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最新推荐文章于 2024-07-04 23:15:22 发布
原创 最新推荐文章于 2024-07-04 23:15:22 发布 · 108 阅读 · 0 ·
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24.24.24.24.24.24.24.24.24.24.24.24.24.24.24.24.24.24.24.
4. pandas使用2
archmagus的博客
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import pandas as pd import numpy as np 示例 股票数据离散化 stock=pd.read_csv("/home/python/练习/stock_day/stock_day.csv") stock.head() open high close low volume price_change p_change ma5 ma10
Assistant Pro 安卓助手v24.24付费版.zip
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【Assistant Pro 安卓助手v24.24付费版】是一个专为安卓用户设计的应用程序，它提供了全面的功能来帮助用户管理和优化他们的设备。这个版本可能是该软件的一个高级版本，通常付费版会包含更多特性、更少的广告以及更...
24.24.24.24.24.24.24.24.24.24.24.
Java Web技术
07-20 123
24.24.24.24.24.24.24.24.24.24.24.24.24.24.24.24.24.24.24.
L212. 单词搜索 II
weixin_41759020的博客
05-23 158
单词搜索 II 给定一个二维网格 board 和一个字典中的单词列表 words，找出所有同时在二维网格和字典中出现的单词。 单词必须按照字母顺序，通过相邻的单元格内的字母构成，其中“相邻”单元格是那些水平相邻或垂直相邻的单元格。同一个单元格内的字母在一个单词中不允许被重复使用。 示例: 输入: words = [“oath”,“pea”,“eat”,“rain”] and board = [ [‘o’,‘a’,‘a’,‘n’], [‘e’,‘t’,‘a’,‘e’], [‘i’,‘h’,‘k’,‘r’].
reader.php,如何安装和使用PHP版的DB-Reader-php
weixin_34365042的博客
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如何安装和使用PHP版的DB-Reader-php由于GEOIP官方的原免费GeoLite Legacy数据库(.dat)已于2019年1月开始不再提供更新和支持。因此基于例如nginx/apache等WEB引擎的Geoip模块均已不可用。官方已变更了ip数据库的格式，需要用新的API方式，来使用Geoip功能。本文介绍使用php版的DB-Reader-php来实现功能也可以使用GeoIP2-PH...
键盘键值 linux.h,linux下input event事件，可以用后台截取键盘按下的键值，后台截取数据.....input.h...
weixin_39727743的博客
05-16 466
如何读取Linux键值，输入子系统，key，dev/input/event，dev/event，C语言键盘？这里先上一段代码，自己拿到linux系统运行就可以，注意代码中keys_fd = open("/dev/input/event2", O_RDONLY); 这里去你的/dev/input/event2的event事件，去查看你的键盘事件是那一个，如果是event3那么你改成even...
NXP i.MX8系列平台开发讲解 - 3.6 Linux 之Input 子系统（二）
hywelstar的嵌入式软件开发笔记
02-26 2020
本章节主要讲述关于Input子系统的应用，获取外部输入和模拟一个虚拟的input 设备，这些都使用Linux 系统，Android 系统也适用，Android同样进行交叉静态编译也可以运行。
免费办公软件 -- LibreOffice v24.2.4
dntkorg的博客
07-04 922
LibreOffice是一款免费且开源的办公软件套件，它为个人和商业用户提供了一套完整的工具，用于处理文本文档、电子表格、演示文稿和其他类型的文件。这款软件兼容 Microsoft Office 的文件格式，如 .docx、.xlsx 和 .pptx，使其成为那些寻找经济实惠且功能全面的办公软件替代品的用户的理想选择。一些显著的改进包括对用户界面的改进，例如NotebookBar界面的改进和打印视图预览的增强；支持从下拉菜单插入特殊字符；对于Calc，功能侧边栏增加了搜索栏，并且改进了科学数字格式的支持。
leetcode 64.最小路径和
zhuruihe2014的博客
07-07 130
原题 64.最小路径和 方法一 动态规划。 此方法java代码示例： /* @v7fgg 执行用时：3 ms, 在所有 Java 提交中击败了89.20%的用户 内存消耗：42.8 MB, 在所有 Java 提交中击败了24.24%的用户 2020年7月7日 20:37 */ class Solution { public int minPathSum(int[][] grid) { int ans[][]=new int[grid.length][grid[0].length];
interface gigabitEthernet 1/1/1 no switchport ip address 24.24.24.2 255.255.255.0 no shutdown vlan 40 interface vlan 40 ip address 40.40.40.254 255.255.255.0 no shutdown interface gigabitEthernet 1/1/2 switchport mode access switchport access vlan 40 interface gigabitEthernet 1/1/3 switchport mode access switchport access vlan 40ip route 0.0.0.0 0.0.0.0 24.24.24.1 line vty 0 4 password admin login翻译什么意思
06-12
- 配置GigabitEthernet 1/1/1接口，不作为交换机端口，设置IP地址为24.24.24.2，子网掩码为255.255.255.0，打开接口，将其加入VLAN 40。 - 配置VLAN 40接口，设置IP地址为40.40.40.254，子网掩码为255.255.255.0，...
