# 导入数据 for index, row in data.iterrows(): sql = "INSERT IGNORE INTO job_list VALUES('%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s')ON DUPLICATE KEY UPDATE company_name = company_name AND job_title = job_title" % ( row['职位名称'], row['地区'], row['能力要求'], row['公司名字'], row['福利待遇'], row['详情链接'], row['工作经验'], row['学历'], row['最低薪资（K）'], row['最高薪资（K）'], row['薪期']) cursor.execute(sql) # 提交更改并关闭连接 conn.commit() cursor.close() conn.close()执行后数据库中存在重复数据

HeidiSQL工具导出导入MySQL数据

接下来，我们讨论如何导入数据。如果你已经有一个SQL脚本文件（例如emp.sql），可以通过以下步骤将其导入到HeidiSQL： 1. 打开HeidiSQL，连接到目标数据库（可能是不同的服务器或数据库）。 2. 选择要导入的SQL...

node-ignore::magnifying_glass_tilted_left:node-ignore是.gitignore规则的管理器和过滤器，由eslint，prettier和许多其他规则使用

ignore是一个管理器，过滤器和解析器，根据，使用纯JavaScript实现。 eslint，gitbook和语言都使用ignore 。请注意，（由fstream-ignore ）不遵循gitignore规范。要根据.gitignore文件过滤文件名，我建议使用...

at91sam9g25.zip_Ignore It!_at91

you can have it by hand ,so ignore me

已有代码# 导入数据 for index, row in data.iterrows(): sql = "INSERT IGNORE INTO job_list VALUES('%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s')ON DUPLICATE KEY UPDATE company_name = company_name" % ( row['职位名称'], row['地区'], row['能力要求'], row['公司名字'], row['福利待遇'], row['详情链接'], row['工作经验'], row['学历'], row['最低薪资（K）'], row['最高薪资（K）'], row['薪期']) cursor.execute(sql) # 提交更改并关闭连接 conn.commit() cursor.close() conn.close()怎么同时进行去重

sql = "INSERT INTO job_list VALUES('%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s') ON DUPLICATE KEY UPDATE company_name = company_name" % ( row['职位名称'], row['地区'], row['能力...

# 连接数据库 db = pymysql.connect( host='localhost', user='root', password='240034', charset='utf8', database='class' ) # 创建游标 cursor = db.cursor() # 查询sql sql = 'show tables' # 把sql语句交给游标执行 cursor.execute(sql) for list,contentss in zip(content,contents): if i == 0: # 创建数据表 sql = f'create table use({list} varchar(800))' cursor.execute(sql) for content in contentss: # 插入数据 # print(content) sql = 'insert ignore into use values(%s)' cursor.execute(sql,(content)) db.commit() i = i + 1 else: sql = 'alter table use add %s varchar(800)'%list cursor.execute(sql) for content in contentss: # 插入数据 # print(content) sql = f'insert ignore into use({list}) values(%s)' cursor.execute(sql,(content)) db.commit()

根据你提供的代码，我发现在执行创建数据表的语句时出现了问题。...sql = f'insert into use ({list}) values (%s)' 带有参数的语句中表名和字段名需要用反引号包围起来，而不是用括号 () 包围。

这段代码的详细解释是什么 for index, row in new_df.iterrows(): series = pd.Series([index]+list(row), index=df.columns) df = df.append(series, ignore_index=True)

这段代码利用 Pandas 库的 iterrows() 方法遍历一个 DataFrame 数据框中的每一行，然后将每一行数据转为 Pandas 库中的 Series 序列，再通过 append() 方法将这个序列添加到一个新的 DataFrame 数据框的末尾，最终...

