<\/span><\/h3>\n\n\n\n# \u8f6c\u6362order_date\u4e3a\u65e5\u671f\u683c\u5f0f\ndf['order_date'] = pd.to_datetime(df['order_date'])\n\n# \u63d0\u53d6\u5e74\u3001\u6708\u3001\u5468\u3001\u661f\u671f\u51e0\ndf['year'] = df['order_date'].dt.year\ndf['month'] = df['order_date'].dt.month\ndf['week_of_year'] = df['order_date'].dt.isocalendar().week.astype(int)\ndf['day_of_week'] = df['order_date'].dt.dayofweek\n\nprint(\"\\n\u63d0\u53d6\u65e5\u671f\u4e2d\u7684\u5e74\u3001\u6708\u3001\u5468\u3001\u661f\u671f\u51e0\u540e:\")\nprint(df)<\/code><\/pre>\n\n\n\n<\/span>2. \u5c06\u5b57\u7b26\u4e32\u5206\u7c7b\u53d8\u91cf\u7f16\u7801\u4e3a\u54d1\u53d8\u91cf\uff08One-Hot Encoding\uff09<\/span><\/h3>\n\n\n\n# \u5c06category_code\u8fdb\u884cone-hot encoding\ndf_encoded = pd.get_dummies(df, columns=['category_code'], prefix='cat')\n\nprint(\"\\n\u5c06\u5b57\u7b26\u4e32\u5206\u7c7b\u53d8\u91cf\u7f16\u7801\u4e3a\u54d1\u53d8\u91cf\u540e:\")\nprint(df_encoded)<\/code><\/pre>\n\n\n\n<\/span>3. \u8ba1\u7b97\u8ba2\u5355\u603b\u91d1\u989d\uff08\u5355\u4ef7\u00d7\u6570\u91cf\uff09<\/span><\/h3>\n\n\n\n# \u8ba1\u7b97\u8ba2\u5355\u603b\u91d1\u989d\ndf_encoded['total_amount'] = df_encoded['amount'] * df_encoded['quantity']\n\nprint(\"\\n\u8ba1\u7b97\u8ba2\u5355\u603b\u91d1\u989d\u540e:\")\nprint(df_encoded)<\/code><\/pre>\n\n\n\n<\/span>4. \u8ba1\u7b97\u6298\u6263\u7387\uff08\u5b9e\u9645\u652f\u4ed8\u4ef7 \/ \u539f\u4ef7\uff09<\/span><\/h3>\n\n\n\n# \u8ba1\u7b97\u6298\u6263\u7387\ndf_encoded['discount_rate'] = (df_encoded['amount'] \/ df_encoded['original_price']).round(2)\n\nprint(\"\\n\u8ba1\u7b97\u6298\u6263\u7387\u540e:\")\nprint(df_encoded)<\/code><\/pre>\n\n\n\n<\/span>5. \u6784\u9020\u7528\u6237\u8d2d\u4e70\u9891\u6b21\u7279\u5f81<\/span><\/h3>\n\n\n\n\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5ba2\u6237\u8d2d\u4e70\u8bb0\u5f55\u7684DataFrame\uff0c\u7528\u4e8e\u6784\u9020\u7528\u6237\u8d2d\u4e70\u9891\u6b21\u7279\u5f81\u3002<\/p>\n\n\n\n
# \u521b\u5efa\u5ba2\u6237\u8d2d\u4e70\u8bb0\u5f55\u7684DataFrame\npurchase_history = {\n 'customer_id': [101, 102, 103, 104, 105, 106, 107, 108, 101, 102, 103, 104],\n 'purchase_count': [2, 3, 1, 2, 1, 3, 2, 1, 1, 2, 1, 3]\n}\ndf_purchase_history = pd.DataFrame(purchase_history)\n\n# \u5408\u5e76\u5ba2\u6237\u8d2d\u4e70\u8bb0\u5f55\ndf_transformed = pd.merge(df_encoded, df_purchase_history, on='customer_id', how='left')\n\nprint(\"\\n\u6784\u9020\u7528\u6237\u8d2d\u4e70\u9891\u6b21\u7279\u5f81\u540e:\")\nprint(df_transformed)<\/code><\/pre>\n\n\n\n<\/span>6. \u8f6c\u6362\u65f6\u95f4\u6233\u4e3a\u53ef\u6392\u5e8f\u7684\u65f6\u95f4\u5e8f\u5217<\/span><\/h3>\n\n\n\n\u5728\u6211\u4eec\u7684\u4f8b\u5b50\u4e2d\uff0corder_date<\/code>\u5df2\u7ecf\u662f\u65e5\u671f\u65f6\u95f4\u683c\u5f0f\uff0c\u53ef\u4ee5\u76f4\u63a5\u6392\u5e8f\u3002<\/p>\n\n\n\n# \u786e\u4fddorder_date\u662fdatetime\u7c7b\u578b\ndf_transformed['order_date'] = pd.to_datetime(df_transformed['order_date'])\n\n# \u6309order_date\u6392\u5e8f\ndf_sorted = df_transformed.sort_values(by='order_date')\n\nprint(\"\\n\u6309order_date\u6392\u5e8f\u540e:\")\nprint(df_sorted)<\/code><\/pre>\n\n\n\n<\/span>7. \u6784\u9020\u5546\u54c1\u9500\u91cf\u6392\u540d\u5b57\u6bb5<\/span><\/h3>\n\n\n\n\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5546\u54c1\u9500\u91cf\u7edf\u8ba1\u7684DataFrame\uff0c\u7528\u4e8e\u6784\u9020\u5546\u54c1\u9500\u91cf\u6392\u540d\u7279\u5f81\u3002<\/p>\n\n\n\n
# \u521b\u5efa\u5546\u54c1\u9500\u91cf\u7edf\u8ba1\u7684DataFrame\nproduct_sales = {\n 'product_id': ['A001', 'B002', 'D004', 'E005', 'F006', 'G007', 'H008', 'I009'],\n 'sales_rank': [1, 2, 3, 4, 5, 6, 7, 8]\n}\ndf_product_sales = pd.DataFrame(product_sales)\n\n# \u5408\u5e76\u5546\u54c1\u9500\u91cf\u7edf\u8ba1\ndf_final = pd.merge(df_sorted, df_product_sales, on='product_id', how='left')\n\nprint(\"\\n\u6784\u9020\u5546\u54c1\u9500\u91cf\u6392\u540d\u5b57\u6bb5\u540e:\")\nprint(df_final)<\/code><\/pre>\n\n\n\n<\/span>8. \u5bf9\u4ef7\u683c\u8fdb\u884c\u6807\u51c6\u5316\uff08Z-score\uff09<\/span><\/h3>\n\n\n\nfrom scipy.stats import zscore\n\n# \u5bf9amount\u5217\u8fdb\u884cz-score\u6807\u51c6\u5316\ndf_final['amount_zscore'] = zscore(df_final['amount'])\n\nprint(\"\\n\u5bf9\u4ef7\u683c\u8fdb\u884c\u6807\u51c6\u5316\u540e:\")\nprint(df_final)<\/code><\/pre>\n\n\n\n<\/span>9. \u6784\u9020\u662f\u5426\u53c2\u4e0e\u4fc3\u9500\u7684\u5e03\u5c14\u6807\u7b7e<\/span><\/h3>\n\n\n\n# \u6784\u9020\u662f\u5426\u53c2\u4e0e\u4fc3\u9500\u7684\u5e03\u5c14\u6807\u7b7e\ndf_final['is_promotion'] = df_final['promotion'].astype(bool)\n\nprint(\"\\n\u6784\u9020\u662f\u5426\u53c2\u4e0e\u4fc3\u9500\u7684\u5e03\u5c14\u6807\u7b7e\u540e:\")\nprint(df_final)<\/code><\/pre>\n\n\n\n\u7efc\u5408\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6700\u7ec8\u7684\u8f6c\u6362\u540e\u7684DataFrame\u5982\u4e0b\uff1a<\/p>\n\n\n\n
\u8fd9\u6bb5\u4ee3\u7801\u5c55\u793a\u4e86\u4ece\u539f\u59cb\u6570\u636e\u5230\u7ecf\u8fc7\u5168\u9762\u8f6c\u6362\u7684\u6570\u636e\u7684\u8fc7\u7a0b\u3002\u4f60\u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u9700\u6c42\u8c03\u6574\u6bcf\u4e00\u6b65\u7684\u64cd\u4f5c\u3002<\/p>\n\n\n\n
import pandas as pd\r\nimport numpy as np\r\nfrom scipy.stats import zscore\r\n\r\n# \u521b\u5efa\u793a\u4f8bDataFrame\r\ndata = {\r\n 'order_id': [1, 2, 3, 4, 5, 6, 7, 8],\r\n 'product_id': ['A001', 'B002', 'D004', 'E005', 'F006', 'G007', 'H008', 'I009'],\r\n 'category_code': ['C1', 'C2', 'C1', 'C3', 'C4', 'C5', 'C6', 'C7'],\r\n 'amount': [100.00, 200.00, 300.00, 400.00, 500.00, 600.00, 700.00, 800.00],\r\n 'original_price': [120.00, 220.00, 320.00, 420.00, 520.00, 620.00, 720.00, 820.00],\r\n 'quantity': [1, 2, 1, 1, 2, 1, 1, 2],\r\n 'order_date': ['2025-06-01', '2025-06-02', '2025-06-03', '2025-06-04', '2025-06-05', '2025-06-06', '2025-06-07', '2025-06-08'],\r\n 'customer_id': [101, 102, 103, 104, 105, 106, 107, 108],\r\n 'promotion': [False, True, False, True, False, True, False, True]\r\n}\r\n\r\ndf = pd.DataFrame(data)\r\n\r\n# \u8f6c\u6362order_date\u4e3a\u65e5\u671f\u683c\u5f0f\r\ndf['order_date'] = pd.to_datetime(df['order_date'])\r\n\r\n# \u63d0\u53d6\u5e74\u3001\u6708\u3001\u5468\u3001\u661f\u671f\u51e0\r\ndf['year'] = df['order_date'].dt.year\r\ndf['month'] = df['order_date'].dt.month\r\ndf['week_of_year'] = df['order_date'].dt.isocalendar().week.astype(int)\r\ndf['day_of_week'] = df['order_date'].dt.dayofweek\r\n\r\n# \u5c06category_code\u8fdb\u884cone-hot