data = cur.execute(sql) text = "" for item in data: text = text + item[0] # print(item[0]) # print(text) cur.close() con.close()

text-to-sql:text生成sql语句

文字转SQL 介绍刚开始，由我自己整理权限脚本... 只有很少数据的时候，还能接受手动复制粘贴... 在之后的功能开发中，发现需要整理的权限脚本越来越多... 可能需要花费半小时或一个小时的时间去整理脚本，还生怕会出错... 于是，就有了这个小工具... 现在，就可以正大光明的偷懒啦〜使用整理成如src下permission.txt的形式，试试吧... 菜单等级=等级（1 2 3），父菜单码菜单代码菜单名称源 level = 1 menu_activity_home 活动管理 /activity level = 2 , menu_activity_home menu_activity_lottery_list 抽奖活动管理 /activity/lotteryList menu_activity_groupon_list 团购活动管理 /activity/grouponList m

数据的操作.sql

文本导入sql

文本导入sql BULK INSERT CSDN FROM 'D:\XX.txt' WITH ( CODEPAGE='ANSI ',

from wordcloud import WordCloud #词云 import jieba #分词 from matplotlib import pyplot as plt #绘图数据可视化 from PIL import Image #图片处理 import numpy as np #矩阵运算 import sqlite3 #数据库 # def show(): con = sqlite3.connect('movie.db') cur = con.cursor() sql = 'select instroduction from movie250' data = cur.execute(sql) text = "" for item in data: text = text + item[0] # print(text) cur.close() con.close() cut = jieba.cut(text) string = ' '.join(cut) print(len(string)) img = Image.open(r'./static/images/old.png') img_array = np.array(img) #将图片转换为数组 wc = WordCloud( background_color='white', mask=img_array, font_path="/Library/Fonts/Songti.ttc" #字体所在位置C:\Windows\Fonts ) wc.generate_from_text(string) #绘制图片 fig = plt.figure(1) plt.imshow(wc) plt.axis('off') #是否显示坐标轴 fig.patch.set_alpha(0) plt.show() #显示生成的词云图片 plt.savefig('static/images/new.png') # return img_array

for item in data: text = text + item[0] cur.close() con.close() cut = jieba.cut(text) string = ' '.join(cut) print(len(string)) img = Image.open(r'./static/images/old.png') img_array = np....

解释每一句import pymysql class MyMysql: def init(self): self.connect = pymysql.connect( host=Mysql服务IP地址, port=Mysql数据库端口, user=数据库用户名, password=数据库密码, database=数据库名, charset='utf8' ) self.cursor = self.connect.cursor()import random from tools.config import MyMysql class UaPool: def init(self): database = MyMysql() db = database.connect cur = database.cursor sql = 'select useragent from ua_pool where types = %s and popularity != %s' cur.execute(sql, ('Windows', 'Uncommon')) data_d = cur.fetchall() desktop_list = [] for data_d_per in data_d: desktop_list.append(data_d_per[0]) self.desktop = desktop_list cur.execute(sql, ('Android', 'Uncommon')) data_m = cur.fetchall() mobile_list = [] for data_m_per in data_m: mobile_list.append(data_m_per[0]) self.mobile = mobile_list cur.close() db.close() def choose_ua(self, platform='desktop'): if platform == 'desktop': return random.choice(self.desktop) elif platform == 'mobile': return random.choice(self.mobile) else: return random.choice(self.desktop) if name == 'main': up = UaPool() print(up.choose_ua())

13. cur.execute(sql, ('Windows', 'Uncommon'))：执行 SQL 查询语句，并传入参数，用于查询 Windows 桌面平台上不常见的用户代理。 14. data_d = cur.fetchall()：获取查询结果集，即 Windows 桌面平台上不...

try: select_sqli = "SELECT distinct time FROM sheji.sale;" cur.execute(select_sqli) data = pd.DataFrame(cur.fetchall(), columns=['time']) except Exception as e: print("读取数据失败:", e) else: print("读取数据成功") data['time'] = pd.to_datetime(data['time'], format='%Y-%m') data = data.sort_index() year_month = time.strftime('%Y-%m', time.localtime(data['time'][0])) print(year_month)哪里有错

if cur.rowcount > 0: data = pd.DataFrame(cur.fetchall(), columns=['time']) else: print("查询结果为空") 3. data['time'][0] 可能会出现 IndexError 的错误，因为 DataFrame 可能为空。需要先判断 ...