24.24 - 容器化一個Python Flask APP.mp4
10-11
Docker技术入门与实战
import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler, LabelEncoder from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import classification_report, confusion_matrix from mlxtend.frequent_patterns import apriori, association_rules import warnings warnings.filterwarnings('ignore') plt.rcParams['font.sans-serif'] = ['SimHei'] # 用于显示中文 plt.rcParams['axes.unicode_minus'] = False # ====================== 加载数据 ====================== file_path = 'final_train_ready.csv' data = pd.read_csv(file_path) # 删除无关列 drop_cols = ['ID', 'Delivery_person_ID', 'Order_Date', 'Time_Orderd', 'Time_Order_picked'] data.drop(columns=[col for col in drop_cols if col in data.columns], inplace=True) # ======== 清洗 Time_taken(min) 字段 ======== data['Time_taken(min)'] = ( data['Time_taken(min)'] .astype(str) .str.replace(r'[^\d.]', '', regex=True) .replace('', np.nan) .astype(float) ) print(f" 数据加载完成：{data.shape[0]} 行 × {data.shape[1]} 列\n") # ================== 关联规则挖掘================== print(" 开始：关联规则挖掘（购物篮分析思想）\n") # 选择分类变量 + 时间段作为维度 cat_vars = ['Weatherconditions', 'Road_traffic_density', 'Type_of_order', 'Type_of_vehicle', 'Festival', 'City', 'Order_Period'] subset_data = data[cat_vars].copy() # 将所有字段转为字符串类别 for col in subset_data.columns: subset_data[col] = subset_data[col].astype(str) # One-Hot 编码（独热编码） oht = pd.get_dummies(subset_data) oht = oht.groupby(level=0, axis=1).sum() # 合并重复列 # 应用 Apriori 获取频繁项集 frequent_items = apriori(oht, min_support=0.1, use_colnames=True) # 生成关联规则 rules = association_rules(frequent_items, metric="lift", min_threshold=1.0) rules = rules.sort_values(['lift', 'confidence'], ascending=False) # 筛选高置信度规则 strong_rules = rules[rules['confidence'] >= 0.5] print(f" 发现 {len(strong_rules)} 条强关联规则（confidence ≥ 0.5）：\n") for idx, row in strong_rules.head(10).iterrows(): # 可调整显示条数 # 展开前件（antecedents） antecedent_terms = [f"{item}" for item in row['antecedents']] antecedent_str = " 且 ".join(antecedent_terms) # 展开后件（consequents） consequent_terms = [f"{item}" for item in row['consequents']] consequent_str = " 且 ".join(consequent_terms) # 格式化输出 print(f" 若 {antecedent_str} → 则 {consequent_str} " f"(支持度={row['support']:.3f}, 置信度={row['confidence']:.3f}, 提升度={row['lift']:.3f})") # ================== 分类模型构建 ================== print(" 开始：分类模型构建 —— 预测配送时间等级\n") # 目标变量：将连续时间划分为三类（短、中、长） time_labels = [0, 1, 2] # 0=短, 1=中, 2=长 data['Time_Category'] = pd.qcut(data['Time_taken(min)'], q=3, labels=time_labels) # 特征工程：编码分类变量 le = LabelEncoder() X_cat = data.select_dtypes(include=['object']).apply(lambda x: le.fit_transform(x.astype(str))) X_num = data.select_dtypes(include=[np.number]).drop(columns=['Time_taken(min)', 