本次分享将介绍如何在Python中使用Pandas库实现MySQL数据库的读写。首先我们需要了解点ORM方面的知识。
ORM技术
对象关系映射技术,即ORM(Object-Relational Mapping)技术,指的是把关系数据库的表结构映射到对象上,通过使用描述对象和数据库之间映射的元数据,将程序中的对象自动持久化到关系数据库中。
在Python中,最有名的ORM框架是SQLAlchemy。Java中典型的ORM中间件有:Hibernate,ibatis,speedframework。
SQLAlchemy
SQLAlchemy是Python编程语言下的一款开源软件。提供了SQL工具包及对象关系映射(ORM)工具,使用MIT许可证发行。
可以使用pip命令安装SQLAlchemy模块:
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pip install sqlalchemy |
SQLAlchemy模块提供了create_engine()函数用来初始化数据库连接,SQLAlchemy用一个字符串表示连接信息:
‘数据库类型+数据库驱动名称://用户名:口令@机器地址:端口号/数据库名’
Pandas读写MySQL数据库
我们需要以下三个库来实现Pandas读写MySQL数据库:
- pandas
- sqlalchemy
- pymysql
其中,pandas模块提供了read_sql_query()函数实现了对数据库的查询,to_sql()函数实现了对数据库的写入,并不需要实现新建MySQL数据表。sqlalchemy模块实现了与不同数据库的连接,而pymysql模块则使得Python能够操作MySQL数据库。
我们将使用MySQL数据库中的mydb数据库以及employee表,内容如下:

下面将介绍一个简单的例子来展示如何在pandas中实现对MySQL数据库的读写:
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<span class="token comment"># -*- coding: utf-8 -*-</span> <span class="token comment"># 导入必要模块</span> <span class="token keyword">import</span> pandas <span class="token keyword">as</span> pd <span class="token keyword">from</span> sqlalchemy <span class="token keyword">import</span> create_engine <span class="token comment"># 初始化数据库连接,使用pymysql模块</span> <span class="token comment"># MySQL的用户:root, 密码:147369, 端口:3306,数据库:mydb</span> engine <span class="token operator">=</span> create_engine<span class="token punctuation">(</span><span class="token string">'mysql+pymysql://root:147369@localhost:3306/mydb'</span><span class="token punctuation">)</span> <span class="token comment"># 查询语句,选出employee表中的所有数据</span> sql <span class="token operator">=</span> <span class="token triple-quoted-string string">''' select * from employee; '''</span> <span class="token comment"># read_sql_query的两个参数: sql语句, 数据库连接</span> df <span class="token operator">=</span> pd<span class="token punctuation">.</span>read_sql_query<span class="token punctuation">(</span>sql<span class="token punctuation">,</span> engine<span class="token punctuation">)</span> <span class="token comment"># 输出employee表的查询结果</span> <span class="token keyword">print</span><span class="token punctuation">(</span>df<span class="token punctuation">)</span> <span class="token comment"># 新建pandas中的DataFrame, 只有id,num两列</span> df <span class="token operator">=</span> pd<span class="token punctuation">.</span>DataFrame<span class="token punctuation">(</span><span class="token punctuation">{</span><span class="token string">'id'</span><span class="token punctuation">:</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span><span class="token number">2</span><span class="token punctuation">,</span><span class="token number">3</span><span class="token punctuation">,</span><span class="token number">4</span><span class="token punctuation">]</span><span class="token punctuation">,</span><span class="token string">'num'</span><span class="token punctuation">:</span><span class="token punctuation">[</span><span class="token number">12</span><span class="token punctuation">,</span><span class="token number">34</span><span class="token punctuation">,</span><span class="token number">56</span><span class="token punctuation">,</span><span class="token number">89</span><span class="token punctuation">]</span><span class="token punctuation">}</span><span class="token punctuation">)</span> <span class="token comment"># 将新建的DataFrame储存为MySQL中的数据表,不储存index列</span> df<span class="token punctuation">.</span>to_sql<span class="token punctuation">(</span><span class="token string">'mydf'</span><span class="token punctuation">,</span> engine<span class="token punctuation">,</span> index<span class="token operator">=</span> <span class="token boolean">False</span><span class="token punctuation">)</span> <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">'Read from and write to Mysql table successfully!'</span><span class="token punctuation">)</span> |
程序的运行结果如下:

在MySQL中查看mydf表格:

这说明我们确实将pandas中新建的DataFrame写入到了MySQL中!
将CSV文件写入到MySQL中
以上的例子实现了使用Pandas库实现MySQL数据库的读写,我们将再介绍一个实例:将CSV文件写入到MySQL中,示例的mpg.CSV文件前10行如下:

示例的Python代码如下:
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<span class="token comment"># -*- coding: utf-8 -*-</span> <span class="token comment"># 导入必要模块</span> <span class="token keyword">import</span> pandas <span class="token keyword">as</span> pd <span class="token keyword">from</span> sqlalchemy <span class="token keyword">import</span> create_engine <span class="token comment"># 初始化数据库连接,使用pymysql模块</span> engine <span class="token operator">=</span> create_engine<span class="token punctuation">(</span><span class="token string">'mysql+pymysql://root:147369@localhost:3306/mydb'</span><span class="token punctuation">)</span> <span class="token comment"># 读取本地CSV文件</span> df <span class="token operator">=</span> pd<span class="token punctuation">.</span>read_csv<span class="token punctuation">(</span><span class="token string">"E://mpg.csv"</span><span class="token punctuation">,</span> sep<span class="token operator">=</span><span class="token string">','</span><span class="token punctuation">)</span> <span class="token comment"># 将新建的DataFrame储存为MySQL中的数据表,不储存index列</span> df<span class="token punctuation">.</span>to_sql<span class="token punctuation">(</span><span class="token string">'mpg'</span><span class="token punctuation">,</span> engine<span class="token punctuation">,</span> index<span class="token operator">=</span> <span class="token boolean">False</span><span class="token punctuation">)</span> <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"Write to MySQL successfully!"</span><span class="token punctuation">)</span> |
在MySQL中查看mpg表格:

仅仅5句Python代码就实现了将CSV文件写入到MySQL中,这无疑是简单、方便、迅速、高效的!
总结
本文主要介绍了ORM技术以及SQLAlchemy模块,并且展示了两个Python程序的实例,介绍了如何使用Pandas库实现MySQL数据库的读写。程序本身并不难,关键在于多多练习。
本次分享到此结束,欢迎大家多多交流~~
作者:山阴少年
链接:https://www.jianshu.com/p/238a13995b2b
来源:简书
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