Python之Pymysql模块操作MySQL增删改查

Python必学模块之pymysql (操作数据库) 阅读详情

Python3 MySQL 数据库连接 - PyMySQL 驱动

PyMySQL 连接数据库,实现增删改查

什么是 PyMySQL?

PyMySQL 是在 Python3.x 版本中用于连接 MySQL 服务器的一个库,Python2中则使用mysqldb。

PyMySQL 遵循 Python 数据库 API v2.0 规范,并包含了 pure-Python MySQL 客户端库。

安装PyMySQL

$ pip install PyMySQL

1、创建数据库连接

在操作mysql之前,首先要与mysql建立连接

conn=pymysql.connect(host=“mysql域名/ip”,user=“用户名”,password=“密码”,db=“库名”,port=端口号3306,charset=‘utf-8’)

2、创建游标对象

当游标建立之时,就自动开始了一个隐形的数据库事务

#使用 cursor() 方法创建一个游标对象 cursor
cursor = conn.cursor()

3、执行sql语句

sql=“select * from user”

cursor.execute(sql) 或 cursor.execute(“select * from user”)

4、提交

conn.commit()

5、回滚

conn.rollback()方法回滚当前游标的所有操作。每一个方法都开始了一个新的事务

5、关闭游标

cursor.close()

6、关闭数据库连接

conn.close()

创建数据库连接


import pymysql
#创建数据库连接
db=pymysql.connect(host="数据库域名/ip",user="账号",password="密码",db="库名",port=3306)
print(db)
#使用 cursor() 方法创建一个游标对象 cursor
cursor = db.cursor()
print(cursor)
#使用 execute() 方法执行 SQL 查询
cursor.execute("select * from user where mobile")
# 使用 fetchone() 方法.
results1= cursor.fetchone()#获取单条(第1条)数据
results2=cursor.fetchmany(3)#获取3条(2、3、4)数据
results3=cursor.fetchall() #获取全部(5-全部)数据
print(results3)
#关闭游标,又从起始位置开始
cursor.close()
# 关闭数据库连接
db.close()


C:\Users\wangli\PycharmProjects\AutoMation\venv\Scripts\python.exe C:/Users/wangli/PycharmProjects/AutoMation/case/test.py
<pymysql.connections.Connection object at 0x00000161DDC90E80>
<pymysql.cursors.Cursor object at 0x00000161DF8E44E0>

数据省略

Process finished with exit code 0

创建数据库表

#!/usr/bin/python3
 
import pymysql
 
# 打开数据库连接
db = pymysql.connect("localhost","testuser","test123","TESTDB" )
 
# 使用 cursor() 方法创建一个游标对象 cursor
cursor = db.cursor()
 
# 使用 execute() 方法执行 SQL,如果表存在则删除
cursor.execute("DROP TABLE IF EXISTS EMPLOYEE")
 
# 使用预处理语句创建表
sql = """CREATE TABLE EMPLOYEE (
         FIRST_NAME  CHAR(20) NOT NULL,
         LAST_NAME  CHAR(20),
         AGE INT,  
         SEX CHAR(1),
         INCOME FLOAT )"""
 
cursor.execute(sql)
 
# 关闭数据库连接
db.close()

数据库插入操作

import pymysql
#创建数据库连接
db=pymysql.connect(host="",user="",password="",db="ck",port=3306)
print(db)
#使用 cursor() 方法创建一个游标对象 cursor
cursor = db.cursor()
print(cursor)
sql="INSERT INTO j.store (id,company_id,title,address,district,lon,lat,remark,updated_at,created_at,deleted_at) VALUES ('195', '61', '虹桥路店', '虹桥路1027号', '', '0.000000', '0.000000', NULL, '2019-03-15 14:54:29', '2019-03-15 14:54:29', NULL)"


#写法2
#cursor.execute('insert into stu(id,name,age) values(%s,%s,%s)',('201611001','xiaoqian',20))

