name = 'Coupon_id' # 折扣率排序 datas[name_prifix + 'discount_rateRank'] = datas.groupby(name)['discount_rate'].rank(ascending = False) datas[name_prifix + 'discount_rateRankAscend'] = datas.groupby(name)['discount_rate'].rank(ascending = True) # 距离排序 datas[name_prifix + 'distanceRank'] = datas.groupby(name)['Distance'].rank(ascending = False) datas[name_prifix + 'distanceRankAscend'] = datas.groupby(name)['Distance'].rank(ascending = True) # 领券日期排序 datas[name_prifix + 'date_receivedRank'] =datas.groupby(name)['Date_received'].rank(ascending = False) datas[name_prifix + 'date_receivedRankAscend'] =datas.groupby(name)['Date_received'].rank(ascending = True) 请帮我用groupby函数改写

select a.*, b.name activityName, c.name productName, c.member_max memberMax, d.status as complete, group_concat(if(e.face_value is null, "", e.face_value) separator "\n") as rewardAmount, group_concat(if(e.coupon_code is null, "", e.coupon_code ) separator "\n") as couponCode , group_concat(if(e.coupon_id is null, "", e.coupon_id) separator "\n") as caId from marketing_group_tool_group_member a left join marketing_group_tool_group_info d on a.group_id = d.id left join marketing_group_tool_activity b on a.activity_id = b.activity_id left join marketing_group_tool_product_base c on a.product_id = c.product_id left join marketing_group_tool_send_coupon_record e on (a.group_id = e.group_id and a.card_no = e.card_no) left join wx_recommend_organization organization on b.organization_id = organization.id where ((organization.tree_id between 2002000000000000 and 2002999999999999) or (organization.tree_id between 1000000000000000 and 1999999999999999) or (organization.tree_id between 2000000000000000 and 2999999999999999) or (organization.tree_id between 3000000000000000 and 3999999999999999) or (organization.tree_id between 4000000000000000 and 4999999999999999) or (organization.tree_id between 6000000000000000 and 6999999999999999) or (organization.tree_id between 5000000000000000 and 5999999999999999) or (organization.tree_id between 10000000000000000 and 10999999999999999) or (organization.tree_id between 8000000000000000 and 8999999999999999) or (organization.tree_id between 9000000000000000 and 9999999999999999) or (organization.tree_id between 11000000000000000 and 11999999999999999) or (organization.tree_id between 12000000000000000 and 12999999999999999) or (organization.tree_id between 13000000000000000 and 13999999999999999) or (organization.tree_id between 14000000000000000 and 14999999999999999) or (organization.tree_id between 15000000000000000 and 15999999999999999) or (organization.tree_id between 16000000000000000 and 16999999999999999) or (organization.tree_id between 17000000000000000 and 17999999999999999) ) and 1 = 1 group by a.id, a.activity_id , a.group_id , a.product_id , a.activity_referral_code, a.product_referral_code , a.openid , a.unionid , a.nickname , a.head_img , a.mobile , a.is_sub_buy , a.is_sub_progress , a.is_sub_success , a.sort , a.card_no , a.create_time , a.is_received_notice , a.is_received_progress , a.is_received_success , b.name, c.name, c.member_max , d.status order by a.id desc limit 0,20;

3. 尽量避免使用 group_concat，这会导致 MySQL 在内部生成临时表，会消耗大量的内存和 CPU 时间，可以考虑在应用层进行拼接。 4. 将复杂查询拆分成多个简单查询，每个查询只涉及少量表和字段，可以利用缓存和...

