\u6570\u5b57\u53d8\u6210\u56fe\u5f62<\/strong>\uff1acharts.py<\/code> \u751f\u6210\u6e29\u5ea6\u6298\u7ebf\u56fe\u548c\u964d\u6c34\u67f1\u72b6\u56fe\uff1bplt.xticks(rotation=30)<\/code> \u9632\u65e5\u671f\u91cd\u53e0\u3001tight_layout<\/code> \u9632\u88c1\u5207\u3001savefig<\/code> \u5b58 PNG\u3001close<\/code> \u91ca\u653e\u5185\u5b58\u3002<\/li>\n\n\n\n
  5. \u722c\u866b\uff08Day 26\uff09\u2014\u2014\u4ece\u7f51\u9875\u62ff\u6570\u636e<\/strong>\uff1a\u9879\u76ee\u7528\u5b98\u65b9 API\uff0c\u4f46\u5408\u89c4\u610f\u8bc6\u548c\u9519\u8bef\u5904\u7406\u4ecd\u7136\u9002\u7528\u2014\u2014timeout=10<\/code> \u9632\u5361\u6b7b\u3001raise_for_status()<\/code> \u68c0\u67e5\u72b6\u6001\u3001\u4e0d\u9891\u7e41\u8bf7\u6c42\u3002<\/li>\n\n\n\n
  6. Web API\uff08Day 27\uff09\u2014\u2014\u548c\u670d\u52a1\u5668\u5bf9\u8bdd<\/strong>\uff1a**\u8fd9\u662f\u9879\u76ee\u7684”\u6c34\u6e90”**\u2014\u2014weather_data.py<\/code> \u7528 requests.get<\/code> + params<\/code> \u53d1\u8bf7\u6c42\uff0cresp.json()[\"daily\"]<\/code> \u53d6\u51fa\u6570\u636e\uff0czip<\/code> \u6253\u5305\u6210\u5217\u8868\u3002\u6ca1\u6709\u8fd9\u4e00\u6b65\uff0c\u540e\u9762\u5168\u90e8\u662f\u7a7a\u8c08\u3002<\/li>\n\n\n\n
  7. SQLite\uff08Day 28\uff09\u2014\u2014\u6570\u636e\u7684”\u4ed3\u5e93”\uff1adate<\/code> \u505a\u4e3b\u952e\u3001INSERT OR IGNORE<\/code> \u4fdd\u8bc1\u540c\u4e00\u65e5\u671f\u53ea\u5b58\u4e00\u6b21\uff08<\/strong>\u5e42\u7b49<\/strong>\uff1a\u8dd1\u4e24\u6b21\u7b2c\u4e8c\u6b21\u65b0\u589e 0 \u6761\uff09\uff1bload_weather()<\/code> \u8bfb\u56de\u6570\u636e\u4f9b\u7f51\u9875\u5c55\u793a\uff1b\u5168\u7a0b\u53c2\u6570\u5316\u67e5\u8be2\u3002<\/li>\n\n\n\n
  8. Flask\uff08Day 29\uff09\u2014\u2014\u628a\u6210\u679c\u5c55\u793a\u51fa\u53bb<\/strong>\uff1a\/<\/code> \u8def\u7531\u8dd1\u4e00\u904d\u5b8c\u6574\u6d41\u6c34\u7ebf\uff0c\u628a\u7edf\u8ba1\u7ed3\u679c\u4f20\u7ed9 report.html<\/code> \u6e32\u67d3\uff1bsummary[\"table\"]<\/code> \u7528 {% for %}<\/code> \u5faa\u73af\u6e32\u67d3\u6210\u8868\u683c\uff1b\u56fe\u7247\u4ece \/static\/<\/code> \u63d0\u4f9b\u3002<\/li>\n<\/ol>\n\n\n\n
    \u9879\u76ee\u62c6\u89e3\uff1a\u4e94\u6b65\u6d41\u6c34\u7ebf \u2192 \u77e5\u8bc6\u70b9\u5bf9\u7167<\/strong><\/p>\n\n\n\n
    \u73af\u8282<\/th>\u6587\u4ef6<\/th>\u7528\u5230\u7684\u77e5\u8bc6\u70b9<\/th>\u6765\u81ea\u54ea\u51e0\u5929<\/th>\u5728\u672c\u9879\u76ee\u4e2d\u7684\u4f5c\u7528<\/th><\/tr><\/thead>
    1. \u6570\u636e\u83b7\u53d6<\/td>weather_data.py<\/code><\/td>requests\u3001params\u3001JSON \u89e3\u6790\u3001\u72b6\u6001\u7801\u3001\u51fd\u6570<\/td>Day 27 \/ 8 \/ 13<\/td>\u4ece Open-Meteo \u62ff\u5230\u5317\u4eac 7 \u5929\u5929\u6c14\uff0c\u7ffb\u8bd1\u5929\u6c14\u4ee3\u7801<\/td><\/tr>
    2. \u6570\u636e\u5206\u6790<\/td>analysis.py<\/code><\/td>DataFrame\u3001describe\u3001loc\u3001idxmax\u3001nlargest<\/td>Day 23 \/ 22<\/td>\u7edf\u8ba1\u3001\u627e\u6700\u70ed\u4e00\u5929\u3001\u964d\u96e8\u524d\u4e09\u3001\u5e73\u5747\u6e29\u5ea6<\/td><\/tr>
    3. \u53ef\u89c6\u5316<\/td>charts.py<\/code><\/td>plot\u3001bar\u3001savefig\u3001close\u3001\u4e2d\u6587\u5b57\u4f53<\/td>Day 25<\/td>\u751f\u6210\u6e29\u5ea6\u6298\u7ebf\u56fe\u548c\u964d\u6c34\u67f1\u72b6\u56fe PNG<\/td><\/tr>
    4. \u6570\u636e\u5b58\u50a8<\/td>database.py<\/code><\/td>\u5efa\u8868\u3001INSERT OR IGNORE\u3001\u4e3b\u952e\u3001\u53c2\u6570\u5316\u67e5\u8be2\u3001\u4e8b\u52a1<\/td>Day 28<\/td>\u6570\u636e\u5165\u5e93\u4e14\u540c\u65e5\u671f\u53bb\u91cd\uff08\u5e42\u7b49\uff09\uff0c\u53ef\u53cd\u590d\u8fd0\u884c<\/td><\/tr>
    5. \u7f51\u9875\u5c55\u793a<\/td>app.py<\/code> + report.html<\/code><\/td>\u8def\u7531\u3001render_template\u3001Jinja2 \u5faa\u73af\u3001static<\/td>Day 29 \/ 18<\/td>\u628a\u5206\u6790\u7ed3\u679c + \u56fe\u8868\u6e32\u67d3\u6210\u6d4f\u89c8\u5668\u62a5\u544a\u9875<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    \n
    \u89c2\u5bdf\u8fd9\u5f20\u8868\uff1a\u9879\u76ee\u7684\u300c\u6570\u636e\u80fd\u529b\u300d\u6765\u81ea Day 22~28\uff0c\u9879\u76ee\u7684\u300c\u51fa\u53e3\u300d\u6765\u81ea Day 29\u3002\u80fd\u8bb2\u6e05\u8fd9\u5f20\u8868\uff0c\u4f60\u5c31\u771f\u6b63\u5b66\u4f1a\u4e86\u8fd9 30 \u5929<\/strong>\u3002<\/p>\n<\/blockquote>\n\n\n\n
    \u6613\u9519\u70b9<\/strong><\/p>\n\n\n\n
      \n
    • \u9519\u8bef\u5199\u6cd5\uff1a\u4e00\u53e3\u6c14\u5199\u5b8c\u4e94\u4e2a\u6587\u4ef6\u518d\u8fd0\u884c \u2192 \u95ee\u9898\uff1a\u51fa\u95ee\u9898\u8303\u56f4\u5927\u3001\u96be\u5b9a\u4f4d \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u6bcf\u5b8c\u6210\u4e00\u4e2a\u73af\u8282\u5c31\u8fd0\u884c\u4e00\u6b21\u3001\u9a8c\u8bc1\u4e00\u6b21<\/strong>\uff08\u5c0f\u6b65\u8fed\u4ee3\uff09\u3002<\/li>\n\n\n\n
    • \u9519\u8bef\u5199\u6cd5\uff1a\u627e\u6700\u5927\u503c\u7528 df[df[\"tmax\"].max()]<\/code> \u2192 \u95ee\u9898\uff1a\u62ff”\u6700\u5927\u503c”\u53bb\u548c\u6574\u5217\u6bd4\u8f83\uff0c\u6761\u4ef6\u6c38\u8fdc\u4e0d\u6210\u7acb\uff0c\u8fd4\u56de\u7a7a \u2192 \u6b63\u786e\u5199\u6cd5\uff1adf.loc[df[\"tmax\"].idxmax()]<\/code>\uff08idxmax \u627e\u884c\u53f7 + loc \u53d6\u6574\u884c\uff09\u3002<\/li>\n\n\n\n
    • \u9519\u8bef\u5199\u6cd5\uff1a\u6bcf\u5237\u65b0\u4e00\u6b21\u9875\u9762\u5c31\u8bf7\u6c42\u4e00\u6b21\u5916\u90e8 API \u2192 \u95ee\u9898\uff1a\u6162\u3001\u8d39\u6d41\u91cf\u3001\u6709\u9891\u7387\u9650\u5236\uff0c\u53ef\u80fd 502 \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u4f18\u5148\u4ece\u6570\u636e\u5e93\u8bfb\uff0c\u6ca1\u6709\u6570\u636e\u624d\u8bf7\u6c42 API \u5e76\u5165\u5e93\uff08\u7f13\u5b58\u601d\u8def\uff09\u3002<\/li>\n<\/ul>\n\n\n\n