import os import pandas as pd from openpyxl import load_workbook # 指定要合并的文件夹路径 folder_path = r"E:\aaaa\aaaa" fields_to_write = ['aaaa', 'aaaa'] # 获取文件夹中所有的 xlsx 文件路径 xlsx_files = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if f.endswith('.xlsx')] # 创建一个空的 DataFrame 用于存储合并后的数据 merged_data = pd.DataFrame() # 循环读取每个 xlsx 文件，将它们合并到 merged_data 中 for xlsx_file in xlsx_files: # 使用 pandas 读取 xlsx 文件，并清理无效字符引用 wb = load_workbook(filename=xlsx_file, read_only=True, data_only=True) for sheet_name in wb.sheetnames: ws = wb[sheet_name] for row in ws.rows: for cell in row: cell.value = cell.value if cell.value is None else str(cell.value).strip() df = pd.read_excel(wb) # 将读取到的数据追加到 merged_data 中 merged_data = merged_data.append(df, ignore_index=True) # 在 merged_data 中添加新的一列数据 merged_data['new_column'] = 'new_value' # 将合并后的数据写入到新的 xlsx 文件中 merged_data[fields_to_write + ['new_column']].to_excel(r"E:\aaaa\aaaa\merged_file.xlsx", index=False)这个代码会出现AttributeError: Cell is read only报错，请优化下

merged_data = merged_data.append(df, ignore_index=True) # 在 merged_data 中添加新的一列数据 merged_data['new_column'] = 'new_value' # 创建一个新的工作簿 wb_new = load_workbook(write_only=True) ...

import pandas as pd import math as mt import numpy as np from sklearn.model_selection import train_test_split from Recommenders import SVDRecommender triplet_dataset_sub_song_merged = triplet_dataset_sub_song_mergedpd triplet_dataset_sub_song_merged_sum_df = triplet_dataset_sub_song_merged[['user','listen_count']].groupby('user').sum().reset_index() triplet_dataset_sub_song_merged_sum_df.rename(columns={'listen_count':'total_listen_count'},inplace=True) triplet_dataset_sub_song_merged = pd.merge(triplet_dataset_sub_song_merged,triplet_dataset_sub_song_merged_sum_df) triplet_dataset_sub_song_merged['fractional_play_count'] = triplet_dataset_sub_song_merged['listen_count']/triplet_dataset_sub_song_merged small_set = triplet_dataset_sub_song_merged user_codes = small_set.user.drop_duplicates().reset_index() song_codes = small_set.song.drop_duplicates().reset_index() user_codes.rename(columns={'index':'user_index'}, inplace=True) song_codes.rename(columns={'index':'song_index'}, inplace=True) song_codes['so_index_value'] = list(song_codes.index) user_codes['us_index_value'] = list(user_codes.index) small_set = pd.merge(small_set,song_codes,how='left') small_set = pd.merge(small_set,user_codes,how='left') mat_candidate = small_set[['us_index_value','so_index_value','fractional_play_count']] data_array = mat_candidate.fractional_play_count.values row_array = mat_candidate.us_index_value.values col_array = mat_candidate.so_index_value.values data_sparse = coo_matrix((data_array, (row_array, col_array)),dtype=float) K=50 urm = data_sparse MAX_PID = urm.shape[1] MAX_UID = urm.shape[0] recommender = SVDRecommender(K) U, S, Vt = recommender.fit(urm) Compute recommendations for test users uTest = [1,6,7,8,23] uTest_recommended_items = recommender.recommend(uTest, urm, 10) Output recommended songs in a dataframe recommendations = pd.DataFrame(columns=['user','song', 'score','rank']) for user in uTest: rank = 1 for song_index in uTest_recommended_items[user, 0:10]: song = small_set.loc[small_set['so_index_value'] == song_index].iloc[0] # Get song details recommendations = recommendations.append({'user': user, 'song': song['title'], 'score': song['fractional_play_count'], 'rank': rank}, ignore_index=True) rank += 1 display(recommendations)这段代码报错了，为什么？给出修改后的代码

for song_index in uTest_recommended_items[user, 0:10]: song = small_set.loc[small_set['so_index_value'] == song_index].iloc[0] recommendations = recommendations.append({'user': user, 'song': song...