SELECT c.stock_id, a.stock_name, a.coupon_amount, a.transaction_minimum, b.company_name, a.start_time, a.end_time, (SELECT COUNT(1) FROM user_coupon WHERE activity_id = a.id) AS grant, (SELECT COUNT(1) FROM user_coupon WHERE activity_id = a.id AND (status = "20" OR status = "30")) AS receive, (SELECT COUNT(1) FROM user_coupon WHERE activity_id = a.id AND status = "30") AS writeOff FROM wx_coupon a LEFT JOIN baishiof_company b ON a.company_id = b.company_id LEFT JOIN user_coupon c ON a.id = c.activity_id GROUP BY c.stock_id, a.stock_name, a.coupon_amount, a.transaction_minimum, b.company_name, a.start_time, a.end_time;以上代码出现了You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near 'grant , ( SELECT COUNT(1) FROM user_coupon WHERE activity_id = a.id AND (status ' at line 1这个错误，修复他

GROUP BY c.stock_id, a.stock_name, a.coupon_amount, a.transaction_minimum, b.company_name, a.start_time, a.end_time; 在上面的修复中，将 grant 列名修改为 total_grant。你可以根据需要选择其他...

第一个SQL： SELECT orders.id,orders.user_id,orders.cityarea_id,orders.batchcode,orders.status, orders.pay_status,orders.real_pay,orders.total,orders.money,orders.pay_type, orders.premium,orders.createtime,orders.paytime,orders.confirmtime, orders.is_evaluate,orders.pay_method,orders.exp_user_id, orders.return_score, orders.is_change,orders.reward_money,orders.isvalid,orders.after_sale_status,orders.remark, orders.use_coupon_detail,orders.block_chain_price, users.name,users.weixin_name,users.weixin_fromuser, users.phone,wcoe.diy_area_name from wsy_o2o.weixin_cityarea_orders orders left join wsy_o2o.weixin_cityarea_supply supply on orders.cityarea_id = supply.id left join wsy_o2o.weixin_cityarea_payonline_orders wcco on orders.batchcode=wcco.batchcode left join wsy_user.weixin_users users on users.id = orders.user_id LEFT JOIN wsy_o2o.weixin_cityarea_orders_extend wcoe ON orders.batchcode = wcoe.batchcode WHERE orders.types=3 and orders.customer_id = 41 AND orders.isvalid=true GROUP BY orders.batchcode ORDER BY orders.id DESC limit 0,20; 第二个SQL： SELECT orders.id,orders.user_id,orders.cityarea_id,orders.batchcode,orders.status, orders.pay_status,orders.real_pay,orders.total,orders.money,orders.pay_type, orders.premium,orders.createtime,orders.paytime,orders.confirmtime, orders.is_evaluate,orders.pay_method,orders.exp_user_id, orders.return_score, orders.is_change,orders.reward_money,orders.isvalid,orders.after_sale_status,orders.remark, orders.use_coupon_detail,orders.block_chain_price, users.name,users.weixin_name,users.weixin_fromuser, users.phone,wcoe.diy_area_name ,writes.ActivityStatus,writes.NewMoney from wsy_o2o.weixin_cityarea_orders orders left join wsy_o2o.weixin_cityarea_supply supply on orders.cityarea_id = supply.id left join wsy_o2o.weixin_cityarea_payonline_orders wcco on orders.batchcode=wcco.batchcode left join wsy_user.weixin_users users on users.id = orders.user_id LEFT JOIN wsy_o2o.weixin_cityarea_orders_extend wcoe ON orders.batchcode = wcoe.batchcode LEFT JOIN wsy_dream.ShopWrite writes on orders.batchcode = writes.Batchcode WHERE orders.types=3 and orders.customer_id = 41 AND orders.isvalid=true GROUP BY orders.batchcode ORDER BY orders.id DESC limit 0,20; 为什么查询结果从0.7秒变成了3.7秒？

查询结果从0.7秒变成3.7秒可能是由于以下原因导致的： 1. SQL语句中的表连接（JOIN）操作过多：第二个SQL语句相较于第一个SQL语句多了一个表连接，即LEFT JOIN wsy_dream.ShopWrite writes on orders.batchcode = ...

def get_predict(datas=datasets_yc,model=model,features=['Discount_rate','Distance'],outfiles='result01.csv'): data_yc = datas[features] pre_yc = model.predict_proba(data_yc)[:,1] result = datas[['User_id','Coupon_id','Date_received']] result['probability'] = pre_yc

result = datas[['User_id', 'Coupon_id', 'Date_received']] result['probability'] = pre_yc result.to_csv(outfiles, index=False) 这里使用了 iloc 函数对 DataFrame 进行切片，可以避免使用列名进行...