      \u8bb0\u5fc6\u53e3\u8bc0<\/strong>\uff1a\u53d6\u6570\u5206\u6790\u753b\u51fa\u56fe\uff0c\u5165\u5e93\u5c55\u793a\u4e94\u6b65\u8d70\uff1b\u4e00\u73af\u6263\u4e00\u73af\uff0c\u7f3a\u4e00\u5c31\u65ad\u94fe\u3002<\/p>\n\n\n\n
      2 \u7b2c 1 \u6b65\uff1a\u6570\u636e\u83b7\u53d6\uff08weather_data.py\uff09<\/h3>\n\n\n\n
      \u662f\u4ec0\u4e48\uff1f\u4e3a\u4ec0\u4e48\u5b66\uff1f<\/strong><\/p>\n\n\n\n
      \u7528 Open-Meteo \u62ff\u5317\u4eac 7 \u5929\u7684\u6700\u9ad8\u6e29\u3001\u6700\u4f4e\u6e29\u3001\u964d\u6c34\u3001\u5929\u6c14\u4ee3\u7801\uff0c\u8fd4\u56de\u6210\u5217\u8868\u3002\u8fd9\u662f\u9879\u76ee\u7684”\u6c34\u6e90”\u2014\u2014\u6ca1\u6709\u8fd9\u4e00\u6b65\uff0c\u540e\u9762\u5168\u90e8\u662f\u7a7a\u8c08\u3002<\/p>\n\n\n\n
      \u5e95\u5c42\u539f\u7406<\/strong><\/p>\n\n\n\n
      requests.get<\/code> + params<\/code> \u628a\u7ecf\u7eac\u5ea6\u3001\u5929\u6570\u3001\u9700\u8981\u7684\u6c14\u8c61\u5b57\u6bb5\u53d1\u7ed9 Open-Meteo \u5b98\u65b9 API\uff1bresp.raise_for_status()<\/code> \u62e6\u622a\u5931\u8d25\u8bf7\u6c42\uff1bresp.json()[\"daily\"]<\/code> \u53d6\u51fa 7 \u5929\u7684\u6e29\u5ea6\u3001\u964d\u6c34\u3001\u5929\u6c14\u4ee3\u7801\uff1bzip<\/code> \u628a\u591a\u4e2a\u5217\u8868\u6253\u5305\u6210\u4e00\u6761\u6761\u8bb0\u5f55\u3002<\/p>\n\n\n\n
      \u4ee3\u7801\u6f14\u793a<\/strong><\/p>\n\n\n\n
      # weather_data.py \u2014\u2014 \u4ece Open-Meteo \u83b7\u53d6\u5317\u4eac 7 \u5929\u5929\u6c14\nimport requests\n\n# \u5317\u4eac\u7ecf\u7eac\u5ea6\nBEIJING = (39.9042, 116.4074)\n\ndef fetch_weather(days=7):\n    \"\"\"\u8fd4\u56de [{date, tmax, tmin, rain, code}, ...] \u5217\u8868\"\"\"\n    params = {\n        \"latitude\": BEIJING[0],\n        \"longitude\": BEIJING[1],\n        \"daily\": (\"temperature_2m_max,temperature_2m_min,\"\n                  \"precipitation_sum,weather_code\"),\n        \"timezone\": \"Asia\/Shanghai\",\n        \"forecast_days\": days,\n    }\n    resp = requests.get(\"https:\/\/api.open-meteo.com\/v1\/forecast\",\n                        params=params, timeout=10)\n    resp.raise_for_status()\n    daily = resp.json()[\"daily\"]\n\n    # weather_code\uff1a0 \u6674\uff0c1~3 \u591a\u4e91\/\u9634\uff0c51+ \u96e8\uff0c71+ \u96ea\uff08Open-Meteo \u5b98\u65b9\u7f16\u7801\uff09\n    def describe(code):\n        if code == 0:\n            return \"\u6674\"\n        if code in (1, 2, 3):\n            return \"\u591a\u4e91\"\n        if 51 <= code <= 67:\n            return \"\u96e8\"\n        if 71 <= code <= 86:\n            return \"\u96ea\"\n        return \"\u5176\u5b83\"\n\n    result = []\n    for d, tmax, tmin, rain, code in zip(\n            daily[\"time\"], daily[\"temperature_2m_max\"],\n            daily[\"temperature_2m_min\"], daily[\"precipitation_sum\"],\n            daily[\"weather_code\"]):\n        result.append({\n            \"date\": d,\n            \"tmax\": tmax,\n            \"tmin\": tmin,\n            \"rain\": rain,\n            \"weather\": describe(code),\n        })\n    return result\n\nif __name__ == \"__main__\":\n    data = fetch_weather()\n    for item in data:\n        print(item)\n<\/code><\/pre>\n\n\n\n
      \u8fd0\u884c\u7ed3\u679c\uff08\u793a\u4f8b\uff0c\u6570\u636e\u968f\u5f53\u5929\u53d8\u5316\uff09\uff1a<\/p>\n\n\n\n
      {'date': '2026-08-04', 'tmax': 34.1, 'tmin': 25.5, 'rain': 0.4, 'weather': '\u96e8'}\n{'date': '2026-08-05', 'tmax': 37.2, 'tmin': 25.6, 'rain': 0.0, 'weather': '\u591a\u4e91'}\n...\n<\/code><\/pre>\n\n\n\n
      \u6ce8\u610f\u4e8b\u9879\u603b\u7ed3<\/strong>\uff1aweather_code<\/code> \u662f Open-Meteo \u5b98\u65b9\u5929\u6c14\u4ee3\u7801\uff0c\u5728 describe<\/code> \u51fd\u6570\u91cc\u7ffb\u8bd1\u6210\u4e2d\u6587\u2014\u2014\u6570\u636e\u5728\u6e90\u5934\u6e05\u7406\u597d\uff0c\u4e0b\u6e38\u5168\u90fd\u7701\u4e8b<\/strong>\uff08Day 24 \u6570\u636e\u6e05\u6d17\u7684\u6838\u5fc3\u601d\u8def + Day 15 \u5355\u4e00\u804c\u8d23\uff09\u3002\u8fd0\u884c\u6210\u529f\u8bf4\u660e\u7b2c 1 \u6b65\u5b8c\u6210\u3002<\/p>\n\n\n\n
      \u6613\u9519\u70b9<\/strong><\/p>\n\n\n\n
        \n
      • \u9519\u8bef\u5199\u6cd5\uff1a\u4e0d\u8bbe timeout<\/code> \u2192 \u95ee\u9898\uff1a\u7f51\u7edc\u5361\u4f4f\u65f6\u7a0b\u5e8f\u65e0\u9650\u7b49\u5f85 \u2192 \u6b63\u786e\u5199\u6cd5\uff1arequests.get(..., timeout=10)<\/code>\u3002<\/li>\n\n\n\n
      • \u9519\u8bef\u5199\u6cd5\uff1a\u4e0d\u68c0\u67e5\u72b6\u6001\u7801\u76f4\u63a5 resp.json()<\/code> \u2192 \u95ee\u9898\uff1a\u8bf7\u6c42\u5931\u8d25\u65f6\u89e3\u6790\u62a5\u9519 \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u5148 resp.raise_for_status()<\/code>\u3002<\/li>\n\n\n\n
      • \u9519\u8bef\u5199\u6cd5\uff1a\u5929\u6c14\u4ee3\u7801\u4e0d\u7ffb\u8bd1\u76f4\u63a5\u5b58 \u2192 \u95ee\u9898\uff1a\u4e0b\u6e38\u6bcf\u4e2a\u73af\u8282\u90fd\u8981\u81ea\u5df1\u7ffb\u8bd1\u4e00\u904d \u2192 \u6b63\u786e\u5199\u6cd5\uff1afetch_weather<\/code> \u8fd4\u56de\u65f6\u5df2\u5e26\u4e2d\u6587 weather<\/code> \u5b57\u6bb5\u3002<\/li>\n<\/ul>\n\n\n\n
        \u8bb0\u5fc6\u53e3\u8bc0<\/strong>\uff1a\u6e90\u5934\u5148\u628a\u6570\u636e\u6e05\uff0c\u4e0b\u6e38\u5168\u90fd\u7701\u529b\u6c14\uff1b\u4ee3\u7801\u7ffb\u8bd1\u653e\u4e00\u5904\uff0c\u5904\u5904\u90fd\u4e0d\u7528\u518d\u8bd1\u3002<\/p>\n\n\n\n
        3 \u7b2c 2 \u6b65\uff1a\u6570\u636e\u5206\u6790\uff08analysis.py\uff09<\/h3>\n\n\n\n
        \u662f\u4ec0\u4e48\uff1f\u4e3a\u4ec0\u4e48\u5b66\uff1f<\/strong><\/p>\n\n\n\n