data = pd.read_csv(path) sql = "INSERT INTO mail_pricequery (com, account, cata,end,weight,country_to) " # 使用游标对象执行 SQL 语句 for row in data: cur.execute(sql, (row[0], row[1],row[2],row[3],row[4],row[5]))代码IndexError: string index out of range怎么修改

这个错误通常是由于尝试访问... cur.execute(sql, (row[0], row[1],row[2],row[3],row[4],row[5])) except IndexError: print("该行数据不完整:", row) 这将跳过包含不完整数据的行，并打印相关信息以供检查。

from flask import Flask, render_template import pymysql app = Flask(name) @app.route("/") def my_echart(): conn = pymysql.connect(host='127.0.0.1', user='你的数据库用户名', password='你的数据库密码', db='你的数据库名，在这里为data')#建立数据库连接 cur = conn.cursor() sqlmale = ' SELECT count(sex) FROM people_flow where sex=1 ' sqlfemale = ' SELECT count(sex) FROM people_flow where sex=0' sqls = ' SELECT sex FROM people_flow ' cur.execute(sqlmale)#执行单条sql语句 maleresult = cur.fetchall()#接收全部的返回结果行 cur.execute(sqlfemale) femaleresult = cur.fetchall() cur.execute(sqls) results = cur.fetchall() male_num = maleresult[0][0] female_num = femaleresult[0][0] num1 = [] for result in results: num1.append(result[0]) cur.close()#关闭指针对象 conn.close()#关闭连接对象 # print(male_num)#测试 # print(female_num) # print(num1) return render_template('bar.html',male_num=male_num,female_num=female_num, num1=num1)#先引入bar.html，同时根据后面传入的参数，对html进行修改渲染 if name == 'main': app.run(debug=True)#启用调试模式

这段代码是使用Python中的Flask框架和pymysql库来连接数据库，并在网页上展示数据。其中，Flask是一个轻量级的Web应用框架，render_template用于渲染模板，pymysql是Python操作MySQL数据库的库。...

def saveData2DB(datalist, dbpath): init_db(dbpath) conn = sqlite3.connect(dbpath) cur = conn.cursor() for data in datalist: for index in range(len(data)): # if (index == 4 or index == 5): # continue data[index] = '"' + data[index] + '"' sql = ''' insert into movie250( info_link, pic_link, cname, score,rated, introduction,year_release,country,category ) values(%s)''' % ",".join(data) # print(sql) cur.execute(sql) conn.commit() cur.close() conn.close

这段代码是用来将一个数据列表...然后对于datalist中的每一个数据，都将其中的每一个字段用双引号括起来，然后拼接成一个SQL语句，使用游标对象的execute方法将其执行并提交到数据库中。最后，关闭游标和连接对象。

import pymysqlimport csvimport pandas as pdcon = pymysql.connect(host='localhost', user='root', port=3306, password='123456', charset="utf8", database='xscj')cur = con.cursor()with open('student.csv', encoding="utf-8") as f: data = [tuple(row) for row in csv.reader(f, delimiter='，')] print(data)sql = "insert into stu(sno, name, major, born, nian, money) values(%s, %s, %s, %s, %s, %s)"cur.executemany(sql, data)cur.execute("select * from stu")result = cur.fetchall()print(result)con.commit()cur.close()con.close()为什么还是会报错

1. pymysql.err.ProgrammingError: (1064, "You have an error in your SQL syntax...")：这种报错信息通常是由于 SQL 语句语法错误导致的。你需要检查 SQL 语句是否正确，特别是字段名、表格名、数据类型、占位符...

import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.arima_model import ARIMA from sklearn.metrics import mean_squared_error import pymysql import time,os import re import requests import urllib from datetime import datetime from statsmodels.graphics.tsaplots import plot_acf, plot_pacf # ---------连接-------------- connect = pymysql.connect(host='localhost', # 本地数据库 user='root', password='123456', port=3306, charset='utf8') #服务器名,账户,密码，数据库名称 cur = connect.cursor() print(cur) # 读取数据 try: select_sqli = "SELECT time,xiaoliang FROM sheji.sale where chexing='海豚';" cur.execute(select_sqli) data = pd.DataFrame(cur.fetchall(), columns=['time', 'xiaoliang']) except Exception as e: print("读取数据失败:", e) else: print("读取数据成功") # 转换时间格式 data['time'] = pd.to_datetime(data['time'], format='%Y-%m') data = data.set_index('time') diff_data = data.diff().dropna() plot_acf(diff_data) plot_pacf(diff_data) print(data)哪里有错

cur.execute(select_sqli) data = pd.DataFrame(cur.fetchall(), columns=['time', 'xiaoliang']) except Exception as e: print("读取数据失败:", e) else: print("读取数据成功") # 转换时间格式 data['time'...

