<\/span><\/h3>\n\n\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# \u521b\u5efa\u793a\u4f8b\u65f6\u95f4\u5e8f\u5217\u6570\u636e\ndates = pd.date_range(start='2023-01-01', end='2025-06-30', freq='D')\nnp.random.seed(42)\nsales_data = np.cumsum(np.random.normal(loc=100, scale=20, size=len(dates)))\n\ndata = {\n 'order_date': dates,\n 'product_id': np.random.choice(['P{}'.format(i) for i in range(1, 101)], len(dates)),\n 'category_code': np.random.choice(['C{}'.format(i) for i in range(1, 11)], len(dates)),\n 'amount': sales_data,\n 'quantity': np.random.randint(1, 5, size=len(dates)),\n 'customer_id': np.random.randint(1, 1001, size=len(dates)),\n 'region': np.random.choice(['Beijing', 'Shanghai', 'Guangzhou', 'Shenzhen'], len(dates)),\n 'promotion': np.random.choice([True, False], len(dates))\n}\n\ndf = pd.DataFrame(data)\n\n# \u8bbe\u7f6e\u65f6\u95f4\u4e3a\u7d22\u5f15\ndf.set_index('order_date', inplace=True)\n\nprint(\"\u539f\u59cb\u6570\u636e:\")\nprint(df.head())<\/code><\/pre>\n\n\n\n<\/span>1. \u7ed8\u5236\u997c\u56fe\u663e\u793aTop\u5546\u54c1\u9500\u552e\u5360\u6bd4<\/span><\/h3>\n\n\n\n# \u6309\u4ea7\u54c1ID\u805a\u5408\u9500\u552e\u989d\ntop_products = df.groupby('product_id')['amount'].sum().sort_values(ascending=False).head(10)\n\n# \u7ed8\u5236\u997c\u56fe\nplt.figure(figsize=(8, 8))\nplt.pie(top_products, labels=top_products.index, autopct='%1.1f%%', startangle=140)\nplt.title('Top 10 \u5546\u54c1\u9500\u552e\u5360\u6bd4')\nplt.show()<\/code><\/pre>\n\n\n\n<\/span>2. \u7ed8\u5236\u5730\u533a\u9500\u552e\u989d\u70ed\u529b\u56fe<\/span><\/h3>\n\n\n\n# \u6309\u5730\u533a\u805a\u5408\u9500\u552e\u989d\nregion_sales = df.groupby('region')['amount'].sum()\n\n# \u5c06\u5730\u533a\u9500\u552e\u989d\u8f6c\u6362\u4e3aDataFrame\nregion_sales_df = region_sales.reset_index()\n\n# \u7ed8\u5236\u70ed\u529b\u56fe\nplt.figure(figsize=(10, 6))\nsns.heatmap(region_sales_df.pivot('region', 'region', 'amount'), annot=True, fmt=\".0f\", cmap=\"YlGnBu\")\nplt.title('\u5730\u533a\u9500\u552e\u989d\u70ed\u529b\u56fe')\nplt.show()<\/code><\/pre>\n\n\n\n<\/span>3. \u7ed8\u5236\u7528\u6237\u8d2d\u4e70\u9891\u7387\u76f4\u65b9\u56fe<\/span><\/h3>\n\n\n\n# \u8ba1\u7b97\u6bcf\u4e2a\u7528\u6237\u7684\u8ba2\u5355\u6b21\u6570\nuser_orders = df.groupby('customer_id')['order_id'].count().reset_index()\nuser_orders.columns = ['customer_id', 'Order_Count']\n\n# \u7ed8\u5236\u7528\u6237\u8d2d\u4e70\u9891\u7387\u76f4\u65b9\u56fe\nplt.figure(figsize=(10, 6))\nsns.histplot(user_orders['Order_Count'], bins=30, kde=True)\nplt.title('\u7528\u6237\u8d2d\u4e70\u9891\u7387\u5206\u5e03')\nplt.xlabel('\u8ba2\u5355\u6b21\u6570')\nplt.ylabel('\u9891\u6570')\nplt.show()<\/code><\/pre>\n\n\n\n<\/span>4. \u7ed8\u5236\u4fc3\u9500\u6d3b\u52a8\u5bf9\u6bd4\u67f1\u72b6\u56fe<\/span><\/h3>\n\n\n\n# \u6309\u4fc3\u9500\u72b6\u6001\u805a\u5408\u9500\u552e\u989d\npromotion_effect = df.groupby('promotion')['amount'].sum()\n\n# \u7ed8\u5236\u4fc3\u9500\u6d3b\u52a8\u5bf9\u6bd4\u67f1\u72b6\u56fe\nplt.figure(figsize=(8, 6))\nsns.barplot(x=promotion_effect.index.map({False: 'No Promotion', True: 'With Promotion'}), y=promotion_effect.values)\nplt.title('\u4fc3\u9500\u6d3b\u52a8\u5bf9\u9500\u552e\u989d\u7684\u5f71\u54cd')\nplt.xlabel('\u4fc3\u9500\u72b6\u6001')\nplt.ylabel('\u9500\u552e\u989d')\nplt.show()<\/code><\/pre>\n\n\n\n<\/span>5. \u7ed8\u5236\u9500\u552e\u989d\u968f\u65f6\u95f4\u53d8\u5316\u7684\u6298\u7ebf\u56fe<\/span><\/h3>\n\n\n\n# \u6309\u5929\u805a\u5408\u9500\u552e\u989d\ndaily_sales = df['amount'].resample('D').sum()\n\n# \u7ed8\u5236\u9500\u552e\u989d\u968f\u65f6\u95f4\u53d8\u5316\u7684\u6298\u7ebf\u56fe\nplt.figure(figsize=(14, 7))\nplt.plot(daily_sales, label='Daily Sales')\nplt.title('\u6bcf\u65e5\u9500\u552e\u989d\u53d8\u5316\u8d8b\u52bf')\nplt.xlabel('\u65e5\u671f')\nplt.ylabel('\u9500\u552e\u989d')\nplt.legend()\nplt.show()<\/code><\/pre>\n\n\n\n\u7efc\u5408\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6700\u7ec8\u7684\u6570\u636e\u53ef\u89c6\u5316\u7ed3\u679c\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\u6570\u636e\u53ef\u89c6\u5316\u7684\u7ed3\u679c\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\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\n\r\n# \u521b\u5efa\u793a\u4f8b\u65f6\u95f4\u5e8f\u5217\u6570\u636e\r\ndates = pd.date_range(start='2023-01-01', end='2025-06-30', freq='D')\r\nnp.random.seed(42)\r\nsales_data = np.cumsum(np.random.normal(loc=100, scale=20, size=len(dates)))\r\n\r\ndata = {\r\n 'order_date': dates,\r\n 'product_id': np.random.choice(['P{}'.format(i) for i in range(1, 101)], len(dates)),\r\n 'category_code': np.random.choice(['C{}'.format(i) for i in range(1, 11)], len(dates)),\r\n 'amount': sales_data,\r\n 'quantity': np.random.randint(1, 5, size=len(dates)),\r\n 'customer_id': np.random.randint(1, 1001, size=len(dates)),\r\n 'region': np.random.choice(['Beijing', 'Shanghai', 'Guangzhou', 'Shenzhen'], len(dates)),\r\n 'promotion': np.random.choice([True, False], len(dates))\r\n}\r\n\r\ndf = pd.DataFrame(data)\r\n\r\n# \u8bbe\u7f6e\u65f6\u95f4\u4e3a\u7d22\u5f15\r\ndf.set_index('order_date', inplace=True)\r\n\r\n# \u6309\u4ea7\u54c1ID\u805a\u5408\u9500\u552e\u989d\r\ntop_products = df.groupby('product_id')['amount'].sum().sort_values(ascending=False).head(10)\r\n\r\n# \u7ed8\u5236\u997c\u56fe\r\nplt.figure(figsize=(8, 8))\r\nplt.pie(top_products, labels=top_products.index, autopct='%1.1f%%', startangle=140)\r\nplt.title('Top 10 \u5546\u54c1\u9500\u552e\u5360\u6bd4')\r\nplt.show()\r\n\r\n# \u6309\u5730\u533a\u805a\u5408\u9500\u552e\u989d\r\nregion_sales = df.groupby('region')['amount'].sum()\r\n\r\n# \u5c06\u5730\u533a\u9500\u552e\u989d\u8f6c\u6362\u4e3aDataFrame\r\nregion_sales_df = region_sales.reset_index()\r\n\r\n# \u7ed8\u5236\u70ed\u529b\u56fe\r\nplt.figure(figsize=(10, 6))\r\nsns.heatmap(region_sales_df.pivot('region', 'region', 