for row in new_day01: if row['type']=='a': row['type']=1 if row['type']=='b': row['type']=2 if row['type']=='c': row['type']=3 if row['type']=='d': row['type']=4 if row['type']=='e': row['type']=5 if row['type']=='f': row['type']=6 if row['type']=='g': row['type']=7 if row['type']=='h': row['type']=8 if row['type']=='i': row['type']=9 if row['type']=='j': row['type']=10 if row['type']=='k': row['type']=11 if row['type']=='l': row['type']=12 if row['type']=='m': row['type']=13 if row['type']=='n': row['type']=14 if row['type']=='o': row['type']=15 if row['type']=='p': row['type']=16 if row['type']=='q': row['type']=17 if row['type']=='r': row['type']=18 if row['type']=='s': row['type']=19 if row['type']=='t': row['type']=20 break new_day01 TypeError: string indices must be integers

import pandas as pd df = pd.read_csv('stock_data.csv') df['four_days_increase'] = df['close'].rolling(window=4).apply(lambda x: all(x[i] < x[i+1] for i in range(3))) * 1 df['three_days_decrease'] = df['close'].rolling(window=3).apply(lambda x: all(x[i] > x[i+1] for i in range(2))) * 1 capital = 1000000 max_stock_per_day = 10 max_stock_value = 100000 start_date = '2020-01-01' end_date = '2023-01-01' df = df[(df['date'] >= start_date) & (df['date'] < end_date)] df = df.reset_index(drop=True) hold_stock = [] for i, row in df.iterrows(): if len(hold_stock) > 0: sell_stock = [] for stock in hold_stock: if i - stock['buy_day'] >= 3: capital += stock['buy_price'] * stock['buy_qty'] * (1 - 0.002) sell_stock.append(stock) hold_stock = [stock for stock in hold_stock if stock not in sell_stock] df_today = df.loc[i:i+3] if i + 3 >= len(df): break if all(df_today['four_days_increase']) and all(df_today['three_days_decrease'].iloc[1:]): available_capital = capital available_stock = max_stock_per_day available_value = max_stock_value for j, stock_row in df_today.iterrows(): if available_capital > 0 and available_stock > 0 and available_value > 0: buy_qty = min(int(available_capital / (stock_row['close'] * 1.002)), available_stock, int(available_value / (stock_row['close'] * 1.002))) if buy_qty > 0: hold_stock.append({'buy_day': i, 'buy_price': stock_row['close'], 'buy_qty': buy_qty}) available_capital -= stock_row['close'] * buy_qty * 1.002 available_stock -= 1 available_value -= stock_row['close'] * buy_qty * 1.002 print('Final capital:', capital)让上述代码在jupyter里不报错

if all(df_today['four_days_increase']) and all(df_today['three_days_decrease'].iloc[1:]): available_capital = capital available_stock = max_stock_per_day available_value = max_stock_value for...

<select id="getContinuitySignCount" resultType="Map"> set @c_day :='2000-01-01'; set @row_number:=1; select s.sign_in_time,@c_day as twoday, @row_number:=case when DATEDIFF(s.sign_in_time,@c_day) = 1 then @row_number + 1 else 1 end as typenum, @c_day := s.sign_in_time as c_day, s.user_id from ums_sign_in s where s .user_id=#{userId} order by s.user_id,s.sign_in_time; </select>

查询结果包括签到时间（sign_in_time）、两天间隔（twoday）、连续签到次数（typenum）、当前日期（c_day）和用户ID（user_id）。请注意，这段代码是一个部分的SQL查询语句，可能需要结合其他代码来完成整个功能。

# 读取第一个 CSV 文件 data1 = [] with open('day01改.csv', 'r', encoding='gbk') as file1: reader = csv.DictReader(file1) for row in reader: data1.append(row) # 读取第二个 CSV 文件 data2 = [] with open('app_class.csv', 'r', encoding='gbk') as file2: reader = csv.DictReader(file2) for row in reader: data2.append(row) # 比较两个数据集并保留相同的内容 result = [] for row in data1: for compare_row in data2: if row['appid'] == compare_row['appid']: row['appid']=compare_row['type'] break

for row in data1: for compare_row in data2: if row['appid'] == compare_row['appid']: row['appid'] = compare_row['type'] break result.append(row) # 将结果写入新的 CSV 文件 fieldnames = data1[0]....