SELECT orders.id,orders.user_id,orders.cityarea_id,orders.batchcode,orders.status, orders.pay_status,orders.real_pay,orders.total,orders.money,orders.pay_type, orders.premium,orders.createtime,orders.paytime,orders.confirmtime, orders.is_evaluate,orders.pay_method,orders.exp_user_id, orders.return_score, orders.is_change,orders.reward_money,orders.isvalid,orders.after_sale_status,orders.remark, orders.use_coupon_detail,orders.block_chain_price, users.name,users.weixin_name,users.weixin_fromuser, users.phone,wcoe.diy_area_name ,writes.ActivityStatus,writes.NewMoney,records.Commission,records.ReturnRadio from wsy_o2o.weixin_cityarea_orders orders left join wsy_o2o.weixin_cityarea_supply supply on orders.cityarea_id = supply.id left join wsy_o2o.weixin_cityarea_payonline_orders wcco on orders.batchcode=wcco.batchcode left join wsy_user.weixin_users users on users.id = orders.user_id LEFT JOIN wsy_o2o.weixin_cityarea_orders_extend wcoe ON orders.batchcode = wcoe.batchcode LEFT JOIN wsy_dream.ShopWrite writes on orders.batchcode = writes.Batchcode LEFT JOIN wsy_o2o.weixin_cityarea_supply_commissons_record records ON orders.cityarea_id = records.SupplyId and records.CreateTime < orders.createtime ORDER BY records.CreateTime DESC LIMIT 1 WHERE orders.types=3 and orders.customer_id = 41 AND orders.isvalid=true GROUP BY orders.batchcode ORDER BY orders.id DESC limit 0,20; 以上代码报错，怎么解决

在 ORDER BY 之前应该是 WHERE 语句，然后是 GROUP BY，最后是 ORDER BY 和 LIMIT。请将这两个语句调整到正确的位置。 2. LEFT JOIN 和 JOIN 语句之间缺少逗号。请在第一个 LEFT JOIN 语句和第二个...

select c.stock_id,a.stock_name,a.coupon_amount, a.transaction_minimum, b.company_name, a.start_time, a.end_time, (select count(1) from user_coupon where activity_id = a.id) as grant,(select count(1) from user_coupon where activity_id = a.id and (status = "20" or status = "30")) as receive,(select count(1) from user_coupon where activity_id = a.id and status = "30") as writeOff from wx_coupon a left join baishiof_company b on a.company_id = b.company_id left join user_coupon c on a.id = c.activity_id group by c.stock_id这段SQL报Expression #2 of SELECT list is not in GROUP BY clause and contains nonaggregated column 'baishiof.a.stock_name' which is not functionally dependent on columns in GROUP BY clause; this is incompatible with sql_mode=only_full_group_by错误修改这条SQL使其可以执行

要修复这个错误，你可以调整 GROUP BY 子句配置，以便在查询中包含所有非聚合列。修改后的 SQL 查询语句如下： sql SELECT c.stock_id, a.stock_name, a.coupon_amount, a.transaction_minimum, b.company_...

nan1 = data_off["Discount_rate"].isnull() nan2 = data_off['Date_received'].isnull() nan3 = data_off['Coupon_id'].isnull() np.all(nan1==nan2),np.all(nan1==nan3) 解释

这段代码的作用是判断 data_off 数据集中的优惠券折扣率（Discount_rate）、领券时间（Date_received）和优惠券编号（Coupon_id）是否存在缺失值（即空值或 NA 值）。首先，通过 nan1 = data_off["Discount_rate...