优化代码 def cluster_format(self, start_time, end_time, save_on=True, data_clean=False, data_name=None): """ local format function is to format data from beihang. :param start_time: :param end_time: :return: """ # 户用簇级数据清洗 if data_clean: unused_index_col = [i for i in self.df.columns if 'Unnamed' in i] self.df.drop(columns=unused_index_col, inplace=True) self.df.drop_duplicates(inplace=True, ignore_index=True) self.df.reset_index(drop=True, inplace=True) dupli_header_lines = np.where(self.df['sendtime'] == 'sendtime')[0] self.df.drop(index=dupli_header_lines, inplace=True) self.df = self.df.apply(pd.to_numeric, errors='ignore') self.df['sendtime'] = pd.to_datetime(self.df['sendtime']) self.df.sort_values(by='sendtime', inplace=True, ignore_index=True) self.df.to_csv(data_name, index=False) # 调用基本格式化处理 self.df = super().format(start_time, end_time) module_number_register = np.unique(self.df['bat_module_num']) # if registered m_num is 0 and not changed, there is no module data if not np.any(module_number_register): logger.logger.warning("No module data!") sys.exit() if 'bat_module_voltage_00' in self.df.columns: volt_ref = 'bat_module_voltage_00' elif 'bat_module_voltage_01' in self.df.columns: volt_ref = 'bat_module_voltage_01' elif 'bat_module_voltage_02' in self.df.columns: volt_ref = 'bat_module_voltage_02' else: logger.logger.warning("No module data!") sys.exit() self.df.dropna(axis=0, subset=[volt_ref], inplace=True) self.df.reset_index(drop=True, inplace=True) self.headers = list(self.df.columns) # time duration of a cluster self.length = len(self.df) if self.length == 0: logger.logger.warning("After cluster data clean, no effective data!") raise ValueError("No effective data after cluster data clean.") self.cluster_stats(save_on) for m in range(self.mod_num): print(self.clusterid, self.mod_num) self.module_list.append(np.unique(self.df[f'bat_module_sn_{str(m).zfill(2)}'].dropna())[0])

Here are some possible optimizations for the given code: 1. Instead of using a list comprehension to find columns with 'Unnamed' in their names, you can use the filter() function along with a ...