import numpy as np from datetime import datetime, timedelta import pandas as pd test = pd.read_excel("C:\\Users\\1data\\Desktop\\DBM成本收入核算\\test.xlsx") start_date = pd.to_datetime(test['合同开始日期']) end_date = pd.to_datetime(test['合同截止日期']) test['合同周期月数'] = round((end_date - start_date) / np.timedelta64(1, 'M')) start_date_col = '合同开始日期' end_date_col = '合同截止日期' new_col = '日期' for index, row in test.iterrows(): start_date = pd.to_datetime(row[start_date_col]) end_date = pd.to_datetime(row[end_date_col]) date_list = [] if start_date.day <= 15: while start_date <= end_date: date_list.append(start_date) start_date = start_date + timedelta(days=30) else: start_date = start_date + timedelta(days=30) while start_date <= end_date + timedelta(days=30): date_list.append(start_date) start_date = start_date + timedelta(days=30) test.loc[index, new_col] = ','.join([str(date.date()) for date in date_list]) df_tmp=test['日期'].str.split(',',expand=True) df_tmp=df_tmp.stack() df_tmp = df_tmp.reset_index(level=1,drop=True) df_tmp.name='日期' df_new = test.drop(['日期'], axis=1).join(df_tmp).reset_index().drop(columns='index') print(df_new) df_new.to_excel('income_test.xlsx',index=False) 将这段代码导出的excel中合同开始日期字段不显示时分秒且新增日期字段的取年月的字段合同周期年月

if start_date.day <= 15: while start_date <= end_date: date_list.append(start_date) start_date = start_date + timedelta(days=30) else: start_date = start_date + timedelta(days=30) while ...

from datetime import datetime, timedelta import pandas as pd test = pd.read_excel("C:\\Users\\1data\\Desktop\\DBM成本收入核算\\test.xlsx") # 定义开始日期和结束日期的列名 start_date_col = test['合同开始日期'] end_date_col = test['合同截止日期'] # 定义新列名 new_col = '日期' # 遍历每一行数据 for index, row in test.iterrows(): start_date = row[start_date_col] end_date = row[end_date_col] date_list = [] # 判断开始日期在每月15号之前还是之后 if start_date.day <= 15: # 开始日期在每月15号之前，直接逐月累加 while start_date <= end_date: date_list.append(start_date) start_date = start_date + timedelta(days=30) else: # 开始日期在每月15号之后，推迟一个月再逐月累加 start_date = start_date + timedelta(days=30) while start_date <= end_date + timedelta(days=30): date_list.append(start_date) start_date = start_date + timedelta(days=30) # 在原表后追加新列 test.loc[index, new_col] = ','.join([str(date.date()) for date in date_list]) # 输出结果 print(test)以上这段代码报错AttributeError: 'Series' object has no attribute 'day'，如何修改

for index, row in test.iterrows(): start_date = pd.to_datetime(row[start_date_col]) # 将字符串转换为 datetime 对象 end_date = pd.to_datetime(row[end_date_col]) # 将字符串转换为 datetime 对象 date_...

create table if not exists tb_log_gps ( id bigint not null, device_id varchar not null, platform_id varchar, location varchar, happen_time varchar, create_time TIMESTAMP NOT NULL DEFAULT now() ); CREATE INDEX idx_tb_log_gps_id ON tb_log_gps(id); CREATE INDEX idx_tb_log_gps_happen_time ON tb_log_gps(happen_time); CREATE OR REPLACE FUNCTION insert_log_gps_partition_func() RETURNS TRIGGER AS $$ DECLARE date_text TEXT; insert_statement TEXT; date_part DATE := '2000-01-01'; date_next DATE := '2000-01-01'; BEGIN SELECT SUBSTRING(NEW.happen_time,1,10) INTO date_text; insert_statement := 'INSERT INTO tb_log_gps_' || date_text ||' VALUES ($1.*);'; EXECUTE insert_statement USING NEW; RETURN NULL; EXCEPTION WHEN UNDEFINED_TABLE THEN date_part := ('''' || date_part('year'::text, to_date(NEW.happen_time,'yyyy-MM-dd hh24:mi:ss')) || '-' || date_part('month'::text, to_date(NEW.happen_time,'yyyy-MM-dd hh24:mi:ss')) || '-' || date_part('day'::text, to_date(NEW.happen_time,'yyyy-MM-dd hh24:mi:ss')))::DATE; date_next := date_part + '1 day'::interval; EXECUTE 'CREATE TABLE IF NOT EXISTS tb_log_gps_' || date_text || '(CHECK(' || 'happen_time' || '>= ''' || date_part::text || ''' AND ' || 'happen_time' || '< ''' || date_next::text || ''')) INHERITS (tb_log_gps);'; EXECUTE 'create index idx_tb_log_gps_' || date_text || '_happen_time on tb_log_gps_' || date_text || '(happen_time);'; EXECUTE 'create index idx_tb_log_gps_' || date_text || '_id on tb_log_gps_' || date_text || '(id);'; EXECUTE insert_statement USING NEW; RETURN NULL; END; $$ LANGUAGE plpgsql VOLATILE; DROP TRIGGER IF EXISTS insert_tb_log_gps_partition_trigger ON tb_log_gps; CREATE TRIGGER insert_tb_log_gps_partition_trigger BEFORE INSERT ON tb_log_gps FOR EACH ROW EXECUTE PROCEDURE insert_log_gps_partition_func(); 插入数据报错,这个分表触发器哪里错了