'Time_Category'], errors='ignore') X = pd.concat([X_cat, X_num], axis=1) y = data['Time_Category'] # 划分训练/测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y) # 训练随机森林分类器 clf = RandomForestClassifier(n_estimators=100, random_state=42, class_weight='balanced') clf.fit(X_train, y_train) y_pred = clf.predict(X_test) # 输出评估结果 print(" 分类性能报告：") print(classification_report(y_test, y_pred, target_names=['Short', 'Medium', 'Long'])) # 可视化混淆矩阵 # plt.figure(figsize=(6, 5)) # cm = confusion_matrix(y_test, y_pred) # sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Short','Medium','Long'], yticklabels=['Short','Medium','Long']) # plt.title('Confusion Matrix - Delivery Time Classification') # plt.xlabel('Predicted') # plt.ylabel('Actual') # plt.tight_layout() # plt.show() print("-" * 60) # ================== 聚类分析 ================== print("📊 开始：聚类分析 —— 订单行为分群\n") # === 提取用于聚类的特征 === cluster_features = [ 'Delivery_person_Ratings', 'Delivery_Distance', 'multiple_deliveries', 'Time_taken(min)', 'Order_Hour' ] X_clust = data[cluster_features].copy() # === 关键步骤1：逐列强制转为数值类型（字符串→float）=== for col in X_clust.columns: if col == 'multiple_deliveries': # 特殊处理 pattern = r'[^\d.]' X_clust[col] = ( X_clust[col].astype(str) .str.replace(pattern, '', regex=True) .replace('', np.nan) .astype(float) ) else: X_clust[col] = pd.to_numeric(X_clust[col], errors='coerce') # === 检查转换结果 === print("\n🔍 各列数据类型（转换后）：") print(X_clust.dtypes) print("\n🧩 缺失值统计（转换后）：") print(X_clust.isnull().sum()) # === 关键步骤2：安全填充均值 === numeric_columns = X_clust.select_dtypes(include=[np.number]).columns.tolist() X_clust_filled = X_clust[numeric_columns].copy() for col in numeric_columns: if X_clust_filled[col].isnull().any(): mean_val = X_clust_filled[col].mean() X_clust_filled[col].fillna(mean_val, inplace=True) # === 再次检查是否还有 NaN === assert not X_clust_filled.isnull().any().any(), "仍有 NaN，请检查！" print("✅ 数据清洗完成，共 {} 个样本".format(len(X_clust_filled))) # === 标准化 === scaler = StandardScaler() X_scaled = scaler.fit_transform(X_clust_filled) X_scaled_df = pd.DataFrame(X_scaled, columns=numeric_columns) # === 肘部法找最优 k === inertias = [] k_range = range(1, 10) for k in k_range: kmeans = KMeans(n_clusters=k, random_state=42, n_init=10) kmeans.fit(X_scaled) inertias.append(kmeans.inertia_) # 可视化肘部图（可选开启） # plt.figure(figsize=(8, 5)) # plt.plot(k_range, inertias, 'bo-', linewidth=2, markersize=6) # plt.axvline(x=3, color='r', linestyle='--', label='k=3') # plt.title('Elbow Method for Optimal k') # plt.xlabel('Number of Clusters (k)') # plt.ylabel('Inertia') # plt.legend() # plt.grid(True) # plt.tight_layout() # plt.show() # === K-Means 聚类（k=3）=== optimal_k = 3 kmeans_final = KMeans(n_clusters=optimal_k, random_state=42, n_init=10) clusters = kmeans_final.fit_predict(X_scaled) # === 写回原数据 === data['Cluster'] = np.nan data.loc[X_clust_filled.index, 'Cluster'] = clusters data['Cluster'] = data['Cluster'].astype('int') # === 输出结果 === print(f"🎯 聚类完成！