#插入多条数据,具体数据用列表来保存,列表元素是元组
#cur.executemany('insert into stu(id,name,age) values(%s,%s,%s)',[('201611001','xiaoqian',20),('201611002','smile',21),('201611003','wood',23)])



try:
    #执行插入sql
    cursor.execute(sql)
    # 提交到数据库执行
    db.commit()
except:
    # 如果发生错误则回滚
    db.rollback()
# 关闭数据库连接
db.close()



C:\Users\wangli\PycharmProjects\AutoMation\venv\Scripts\python.exe C:/Users/wangli/PycharmProjects/AutoMation/case/test.py
<pymysql.connections.Connection object at 0x00000295DEA90EB8>
<pymysql.cursors.Cursor object at 0x00000295E06B44E0>

Process finished with exit code 0

数据库查询操作

Python查询Mysql使用 fetchone() 方法获取单条数据, 使用fetchall() 方法获取多条数据。

  • fetchone(): 该方法获取下一个查询结果集。结果集是一个对象
  • fetchall(): 接收全部的返回结果行.
  • rowcount: 这是一个只读属性,并返回执行execute()方法后影响的行

1、单条sql语句查询

sql = “SELECT * FROM EMPLOYEE WHERE INCOME > %s” % (1000)

2、多条sql语句查询

    import pymysql
    #创建数据库连接
    db=pymysql.connect(host="",user="",password="",db="",port=3306)
    print(db)
    #使用 cursor() 方法创建一个游标对象 cursor
    cursor = db.cursor()
    print(cursor)
    sql="select * from c.user limit 10"
    try:
        #执行sql语句
        cursor.execute(sql)
        #查询所有记录
        results=cursor.fetchall()
        print(results)#打印10条数据
        for row in results:
            print(row)#循环打印每条数据
            user_id=row[0]#第1列字段
            mobile=row[1]#第2列字段
            print(user_id,mobile)#循环打印第1、2列字段
    except:
        print('查询失败')
    #关闭数据库连接
    db.close()
    