insert_statement := 'INSERT INTO tb_log_gps_' || date_text ||' VALUES ($1.*);'; 2. 函数中的单引号使用错误在函数中拼接 SQL 语句时，需要使用单引号将字符串括起来，但是在代码中出现了多余的单引号，...

# 读取 CSV 文件 data1 = [] with open('day01改改.csv', 'r') as file: reader = csv.DictReader(file) for row in reader: data1.append(row) for row in data1: if row['duration'] <=0: data1.append(row) break fieldnames = data1[0].keys() # 获取字段名 with open('day01改改.csv', 'w', newline='') as result_file: writer = csv.DictWriter(result_file, fieldnames=fieldnames) writer.writeheader() # 写入字段名 writer.writerows(data1) TypeError: '<=' not supported between instances of 'str' and 'int'

for row in data1: if int(row['duration']) > 0: updated_data.append(row) fieldnames = data1[0].keys() # 获取字段名 with open('day01改改.csv', 'w', newline='') as result_file: writer = csv....

SELECT '${date.start(0,day,%Y%m)}', substr( userid, 1, 4 ) "本地网ID", userid "号码", result "返回码", errordesc "描述", req_date "请求时间", action "操作类型", '' FROM ( SELECT userid, result, req_date, resp_date, errordesc, action, IF ( @unmber_row = userid, @rm := @rm + 1, @rm := 1 ) rum, @unmber_row := userid unmber_row FROM boss_workflow, ( SELECT @unmber_row := NULL, @rm := 1 ) c WHERE result = '0' AND date_format( req_date, '%Y%m' ) <= '2023-04-01 00:00:00' AND userid LIKE '08%iptv%' ORDER BY userid, req_date DESC ) a WHERE a.rum = '1' AND ( a.action = '1' OR a.action = '4' )，检查此条语句是否有错误

IF(@unmber_row = userid, @rm := @rm + 1, @rm := 1) rum, @unmber_row := userid unmber_row FROM boss_workflow, (SELECT @unmber_row := NULL, @rm := 1) c WHERE result = '0' AND date_format(req_...

import pandas as pd # 读取CSV文件 df = pd.read_csv('day01改改.csv') # 遍历指定列，删除不符合条件的行 for index, row in df.iterrows(): if row['app_type'] != 0 and row['app_type'] != 1: df.drop(index, inplace=True) break # 重置行索引 df.reset_index(drop=True, inplace=True) # 将更新后的数据写入CSV文件 df.to_csv('day01改改.csv', index=False) #%% 为什么一直运行没结果

if row['app_type'] == 0 or row['app_type'] == 1: updated_df = updated_df.append(row) # 重置行索引 updated_df.reset_index(drop=True, inplace=True) # 将更新后的数据写入新的CSV文件 updated_df.to_csv...