共分为 {optimal_k} 类：") print(data['Cluster'].value_counts().sort_index()) # print(data[['multiple_deliveries', 'Delivery_Distance']].dtypes) # # data['multiple_deliveries'] = pd.to_numeric(data['multiple_deliveries'], errors='coerce') # data['Delivery_Distance'] = pd.to_numeric(data['Delivery_Distance'], errors='coerce') # data['Time_taken(min)'] = pd.to_numeric(data['Time_taken(min)'], errors='coerce') # data['Order_Hour'] = pd.to_numeric(data['Order_Hour'], errors='coerce') # # for col in numeric_columns: # data[col].fillna(data[col].mean(), inplace=True) # print(data[numeric_columns].dtypes) # # print(data[['multiple_deliveries', 'Delivery_Distance']].dtypes) # 各簇特征均值 cluster_profile = data.groupby('Cluster')[cluster_features].mean() print("\n📌 各类特征均值对比：") print(cluster_profile.round(2)) # === 可视化聚类结果 === # plt.figure(figsize=(8, 6)) # sns.scatterplot( # data=data, # x='Delivery_Distance', # y='Time_taken(min)', # hue='Cluster', # palette='Set1', # alpha=0.7 # ) # plt.title('K-Means Clustering: Delivery Distance vs Time Taken') # plt.xlabel('配送距离 (km)') # plt.ylabel('配送时间 (分钟)') # plt.legend(title='簇编号') # plt.grid(True) # plt.tight_layout() # plt.show() print("\n🎉 聚类分析成功完成！") C:\Users\32773\.conda\envs\mining\python.exe "C:\Users\32773\Desktop\外卖消费行为处理好的数据1\外卖消费行为处理好的数据\data mining.py" 数据加载完成：45593 行 × 18 列 开始：关联规则挖掘（购物篮分析思想） 发现 212 条强关联规则（confidence ≥ 0.5）： 若 Order_Period_下午茶 → 则 Road_traffic_density_Medium 且 Festival_No (支持度=0.125, 置信度=0.857, 提升度=3.626) 若 Road_traffic_density_Medium 且 Festival_No → 则 Order_Period_下午茶 (支持度=0.125, 置信度=0.529, 提升度=3.626) 若 Order_Period_下午茶 且 Festival_No → 则 Road_traffic_density_Medium (支持度=0.125, 置信度=0.869, 提升度=3.620) 若 Road_traffic_density_Medium → 则 Order_Period_下午茶 且 Festival_No (支持度=0.125, 置信度=0.521, 提升度=3.620) 若 Order_Period_下午茶 → 则 Road_traffic_density_Medium (支持度=0.127, 置信度=0.868, 提升度=3.616) 若 Road_traffic_density_Medium → 则 Order_Period_下午茶 (支持度=0.127, 置信度=0.527, 提升度=3.616) 若 Order_Period_早餐 → 则 Festival_No 且 Road_traffic_density_Low (支持度=0.125, 置信度=0.990, 提升度=2.950) 若 Order_Period_早餐 → 则 Road_traffic_density_Low (支持度=0.126, 置信度=1.000, 提升度=2.946) 若 Order_Period_早餐 且 Festival_No → 则 Road_traffic_density_Low (支持度=0.125, 置信度=1.000, 提升度=2.946) 若 Road_traffic_density_Jam 且 Type_of_vehicle_motorcycle → 则 City_Metropolitian 且 Order_Period_晚餐 (支持度=0.143, 置信度=0.791, 提升度=1.610) 开始：分类模型构建 —— 预测配送时间等级 分类性能报告： precision recall f1-score support Short 0.79 0.76 0.77 3150 Medium 0.63 0.71 0.67 3004 Long 0.90 0.82 0.85 2965 accuracy 0.76 9119 macro avg 