将上述代码放入了Recommenders.py文件中，作为一个自定义工具包。将下列代码中调用scipy包中svd的部分。转为使用Recommenders.py工具包中封装的svd方法。给出修改后的完整代码。import pandas as pd import math as mt import numpy as np from sklearn.model_selection import train_test_split from Recommenders import * from scipy.sparse.linalg import svds from scipy.sparse import coo_matrix from scipy.sparse import csc_matrix # Load and preprocess data triplet_dataset_sub_song_merged = triplet_dataset_sub_song_mergedpd # load dataset triplet_dataset_sub_song_merged_sum_df = triplet_dataset_sub_song_merged[['user','listen_count']].groupby('user').sum().reset_index() triplet_dataset_sub_song_merged_sum_df.rename(columns={'listen_count':'total_listen_count'},inplace=True) triplet_dataset_sub_song_merged = pd.merge(triplet_dataset_sub_song_merged,triplet_dataset_sub_song_merged_sum_df) triplet_dataset_sub_song_merged['fractional_play_count'] = triplet_dataset_sub_song_merged['listen_count']/triplet_dataset_sub_song_merged['total_listen_count'] # Convert data to sparse matrix format small_set = triplet_dataset_sub_song_merged user_codes = small_set.user.drop_duplicates().reset_index() song_codes = small_set.song.drop_duplicates().reset_index() user_codes.rename(columns={'index':'user_index'}, inplace=True) song_codes.rename(columns={'index':'song_index'}, inplace=True) song_codes['so_index_value'] = list(song_codes.index) user_codes['us_index_value'] = list(user_codes.index) small_set = pd.merge(small_set,song_codes,how='left') small_set = pd.merge(small_set,user_codes,how='left') mat_candidate = small_set[['us_index_value','so_index_value','fractional_play_count']] data_array = mat_candidate.fractional_play_count.values row_array = mat_candidate.us_index_value.values col_array = mat_candidate.so_index_value.values data_sparse = coo_matrix((data_array, (row_array, col_array)),dtype=float) # Compute SVD def compute_svd(urm, K): U, s, Vt = svds(urm, K) dim = (len(s), len(s)) S = np.zeros(dim, dtype=np.float32) for i in range(0, len(s)): S[i,i] = mt.sqrt(s[i]) U = csc_matrix(U, dtype=np.float32) S = csc_matrix(S, dtype=np.float32) Vt = csc_matrix(Vt, dtype=np.float32) return U, S, Vt def compute_estimated_matrix(urm, U, S, Vt, uTest, K, test): rightTerm = SVt max_recommendation = 10 estimatedRatings = np.zeros(shape=(MAX_UID, MAX_PID), dtype=np.float16) recomendRatings = np.zeros(shape=(MAX_UID,max_recommendation ), dtype=np.float16) for userTest in uTest: prod = U[userTest, :]rightTerm estimatedRatings[userTest, :] = prod.todense() recomendRatings[userTest, :] = (-estimatedRatings[userTest, :]).argsort()[:max_recommendation] return recomendRatings K=50 # number of factors urm = data_sparse MAX_PID = urm.shape[1] MAX_UID = urm.shape[0] U, S, Vt = compute_svd(urm, K) # Compute recommendations for test users # Compute recommendations for test users uTest = [1,6,7,8,23] uTest_recommended_items = compute_estimated_matrix(urm, U, S, Vt, uTest, K, True) # Output recommended songs in a dataframe recommendations = pd.DataFrame(columns=['user','song', 'score','rank']) for user in uTest: rank = 1 for song_index in uTest_recommended_items[user, 0:10]: song = small_set.loc[small_set['so_index_value'] == song_index].iloc[0] # Get song details recommendations = recommendations.append({'user': user, 'song': song['title'], 'score': song['fractional_play_count'], 'rank': rank}, ignore_index=True) rank += 1 display(recommendations)

for song_index in uTest_recommended_items[user, 0:10]: song = small_set.loc[small_set['so_index_value'] == song_index].iloc[0] # Get song details recommendations = recommendations.append({'user': ...

wordData = wordData.append(data3.ignore_index=True)

这段代码有语法错误。正确的写法应该是： ...这行代码的作用是将data3这个数据框添加到wordData这个数据框的末尾，并忽略data3中原有的索引，重新生成新的索引。ignore_index=True的作用就是实现这一功能。

将C++#include <ros/ros.h> #include <ros/package.h> #include <quadrotor_msgs/PositionCommand.h> #include #include <sensor_msgs/Joy.h> #include<mavros_msgs/AttitudeTarget.h> #include <tf/tf.h> #include <math.h> using namespace std; int main(int argc, char **argv) { ros::init(argc, argv, "traj_pub"); //##节点名traj_pub ros::NodeHandle nh; // ros::Publisher local_pos_pub3 = nh.advertise<quadrotor_msgs::PositionCommand> ("/position_cmd", 10); //##话题名字为/——position_cmd 10为缓存长度 quadrotor_msgs::PositionCommand poscom; //ros::Publisher local_pos_pub3 = nh.advertise<mavros_msgs::AttitudeTarget> // ("/mavros/setpoint_raw/attitude", 10); //mavros_msgs::AttitudeTarget msg; ros::Rate rate(20.0); //##循环频率20 int i = 0; while(ros::ok()) { poscom.position.x = 2.5sin(M_PIi/400); poscom.position.y = 5sin(M_PIi/800); poscom.position.z = 2; poscom.velocity.x = 0; poscom.velocity.y = 0; poscom.velocity.z = 0; poscom.acceleration.x = 0; poscom.acceleration.y = 0; poscom.acceleration.z = 0; poscom.yaw = 0; poscom.jerk.x=0; poscom.jerk.y=0; poscom.jerk.z=0; local_pos_pub3.publish(poscom); // tf::Quaternion q = tf::createQuaternionFromRPY(0, 0, 0.5); // msg.type_mask = msg.IGNORE_ROLL_RATE || msg.IGNORE_PITCH_RATE || msg.IGNORE_YAW_RATE; // msg.orientation.x = q.x(); // msg.orientation.y = q.y(); // msg.orientation.z = q.z(); // msg.orientation.w = q.w(); // msg.thrust = 0.75; // local_pos_pub3.publish(msg); ros::spinOnce(); rate.sleep(); i++; } } 转成pyrhon