        \u628a\u6570\u636e\u653e\u8fdb Pandas DataFrame\uff08Day 22\/23 \u5b66\u7684\uff09\uff0c\u505a\u7edf\u8ba1\u3001\u6392\u5e8f\u3001\u7b5b\u9009\u3002\u8fd9\u662f\u9879\u76ee\u7684”\u5206\u6790\u5927\u8111”\u3002<\/p>\n\n\n\n
        \u4ee3\u7801\u6f14\u793a<\/strong><\/p>\n\n\n\n
        # analysis.py \u2014\u2014 \u7528 Pandas \u5206\u6790\u5929\u6c14\u6570\u636e\nimport pandas as pd\nfrom weather_data import fetch_weather\n\ndef analyze(data):\n    \"\"\"\u628a\u5929\u6c14\u6570\u636e\u8f6c\u6210 DataFrame \u5e76\u505a\u7edf\u8ba1\"\"\"\n    df = pd.DataFrame(data)\n    print(\"\u6570\u636e\u5f62\u72b6\uff1a\", df.shape)\n    print(\"\\n\u524d 3 \u884c\uff1a\")\n    print(df.head(3))\n\n    print(\"\\n\u57fa\u672c\u7edf\u8ba1\uff1a\")\n    print(df[[\"tmax\", \"tmin\", \"rain\"]].describe())\n\n    print(\"\\n\u6700\u70ed\u7684\u4e00\u5929\uff1a\")\n    hottest = df.loc[df[\"tmax\"].idxmax()]\n    print(f\"{hottest['date']}\uff0c\u6700\u9ad8 {hottest['tmax']}\u2103\")\n\n    print(\"\\n\u964d\u96e8\u6700\u591a\u7684\u4e09\u5929\uff1a\")\n    print(df.nlargest(3, \"rain\")[[\"date\", \"rain\"]])\n\n    print(\"\\n\u5e73\u5747\u6e29\u5ea6\uff08\u6444\u6c0f\u5ea6\uff0c\u4fdd\u7559 1 \u4f4d\uff09\uff1a\")\n    avg = (df[\"tmax\"] + df[\"tmin\"]) \/ 2\n    print(round(avg.mean(), 1))\n\n    return df\n\nif __name__ == \"__main__\":\n    df = analyze(fetch_weather())\n<\/code><\/pre>\n\n\n\n
        \u8fd0\u884c\u7ed3\u679c\uff08\u793a\u4f8b\uff0c\u6570\u636e\u968f\u5f53\u5929\u53d8\u5316\uff09\uff1a<\/p>\n\n\n\n
        \u6570\u636e\u5f62\u72b6\uff1a (7, 5)\n\n\u524d 3 \u884c\uff1a\n         date  tmax  tmin  rain weather\n0  2026-08-04  34.1  25.5   0.4       \u96e8\n1  2026-08-05  37.2  25.6   0.0      \u591a\u4e91\n2  2026-08-06  39.3  27.6   0.0      \u591a\u4e91\n...\n\u6700\u70ed\u7684\u4e00\u5929\uff1a\n2026-08-06\uff0c\u6700\u9ad8 39.3\u2103\n<\/code><\/pre>\n\n\n\n
        \u6ce8\u610f\u4e8b\u9879\u603b\u7ed3<\/strong>\uff1adf.loc[df[\"tmax\"].idxmax()]<\/code> \u662f\u5148\u627e\u6700\u5927\u503c\u7684\u884c\u53f7\uff08idxmax<\/code>\uff09\uff0c\u518d\u7528 loc<\/code> \u53d6\u90a3\u4e00\u884c\u2014\u2014Pandas \u91cc\u6700\u5e38\u7528\u7684”\u627e\u6781\u503c”\u5957\u8def<\/strong>\u3002describe()<\/code> \u51fa\u7edf\u8ba1\u3001nlargest(3, \"rain\")<\/code> \u627e\u964d\u96e8\u524d\u4e09\u3001(df[\"tmax\"] + df[\"tmin\"]) \/ 2<\/code> \u662f\u5411\u91cf\u5316\u8ba1\u7b97\u5e73\u5747\u6e29\u5ea6\u3002<\/p>\n\n\n\n
        \u6613\u9519\u70b9<\/strong><\/p>\n\n\n\n
          \n
        • \u9519\u8bef\u5199\u6cd5\uff1adf[df[\"tmax\"].max()]<\/code> \u2192 \u95ee\u9898\uff1a\u62ff”\u6700\u5927\u503c 39.3″\u548c\u6574\u5217\u6bd4\u8f83\uff0c\u6ca1\u6709\u4efb\u4f55\u4e00\u884c”\u7b49\u4e8e”\u6700\u5927\u503c\u672c\u8eab\uff0c\u8fd4\u56de\u7a7a \u2192 \u6b63\u786e\u5199\u6cd5\uff1adf.loc[df[\"tmax\"].idxmax()]<\/code>\uff08\u5148\u627e\u4f4d\u7f6e\u518d\u53d6\u884c\uff09\u3002<\/li>\n\n\n\n
        • \u9519\u8bef\u5199\u6cd5\uff1a\u5fd8\u4e86 round(avg.mean(), 1)<\/code> \u2192 \u95ee\u9898\uff1a\u5e73\u5747\u6e29\u5ea6\u4e00\u957f\u4e32\u5c0f\u6570 \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u4fdd\u7559 1 \u4f4d\u5c0f\u6570\u5c55\u793a\u3002<\/li>\n\n\n\n
        • \u9519\u8bef\u5199\u6cd5\uff1a\u76f4\u63a5\u5728 print<\/code> \u91cc\u53cd\u590d\u7b97 df[\"tmax\"].mean()<\/code> \u2192 \u95ee\u9898\uff1a\u91cd\u590d\u8ba1\u7b97\u3001\u4ee3\u7801\u5570\u55e6 \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u5148\u7528 avg = (df[\"tmax\"] + df[\"tmin\"]) \/ 2<\/code> \u5b58\u53d8\u91cf\u518d\u7528\u3002<\/li>\n<\/ul>\n\n\n\n
          \u8bb0\u5fc6\u53e3\u8bc0<\/strong>\uff1a\u627e\u6781\u503c\u5148 idxmax \u5b9a\u4f4d\uff0c\u518d\u7528 loc \u53d6\u6574\u884c\uff1bPandas \u5957\u8def\u8bb0\u5fc3\u5934\u3002<\/p>\n\n\n\n
          4 \u7b2c 3 \u6b65\uff1a\u53ef\u89c6\u5316\uff08charts.py\uff09<\/h3>\n\n\n\n
          \u662f\u4ec0\u4e48\uff1f\u4e3a\u4ec0\u4e48\u5b66\uff1f<\/strong><\/p>\n\n\n\n
          \u7528 matplotlib \u751f\u6210\u4e24\u5f20\u56fe\uff1a\u6e29\u5ea6\u6298\u7ebf\u56fe\u3001\u964d\u6c34\u67f1\u72b6\u56fe\uff0c\u4fdd\u5b58\u6210 PNG \u4f9b\u7f51\u9875\u5c55\u793a\u3002\u56fe\u662f\u7ed9”\u4eba”\u770b\u7684\uff0c\u8fd9\u4e00\u6b65\u8ba9\u9879\u76ee\u6709\u4e86”\u95e8\u9762”\u3002<\/p>\n\n\n\n
          \u4ee3\u7801\u6f14\u793a<\/strong><\/p>\n\n\n\n
          # charts.py \u2014\u2014 \u751f\u6210\u5929\u6c14\u56fe\u8868\nimport matplotlib.pyplot as plt\n\n# \u4e2d\u6587\u5b57\u4f53\u8bbe\u7f6e\uff08\u753b\u56fe\u811a\u672c\u5fc5\u5907\uff0c\u89c1 Day 25\uff09\nplt.rcParams[\"font.sans-serif\"] = [\"SimHei\", \"Microsoft YaHei\"]\nplt.rcParams[\"axes.unicode_minus\"] = False\n\ndef make_charts(df):\n    \"\"\"\u6839\u636e DataFrame \u751f\u6210\u4e24\u5f20\u56fe\uff1atemperature.png \u548c rain.png\"\"\"\n    dates = df[\"date\"].tolist()\n\n    # \u56fe 1\uff1a\u6700\u9ad8\/\u6700\u4f4e\u6e29\u6298\u7ebf\u56fe\n    plt.figure(figsize=(8, 4))\n    plt.plot(dates, df[\"tmax\"], marker=\"o\", label=\"\u6700\u9ad8\u6e29\")\n    plt.plot(dates, df[\"tmin\"], marker=\"s\", label=\"\u6700\u4f4e\u6e29\")\n    plt.title(\"\u5317\u4eac\u672a\u6765 7 \u5929\u6e29\u5ea6\u8d70\u52bf\")\n    plt.xlabel(\"\u65e5\u671f\")\n    plt.ylabel(\"\u6e29\u5ea6\uff08\u2103\uff09\")\n    plt.legend()\n    plt.grid(True, linestyle=\"--\", alpha=0.5)\n    plt.xticks(rotation=30)      # \u65e5\u671f\u659c\u7740\u663e\u793a\uff0c\u907f\u514d\u91cd\u53e0\n    plt.tight_layout()\n    plt.savefig(\"temperature.png\", dpi=150)\n    plt.close()                  # \u5173\u95ed\u753b\u5e03\uff0c\u91ca\u653e\u5185\u5b58\n\n    # \u56fe 2\uff1a\u964d\u6c34\u91cf\u67f1\u72b6\u56fe\n    plt.figure(figsize=(8, 4))\n    plt.bar(dates, df[\"rain\"], color=\"steelblue\")\n    plt.title(\"\u5317\u4eac\u672a\u6765 7 \u5929\u964d\u6c34\u91cf\")\n    plt.xlabel(\"\u65e5\u671f\")\n    plt.ylabel(\"\u964d\u6c34\u91cf\uff08mm\uff09\")\n    plt.xticks(rotation=30)\n    plt.tight_layout()\n    plt.savefig(\"rain.png\", dpi=150)\n    plt.close()\n\n    print(\"\u5df2\u751f\u6210 temperature.png \u548c rain.png\")\n\nif __name__ == \"__main__\":\n    import pandas as pd\n    from weather_data import fetch_weather\n    make_charts(pd.DataFrame(fetch_weather()))\n<\/code><\/pre>\n\n\n\n