0.77 0.76 0.77 9119 weighted avg 0.77 0.76 0.77 9119 ------------------------------------------------------------ 📊 开始：聚类分析 —— 订单行为分群 🔍 各列数据类型（转换后）： Delivery_person_Ratings float64 Delivery_Distance float64 multiple_deliveries float64 Time_taken(min) float64 Order_Hour int64 dtype: object 🧩 缺失值统计（转换后）： Delivery_person_Ratings 1908 Delivery_Distance 0 multiple_deliveries 993 Time_taken(min) 0 Order_Hour 0 dtype: int64 ✅ 数据清洗完成，共 45593 个样本 🎯 聚类完成！共分为 3 类： Cluster 0 12727 1 32704 2 162 Name: count, dtype: int64 Traceback (most recent call last): File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\groupby\groupby.py", line 1944, in _agg_py_fallback res_values = self._grouper.agg_series(ser, alt, preserve_dtype=True) File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\groupby\ops.py", line 873, in agg_series result = self._aggregate_series_pure_python(obj, func) File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\groupby\ops.py", line 894, in _aggregate_series_pure_python res = func(group) File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\groupby\groupby.py", line 2461, in <lambda> alt=lambda x: Series(x, copy=False).mean(numeric_only=numeric_only), ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\series.py", line 6570, in mean return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs) ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\generic.py", line 12485, in mean return self._stat_function( ~~~~~~~~~~~~~~~~~~~^ "mean", nanops.nanmean, axis, skipna, numeric_only, **kwargs ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ) ^ File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\generic.py", line 12442, in _stat_function return self._reduce( ~~~~~~~~~~~~^ func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ) ^ File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\series.py", line 6478, in _reduce return op(delegate, skipna=skipna, **kwds) File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\nanops.py", line 147, in f result = alt(values, axis=axis, skipna=skipna, **kwds) File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\nanops.py", line 404, in new_func result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs) File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\nanops.py", line 720, in nanmean the_sum = _ensure_numeric(the_sum) File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\nanops.py", line 1701, in _ensure_numeric raise TypeError(f"Could not convert string '{x}' to numeric") TypeError: Could not convert string '4.54.74.64.84.24.744.24.94.14.344.344.13.54.64.3NaN 4.74.84.24.44.64.54.14.64.74.44.64.24.44.64.34.23.84.94.24.74.94.64.24.13.94.644.44.14.84.34.74.6NaN 