def get_feat_importance(dtest,model): # 预测 predict = model.predict(dtest) # 处理结果 predict = pd.DataFrame(predict, columns=['prob']) result = pd.concat([test[['User_id', 'Coupon_id', 'Date_received']], predict], axis=1) train_data=xgb.DMatrix(offline_train,label=get_label) params={'max_width':3} bst=xgb.train(params,train_data,num_boost_round=1) for importance_type in('weight','gain','cover','total_gain','total_cover'): print('%s: ' % importance_type,bst.get_score(importance_type=importance_type)) return result def get_result(model,test):#线上测试集 dtest = xgb.DMatrix(test.drop(['User_id', 'Coupon_id', 'Date_received'], axis=1)) # 预测 predict = model.predict(dtest) # 处理结果 predict = pd.DataFrame(predict, columns=['pred']) result = pd.concat([test[['User_id', 'Coupon_id', 'Date_received']], predict], axis=1) result.to_csv('result/result.csv', index=False, header=None) print("results are saved.")改写代码，不改变功能

result = pd.concat([test[['User_id', 'Coupon_id', 'Date_received']], predict], axis=1) train_data=xgb.DMatrix(offline_train,label=get_label) params={'max_width':3} bst=xgb.train(params,train_...

select c.stock_id,a.stock_name,a.coupon_amount, a.transaction_minimum, b.company_name, a.start_time, a.end_time, (select count(1) from user_coupon where activity_id = a.id) as grant,(select count(1) from user_coupon where activity_id = a.id and (status = "20" or status = "30")) as receive,(select count(1) from user_coupon where activity_id = a.id and status = "30") as writeOff from wx_coupon a left join baishiof_company b on a.company_id = b.company_id left join user_coupon c on a.id = c.activity_id group by c.stock_id

这是一个查询语句，根据不同的条件从三个表（wx_coupon, baishiof_company, user_coupon）中获取数据。以下是查询结果的字段： - c.stock_id: 股票ID - a.stock_name: 股票名称 - a.coupon_amount: 优惠券金额 - a....