          \u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n\n\n\n
          \u5df2\u751f\u6210 temperature.png \u548c rain.png\n<\/code><\/pre>\n\n\n\n
          \u6ce8\u610f\u4e8b\u9879\u603b\u7ed3<\/strong>\uff1a\u5de5\u7a0b\u5316\u6ce8\u610f<\/strong>\u2014\u2014\u751f\u6210\u56fe\u8868\u7528 plt.close()<\/code> \u5173\u95ed\u753b\u5e03\uff0c\u5426\u5219\u5728\u811a\u672c\u91cc\u591a\u6b21 plt.figure()<\/code> \u4f1a\u7d2f\u8ba1\u5185\u5b58\u3002\u56fe\u7247\u4fdd\u5b58\u5728\u548c charts.py<\/code> \u76f8\u540c\u7684\u76ee\u5f55\uff08project\/backend\/<\/code> \u4e0b\uff09\uff0c\u7b2c 5 \u6b65\u7684 Flask \u76f4\u63a5\u5f15\u7528\uff08\u9700\u632a\u5230 static\/<\/code>\uff09\u3002<\/p>\n\n\n\n
          \u6613\u9519\u70b9<\/strong><\/p>\n\n\n\n
            \n
          • \u9519\u8bef\u5199\u6cd5\uff1a\u753b\u56fe\u811a\u672c\u5c11\u4e86\u4e2d\u6587\u5b57\u4f53\u8bbe\u7f6e \u2192 \u95ee\u9898\uff1a\u56fe\u8868\u4e2d\u6587\u663e\u793a\u6210\u65b9\u6846 \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u5f00\u5934\u52a0 plt.rcParams[\"font.sans-serif\"] = [\"SimHei\", \"Microsoft YaHei\"]<\/code>\u3002<\/li>\n\n\n\n
          • \u9519\u8bef\u5199\u6cd5\uff1aplt.figure()<\/code> \u540e\u4e0d plt.close()<\/code> \u2192 \u95ee\u9898\uff1a\u591a\u6b21\u753b\u56fe\u5185\u5b58\u7d2f\u8ba1 \u2192 \u6b63\u786e\u5199\u6cd5\uff1asavefig<\/code> \u540e plt.close()<\/code>\u3002<\/li>\n\n\n\n
          • \u9519\u8bef\u5199\u6cd5\uff1a\u65e5\u671f\u6807\u7b7e\u6a2a\u6392 \u2192 \u95ee\u9898\uff1a7 \u4e2a\u65e5\u671f\u6324\u5728\u4e00\u8d77\u770b\u4e0d\u6e05 \u2192 \u6b63\u786e\u5199\u6cd5\uff1aplt.xticks(rotation=30)<\/code> \u659c\u7740\u663e\u793a + tight_layout<\/code> \u9632\u88c1\u5207\u3002<\/li>\n<\/ul>\n\n\n\n
            \u8bb0\u5fc6\u53e3\u8bc0<\/strong>\uff1a\u4e2d\u6587\u5b57\u4f53\u5148\u8bbe\u597d\uff0c\u753b\u5b8c\u8bb0\u5f97 close \u6389\uff1b\u65e5\u671f\u659c\u653e\u4e0d\u91cd\u53e0\uff0csavefig \u5b58\u56fe\u7f51\u9875\u8981\u3002<\/p>\n\n\n\n
            5 \u7b2c 4 \u6b65\uff1a\u6570\u636e\u5b58\u50a8\uff08database.py\uff09<\/h3>\n\n\n\n
            \u662f\u4ec0\u4e48\uff1f\u4e3a\u4ec0\u4e48\u5b66\uff1f<\/strong><\/p>\n\n\n\n
            \u628a\u5929\u6c14\u6570\u636e\u5b58\u8fdb SQLite\uff0c\u5173\u952e\u70b9\uff1a\u540c\u4e00\u65e5\u671f\u53ea\u5b58\u4e00\u6b21<\/strong>\uff08\u91cd\u590d\u5165\u5e93\u4f1a\u8df3\u8fc7\uff09\u3002\u7528 Day 28 \u5b66\u7684\u53c2\u6570\u5316\u67e5\u8be2\u548c\u4e8b\u52a1\u3002\u8fd9\u4e00\u6b65\u8ba9\u6570\u636e”\u6c89\u6dc0”\u4e0b\u6765\uff0c\u4e0d\u4f9d\u8d56\u6bcf\u6b21\u73b0\u6293\u3002<\/p>\n\n\n\n
            \u4ee3\u7801\u6f14\u793a<\/strong><\/p>\n\n\n\n
            # database.py \u2014\u2014 \u5929\u6c14\u6570\u636e\u5b58\u5165 SQLite\nimport sqlite3\n\nDB_FILE = \"weather.db\"\n\ndef init_db():\n    conn = sqlite3.connect(DB_FILE)\n    conn.execute(\"\"\"\n    CREATE TABLE IF NOT EXISTS daily_weather (\n        date TEXT PRIMARY KEY,     -- \u65e5\u671f\u505a\u4e3b\u952e\uff0c\u5929\u7136\u53bb\u91cd\n        tmax REAL,\n        tmin REAL,\n        rain REAL,\n        weather TEXT\n    )\n    \"\"\")\n    conn.commit()\n    conn.close()\n\ndef save_weather(data):\n    \"\"\"\u628a\u5929\u6c14\u6570\u636e\u5199\u5165\u6570\u636e\u5e93\uff1b\u540c\u4e00\u65e5\u671f\u5df2\u5b58\u5728\u5219\u8df3\u8fc7\uff08INSERT OR IGNORE\uff09\"\"\"\n    conn = sqlite3.connect(DB_FILE)\n    inserted = 0\n    for item in data:\n        cur = conn.execute(\n            \"INSERT OR IGNORE INTO daily_weather \"\n            \"(date, tmax, tmin, rain, weather) VALUES (?, ?, ?, ?, ?)\",\n            (item[\"date\"], item[\"tmax\"], item[\"tmin\"],\n             item[\"rain\"], item[\"weather\"]),\n        )\n        inserted += cur.rowcount      # 0 \u8868\u793a\u5df2\u5b58\u5728\uff0c\u8df3\u8fc7\n    conn.commit()\n    conn.close()\n    return inserted\n\ndef load_weather():\n    \"\"\"\u4ece\u6570\u636e\u5e93\u8bfb\u56de\u5168\u90e8\u6570\u636e\uff08\u7528\u4e8e\u7f51\u9875\u5c55\u793a\uff09\"\"\"\n    conn = sqlite3.connect(DB_FILE)\n    rows = conn.execute(\n        \"SELECT date, tmax, tmin, rain, weather \"\n        \"FROM daily_weather ORDER BY date\"\n    ).fetchall()\n    conn.close()\n    return [\n        {\"date\": r[0], \"tmax\": r[1], \"tmin\": r[2], \"rain\": r[3], \"weather\": r[4]}\n        for r in rows\n    ]\n\nif __name__ == \"__main__\":\n    from weather_data import fetch_weather\n    init_db()\n    new = save_weather(fetch_weather())\n    print(f\"\u65b0\u589e {new} \u6761\uff08\u91cd\u590d\u65e5\u671f\u4f1a\u81ea\u52a8\u8df3\u8fc7\uff09\")\n    print(\"\u6570\u636e\u5e93\u4e2d\u73b0\u6709\u6570\u636e\uff1a\")\n    for row in load_weather():\n        print(row)\n<\/code><\/pre>\n\n\n\n
            \u8fd0\u884c\u7ed3\u679c\uff08\u793a\u4f8b\uff09\uff1a<\/p>\n\n\n\n