    
    C:\Users\wangli\PycharmProjects\AutoMation\venv\Scripts\python.exe C:/Users/wangli/PycharmProjects/AutoMation/case/test.py
    <pymysql.connections.Connection object at 0x0000018E8BEDB748>
    <pymysql.cursors.Cursor object at 0x0000018E8DB34550>
    ((2, '13888888888', '新', '', 0, '', '', '', 0, 1, '', '', '', '', '', 30, 1554291602, 0, 1, 0, datetime.datetime(2019, 4, 3, 19, 37, 21), datetime.datetime(2019, 4, 3, 19, 49, 19), None), (75, '13888888888', '小', '', 0, '', '', '', 0, 1, '', '', '', '', '', 30, 1554291602, 0, 1, 0, datetime.datetime(2019, 4, 3, 19, 37, 21), datetime.datetime(2019, 4, 3, 19, 49, 19), None), (139, '13888888888', '林', '', 0, '', '', '', 0, 1, '', '', '', '', '', 30, 1554088503, 0, 2, 0, datetime.datetime(2019, 4, 1, 11, 11, 37), datetime.datetime(2019, 4, 1, 11, 15, 3), None), (190, '13888888888', '大', '', 0, '', '', '', 0, 1, '', '', '', '', '', 30, 1554086702, 0, 1, 0, datetime.datetime(2019, 4, 1, 10, 40, 28), datetime.datetime(2019, 4, 1, 10, 45, 2), None), (192, '13888888888', '磊', '33010019900530156x', 0, '', '', '', 1, 1, '', '', '', '', '', 30, 13888888888, 0, 2, 0, datetime.datetime(2019, 3, 29, 17, 26, 56), datetime.datetime(2019, 4, 3, 19, 49, 19), None), (199, '13888888888', '一', '', 0, '', '', '', 0, 1, '', '', '', '', '', 30, 1554086702, 0, 2, 0, datetime.datetime(2019, 4, 1, 10, 40, 28), datetime.datetime(2019, 4, 1, 16, 8, 3), None), (202, '13888888888', '三', '', 0, '', '', '', 0, 1, '', '', '', '', '', 30, 1554086702, 0, 1, 0, datetime.datetime(2019, 4, 1, 10, 40, 29), datetime.datetime(2019, 4, 1, 10, 45, 2), None), (203, '13888888888', '四', '', 0, '', '', '', 0, 1, '', '', '', '', '', 30, 1554086702, 0, 1, 0, datetime.datetime(2019, 4, 1, 10, 40, 29), datetime.datetime(2019, 4, 1, 10, 45, 2), None), (230, '13888888888', '大', '320382199303242813', 0, '', '', '', 1, 1, '', '', '', '', '', 30, 1554291602, 0, 2, 0, datetime.datetime(2019, 4, 3, 19, 37, 20), datetime.datetime(2019, 4, 3, 20, 11, 18), None), (278, '13700000003', '三', '', 0, '', '', '', 0, 1, '', '', '', '', '', 30, 1554171902, 0, 2, 0, datetime.datetime(2019, 4, 2, 10, 24, 35), datetime.datetime(2019, 4, 2, 10, 25, 8), None))
    (2, '13888888888', '林', '', 0, '', '', '', 0, 1, '', '', '', '', '', 30, 1554291602, 0, 1, 0, datetime.datetime(2019, 4, 3, 19, 37, 21), datetime.datetime(2019, 4, 3, 19, 49, 19), None)
    2 13888888888
    (75, '13888888888', '马', '', 0, '', '', '', 0, 1, '', '', '', '', '', 30, 1554291602, 0, 1, 0, datetime.datetime(2019, 4, 3, 19, 37, 21), datetime.datetime(2019, 4, 3, 19, 49, 19), None)
    75 18817893609
    (139, '13888888888', '林', '', 0, '', '', '', 0, 1, '', '', '', '', '', 30, 1554088503, 0, 2, 0, datetime.datetime(2019, 4, 1, 11, 11, 37), datetime.datetime(2019, 4, 1, 11, 15, 3), None)
    139 13888888888
    (190, '13888888888', '徐', '', 0, '', '', '', 0, 1, '', '', '', '', '', 30, 1554086702, 0, 1, 0, datetime.datetime(2019, 4, 1, 10, 40, 28), datetime.datetime(2019, 4, 1, 10, 45, 2), None)
    190 13888888888
    (192, '13888888888', '王', '330', 0, '', '', '', 1, 1, '', '', '', '', '', 30, 1553851802, 0, 2, 0, datetime.datetime(2019, 3, 29, 17, 26, 56), datetime.datetime(2019, 4, 3, 19, 49, 19), None)
    192 13888888888
    (199, '13888888888', '李', '', 0, '', '', '', 0, 1, '', '', '', '', '', 30, 1554086702, 0, 2, 0, datetime.datetime(2019, 4, 1, 10, 40, 28), datetime.datetime(2019, 4, 1, 16, 8, 3), None)
    199 13888888888
    (202, '13888888888', '三', '', 0, '', '', '', 0, 1, '', '', '', '', '', 30, 1554086702, 0, 1, 0, datetime.datetime(2019, 4, 1, 10, 40, 29), datetime.datetime(2019, 4, 1, 10, 45, 2), None)
    202 13888888888
    (203, '13888888888', '四', '', 0, '', '', '', 0, 1, '', '', '', '', '', 30, 1554086702, 0, 1, 0, datetime.datetime(2019, 4, 1, 10, 40, 29), datetime.datetime(2019, 4, 1, 10, 45, 2), None)
    203 13888888888
    (230, '13888888888', '小', '320', 0, '', '', '', 1, 1, '', '', '', '', '', 30, 1554291602, 0, 2, 0, datetime.datetime(2019, 4, 3, 19, 37, 20), datetime.datetime(2019, 4, 3, 20, 11, 18), None)
    230 13888888888
    (278, '13700000003', '王', '', 0, '', '', '', 0, 1, '', '', '', '', '', 30, 1554171902, 0, 2, 0, datetime.datetime(2019, 4, 2, 10, 24, 35), datetime.datetime(2019, 4, 2, 10, 25, 8), None)
    278 13700000003
    