20210150章明一班男 20210123李悦灵二班女 20210913欧阳紫玉一班女读取文本文件data.txt，将学号、姓名、班级、性别插入到数据库Student.db的stu表中 import salite3 con = sqlite3.connect('student.db' cur = con.cursor0) cur.execute('create table if not exists stus(id primary key,name text,cla text,sex text)fr = open(dat.txt',r) for line in frreadlines(): L=len(line) if line[-1]=='n' L=L-1line=line[o:L]xh=line[0:8] xm=line[8:L-3] bj=line[L xb=line[L-1:]print(xh,xm,xb) values(?,?,?,?),(xh,xm,bj,xb))cur.execute(insert intofr.close()con.commit0)cur.close() con.close()

cur.execute('create table if not exists stus(id primary key, name text, cla text, sex text)') # 读取文件并插入数据 with open('data.txt', 'r') as fr: for line in fr.readlines(): # 去除换行符 line =...

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom statsmodels.tsa.arima_model import ARIMAfrom sklearn.metrics import mean_squared_errorimport pymysqlimport time, osimport reimport requestsimport urllibfrom datetime import datetimefrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf# 导入模块import pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom statsmodels.tsa.arima_model import ARIMAfrom sklearn.metrics import mean_squared_errorimport pymysqlfrom datetime import datetimefrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf# 连接数据库connect = pymysql.connect(host='localhost', # 本地数据库 user='root', password='123456', port=3306, charset='utf8', database='sheji') # 数据库名称cur = connect.cursor()# 读取数据try: select_sqli = "SELECT time,xiaoliang FROM sale where chexing='海豚';" cur.execute(select_sqli) data = pd.DataFrame(cur.fetchall(), columns=['time', 'xiaoliang'])except Exception as e: print("读取数据失败:", e)else: print("读取数据成功")# 转换时间格式data['time'] = pd.to_datetime(data['time'], format='%Y-%m')data = data.set_index('time')diff_data = data.diff().dropna()# 绘制自相关图和偏自相关图plot_acf(diff_data)plot_pacf(diff_data)# 确定 ARIMA 模型的参数p = 1d = 1q = 1model = ARIMA(data, order=(p, d, q))model_fit = model.fit(disp=0)# 预测销量y_pred = model_fit.predict(len(data), len(data) + 11, typ='levels')# 绘制预测结果plt.plot(data)plt.plot(y_pred, color='red')plt.show()# 关闭数据库连接cur.close()connect.close()请将这段代码改为移动平均模型

model = ARIMA(data_ma, order=(0, 0, q)) model_fit = model.fit(disp=False) # 预测销量并绘图 y_pred = model_fit.predict(len(data_ma), len(data_ma) + 11, typ='levels') plt.plot(data_ma, label='Original ...

import pymysql import csv import pandas as pd con=pymysql.connect(host='localhost',user='root',port=3306,password='123456',charset="utf8",database='xscj') cur=con.cursor() with open('student.csv',encoding="utf-8") as f: gay=[tuple(row)for row in csv.reader(f,delimiter='，')] print(gay) sql="insert into stu(sno,name,major,born,nian,money) values(?,?,?,?,?,?)" cur.executemany(sql,gay) cur.execute("show stu") con.commit() con.rollback() cur.close() con.close()为什么运行会报错

data = [tuple(row) for row in csv.reader(f, delimiter='，')] print(data) sql = "insert into stu(sno, name, major, born, nian, money) values(%s, %s, %s, %s, %s, %s)" cur.executemany(sql, data) cur....