            \u65b0\u589e 7 \u6761\uff08\u91cd\u590d\u65e5\u671f\u4f1a\u81ea\u52a8\u8df3\u8fc7\uff09\n\u6570\u636e\u5e93\u4e2d\u73b0\u6709\u6570\u636e\uff1a\n{'date': '2026-08-04', 'tmax': 34.1, 'tmin': 25.5, 'rain': 0.4, 'weather': '\u96e8'}\n...\n<\/code><\/pre>\n\n\n\n
            \u6ce8\u610f\u4e8b\u9879\u603b\u7ed3<\/strong>\uff1aINSERT OR IGNORE<\/code> \u914d\u5408\u4e3b\u952e date<\/code>\uff1a\u65e5\u671f\u5df2\u5b58\u5728\u5c31\u5ffd\u7565\u3002\u518d\u8fd0\u884c\u4e00\u6b21\uff0c\u65b0\u589e\u662f 0 \u6761<\/strong>\u2014\u2014\u8fd9\u5c31\u662f”\u5e42\u7b49”\uff0c\u91cd\u590d\u6267\u884c\u4e0d\u4ea7\u751f\u526f\u4f5c\u7528\u3002\u76f8\u6bd4\u6bcf\u6b21\u5148 SELECT \u67e5\u91cd\uff0c\u8fd9\u4e00\u884c SQL \u66f4\u7b80\u6d01\u53ef\u9760\u3002\u5168\u7a0b\u53c2\u6570\u5316\u67e5\u8be2\u9632\u6ce8\u5165 + commit()<\/code> \u4e8b\u52a1\u3002<\/p>\n\n\n\n
            \u6613\u9519\u70b9<\/strong><\/p>\n\n\n\n
              \n
            • \u9519\u8bef\u5199\u6cd5\uff1a\u76f4\u63a5 INSERT<\/code> \u649e\u4e86\u4e3b\u952e \u2192 \u95ee\u9898\uff1a\u62a5 sqlite3.IntegrityError: UNIQUE constraint failed<\/code> \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u7528 INSERT OR IGNORE<\/code>\uff0c\u6216\u5148\u67e5\u91cd\u518d\u63d2\u5165\u3002<\/li>\n\n\n\n
            • \u9519\u8bef\u5199\u6cd5\uff1a\u5fd8\u4e86 conn.commit()<\/code> \u2192 \u95ee\u9898\uff1a\u6570\u636e\u6ca1\u771f\u6b63\u5199\u5165\u6587\u4ef6 \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u5199\u64cd\u4f5c\u540e conn.commit()<\/code>\u3002<\/li>\n\n\n\n
            • \u9519\u8bef\u5199\u6cd5\uff1aSQL \u91cc\u5b57\u7b26\u4e32\u62fc\u63a5 \u2192 \u95ee\u9898\uff1aSQL \u6ce8\u5165\u98ce\u9669 \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u4e00\u5f8b ?<\/code> \u5360\u4f4d + \u5143\u7ec4\u4f20\u53c2\u3002<\/li>\n<\/ul>\n\n\n\n
              \u8bb0\u5fc6\u53e3\u8bc0<\/strong>\uff1a\u4e3b\u952e\u5929\u7136\u53bb\u91cd\uff0cIGNORE \u8df3\u8fc7\u91cd\u590d\uff1b\u5e42\u7b49\u8dd1\u4e24\u904d\uff0c\u65b0\u589e\u4ecd\u662f\u96f6\u3002<\/p>\n\n\n\n
              6 \u7b2c 5 \u6b65\uff1a\u7f51\u9875\u5c55\u793a\uff08app.py + report.html\uff09<\/h3>\n\n\n\n
              \u662f\u4ec0\u4e48\uff1f\u4e3a\u4ec0\u4e48\u5b66\uff1f<\/strong><\/p>\n\n\n\n
              \u6700\u540e\u7528 Flask \u628a\u5206\u6790\u7ed3\u679c\u548c\u4e24\u5f20\u56fe\u5c55\u793a\u6210\u7f51\u9875\uff08\u7aef\u53e3 2050\uff09\u3002\u8fd9\u4e00\u6b65\u8ba9\u6574\u4e2a\u9879\u76ee\u7684\u6210\u679c\u53d8\u6210\u6d4f\u89c8\u5668\u91cc\u7684\u4e00\u9875\u62a5\u544a\u3002<\/p>\n\n\n\n
              \u4ee3\u7801\u6f14\u793a<\/strong><\/p>\n\n\n\n
              # app.py \u2014\u2014 Flask \u5c55\u793a\u5929\u6c14\u5206\u6790\u62a5\u544a\nfrom flask import Flask, render_template\nimport pandas as pd\nfrom weather_data import fetch_weather\nfrom analysis import analyze\nfrom charts import make_charts\nfrom database import init_db, save_weather, load_weather\n\napp = Flask(__name__)\n\ndef run_pipeline():\n    \"\"\"\u6267\u884c\u5b8c\u6574\u6d41\u6c34\u7ebf\uff1a\u83b7\u53d6 -> \u5206\u6790 -> \u753b\u56fe -> \u5165\u5e93 -> \u8fd4\u56de\u6570\u636e\"\"\"\n    data = fetch_weather()\n    df = analyze(data)\n    make_charts(df)\n    init_db()\n    save_weather(data)\n    return df\n\n@app.route(\"\/\")\ndef report():\n    # \u9996\u6b21\u8bbf\u95ee\u81ea\u52a8\u8dd1\u4e00\u904d\u6d41\u6c34\u7ebf\uff1b\u4e5f\u53ef\u4ee5\u6539\u6210\u4ece\u6570\u636e\u5e93\u8bfb\n    df = run_pipeline()\n\n    hottest = df.loc[df[\"tmax\"].idxmax()]\n    avg_temp = round(((df[\"tmax\"] + df[\"tmin\"]) \/ 2).mean(), 1)\n    total_rain = round(df[\"rain\"].sum(), 1)\n\n    summary = {\n        \"days\": len(df),\n        \"avg_temp\": avg_temp,\n        \"total_rain\": total_rain,\n        \"hottest_date\": hottest[\"date\"],\n        \"hottest_max\": hottest[\"tmax\"],\n        \"table\": df.to_dict(\"records\"),   # DataFrame \u8f6c\u6210\u5217\u8868\u7ed9\u6a21\u677f\u7528\n    }\n    return render_template(\"report.html\", summary=summary)\n\nif __name__ == \"__main__\":\n    app.run(host=\"127.0.0.1\", port=2050, debug=True)\n<\/code><\/pre>\n\n\n\n
              \u6a21\u677f templates\/report.html<\/code> \u8981\u70b9\uff1a\u6570\u636e\u5361\u7247\uff08\u5e73\u5747\u6e29\u5ea6 \/ \u603b\u964d\u6c34\u91cf \/ \u6700\u70ed\u4e00\u5929\uff09+ \u6bcf\u65e5\u5929\u6c14\u660e\u7ec6\u8868\u683c\uff08{% for row in summary[\"table\"] %}<\/code> \u5faa\u73af\uff09+ \u4e24\u5f20\u56fe\uff08<img src=\"\/static\/temperature.png\"><\/code>\uff09\u3002<\/p>\n\n\n\n
              \u6ce8\u610f\u4e8b\u9879\u603b\u7ed3<\/strong>\uff1aFlask \u9ed8\u8ba4\u4ece static\/<\/code> \u76ee\u5f55\u63d0\u4f9b\u56fe\u7247\uff0c\u6240\u4ee5\u628a\u4e24\u5f20\u56fe\u632a\u8fc7\u53bb\uff1a<\/p>\n\n\n\n
              # \u5728 project\/backend \u4e0b\u6267\u884c\nmkdir static\nmove temperature.png rain.png static\/\n<\/code><\/pre>\n\n\n\n
              \u542f\u52a8\uff1a<\/p>\n\n\n\n
              cd project\/backend\npython app.py\n<\/code><\/pre>\n\n\n\n
              \u6d4f\u89c8\u5668\u6253\u5f00 http:\/\/127.0.0.1:2050\/<\/code> \u5c31\u80fd\u770b\u5230\u5b8c\u6574\u7684\u5206\u6790\u62a5\u544a\u3002\u5347\u7ea7\u6311\u6218<\/strong>\uff1a\u628a run_pipeline<\/code> \u6539\u6210”\u4f18\u5148\u4ece\u6570\u636e\u5e93\u8bfb\uff0c\u6ca1\u6709\u6570\u636e\u624d\u53bb\u8bf7\u6c42 API”\uff1b\u6216\u52a0 ?city=<\/code> \u53c2\u6570\u652f\u6301\u591a\u4e2a\u57ce\u5e02\u3002<\/p>\n\n\n\n
              \u6613\u9519\u70b9<\/strong><\/p>\n\n\n\n
                \n