# msg.type_mask = msg.IGNORE_ROLL_RATE | msg.IGNORE_PITCH_RATE | msg.IGNORE_YAW_RATE # msg.orientation.x = q[0] # msg.orientation.y = q[1] # msg.orientation.z = q[2] # msg.orientation.w = q[3] #...

# 循环处理每一行 for i, row in df[mask].iterrows(): # 计算前后平均值 average_values = {} for column in df.columns: if column in ['车速']: average_values[column] = (row[column] + df.loc[i+1, column]) / 2 # 创建新行并插入数据 new_row = {'数据采集时间': row['数据采集时间'] + pd.Timedelta(seconds=15), '时间差': 15} # 设置新行的时间差为15秒 new_row.update(average_values) # 添加到新行DataFrame中 new_rows = new_rows.append(new_row, ignore_index=True)这段代码是否会无限循环

在循环中，每一行的数据被用于计算前后平均值，并创建一个新的行数据 new_row。然后将新行添加到 new_rows DataFrame 中。这个过程会在满足条件的行上进行迭代，直到遍历完所有满足条件的行为止。循环不会无限...

pytorch代码如下：class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s if weight is not None: weight = torch.FloatTensor(weight).cuda() self.weight = weight self.cls_num_list = cls_num_list def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0)) # 0,1 batch_m = batch_m.view((-1, 1)) # size=(batch_size, 1) (-1,1) x_m = x - batch_m output = torch.where(index, x_m, x) if self.weight is not None: output = output * self.weight[None, :] logit = output * self.s return F.cross_entropy(logit, target, weight=self.weight) classes=7, cls_num_list = np.zeros(classes) for , label in train_loader.dataset: cls_num_list[label] += 1 criterion_train = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30) criterion_val = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30) for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device, non_blocking=True), Variable(target).to(device,non_blocking=True) # 3、将数据输入mixup_fn生成mixup数据 samples, targets = mixup_fn(data, target) targets = torch.tensor(targets).to(torch.long) # 4、将上一步生成的数据输入model，输出预测结果，再计算loss output = model(samples) # 5、梯度清零（将loss关于weight的导数变成0） optimizer.zero_grad() # 6、若使用混合精度 if use_amp: with torch.cuda.amp.autocast(): # 开启混合精度 loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss scaler.scale(loss).backward() # 梯度放大 torch.nn.utils.clip_grad_norm(model.parameters(), CLIP_GRAD) # 梯度裁剪，防止梯度爆炸 scaler.step(optimizer) # 更新下一次迭代的scaler scaler.update() 报错：File "/home/adminis/hpy/ConvNextV2_Demo/models/losses.py", line 53, in forward return F.cross_entropy(logit, target, weight=self.weight) File "/home/adminis/anaconda3/envs/wln/lib/python3.9/site-packages/torch/nn/functional.py", line 2824, in cross_entropy return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index) RuntimeError: multi-target not supported at /pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:15

batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0)) # 0,1 batch_m = batch_m.view((-1, 1)) # size=(batch_size, 1) x_m = x - batch_m output = torch.where(index, x_m, x) if self...