#!/usr/bin/env python #coding: utf-8 import os from time import time from datetime import datetime from netmiko import ConnectHandler from openpyxl import Workbook from openpyxl import load_workbook def read_device_excel( ): ip_list = [] wb1 = load_workbook('E:\/Users/Wayne_Peng/Desktop/cs_lab.xlsx') ws1 = wb1.get_sheet_by_name("Sheet1") for cow_num in range(2,ws1.max_row+1): ipaddr = ws1["a"+str(cow_num)].value ip_list.append(ipaddr) return ip_list def get_config(ipaddr): session = ConnectHandler(device_type="huawei", ip=ipaddr, username="mtlops", password="cisco,123", banner_timeout=300) print("connecting to "+ ipaddr) print ("---- Getting HUAWEI configuration from {}-----------".format(ipaddr)) # config_data = session.send_command('screen-length 0 temporary') # config_data = session.send_command('dis cu | no-more ') # command = 'display version | display cpu-usage | display memory-usage' # config_data = session.send_command(command) commands = ['display version', 'display cpu-usage', 'display memory-usage'] config_data = '' for cmd in commands: output = session.send_command_timing(cmd) config_data += f'{cmd}\n{output}\n' session.disconnect() return config_data def write_config_to_file(config_data,ipaddr): now = datetime.now() date= "%s-%s-%s"%(now.year,now.month,now.day) time_now = "%s-%s"%(now.hour,now.minute) #---- Write out configuration information to file config_path = 'E:\/Users/Wayne_Peng/Desktop/' +date verify_path = os.path.exists(config_path) if not verify_path: os.makedirs(config_path) config_filename = config_path+"/"+'config_' + ipaddr +"_"+date+"_" + time_now # Important - create unique configuration file name print ('---- Writing configuration: ', config_filename) with open( config_filename, "w",encoding='utf-8' ) as config_out: config_out.write( config_data ) return def main(): starting_time = time() ip_list = read_device_excel() for ipaddr in ip_list: hwconfig = get_config(ipaddr) write_config_to_file(hwconfig,ipaddr) print ('\n---- End get config threading, elapsed time=', time() - starting_time) #======================================== # Get config of HUAWEI #======================================== if name == 'main': main() 加一段gevent，def run_gevent(）

for cow_num in range(2,ws1.max_row+1): ipaddr = ws1["a"+str(cow_num)].value ip_list.append(ipaddr) return ip_list def get_config(ipaddr): session = ConnectHandler(device_type="huawei", ip=ip...

one_day_ago = datetime.datetime.now() - datetime.timedelta(days=3) result = mongo_db.user_collection.aggregate([{"$lookup":{"from": "payCondition","localField": "i_ui_identifier","foreignField": "userId","as": "nvc_user_name"}},{"$match": {"nvc_user_name.addtime": {"$gt": one_day_ago}}}]) print(result) # 连接mysql数据库 mysql_conn = mysql.connector.connect(host='172.16.2.246', port='33060', user='root', password='8ach4b6f9c', database='testdata_analyse',) mysql_cursor = mysql_conn.cursor() for row in result: var_title = row[0], var_type_name = row[1], var_company_name = row[2], var_source = row[3], var_succees_cause = row[4], is_succeed = row[5], dt_add_time = row[6], var_user_name = row[7] mysql_cursor.execute("INSERT INTO t_wuliu_information_pay_statistic VALUES (%s, %s, %s, %s, %s,%s,%s,%s)", (var_title,var_type_name,var_company_name,var_source,var_succees_cause,is_succeed,dt_add_time,var_user_name)) mysql_result = mysql_cursor.fetchall() print(mysql_result) mysql_conn.commit()这段python代码为什么result还有结果，但是就不插入到mysql的表中呢？怎么修改这个python代码

for row in result: var_title = row[0], var_type_name = row[1], var_company_name = row[2], var_source = row[3], var_succees_cause = row[4], is_succeed = row[5], dt_add_time = row[6], var_user_...

# 读取 CSV 文件 data = [] with open('day01改改.csv', 'r') as file: reader = csv.DictReader(file) for row in reader: data.append(row) # 创建一个新的列表来存储更新后的数据 updated_data = [] # 遍历数据，筛选出符合条件的行 for row in data: if float(row['duration']) >= 10: # 将 'column_name' 替换为你要筛选的列名 updated_data.append(row) # 写入更新后的数据到新的 CSV 文件 fieldnames = data[0].keys() # 获取字段名 with open('day01改改.csv', 'w', newline='') as result_file: writer = csv.DictWriter(result_file, fieldnames=fieldnames) writer.writeheader() # 写入字段名 writer.writerows(updated_data) ValueError: could not convert string to float: ''

for row in reader: data.append(row) # 创建一个新的列表来存储更新后的数据 updated_data = [] # 遍历数据，筛选出符合条件的行 for row in data: duration = row['duration'] if duration != '' and float...

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