帮我解析一下这个java错误，Caused by: org.h2.jdbc.JdbcSQLSyntaxErrorException: Syntax error in SQL statement "SELECT C.PERIOD_NO, C.BILL_MONTH, C.LOCAL_AIR_CODE, C.INT_DOM_INDICATOR, 'GLOBAL' AS CHECK_CODE, CASE WHEN COUPON_COUNT_GLOBAL = COUPON_COUNT_REPORT AND TAX_AMT_GLOBAL = TAX_AMT_REPORT AND ISC_AMT_GLOBAL = ISC_AMT_REPORT AND UATP_AMT_GLOBAL = UATP_AMT_REPORT AND GROSS_AMT_GLOBAL = GROSS_AMT_REPORT THEN 'S' ELSE 'F' END AS CHECK_RESULT_IND, TAX_AMT_GLOBAL AS TAX_AMT_ACTUAL, TAX_AMT_REPORT AS TAX_AMT_EXPECTED, ISC_AMT_GLOBAL AS ISC_AMT_ACTUAL, ISC_AMT_REPORT AS ISC_AMT_EXPECTED, UATP_AMT_GLOBAL AS UATP_AMT_ACTUAL, UATP_AMT_REPORT AS UATP_AMT_EXPECTED, GROSS_AMT_GLOBAL AS GROSS_AMT_ACTUAL, GROSS_AMT_REPORT AS GROSS_AMT_EXPECTED, COUPON_COUNT_GLOBAL AS COUPON_COUNT_ACTUAL, COUPON_COUNT_REPORT AS COUPON_COUNT_EXPECTED FROM ( ( SELECT BILL_MONTH, PERIOD_NO, LOCAL_AIR_CODE, INT_DOM_INDICATOR, SUM(COUPON_COUNT) AS COUPON_COUNT_GLOBAL, CASE WHEN SUM(GROSS_AMT_BILLING) IS NULL THEN 0 ELSE SUM(GROSS_AMT_BILLING) END AS GROSS_AMT_GLOBAL, CASE WHEN SUM(BILL_TAX_BILLING) IS NULL THEN 0 ELSE SUM(BILL_TAX_BILLING) END AS TAX_AMT_GLOBAL, CASE WHEN SUM(ISC_AMT_BILLING) IS NULL THEN 0 ELSE SUM(ISC_AMT_BILLING) END AS ISC_AMT_GLOBAL, CASE WHEN SUM(UATP_AMT_BILLING) IS NULL THEN 0 ELSE SUM(UATP_AMT_BILLING) END AS UATP_AMT_GLOBAL FROM OWB_GLOBAL_REPORT WHERE REPORT_TYPE = 'R' AND DELETE_IND IS NULL AND PERIOD_NO = ? AND BILL_MONTH = ? AND INT_DOM_INDICATOR = ? AND LOCAL_AIR_CODE = ? GROUP BY BILL_MONTH, PERIOD_NO, LOCAL_AIR_CODE, INT_DOM_INDICATOR) C[*] INNER JOIN ( SELECT BILL_MONTH, PERIOD_NO, LOCAL_AIR_CODE, INT_DOM_INDICATOR, SUM(COUPON_COUNT) AS COUPON_COUNT_REPORT, CASE WHEN SUM(GROSS_AMT_BILLING) IS NULL THEN 0 ELSE SUM(GROSS_AMT_BILLING) END AS GROSS_AMT_REPORT, CASE WHEN SUM(BILL_TAX_BILLING) IS NULL THEN 0 ELSE SUM(BILL_TAX_BILLING) END AS TAX_AMT_REPORT, CASE WHEN SUM(ISC_AMT_BILLING) IS NULL THEN 0 ELSE SUM(ISC_AMT_BILLING) END AS ISC_AMT_REPORT, CASE WHEN SUM(UATP_AMT_BILLING) IS NULL THEN 0 ELSE SUM(UATP_AMT_BILLING) END AS UATP_AMT_REPORT FROM OWB_REPORT WHERE ADJ_MONTH IS NULL AND PERIOD_NO = ? AND BILL_MONTH = ? AND INT_DOM_INDICATOR = ? AND LOCAL_AIR_CODE = ? GROUP BY BILL_MONTH, PERIOD_NO, LOCAL_AIR_CODE, INT_DOM_INDICATOR) R ON C.BILL_MONTH = R.BILL_MONTH AND C.PERIOD_NO = R.PERIOD_NO AND C.LOCAL_AIR_CODE = R.LOCAL_AIR_CODE AND C.INT_DOM_INDICATOR = R.INT_DOM_INDICATOR )"; expected "UNION, EXCEPT, MINUS, INTERSECT, ORDER, OFFSET, FETCH, LIMIT, SAMPLE_SIZE, FOR, )";

这个错误是由于Java程序中的SQL语句存在语法错误导致的。根据错误信息，语法错误出现在SQL查询语句的最后部分。错误提示指出在预期的位置上应该是"UNION, EXCEPT, MINUS, INTERSECT, ORDER, OFFSET, FETCH, LIMIT, ...

def get_dataset(history_field, middle_field, label_field): """构造数据集 Args: Returns: """ # 特征工程 week_feat = get_week_feature(label_field) # 日期特征 simple_feat = get_simple_feature(label_field) # 示例简单特征 # 构造数据集 share_characters = list( set(simple_feat.columns.tolist()) & set(week_feat.columns.tolist())) # 共有属性,包括id和一些基础特征,为每个特征块的交集 dataset = pd.concat([week_feat, simple_feat.drop(share_characters, axis=1)], axis=1) # 删除无用属性并将label置于最后一列 if 'Date' in dataset.columns.tolist(): # 表示训练集和验证集 dataset.drop(['Merchant_id', 'Discount_rate', 'Date', 'date_received', 'date'], axis=1, inplace=True) label = dataset['label'].tolist() dataset.drop(['label'], axis=1, inplace=True) dataset['label'] = label else: # 表示测试集 dataset.drop(['Merchant_id', 'Discount_rate', 'date_received'], axis=1, inplace=True) # 修正数据类型 dataset['User_id'] = dataset['User_id'].map(int) dataset['Coupon_id'] = dataset['Coupon_id'].map(int) dataset['Date_received'] = dataset['Date_received'].map(int) dataset['Distance'] = dataset['Distance'].map(int) if 'label' in dataset.columns.tolist(): dataset['label'] = dataset['label'].map(int) # 去重 dataset.drop_duplicates(keep='first', inplace=True) dataset.index = range(len(dataset)) # 返回 return dataset为什么