imp c##shiwmstest/shIwms_202305@orcl file="D:\oracle\backup\data_20230430.dmp" full=y ignore=y

这是一个使用 imp 工具导入 Oracle 数据库备份文件的命令。其中： - c##shiwmstest/shIwms_202305@orcl 是登录数据库的用户名、密码和服务名。 - file="D:\oracle\backup\data_20230430.dmp" 指定备份文件的路径和...

import pandas as pd pd.set_option('display.unicode.east_asian_width',True) df = pd.read_excel('C:\yh\PycharmProjects\study\A5204990500002023050006\study\总表.xlsx',index_col=0,dtype=str) first_card = '6228480269035098277' level_cards = pd.DataFrame({'卡级':[],'账号':[],'账号姓名':[]}) for index,row in df.iterrows(): if row[1] == first_card: level_cards = pd.concat([level_cards,pd.DataFrame({'卡级':['一级卡'],'账号':[row[1]]})],ignore_index=True) level_cards.drop_duplicates(subset='账号',keep='first',inplace=True) for index,row in df.iterrows(): if row[1] == first_card: level_cards = pd.concat([level_cards,pd.DataFrame({'卡级':['二级卡'],'账号':row[3],'账号姓名':row[2]})],ignore_index=True) level_cards.drop_duplicates(subset='账号',keep='first',inplace=True) print(level_cards) for index,row in df.iterrows(): if level_cards in row[1]: level_cards = pd.concat([level_cards, pd.DataFrame({'卡级': ['三级卡'], '账号': row[3], '账号姓名': row[2]})], ignore_index=True) print(level_cards)

具体来说，代码首先通过 pandas 库中的 read_excel() 函数来读取指定路径下的 Excel 文件，然后使用 iterrows() 函数来遍历每一行数据。在第一个 for 循环中，如果某一行的第二列（即“卡号”）等于指定的“一级卡”...

import pandas as pd pd.set_option('display.unicode.east_asian_width', True) df = pd.read_excel('G:\A5204990500002023050006\一级卡6228480269035098277.xlsx',index_col=0) print(df) first_card = 6228480269035098277 level_cards = pd.DataFrame({'卡级': [], '账号': []}) for index, row in df.iterrows(): if row[0] == first_card: level_cards = pd.concat([level_cards, pd.DataFrame({'卡级': ['一级卡'], '账号': [row[0]]})], ignore_index=True) print(level_cards) level_cards.to_excel('E:\projects\study\study_2.xlsx',engine=)

for index, row in df.iterrows(): if row[0] == first_card: level_cards = pd.concat([level_cards, pd.DataFrame({'卡级': ['一级卡'], '账号': [row[0]]})], ignore_index=True) # 将数据写入 Excel 文件 ...

ignore_flag = False # 是否忽略后面的字符的标志 for ch in s: if ch == '#': ignore_flag = True break

- for ch in s:：遍历字符串 s 中的每一个字符，赋值给变量 ch。 - if ch == '#'：判断当前字符 ch 是否为 #。 - ignore_flag = True：如果当前字符为 #，则将 ignore_flag 标志设置为 True。 -...

def destroy(self):#摧毁 for p in range(self._partitions): with self._get_env_for_partition(p, write=True) as env: db = env.open_db() with env.begin(write=True) as txn: txn.drop(db) table_key = f"{self._namespace}.{self._name}" _get_meta_table().delete(table_key) path = _get_storage_dir(self._namespace, self._name) shutil.rmtree(path, ignore_errors=True)

这段代码看起来是在执行一个摧毁操作，它会删除数据库中的表和对应的数据，并删除存储路径下的文件。根据代码的逻辑，它首先会遍历每个分区，然后在每个分区上打开数据库，并在事务中删除表。接着，它会从元数据表中...

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