4.94.73.94.74.344.14.643.94.84.54.14.44.64.74.84.44.74.644.84NaN 4.44.24.24.94.44.94.14.34.54.94.74.14.44.53.84.44NaN 3.94.34.64.74.54.44.44.24.53.94.6NaN 44.54.14.13.73.8444.84.84.34.24.22.64.34.94444.94.84.64.44.14.44.74.954.94.84.24.44.34.743.54.64.73.74.64.554.74.24.44.94.74.23.84.14.34.153.544.54.14.14.54.14.34.84.74.84.32.53.63.94.83.944.34.13.53.64.44.64.14.34.24.24.94.454.24.84.73.94.844.74.43.83.54.24.444.84.64.94.44.84.24.94.44.94.14.64.154.34.44.14.94.14.244.94.44.54.34.64.54.93.94.154.24.84.254.73.844.73.84.44.14.13.64.74.9NaN 4.14.24.34.14.14.24.454.84.94.34.42.54.63.74.74.14.44.34.54.34.254.34.74.33.53.84.64.64.44.94.84.84.94.13.84.54.64.94.34.64.24.64.64.84.64.15NaN 44.54.34.64.244.14.6444.64.24.44.64.14.34.8NaN 4.74.44.544.84.44.34.84.14.54.54.554.94.14.14.34.64.34.9NaN 4.84.544.2444.54.74.94.74.24.3NaN 4.14.84.34.84.14.454.544.33.74.34.94.44.94.84.64.72.54.64.64.64.64.14.14.644.64.74.62.644.944.24NaN 4.544.644.24.544.13.144.54.34.44.1NaN 3.54.84.94.74.14.34.74.94.3NaN 4.44.94.93.84.94.64.64.84.244.24.84.72.7NaN 4.14.94.44.84.14.244.44.54.64.64.34.73.54.454.44.13.54.14.84.54.13.84.73.754.84.64.64NaN 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54.74.64.54.64.94.74.23.84.44.74.74.844.44.85NaN 3.84.14.44.3NaN 434.74.64.74.244.64.14.24.34.63.94.74.644.33.74.44.94.84.23.754.64.23.9553.44.94NaN 4.14.24.94.84.13.84.64.14.34.83.94.54.54.13.84.14.34.84.24.13.54.34.44.44.84.4NaN NaN 4.64.13.844.94.93.74.94.94.64.24.24.64.44.243.64.24.34.84.64.94.44.54.54.84.14.64.6NaN 4.74.24.34.94.44.94.64.14.344NaN 4.34.34.64.24.444.24.34.64.34.94.64.34.64.64.24.64.9NaN NaN 4.454.94.54.14.94.64.34.14.14.24.34.94.544.354.34.54.24.94.34.34.84.64.644.94.44.844.74.64.64.44.34.3NaN 3.84.44.44.14.844.944.63.65453.34.64.34.8444.44.34.24.84.94.14NaN 4.74.14.74.24.44.34.24.54.74.454.64.14.24.94.13.74.34.94.54.44.744.64.63.444.43.54.24.24.84.954.24.24.24.14.74.24.34.64.34.94.24.14.14.74.64.73.94.34.54.44.144.34.74.843.84.44.64.24.83.94.244.24.244.24.44.94.94.64.34.94.34.24.74.64.24.24.84.24.14.654.6543.54.144.644.54.54.74.84.64.34.94.84.24.24.44.44.24.84.944.144.84.24.74.34.14.14.14.43.84.24.54.5NaN 4.644.74.14.74.8NaN 4.44.343.64.64.44.24.24.24.94.454.843.64.34.84.74.94NaN 4.344.44.44.6NaN NaN 44.53.54.74.54.44.64.3444.24.244.24.14.33.944.44.74.74.64.64.24.14.64.34.44.7NaN 3.44.74.64.14.23.74.64.73.7NaN 4.34.83.74.34.44.93.44.53.8NaN 4.34.754.14.33.83.74.44.14.24.34.14.8NaN 4.84.244.44.44.3NaN 3.244.34.643.4NaN 3.64.34.34.844.14.34.74.54.54.1NaN 4.34.44.24.44.154.14.65NaN 4.74.63.84.24.94.14.34.24.644.83.84.44.4NaN 4.74.53.7NaN 4.34.84.84.7NaN 4.54.34.84.14.64.94.44.63.64.84.34.34.44.354.24.33.54.9NaN NaN 4.343.64.12.94.74.64.23.64.93.74.74.84.24.24.24.64.754.254.34.24.64.74.24.54.44.14.44.24.14.13.84.54.244.1NaN 