              • \u9519\u8bef\u5199\u6cd5\uff1a\u56fe\u8868\u4fdd\u5b58\u540e\u7f51\u9875\u663e\u793a 404 \u2192 \u95ee\u9898\uff1aFlask \u9ed8\u8ba4\u53ea\u4ece static\/<\/code> \u76ee\u5f55\u63d0\u4f9b\u9759\u6001\u6587\u4ef6\uff0c\u800c\u56fe\u5728\u6839\u76ee\u5f55 \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u628a PNG \u632a\u8fdb static\/<\/code>\uff0c\u6a21\u677f\u8def\u5f84\u5199 \/static\/...<\/code>\u3002<\/li>\n\n\n\n
              • \u9519\u8bef\u5199\u6cd5\uff1a\u6bcf\u6b21\u8bbf\u95ee\u9875\u9762\u90fd\u8bf7\u6c42\u5916\u90e8 API \u2192 \u95ee\u9898\uff1a\u6162\u3001\u8d39\u6d41\u91cf\u3001\u53ef\u80fd 502 \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u4f18\u5148 load_weather()<\/code> \u4ece\u6570\u636e\u5e93\u8bfb\uff0c\u6ca1\u6709\u624d\u8bf7\u6c42\u5e76\u5165\u5e93\u3002<\/li>\n\n\n\n
              • \u9519\u8bef\u5199\u6cd5\uff1amove<\/code> \u547d\u4ee4\u5728 PowerShell \u4e0d\u8bc6\u522b \u2192 \u95ee\u9898\uff1a\u547d\u4ee4\u6267\u884c\u5931\u8d25 \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u7528 mv temperature.png rain.png static\/<\/code>\uff08Git Bash\uff09\u6216\u76f4\u63a5\u5728\u6587\u4ef6\u7ba1\u7406\u5668\u62d6\u52a8\u3002<\/li>\n<\/ul>\n\n\n\n
                \u8bb0\u5fc6\u53e3\u8bc0<\/strong>\uff1a\u9759\u6001\u56fe\u7247\u653e static\uff0c\u7f51\u9875\u624d\u80fd\u627e\u5f97\u5230\uff1b\u6570\u636e\u5148\u8bfb\u5e93\u91cc\u5b58\uff0c\u7701\u65f6\u7701\u529b\u4e0d\u6015\u65ad\u3002<\/p>\n\n\n\n
                7 30 \u5929\u590d\u76d8\u6e05\u5355 \u2014\u2014 \u67e5\u6f0f\u8865\u7f3a<\/h3>\n\n\n\n
                \u662f\u4ec0\u4e48\uff1f\u4e3a\u4ec0\u4e48\u5b66\uff1f<\/strong><\/p>\n\n\n\n
                \u590d\u76d8\u662f\u4eca\u5929\u6700\u91cd\u8981\u7684\u4e8b\uff1a\u5bf9\u7167\u6e05\u5355\u9010\u6761\u6253\u52fe\uff0c\u52fe\u4e0d\u4e0a\u7684\u56de\u53bb\u91cd\u770b\u5bf9\u5e94\u5929\u6570\u7684\u6587\u6863<\/strong>\u3002\u56db\u4e2a\u9636\u6bb5\u7684\u4ea7\u51fa\u9012\u8fdb\uff1a\u9636\u6bb5\u4e00\u300c\u4f1a\u5199\u811a\u672c\u300d\u2192 \u9636\u6bb5\u4e8c\u300c\u4f1a\u5c01\u88c5\u6a21\u5757\u300d\u2192 \u9636\u6bb5\u4e09\u300c\u4f1a\u8bbe\u8ba1\u7c7b\u4e0e\u7528\u7b2c\u4e09\u65b9\u5e93\u300d\u2192 \u9636\u6bb5\u56db\u300c\u80fd\u4ea4\u4ed8\u6570\u636e\u4ea7\u54c1\u300d\u3002<\/p>\n\n\n\n
                \u6ce8\u610f\u4e8b\u9879\u603b\u7ed3<\/strong><\/p>\n\n\n\n
                \u56db\u4e2a\u9636\u6bb5\u81ea\u67e5\u6e05\u5355\uff1a<\/p>\n\n\n\n
                  \n
                1. \u8bed\u6cd5\u57fa\u7840\uff08Day 1~7\uff09<\/strong>\uff1a\u53d8\u91cf\u4e0e\u7c7b\u578b\u8f6c\u6362\u3001if \/ elif \/ else<\/code> \u4e0e\u903b\u8f91\u8fd0\u7b97\u3001for \/ while<\/code> \u5faa\u73af\u4e0e break \/ continue<\/code>\u3001\u5b57\u7b26\u4e32\u65b9\u6cd5\u3001\u5217\u8868\/\u5143\u7ec4\/\u5b57\u5178\/\u96c6\u5408\u7684\u589e\u5220\u6539\u67e5\u3002<\/li>\n\n\n\n
                2. \u51fd\u6570\u4e0e\u8fdb\u9636\uff08Day 8~14\uff09<\/strong>\uff1a\u51fd\u6570\u5b9a\u4e49\u4e0e\u53c2\u6570\uff08\u9ed8\u8ba4\u503c\/\u5173\u952e\u5b57\/\u53ef\u53d8\uff09\u3001\u6a21\u5757\u4e0e\u5305\uff08import<\/code>\u3001__main__<\/code>\uff09\u3001\u6587\u4ef6\u8bfb\u5199\u4e0e CSV\/JSON\u3001\u5f02\u5e38\u5904\u7406\uff08try \/ except \/ else \/ finally<\/code>\uff09\u3001f-string\u3002<\/li>\n\n\n\n
                3. \u9762\u5411\u5bf9\u8c61\u4e0e\u6807\u51c6\u5e93\uff08Day 15~21\uff09<\/strong>\uff1a\u7c7b\u4e0e\u5bf9\u8c61\u3001__init__<\/code>\u3001\u5b9e\u4f8b\/\u7c7b\/\u9759\u6001\u65b9\u6cd5\u3001\u7ee7\u627f\u5c01\u88c5\u591a\u6001\u3001\u9b54\u6cd5\u65b9\u6cd5\u3001os<\/code>\/pathlib<\/code>\/datetime<\/code>\u3001pip install<\/code>\u3001\u5217\u8868\u63a8\u5bfc\u5f0f\u4e0e\u751f\u6210\u5668\u3002<\/li>\n\n\n\n
                4. \u6570\u636e\u5206\u6790\u4e0e\u5b9e\u6218\uff08Day 22~30\uff09<\/strong>\uff1aPandas\uff08DataFrame \u521b\u5efa\u3001\u7b5b\u9009\/\u7edf\u8ba1\/\u6392\u5e8f\uff09\u3001matplotlib\uff08\u56db\u79cd\u56fe + \u4fdd\u5b58\uff09\u3001requests + BeautifulSoup\uff08\u722c\u6570\u636e + \u5408\u89c4\uff09\u3001Web API\uff08GET\/POST\/JSON\uff09\u3001SQLite\uff08\u5efa\u8868\/CRUD\/\u9632\u6ce8\u5165\/\u4e8b\u52a1\uff09\u3001Flask\uff08\u8def\u7531\/\u6a21\u677f\/\u8868\u5355\/JSON \u63a5\u53e3\uff09\u3001\u7efc\u5408\u9879\u76ee\u5168\u6d41\u7a0b\u8dd1\u901a\u3002<\/li>\n<\/ol>\n\n\n\n
                  \u6613\u9519\u70b9<\/strong><\/p>\n\n\n\n
                    \n
                  • \u9519\u8bef\u5199\u6cd5\uff1a\u590d\u76d8\u53ea”\u770b”\u4e0d”\u52a8\u624b” \u2192 \u95ee\u9898\uff1a\u773c\u775b\u4f1a\u4e86\u624b\u4e0d\u4f1a\uff0c\u52fe\u4e0a\u4e5f\u6ca1\u7528 \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u52fe\u4e0d\u4e0a\u7684\u6761\u76ee\u56de\u53bb\u91cd\u505a\u5bf9\u5e94\u5929\u6570\u7684\u7ec3\u4e60<\/strong>\u3002<\/li>\n\n\n\n
                  • \u9519\u8bef\u5199\u6cd5\uff1a\u9879\u76ee\u505a\u4e0d\u51fa\u6765\u5c31\u786c\u625b\u4eca\u5929 \u2192 \u95ee\u9898\uff1a\u6d6a\u8d39\u4e00\u5929\u8fd8\u6253\u51fb\u4fe1\u5fc3 \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u9879\u76ee\u505a\u4e0d\u51fa\u6765\u5f80\u5f80\u4e0d\u662f\u4eca\u5929\u7684\u95ee\u9898\uff0c\u800c\u662f\u524d\u9762\u67d0\u5929\u7684\u57fa\u7840\u6ca1\u6253\u7262\uff0c\u56de\u5934\u7ffb\u5bf9\u5e94\u5929\u6570\u7684\u4eca\u65e5\u603b\u7ed3\u3002<\/li>\n\n\n\n
                  • \u9519\u8bef\u5199\u6cd5\uff1a\u4ee5\u4e3a”\u4f1a\u505a\u9898”\u5c31\u662f”\u80fd\u4ea4\u4ed8” \u2192 \u95ee\u9898\uff1a\u53ea\u61c2\u77e5\u8bc6\u4e0d\u61c2\u7ec4\u88c5 \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u5bf9\u7167\u9879\u76ee\u62c6\u89e3\u8868\uff0c\u80fd\u8bb2\u6e05\u6bcf\u4e2a\u73af\u8282\u7528\u5230\u4e86\u54ea\u51e0\u5929\u7684\u54ea\u4e9b\u77e5\u8bc6\u70b9\u3002<\/li>\n<\/ul>\n\n\n\n