    Process finished with exit code 0

数据库更新操作

sql语句

sql = "UPDATE EMPLOYEE SET AGE = AGE + 1 WHERE SEX = '%c'" % ('M')

sql="update c.user set sex=2 where mobile=18221124104"

cur.execute('update stu set age = %s where id = %s',(25,'201611006'))
import pymysql
#创建数据库连接
conn=pymysql.connect(host="",user="qa",password="Qa",db="c",port=3306)
print(conn)
#使用 cursor() 方法创建一个游标对象 cursor
cursor = conn.cursor()
print(cursor)
sql="update c.user set sex=2 where mobile=18221124104"
try:
    cursor.execute(sql)
    conn.commit()
except:
    conn.rollback()
conn.close()


C:\Users\wangli\PycharmProjects\AutoMation\venv\Scripts\python.exe C:/Users/wangli/PycharmProjects/AutoMation/case/test.py
<pymysql.connections.Connection object at 0x000002C281555A20>
<pymysql.cursors.Cursor object at 0x000002C2831944A8>

Process finished with exit code 0


删除操作

#!/usr/bin/python3
 
import pymysql
 
# 打开数据库连接
db = pymysql.connect("localhost","testuser","test123","TESTDB" )
 
# 使用cursor()方法获取操作游标 
cursor = db.cursor()
 
# SQL 删除语句
sql = "DELETE FROM EMPLOYEE WHERE AGE > %s" % (20)
try:
   # 执行SQL语句
   cursor.execute(sql)
   # 提交修改
   db.commit()
except:
   # 发生错误时回滚
   db.rollback()
 
# 关闭连接
db.close()

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Python系列:操作MySQL详解——PyMySQL Python操作MySQL详解——PyMySQL 阅读详情

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首先你得学会基本的mysql操作语句:mysql学习 其次,python要想操作mysql,靠python的内置模块是不行的,而如果通过os模块调用cmd命令虽然原理上是可以的,但是还是不太方便,那么这个问题,很早就有人想过了,而且还做出来了,不废话,就是第三方模块pymysql和mysqldb了。 但是,python3暂不支持mysqldb,pymysql却是python2和3都支...

dingyu6453的博客 294

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

navicat可视化界面操作数据库 python如何操作MySQL(pymysql模块) sql注入问题 pymysql模块增删改查数据操作

今日内容详细 Navicat软件 """ 一开始学习python的时候 >>>下载python解释器然后直接在终端书写 >>> pycharm能够更加方便快捷的帮助你书写python代码 excel word pdf 我们在终端操作MySQL 也没有自动提示也无法保存等等 不方便开发 Navicat内部封装了所有的操作数据库的命令 用户在使用它的时候只需要鼠标点点即可完成操作 无需书写sql语句 """ 安装 直接百度搜索 有破解版的也有非破解 非破解的有

lwj_07的博客 2186

Probability and Statistics - 4th edition

Probability and Statistics - 4th edition

VB摄像头监控

该VB模块可对摄像头捕获的画面进行录像等,可在创建的窗口进行控制

python pymysql模块下载_pymysql模块使用简介与示例

pymysql模块pymysql是一个连接mysql数据库的第三方模块,可作为连接mysql数据库的客户端,对数据库进行增删改查操作# 下载第三方模块:pip3 install pymysql如何连接数据库并对数据库进行操作:import pymysql # 连接mysql数据库的模块# 1.连接数据库"""需要传入的参数:host=None,user=None,password="",datab...

weixin_36044132的博客 395

python pymysql insert_mysql python pymysql模块 增删改查 插入数据 介绍 commit() execute() executemany() 函数...

import pymysqlmysql_host = '192.168.0.106'port = 3306mysql_user = 'root'mysql_pwd = ''encoding = 'utf8'# 建立 连接mysql服务端conn = pymysql.connect(host=mysql_host, # mysql服务端ipport=port, # mysql端口user=mys...

weixin_39550258的博客 817
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