4.94.94.24.944.64.64.2NaN 4.54.94.64.14.44.94.24.64.74.44.34.64.44.54.64.44.54.354.14.34.44.244.84.64.34.74.24.44.34.63.94.24.654.13.54.44.43.644.24.14.154.54.44.24.14.34.54.74.84.74.54.84.34.44.94.14.414.64.34.54.34.24.62.74.63.7NaN 4.44.94.64.44.24.74.54.92.64.94.14.74.34.94.14.144.643.7NaN 4.23.54.54.344.14.64.14.944.84.44.13.63.74.84.24.64.94.54.34.24.24.62.74.54.54.2NaN 4.6NaN 4.34.83.54.65NaN 4.94.63.644.4NaN 4.74.344.64.34.14.53.94.44.64.63.34.6NaN 3.54.544.94.33.94.24.64.14.74.74.9443.944.14.24.94.44.64.3NaN 4.244.143.94.34.24.84514.23.64.24.644.24.84.74.34.74.14.64.754.24.2NaN 4.34.84.253.654.13.54.23.54.23.64.754.6NaN 4.24.3NaN NaN 4.64.7444.64.454.34.54.44.84.24.2NaN 4.74.64.13.84.44.93.544.44.34.944.34.14.543.43.64.554.64.24.64.14NaN 3.74.64.74.653.94.84.24.14.14.14.24.84.544.14.64.94.64.94.93.94.84.64.24.74.64.24.14.54.43.54.84.74.44.64.14.44.33.82.94.44.24.74.74.64.1NaN 4.44.74.34.2NaN 4.34.13.9NaN 2.744.53.43.8NaN 4.24.24.64.24.94.24.84.354.84.43.74.14.64.64.24.24.64.24.74.14.14.94.34.94.454.64.24.64.8NaN 4.94.9NaN 4.64.93.94.34.14.34.34.154.244.64.64.24.24.24.6' to numeric The above exception was the direct cause of the following exception: Traceback (most recent call last): File "C:\Users\32773\Desktop\外卖消费行为处理好的数据1\外卖消费行为处理好的数据\data mining.py", line 227, in <module> cluster_profile = data.groupby('Cluster')[cluster_features].mean() File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\groupby\groupby.py", line 2459, in mean result = self._cython_agg_general( "mean", alt=lambda x: Series(x, copy=False).mean(numeric_only=numeric_only), numeric_only=numeric_only, ) File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\groupby\groupby.py", line 2005, in _cython_agg_general new_mgr = data.grouped_reduce(array_func) File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\internals\managers.py", line 1488, in grouped_reduce applied = sb.apply(func) File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\internals\blocks.py", line 395, in apply result = func(self.values, **kwargs) File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\groupby\groupby.py", line 2002, in array_func result = self._agg_py_fallback(how, values, ndim=data.ndim, alt=alt) File "C:\Users\32773\.conda\envs\mining\Lib\site-packages\pandas\core\groupby\groupby.py", line 1948, in _agg_py_fallback raise type(err)(msg) from err TypeError: agg function failed [how->mean,dtype->object] 进程已结束，退出代码为 1 纠错
11-25
你遇到的错误： ``` TypeError: Could not convert string '4.54.74.6...' to numeric ``` 以及最终报错： ``` TypeError: agg function failed [how->mean,dtype->object] ``` ...说明在进行 `groupby('Cluster')...
285个地级市邻接矩阵、经济地理矩阵等8个矩阵数据（2003-2023年）
11-25