帮我解读下这个代码：import csv import os import numpy as np import pandas as pd import pymysql from pymysql import connect # %% # drug_table = pd.read_excel('./data/drug.xlsx') drug_table_an = pd.read_excel('./data/mimiciv_feature_info.xlsx', sheet_name='antibiotic') drug_table_sa = pd.read_excel('./data/mimiciv_feature_info.xlsx', sheet_name='sedatives_and_analgesics') drug_table_co = pd.read_excel('./data/mimiciv_feature_info.xlsx', sheet_name='anticoagulant') prescriptions = pd.read_csv('/data/check_in/EHR_data/MIMIC_III/CSV/PRESCRIPTIONS.csv') item = pd.read_csv('/data/check_in/EHR_data/MIMIC_III/CSV/D_ITEMS.csv') labitem = pd.read_csv('/data/check_in/EHR_data/MIMIC_III/CSV/D_LABITEMS.csv') columns_pre = prescriptions.columns.tolist() columns_item = item.columns.tolist() columns_labitem = labitem.columns.tolist() # drugs = (drug_table['anticoagulant'].to_list()+drug_table['antiplatelet'].to_list())[:-4] drugs = ['barbital' ,'zepam' ,'zolam' ,'zolpidem' ,'propofol' ,'dexmedetomidine' ,'pentobarbital' ,'clonazepam' ,'alprazolam' ,'estazolam' ,'Zolpidem Tartrate'] drug_test_tsv = open('drug_patients_sedative.csv', 'w', newline='', encoding='utf-8') drug_test = csv.writer(drug_test_tsv, delimiter=',') drug_test.writerow(columns_pre) item_test_tsv = open('item_patients_sedative.csv', 'w', newline='', encoding='utf-8') item_test = csv.writer(item_test_tsv, delimiter=',') item_test.writerow(columns_item) labitem_test_tsv = open('labitem_patients_sedative.csv', 'w', newline='', encoding='utf-8') labitem_test = csv.writer(labitem_test_tsv, delimiter=',') labitem_test.writerow(columns_labitem) # import pdb;pdb.set_trace() for drug in drugs: # print(type(drug)) sql = "select * FROM PRESCRIPTIONS where drug like '%"+ drug + "%' or drug_name_poe like '%"+ drug + "%' or drug_name_generic like '%"+ drug + "%'" print(sql) conn = connect(host='127.0.0.1', port=3306, user='root', passwd='root', db='mimiciii') cursor = conn.cursor() cursor.execute(sql) data_tmp = cursor.fetchall() # print(data_tmp is None) if len(data_tmp) != 0: for data_cur in data_tmp: print(data_cur[1], data_cur[2], data_cur[3], data_cur[7], data_cur[8], data_cur[9]) drug_test.writerow(list(data_cur)) # import pdb;pdb.set_trace() for drug in drugs: # print(type(drug)) sql = "select * FROM D_ITEMS where label like '%{}%'" .format(drug) print(sql) conn1 = connect(host='127.0.0.1', port=3306, user='root', passwd='root', db='mimiciii') cursor1 = conn1.cursor() cursor1.execute(sql) data_tmp = cursor1.fetchall() if len(data_tmp) != 0: for data_cur in data_tmp: print(data_cur[1], data_cur[2]) item_test.writerow(list(data_cur)) # import pdb;pdb.set_trace() for drug in drugs: # print(type(drug)) sql = "select * FROM D_LABITEMS where label like '%{}%'" .format(drug) print(sql) conn1 = connect(host='127.0.0.1', port=3306, user='root', passwd='root', db='mimiciii') cursor1 = conn1.cursor() cursor1.execute(sql) data_tmp = cursor1.fetchall() if len(data_tmp) != 0: for data_cur in data_tmp: print(data_cur[1], data_cur[2]) labitem_test.writerow(list(data_cur)) # import pdb;pdb.set_trace() # %% import pandas as pd drug = pd.read_csv('drug_patients_sedative.csv') print(drug.DRUG.unique()) # %% print(drug.DRUG_NAME_POE.unique()) # %% print(drug.DRUG_NAME_GENERIC.unique()) # %%

这段代码是Python语言的导入模块语句，它导入了csv、os、numpy、pandas和pymysql等模块，并从pymysql模块中导入connect函数。这些模块提供了各种功能，如读写CSV文件、操作操作系统、进行数值计算、处理数据和连接...