'amount'), annot=True, fmt=\".0f\", cmap=\"YlGnBu\")\r\nplt.title('\u5730\u533a\u9500\u552e\u989d\u70ed\u529b\u56fe')\r\nplt.show()\r\n\r\n# \u8ba1\u7b97\u6bcf\u4e2a\u7528\u6237\u7684\u8ba2\u5355\u6b21\u6570\r\nuser_orders = df.groupby('customer_id')['order_id'].count().reset_index()\r\nuser_orders.columns = ['customer_id', 'Order_Count']\r\n\r\n# \u7ed8\u5236\u7528\u6237\u8d2d\u4e70\u9891\u7387\u76f4\u65b9\u56fe\r\nplt.figure(figsize=(10, 6))\r\nsns.histplot(user_orders['Order_Count'], bins=30, kde=True)\r\nplt.title('\u7528\u6237\u8d2d\u4e70\u9891\u7387\u5206\u5e03')\r\nplt.xlabel('\u8ba2\u5355\u6b21\u6570')\r\nplt.ylabel('\u9891\u6570')\r\nplt.show()\r\n\r\n# \u6309\u4fc3\u9500\u72b6\u6001\u805a\u5408\u9500\u552e\u989d\r\npromotion_effect = df.groupby('promotion')['amount'].sum()\r\n\r\n# \u7ed8\u5236\u4fc3\u9500\u6d3b\u52a8\u5bf9\u6bd4\u67f1\u72b6\u56fe\r\nplt.figure(figsize=(8, 6))\r\nsns.barplot(x=promotion_effect.index.map({False: 'No Promotion', True: 'With Promotion'}), y=promotion_effect.values)\r\nplt.title('\u4fc3\u9500\u6d3b\u52a8\u5bf9\u9500\u552e\u989d\u7684\u5f71\u54cd')\r\nplt.xlabel('\u4fc3\u9500\u72b6\u6001')\r\nplt.ylabel('\u9500\u552e\u989d')\r\nplt.show()\r\n\r\n# \u6309\u5929\u805a\u5408\u9500\u552e\u989d\r\ndaily_sales = df['amount'].resample('D').sum()\r\n\r\n# \u7ed8\u5236\u9500\u552e\u989d\u968f\u65f6\u95f4\u53d8\u5316\u7684\u6298\u7ebf\u56fe\r\nplt.figure(figsize=(14, 7))\r\nplt.plot(daily_sales, label='Daily Sales')\r\nplt.title('\u6bcf\u65e5\u9500\u552e\u989d\u53d8\u5316\u8d8b\u52bf')\r\nplt.xlabel('\u65e5\u671f')\r\nplt.ylabel('\u9500\u552e\u989d')\r\nplt.legend()\r\nplt.show()\r\n\r\n\r\n\r\n<\/code><\/pre>\n\n\n\n<\/p>\n","protected":false},"excerpt":{"rendered":"
\u5de5\u4f5c\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u9884\u6d4b\u6848\u4f8b\uff1a \u7ed8\u5236\u997c\u56fe\u663e\u793aTop\u5546\u54c1\u9500\u552e\u5360\u6bd4\u7ed8\u5236\u5730\u533a\u9500\u552e\u989d\u70ed\u529b\u56fe\u7ed8\u5236\u7528\u6237\u8d2d\u4e70\u9891\u7387\u76f4\u65b9… \u7ee7\u7eed\u9605\u8bfb \u3010Python10\u5e74\u7ecf\u9a8c\u603b\u7ed3\u3011\u7b2c\u4e5d\u8bfe \u7535\u5546\u5e73\u53f0\u9500\u552e\u6570\u636e\u5206\u6790\u5b9e\u8df5 -\u6570\u636e\u53ef\u89c6\u5316\uff08Data Visualization\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":1234,"_links":{"self":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/3552"}],"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=3552"}],"version-history":[{"count":2,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/3552\/revisions"}],"predecessor-version":[{"id":3567,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/3552\/revisions\/3567"}],"wp:attachment":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/media?parent=3552"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/categories?post=3552"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/tags?post=3552"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}