01、数据简介 共八个矩阵，各类矩阵通过量化空间关系，为区域政策制定（如交通规划、产业布局）和学术研究（如空间溢出效应、区域收敛）提供关键工具，需根据研究目标灵活选择或组合使用。 数据名称：285个地级市邻接矩阵、经济地理矩阵等8个矩阵数据 数据年份：2003-2023年 参考文献：邵帅,李欣,曹建华,杨莉莉.中国雾霾污染治理的经济政策选择——基于空间溢出效应的视角[J].经济研究,2016,51(09):73-88. 02、相关数据 地级市人均GDP、空间邻接矩阵、空间经济距离矩阵（GDP）、空间地理距离矩阵（经纬度）、空间地理距离倒数平方矩阵（经纬度）、经济地理权重矩阵（GDP和经纬度）、经济地理嵌套矩阵（GDP和经纬度）、空间经济矩阵（非对称）、空间经济地理矩阵（非对称）、纬度、经度、距离
【影视数据分析】基于C++的多维度可视化系统设计：实现高效实时数据处理与交互式决策支持 项目介绍 基于C++的影视数据可视化系统设计和实现的详细项目实例（含模型描述及部分示例代码）
最新发布
11-25
内容概要：本文详细介绍了一个基于C++的影视数据可视化系统的设计与实现，旨在应对影视行业海量、多源数据带来的分析挑战。系统利用C++的高性能优势，实现了大规模数据的高效处理与实时更新，支持多维度数据分析，涵盖票房、用户评价、社交媒体热度等，并通过柱状图、折线图、热力图、词云等多种可视化方式直观展示数据。项目强调用户友好的界面设计、跨平台兼容性、可扩展性与可定制性，结合创新的交互设计，提升用户体验与决策效率。系统不仅服务于影视创作者和营销团队，也为行业数字化转型和创新发展提供数据驱动的支持。; 适合人群：具备一定C++编程基础，从事数据分析、可视化开发或影视行业技术研究的研发人员、软件工程师及高校学生。; 使用场景及目标：①学习如何利用C++构建高性能数据可视化系统；②掌握多源数据融合、实时处理与图形渲染的技术方案；③为影视项目提供数据支持，优化内容创作与市场策略； 阅读建议：建议结合文中提到的模型设计与示例代码进行实践，重点关注数据处理流程、可视化模块实现及系统架构设计，同时可联系作者获取完整代码与GUI资源以加深理解。
CSS插入图片方法[可运行源码]
11-25
本文详细介绍了在CSS中插入图片的多种方法，包括使用background-image属性和background简写属性。通过设置不同的背景属性值，如background-color、background-position、background-size等，可以灵活控制背景图片的显示效果。文章还提供了具体的HTML示例代码，展示了如何在实际项目中应用这些属性。此外，还解释了背景图像默认位于元素左上角并在水平和垂直方向上重复的特性，以及如何通过background-repeat属性调整平铺方式。
jQuery与HTML设置只读/禁用属性[项目代码]
11-25
本文详细介绍了在jQuery和HTML中如何设置和移除表单元素的只读（readonly）和禁用（disabled）属性。在jQuery部分，通过attr()和removeAttr()方法演示了属性的动态操作，并对比了readonly与disabled的区别：disabled会阻止元素获取焦点且表单提交时排除该字段，而readonly仅限制编辑但仍可聚焦和提交。HTML部分列举了三种实现方式（onfocus=this.blur()、readonly、disabled），并附代码示例说明其视觉效果及交互差异（如灰色显示、Tab键切换等）。最后指出两者可结合使用，并补充了CSS屏蔽输入的技巧。
SQL编码规范指南[项目源码]
11-25
本文详细介绍了SQL编码规范的重要性及其具体实施方法。规范化的SQL代码能显著提升可读性、便于问题定位和团队协作。文章强调了大小写的正确使用、单引号与双引号的应用场景、缩进对齐原则、禁止使用SELECT *操作、注释的添加规范以及子句的排版规则等关键点。此外，还提供了表别名的命名建议、字段逗号放置位置等实用技巧，旨在帮助开发者编写清晰、整齐、结构化的SQL代码，从而提升编程效率和代码质量。
Layui输入事件监听[代码]
11-25
本文详细介绍了Layui框架中各种表单元素的输入事件监听方法，包括单选框、复选框、下拉菜单、输入框内容变动以及提交按钮的监听。通过示例代码展示了如何实时获取用户输入的值、监听表单元素的变化以及处理提交事件。此外，还提供了带注释的代码片段，帮助开发者理解每个事件监听器的具体功能和用法。这些方法可以广泛应用于表单验证、动态数据更新等场景，提升用户交互体验。
UAC-BOF-Bonanza[项目源码]
11-25
UAC-BOF-Bonanza is a GitHub repository that compiles various UAC (User Account Control) bypass techniques, weaponized as Beacon Object Files (BOFs). The project includes a module for the Havoc C2 Framework and extension.json files for Sliver C2, enabling users to leverage these bypass methods in red team operations. Techniques include exploiting elevated COM objects, registry modifications, DLL hijacking, and SSPI token forgery. The repository provides detailed usage instructions, OpSec considerations, and credits to original researchers. All bypasses were tested on Windows 10 and 11, though they may be detected by SOCs and EDR solutions. The project is licensed under GPL-3.0 and includes standalone implementations for each bypass.
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