给出上述代码所使用的每个数据结构的名称、标识符以及它们之中每个数据项、记录、文卷和系的标识、定义、长度及它们之间的层次的或表格的相互关系。def askURL(url): head = { #模拟浏览器头部信息，向豆瓣服务器发送消息 "User-Agent": "Mozilla / 5.0(Windows NT 10.0; Win64; x64) AppleWebKit / 537.36(KHTML, like Gecko) Chrome / 80.0.3987.122 Safari / 537.36" } #用户代理，表示告诉豆瓣服务器，我们是什么类型的机器、浏览器（本质上是告诉浏览器，我们可以接收什么水平的文件内容） request = urllib.request.Request(url,headers=head) html = "" try: response = urllib.request.urlopen(request) html = response.read().decode("utf-8") except urllib.error.URLError as e: if hasattr(e,"code"): print(e.code) if hasattr(e,"reason"): print(e.reason) return html #保存数据 def saveData(datalist,savepath): print("save....") book = xlwt.Workbook(encoding="utf-8",style_compression=0) #创建workbook对象 sheet = book.add_sheet('豆瓣电影Top250',cell_overwrite_ok=True) #创建工作表 col = ("电影详情链接","图片链接","影片中文名","影片外国名","评分","评价数","概况","相关信息") for i in range(0,8): sheet.write(0,i,col[i]) #列名 for i in range(0,250): print("第%d条" %(i+1)) data = datalist[i] for j in range(0,8): sheet.write(i+1,j,data[j]) #数据 book.save(savepath) #保存 def saveDataDB(datalist,dbpath): init_db(dbpath) conn = sqlite3.connect(dbpath) cur = conn.cursor() for data in datalist: for index in range(len(data)): if index == 4 or index == 5: continue data[index] = '"'+data[index]+'"' sql = ''' insert into movie250 ( info_link,pic_link,cname,ename,score,rated,instroduction,info) values(%s)'''%",".join(data) # print(sql) cur.execute(sql) conn.commit() cur.close() conn.close() def init_db(dbpath): sql = ''' create table movie250 ( id integer primary key autoincrement, info_link text, pic_link text, cname varchar, ename varchar, score numeric , rated numeric , instroduction text, info text ) ''' #创建数据表 conn = sqlite3.connect(dbpath) cursor = conn.cursor() cursor.execute(sql) conn.commit() conn.close()

4. saveData函数使用datalist和savepath参数，将数据保存到Excel表格中。 5. saveDataDB函数使用datalist和dbpath参数，将数据保存到SQLite数据库中。 6. init_db函数用于初始化SQLite数据库，创建...

from flask import Flask,request import json import pymssql #创建连接字符串（sqlserver默认端口为1433） conn =pymssql.connect( server="db53.oaqi.com",#本地服务器 port="9167",#TCP端口 user="dev", password="Wy@2012!", database="fstask_new", charset = "CP936" #这里设置全局的GBK，如果设置的是UTF—8需要将数据库默认的GBK转化成UTF-8 ) if conn: print('连接数据库成功!')#测试是否连接上 app=Flask(name) @app.route("/test_1.0",methods=["GET","POST"]) def check(): # 默认返回内容 return_dict= {'status': False} get_data=request.args.to_dict() siteid=get_data.get('siteid') client=get_data.get('client') # 对参数进行操作 return_dict=tt(siteid,client) return json.dumps(return_dict, ensure_ascii=False) def tt(siteid,client): result_str="站点:%s 终端:%s" %(siteid,client) return result_str cur = conn.cursor() cur.callproc('p_fstask_new_siteid_client', ['4567','4567354']) result = cur.fetchall() for row in result: status = row[0] print('{0}'.format(status)) conn.close() if name == 'main': app.run(debug=True)

这段代码似乎是一个使用Flask搭建的Web应用程序，连接到了一个名为fstask_new的SQL ...另外，使用存储过程时也可以使用游标对象的execute()方法，不过需要把存储过程名和参数拼成一条SQL语句传递给execute()方法。

pycharm连接sql数据库

在PyCharm中连接SQL数据库，可以使用pymssql库来完成。首先，确保你已经成功安装了pymssql库。接下来，在PyCharm中创建一个新的Python文件，然后输入以下代码并执行: python import pymssql connect = ...

AttributeError: 'Mysql' object has no attribute 'conn'

AttributeError: 'Mysql' object has no... self.cur.execute(sql) data = self.cur.fetchall() return data if __name__ == '__main__': mydb = MysqlDb() r = mydb.query("select * from case") print(r)

data = cur.execute(sql) text = "" for item in data: text = text + item[0] # print(item[0]) # print(text) cur.close() con.close()

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data = pd.read_csv(path) sql = "INSERT INTO mail_pricequery (com, account, cata,end,weight,country_to) " # 使用游标对象执行 SQL 语句 for row in data: cur.execute(sql, (row[0], row[1],row[2],row[3],row[4],row[5]))代码IndexError: string index out of range怎么修改

pycharm连接sql数据库

AttributeError: 'Mysql' object has no attribute 'conn'

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