                    \u8bb0\u5fc6\u53e3\u8bc0<\/strong>\uff1a\u56db\u4e2a\u9636\u6bb5\u9012\u8fdb\u8d70\uff0c\u811a\u672c\u5230\u4ea7\u54c1\u4e00\u6761\u7ebf\uff1b\u52fe\u4e0d\u4e0a\u7684\u56de\u53bb\u8865\uff0c\u80fd\u8bb2\u6e05\u8868\u624d\u7b97\u5b8c\u3002<\/p>\n\n\n\n
                    8 \u540e\u7eed\u8fdb\u9636\u8def\u7ebf \u2014\u2014 30 \u5929\u53ea\u662f\u8d77\u70b9<\/h3>\n\n\n\n
                    \u662f\u4ec0\u4e48\uff1f\u4e3a\u4ec0\u4e48\u5b66\uff1f<\/strong><\/p>\n\n\n\n
                    30 \u5929\u53ea\u662f\u8d77\u70b9\u3002\u6309\u5174\u8da3\u9009\u4e00\u4e2a\u65b9\u5411\u6df1\u5165\uff0c\u6bcf\u4e2a\u65b9\u5411\u90fd\u7ed9\u51fa\u5148\u5b66\u4ec0\u4e48\u3001\u518d\u505a\u4ec0\u4e48<\/strong>\u3002<\/p>\n\n\n\n
                    \u6ce8\u610f\u4e8b\u9879\u603b\u7ed3<\/strong><\/p>\n\n\n\n
                    \u4e94\u4e2a\u65b9\u5411\uff1a<\/p>\n\n\n\n
                      \n
                    1. \u65b9\u5411\u4e00\uff1a\u81ea\u52a8\u5316\u529e\u516c<\/strong> \u2014\u2014 \u5b66\u4e60 openpyxl<\/code>\uff08Excel\uff09\u3001python-docx<\/code>\uff08Word\uff09\u3001PyPDF2<\/code>\uff08PDF\uff09\u3001smtplib<\/code>\uff08\u90ae\u4ef6\uff09\uff1b\u7ec3\u624b\u6279\u91cf\u6574\u7406\u6210\u7ee9\u5355\u3001\u81ea\u52a8\u751f\u6210\u5468\u62a5\u3001\u6279\u91cf\u91cd\u547d\u540d\u6587\u4ef6\u3002<\/li>\n\n\n\n
                    2. \u65b9\u5411\u4e8c\uff1a\u6570\u636e\u5206\u6790<\/strong> \u2014\u2014 \u5b66\u4e60 Pandas \u8fdb\u9636\uff08groupby \/ merge \/ \u900f\u89c6\u8868\uff09\u3001matplotlib<\/code> + seaborn<\/code>\u3001numpy<\/code>\uff1b\u7ec3\u624b\u5206\u6790 Kaggle \u771f\u5b9e\u6570\u636e\u96c6\uff0c\u51fa\u53ef\u89c6\u5316\u62a5\u544a\u3002<\/li>\n\n\n\n
                    3. \u65b9\u5411\u4e09\uff1aWeb \u5f00\u53d1<\/strong> \u2014\u2014 \u5b66\u4e60 Flask \u8fdb\u9636\uff08ORM SQLAlchemy<\/code>\u3001\u7528\u6237\u767b\u5f55\u3001\u90e8\u7f72\uff09\u6216 Django\uff1b\u7ec3\u624b\u628a day29 \u5929\u6c14\u7f51\u9875\u5347\u7ea7\u6210”\u7528\u6237\u7cfb\u7edf + \u6536\u85cf\u57ce\u5e02”\u5e76\u90e8\u7f72\u3002<\/li>\n\n\n\n
                    4. \u65b9\u5411\u56db\uff1a\u722c\u866b<\/strong> \u2014\u2014 \u5b66\u4e60 scrapy<\/code> \u6846\u67b6\u3001Selenium\uff08\u52a8\u6001\u7f51\u9875\uff09\u3001\u53cd\u722c\u5e94\u5bf9\u4e0e\u5408\u89c4\u8fb9\u754c<\/strong>\uff1b\u7ec3\u624b\u722c\u516c\u5f00\u8d44\u8baf\u7ad9\u70b9\u7684\u5206\u7c7b\u5217\u8868\uff08\u52a1\u5fc5\u9075\u5b88 robots.txt\uff09\u3002<\/li>\n\n\n\n
                    5. \u65b9\u5411\u4e94\uff1a\u4eba\u5de5\u667a\u80fd<\/strong> \u2014\u2014 \u524d\u7f6e Python + numpy \u77e9\u9635\u8fd0\u7b97 + \u6570\u5b66\u57fa\u7840\uff08\u7ebf\u6027\u4ee3\u6570\u3001\u6982\u7387\u8bba\uff09\uff1b\u5b66\u4e60 scikit-learn<\/code>\u3001pytorch<\/code>\uff1b\u7ec3\u624b\u623f\u4ef7\u9884\u6d4b\u3001\u624b\u5199\u6570\u5b57\u8bc6\u522b\uff08MNIST\uff09\u3002<\/li>\n<\/ol>\n\n\n\n
                      \u901a\u7528\u5efa\u8bae<\/strong>\uff1a<\/p>\n\n\n\n
                        \n
                      1. \u9879\u76ee\u9a71\u52a8<\/strong>\uff1a\u6bcf\u5b66\u4e00\u4e2a\u65b0\u5e93\uff0c\u7acb\u523b\u505a\u4e00\u4e2a\u80fd\u8dd1\u7684\u5c0f\u9879\u76ee\uff0c\u522b\u53ea\u770b\u6587\u6863\u3002<\/li>\n\n\n\n
                      2. Git \u4e0e\u4ee3\u7801\u6258\u7ba1<\/strong>\uff1a\u628a\u4ee3\u7801\u653e\u5230 GitHub\uff0c\u517b\u6210\u7248\u672c\u7ba1\u7406\u4e60\u60ef\uff08\u627e\u4e00\u5929\u8865\u4e0a Git\uff09\u3002<\/li>\n\n\n\n
                      3. \u575a\u6301\u5199\u4ee3\u7801<\/strong>\uff1a\u6bcf\u5929\u81f3\u5c11 30 \u5206\u949f\uff0c\u7b97\u6cd5\u9898\u7f51\u7ad9\uff08LeetCode \u7b80\u5355\u9898\uff09\u7ec3\u624b\u611f\u3002<\/li>\n\n\n\n
                      4. \u5b66\u4f1a\u63d0\u95ee<\/strong>\uff1a\u62a5\u9519\u5148\u81ea\u5df1\u641c\uff08\u590d\u5236\u62a5\u9519\u4fe1\u606f\u5230\u641c\u7d22\u5f15\u64ce\uff09\uff0c\u641c\u4e0d\u5230\u518d\u95ee\u4eba\u3002<\/li>\n\n\n\n
                      5. \u5199\u535a\u5ba2\u8bb0\u5f55<\/strong>\uff1a\u628a\u5b66\u5230\u7684\u5199\u6210\u7b14\u8bb0\uff0c\u8f93\u51fa\u662f\u6700\u597d\u7684\u5de9\u56fa\u3002<\/li>\n<\/ol>\n\n\n\n
                        \u6613\u9519\u70b9<\/strong><\/p>\n\n\n\n
                          \n
                        • \u9519\u8bef\u5199\u6cd5\uff1a\u4e94\u4e2a\u65b9\u5411\u5168\u90fd\u60f3\u5b66 \u2192 \u95ee\u9898\uff1a\u8d2a\u591a\u56bc\u4e0d\u70c2\uff0c\u4e00\u4e2a\u90fd\u5b66\u4e0d\u6df1 \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u6309\u5174\u8da3\u548c\u804c\u4e1a\u65b9\u5411\u9009\u4e00\u4e2a<\/strong>\uff0c\u5148\u5b66\u8fdb\u53bb\u518d\u6269\u5c55\u3002<\/li>\n\n\n\n
                        • \u9519\u8bef\u5199\u6cd5\uff1a\u5b66\u65b0\u5e93\u53ea\u770b\u6587\u6863\u4e0d\u5199\u4ee3\u7801 \u2192 \u95ee\u9898\uff1a\u770b\u5b8c\u5c31\u5fd8 \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u9879\u76ee\u9a71\u52a8\uff0c\u5b66\u4e00\u4e2a\u505a\u4e00\u4e2a\u80fd\u8dd1\u7684\u5c0f\u9879\u76ee\u3002<\/li>\n\n\n\n
                        • \u9519\u8bef\u5199\u6cd5\uff1aAI \u65b9\u5411\u57fa\u7840\u6ca1\u6253\u7262\u5c31\u786c\u4e0a \u2192 \u95ee\u9898\uff1a\u6570\u5b66\u548c\u77e9\u9635\u4e0d\u8fc7\u5173\uff0c\u4ee3\u7801\u5168\u662f\u6284 \u2192 \u6b63\u786e\u5199\u6cd5\uff1a\u5148\u8865 NumPy \u548c\u6570\u5b66\u57fa\u7840\u518d\u8fdb AI\u3002<\/li>\n<\/ul>\n\n\n\n