接着，对数据类型进行修正，包括将用户ID、优惠券ID、领取日期和距离转换为整数类型。如果数据集中包含标签列，则将标签列也转换为整数类型。最后，通过“drop_duplicates”函数去重，并重新设置数据集的索引。...

def model_xgb(train, test): """xgb模型 Args: Returns: """ # xgb参数 params = {'booster': 'gbtree', 'objective': 'binary:logistic', 'eval_metric': 'auc', 'silent': 1, 'eta': 0.01, 'max_depth': 5, 'min_child_weight': 1, 'gamma': 0, 'lambda': 1, 'colsample_bylevel': 0.7, 'colsample_bytree': 0.7, 'subsample': 0.9, 'scale_pos_weight': 1} # 数据集 dtrain = xgb.DMatrix(train.drop(['User_id', 'Coupon_id', 'Date_received', 'label'], axis=1), label=train['label']) dtest = xgb.DMatrix(test.drop(['User_id', 'Coupon_id', 'Date_received'], axis=1)) # 训练 watchlist = [(dtrain, 'train')] model = xgb.train(params, dtrain, num_boost_round=500, evals=watchlist) # 预测 predict = model.predict(dtest) # 处理结果 predict = pd.DataFrame(predict, columns=['prob']) result = pd.concat([test[['User_id', 'Coupon_id', 'Date_received']], predict], axis=1) # 特征重要性 feat_importance = pd.DataFrame(columns=['feature_name', 'importance']) feat_importance['feature_name'] = model.get_score().keys() feat_importance['importance'] = model.get_score().values() feat_importance.sort_values(['importance'], ascending=False, inplace=True) # 返回 return result, feat_importance解释一下

将预测结果和测试数据集中的 User_id、Coupon_id、Date_received 列进行合并，得到最终的预测结果。 5. 使用 XGBoost 的 get_score 函数获取特征重要性，并将其保存为一个 DataFrame 对象，包括每个特征的...

datasets_yc = pd.merge(datasets_yc,coupon_rate,how='left',on='Coupon_id') datasets_yc = pd.merge(datasets_yc,user_mer_num,how='left',on=['User_id','Merchant_id']) datasets_yc = pd.merge(datasets_yc,user_mer_coupon,how='left',on=['User_id','Merchant_id']) datasets_yc = pd.merge(datasets_yc,user_mer,how='left',on=['User_id','Merchant_id']) datasets_yc['user_mer_cop_rate'] = datasets_yc['user_mer_num'] / datasets_yc['user_mer_coupon']

.merge(user_mer_coupon, on=['User_id','Merchant_id'], how='left') \ .merge(user_mer, on=['User_id','Merchant_id'], how='left') datasets_yc['user_mer_cop_rate'] = datasets_yc['user_mer_num'] / ...

# 以Date_received为分组目标并统计优惠券的数量 tmp = df_1.groupby('Date_received', as_index=False)['Coupon_id'].count() as_index什么意思

因此，执行 tmp = df_1.groupby('Date_received', as_index=False)['Coupon_id'].count() 代码后，得到的结果会包含一个名为 'Date_received' 的列作为分组的依据，并且 'Coupon_id' 列的值将被统计计数。...

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def get_predict(datas=datasets_yc,model=model,features=['Discount_rate','Distance'],outfiles='result01.csv'): data_yc = datas[features] pre_yc = model.predict_proba(data_yc)[:,1] result = datas[['User_id','Coupon_id','Date_received']] result['probability'] = pre_yc

nan1 = data_off["Discount_rate"].isnull() nan2 = data_off['Date_received'].isnull() nan3 = data_off['Coupon_id'].isnull() np.all(nan1==nan2),np.all(nan1==nan3) 解释

# 以Date_received为分组目标并统计优惠券的数量 tmp = df_1.groupby('Date_received', as_index=False)['Coupon_id'].count() as_index什么意思

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