encoding\r\ndf_encoded = pd.get_dummies(df, columns=['category_code'], prefix='cat')\r\n\r\n# \u8ba1\u7b97\u8ba2\u5355\u603b\u91d1\u989d\r\ndf_encoded['total_amount'] = df_encoded['amount'] * df_encoded['quantity']\r\n\r\n# \u8ba1\u7b97\u6298\u6263\u7387\r\ndf_encoded['discount_rate'] = (df_encoded['amount'] \/ df_encoded['original_price']).round(2)\r\n\r\n# \u521b\u5efa\u5ba2\u6237\u8d2d\u4e70\u8bb0\u5f55\u7684DataFrame\r\npurchase_history = {\r\n 'customer_id': [101, 102, 103, 104, 105, 106, 107, 108, 101, 102, 103, 104],\r\n 'purchase_count': [2, 3, 1, 2, 1, 3, 2, 1, 1, 2, 1, 3]\r\n}\r\ndf_purchase_history = pd.DataFrame(purchase_history)\r\n\r\n# \u5408\u5e76\u5ba2\u6237\u8d2d\u4e70\u8bb0\u5f55\r\ndf_transformed = pd.merge(df_encoded, df_purchase_history, on='customer_id', how='left')\r\n\r\n# \u786e\u4fddorder_date\u662fdatetime\u7c7b\u578b\r\ndf_transformed['order_date'] = pd.to_datetime(df_transformed['order_date'])\r\n\r\n# \u6309order_date\u6392\u5e8f\r\ndf_sorted = df_transformed.sort_values(by='order_date')\r\n\r\n# \u521b\u5efa\u5546\u54c1\u9500\u91cf\u7edf\u8ba1\u7684DataFrame\r\nproduct_sales = {\r\n 'product_id': ['A001', 'B002', 'D004', 'E005', 'F006', 'G007', 'H008', 'I009'],\r\n 'sales_rank': [1, 2, 3, 4, 5, 6, 7, 8]\r\n}\r\ndf_product_sales = pd.DataFrame(product_sales)\r\n\r\n# \u5408\u5e76\u5546\u54c1\u9500\u91cf\u7edf\u8ba1\r\ndf_final = pd.merge(df_sorted, df_product_sales, on='product_id', how='left')\r\n\r\n# \u5bf9amount\u5217\u8fdb\u884cz-score\u6807\u51c6\u5316\r\ndf_final['amount_zscore'] = zscore(df_final['amount'])\r\n\r\n# \u6784\u9020\u662f\u5426\u53c2\u4e0e\u4fc3\u9500\u7684\u5e03\u5c14\u6807\u7b7e\r\ndf_final['is_promotion'] = df_final['promotion'].astype(bool)\r\n\r\nprint(\"\u6700\u7ec8\u8f6c\u6362\u540e\u7684\u6570\u636e:\")\r\nprint(df_final)\r\n\r\n\r\n\r\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"\u5e38\u89c1\u7528\u7684\u6570\u636e\u8f6c\u6362\u5206\u7c7b\uff1a \u6211\u4eec\u5c06\u5c55\u793a\u5982\u4f55\u8fdb\u884c\u8fd9\u4e9b\u5e38\u89c1\u7684\u6570\u636e\u8f6c\u6362\u4efb\u52a1\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u9500\u552e\u8ba2\u5355\u6570\u636e\u7684D… \u7ee7\u7eed\u9605\u8bfb \u3010Python10\u5e74\u7ecf\u9a8c\u603b\u7ed3\u3011\u7b2c\u4e09\u8bfe \u7535\u5546\u5e73\u53f0\u9500\u552e\u6570\u636e\u5206\u6790\u5b9e\u8df5\u5206\u89e3 \u2013 \u6570\u636e\u8f6c\u6362\uff08Data Transformation\uff09<\/span><\/i><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[28],"views":797,"_links":{"self":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/3538"}],"collection":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/comments?post=3538"}],"version-history":[{"count":3,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/3538\/revisions"}],"predecessor-version":[{"id":3561,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/3538\/revisions\/3561"}],"wp:attachment":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/media?parent=3538"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/categories?post=3538"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/tags?post=3538"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}