                          \u8bb0\u5fc6\u53e3\u8bc0<\/strong>\uff1a\u4e09\u5341\u5929\u662f\u8d77\u70b9\u4e0d\u662f\u7ec8\u70b9\uff0c\u9009\u4e00\u4e2a\u65b9\u5411\u6df1\u6316\uff1b\u9879\u76ee\u9a71\u52a8\u5199\u535a\u5ba2\uff0c\u6bcf\u5929\u534a\u5c0f\u65f6\u7ec3\u624b\u3002<\/p>\n\n\n\n
                          \u4eca\u65e5\u603b\u7ed3<\/strong><\/p>\n\n\n\n
                          \u628a 30 \u5929\u5b66\u7684\u77e5\u8bc6\u7ec4\u88c5\u6210\u4e94\u6b65\u6d41\u6c34\u7ebf\uff1aAPI \u83b7\u53d6 \u2192 Pandas \u5206\u6790 \u2192 matplotlib \u53ef\u89c6\u5316 \u2192 SQLite \u5b58\u50a8 \u2192 Flask \u5c55\u793a\uff0c\u4e94\u4e2a\u73af\u8282\u5168\u90e8\u8dd1\u901a\n\u6bcf\u4e2a\u73af\u8282\u72ec\u7acb\u6210\u6587\u4ef6\uff08weather_data \/ analysis \/ charts \/ database \/ app\uff09\uff0c\u6bcf\u6b65\u90fd\u80fd\u5355\u72ec\u8fd0\u884c\u9a8c\u8bc1\uff08if __name__ == \"__main__\"<\/code>\uff09\n\u9636\u6bb5\u77e5\u8bc6\u5730\u56fe\uff1aNumPy \u6253\u5e95 \u2192 Pandas \u7ba1\u8868 \u2192 \u6570\u636e\u6e05\u6d17\u4fdd\u8d28\u91cf \u2192 matplotlib \u51fa\u56fe \u2192 \u722c\u866b\u53d6\u7f51\u9875 \u2192 API \u8c03\u670d\u52a1 \u2192 SQLite \u843d\u5730 \u2192 Flask \u5c55\u793a\uff0c\u4e00\u73af\u6263\u4e00\u73af\n\u5e42\u7b49\u5165\u5e93\uff1aINSERT OR IGNORE<\/code> + \u4e3b\u952e\u53bb\u91cd\uff0c\u91cd\u590d\u8fd0\u884c\u65b0\u589e 0 \u6761\n\u56fe\u8868 404 \u6392\u67e5\uff1aFlask \u9759\u6001\u6587\u4ef6\u5fc5\u987b\u5728 static\/<\/code> \u76ee\u5f55\n\u7f51\u9875 http:\/\/127.0.0.1:2050\/<\/code> \u5c55\u793a\u5b8c\u6574\u5206\u6790\u62a5\u544a\uff08\u6570\u636e\u5361\u7247 + \u660e\u7ec6\u8868\u683c + \u4e24\u5f20\u56fe\uff09\n30 \u5929\u590d\u76d8\u6e05\u5355\u67e5\u6f0f\u8865\u7f3a\uff1a\u56db\u4e2a\u9636\u6bb5\uff08\u8bed\u6cd5\u57fa\u7840 \/ \u51fd\u6570\u8fdb\u9636 \/ \u9762\u5411\u5bf9\u8c61 \/ \u6570\u636e\u5206\u6790\u5b9e\u6218\uff09\u9010\u9879\u6253\u52fe\n\u9009\u5b9a\u4e00\u4e2a\u8fdb\u9636\u65b9\u5411\uff1a\u81ea\u52a8\u5316\u529e\u516c \/ \u6570\u636e\u5206\u6790 \/ Web \u5f00\u53d1 \/ \u722c\u866b \/ AI\uff0c\u5199\u4e0b\u4e0b\u4e00\u6b65\u5b66\u4e60\u8ba1\u5212<\/p>\n\n\n\n
                          \u62a5\u9519<\/th>\u539f\u56e0<\/th>\u4fee\u590d<\/th><\/tr><\/thead>
                          ModuleNotFoundError: No module named 'pandas'<\/code><\/td>\u6ca1\u5b89\u88c5 Pandas<\/td>pip install pandas<\/code>\uff08\u7b2c 2 \u6b65\u9700\u8981\uff09<\/td><\/tr>
                          \u7f51\u9875\u4e0a\u56fe\u7247\u4e0d\u663e\u793a\uff08404\uff09<\/td>\u56fe\u7247\u6ca1\u5728 static\/<\/code> \u76ee\u5f55\uff0c\u6216\u8def\u5f84\u4e0d\u5bf9<\/td>\u786e\u8ba4 project\/backend\/static\/temperature.png<\/code> \u5b58\u5728\uff0c\u6a21\u677f\u8def\u5f84\u5199 \/static\/...<\/code><\/td><\/tr>
                          \u9875\u9762\u8bbf\u95ee\u5f88\u6162\u6216\u62a5 502<\/td>\u6bcf\u6b21\u8bbf\u95ee\u90fd\u8bf7\u6c42 Open-Meteo\uff0c\u7f51\u7edc\u6162\u6216\u5931\u8d25<\/td>\u6539\u6210”\u6570\u636e\u5e93\u6709\u6570\u636e\u5c31\u4e0d\u8bf7\u6c42 API”\uff1b\u52a0 timeout<\/code><\/td><\/tr>
                          \u56fe\u8868\u4e2d\u6587\u663e\u793a\u6210\u65b9\u6846<\/td>\u753b\u56fe\u811a\u672c\u5c11\u4e86\u4e2d\u6587\u5b57\u4f53\u8bbe\u7f6e<\/td>\u5728 charts.py<\/code> \u5f00\u5934\u52a0 plt.rcParams[\"font.sans-serif\"] = [\"SimHei\", \"Microsoft YaHei\"]<\/code><\/td><\/tr>
                          sqlite3.IntegrityError: UNIQUE constraint failed<\/code><\/td>\u76f4\u63a5 INSERT \u649e\u4e86\u4e3b\u952e<\/td>\u7528 INSERT OR IGNORE<\/code>\uff0c\u6216\u5148\u67e5\u91cd\u518d\u63d2\u5165<\/td><\/tr>
                          \u5bfc\u5165\u81ea\u5df1\u5199\u7684\u6a21\u5757\u62a5\u9519\uff08No module named ‘weather_data’\uff09<\/td>\u8fd0\u884c\u76ee\u5f55\u4e0d\u5bf9<\/td>\u5728 project\/backend<\/code> \u76ee\u5f55\u4e0b\u8fd0\u884c python app.py<\/code>\uff0c\u8ba9\u811a\u672c\u548c\u6a21\u5757\u5728\u540c\u4e00\u76ee\u5f55<\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"
                          1 \u9636\u6bb5\u77e5\u8bc6\u4e32\u8bb2 \u2014\u2014 \u628a Day 22~30 \u4e32\u6210\u4e00\u5f20\u7f51 \u662f\u4ec0\u4e48\uff1f\u4e3a\u4ec0\u4e48\u5b66\uff1f \u4eca\u5929\u662f\u6536\u5b98\u65e5\uff0c\u4e0d\u5b66\u65b0\u77e5\u8bc6\u2014\u2014\u628a […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","footnotes":""},"categories":[16,9],"tags":[17],"class_list":["post-128","post","type-post","status-publish","format-standard","hentry","category-python","category-code-dev","tag-python-day"],"_links":{"self":[{"href":"https:\/\/ltrainyard.icu\/index.php\/wp-json\/wp\/v2\/posts\/128","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ltrainyard.icu\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ltrainyard.icu\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ltrainyard.icu\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ltrainyard.icu\/index.php\/wp-json\/wp\/v2\/comments?post=128"}],"version-history":[{"count":2,"href":"https:\/\/ltrainyard.icu\/index.php\/wp-json\/wp\/v2\/posts\/128\/revisions"}],"predecessor-version":[{"id":159,"href":"https:\/\/ltrainyard.icu\/index.php\/wp-json\/wp\/v2\/posts\/128\/revisions\/159"}],"wp:attachment":[{"href":"https:\/\/ltrainyard.icu\/index.php\/wp-json\/wp\/v2\/media?parent=128"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ltrainyard.icu\/index.php\/wp-json\/wp\/v2\/categories?post=128"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ltrainyard.icu\/index.php\/wp-json\/wp\/v2\/tags?post=128"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}