修改一些code block的形式

cookbook/c06/p08_database.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python
+# -*- encoding: utf-8 -*-
+"""
+Topic: 关系型数据库处理
+Desc : 
+"""
+import sqlite3
+
+
+def db_operation():
+    db = sqlite3.connect('database.db')
+    c = db.cursor()
+    c.execute('create table portfolio (symbol text, shares integer, price real)')
+    db.commit()
+
+    stocks = [
+        ('GOOG', 100, 490.1),
+        ('AAPL', 50, 545.75),
+        ('FB', 150, 7.45),
+        ('HPQ', 75, 33.2),
+    ]
+    c.executemany('insert into portfolio values (?,?,?)', stocks)
+    db.commit()
+
+    for row in db.execute('select * from portfolio'):
+        print(row)
+
+    min_price = 12
+    for row in db.execute('select * from portfolio where price >= ?', (min_price,)):
+        print(row)
+
+if __name__ == '__main__':
+    db_operation()
+

source/c06/p01_read_write_csv_data.rst

@@ -15,7 +15,7 @@
 对于大多数的CSV格式的数据读写问题，都可以使用 ``csv`` 库。、
 例如，假设你在一个名叫stocks.csv文件中有一些股票市场数据，就像这样：
 
-.. code-block:: python
+.. code-block::
 
     Symbol,Price,Date,Time,Change,Volume
     "AA",39.48,"6/11/2007","9:36am",-0.18,181800
@@ -140,7 +140,7 @@ row[4]访问Change。
 如果你正在读取CSV数据并将它们转换为命名元组，需要注意对列名进行合法性认证。
 例如，一个CSV格式文件有一个包含非法标识符的列头行，类似下面这样：
 
-.. code-block:: python
+.. code-block::
 
     Street Address,Num-Premises,Latitude,Longitude
     5412 N CLARK,10,41.980262,-87.668452
@@ -200,5 +200,3 @@ row[4]访问Change。
 它可以加载CSV数据到一个 ``DataFrame`` 对象中去。
 然后利用这个对象你就可以生成各种形式的统计、过滤数据以及执行其他高级操作了。
 在6.13小节中会有这样一个例子。
-
-

source/c06/p04_parse_huge_xml_files_incrementally.rst

@@ -46,7 +46,7 @@
 例如，你可以下载XML格式的芝加哥城市道路坑洼数据库。
 在写这本书的时候，下载文件已经包含超过100,000行数据，编码格式类似于下面这样：
 
-.. code-block:: python
+.. code-block::
 
     <response>
         <row>

source/c06/p06_parse_modify_rewrite_xml.rst

@@ -15,7 +15,7 @@
 使用 ``xml.etree.ElementTree`` 模块可以很容易的处理这些任务。
 第一步是以通常的方式来解析这个文档。例如，假设你有一个名为 ``pred.xml`` 的文档，类似下面这样：
 
-.. code-block:: python
+.. code-block::
 
     <?xml version="1.0"?>
     <stop>
@@ -67,7 +67,7 @@
 
 处理结果是一个像下面这样新的XML文件：
 
-.. code-block:: python
+.. code-block::
 
     <?xml version='1.0' encoding='us-ascii'?>
     <stop>

source/c06/p08_interact_with_relational_database.rst

@@ -5,14 +5,126 @@
 ----------
 问题
 ----------
-todo...
+你想在关系型数据库中查询、增加或删除记录。
+
+|
 
 ----------
 解决方案
 ----------
-todo...
+Python中表示多行数据的标准方式是一个由元组构成的序列。例如：
+
+.. code-block:: python
+
+    stocks = [
+        ('GOOG', 100, 490.1),
+        ('AAPL', 50, 545.75),
+        ('FB', 150, 7.45),
+        ('HPQ', 75, 33.2),
+    ]
+
+依据PEP249，通过这种形式提供数据，
+可以很容易的使用Python标准数据库API和关系型数据库进行交互。
+所有数据库上的操作都通过SQL查询语句来完成。每一行输入输出数据用一个元组来表示。
+
+为了演示说明，你可以使用Python标准库中的 ``sqlite3`` 模块。
+如果你使用的是一个不同的数据库(比如MySql、Postgresql或者ODBC)，
+还得安装相应的第三方模块来提供支持。
+不过相应的编程接口几乎都是一样的，除了一点点细微差别外。
+
+The first step is to connect to the database. Typically, you execute a connect() function,
+supplying parameters such as the name of the database, hostname, username, password,
+and other details as needed. For example:
+第一步是连接到数据库。通常你要执行 ``connect()`` 函数，
+给它提供一些数据库名、主机、用户名、密码和其他必要的一些参数。例如：
+
+.. code-block:: python
+
+    >>> import sqlite3
+    >>> db = sqlite3.connect('database.db')
+    >>>
+
+为了处理数据，下一步你需要创建一个游标。
+一旦你有了游标，那么你就可以执行SQL查询语句了。比如：
+
+.. code-block:: python
+
+    >>> c = db.cursor()
+    >>> c.execute('create table portfolio (symbol text, shares integer, price real)')
+    <sqlite3.Cursor object at 0x10067a730>
+    >>> db.commit()
+    >>>
+
+为了向数据库表中插入多条记录，使用类似下面这样的语句：
+
+.. code-block:: python
+
+    >>> c.executemany('insert into portfolio values (?,?,?)', stocks)
+    <sqlite3.Cursor object at 0x10067a730>
+    >>> db.commit()
+    >>>
+
+To perform a query, use a statement such as this:
+为了执行某个查询，使用像下面这样的语句：
+
+.. code-block:: python
+
+    >>> for row in db.execute('select * from portfolio'):
+    ...     print(row)
+    ...
+    ('GOOG', 100, 490.1)
+    ('AAPL', 50, 545.75)
+    ('FB', 150, 7.45)
+    ('HPQ', 75, 33.2)
+    >>>
+
+如果你想接受用户输入作为参数来执行查询操作，必须确保你使用下面这样的占位符?来进行参数转义：
+
+.. code-block:: python
+
+    >>> min_price = 100
+    >>> for row in db.execute('select * from portfolio where price >= ?',
+                              (min_price,)):
+    ...     print(row)
+    ...
+    ('GOOG', 100, 490.1)
+    ('AAPL', 50, 545.75)
+    >>>
 
 ----------
 讨论
 ----------
-todo...
+At a low level, interacting with a database is an extremely straightforward thing to do.
+You simply form SQL statements and feed them to the underlying module to either
+update the database or retrieve data. That said, there are still some tricky details you’ll
+need to sort out on a case-by-case basis.
+
+
+One complication is the mapping of data from the database into Python types. For
+entries such as dates, it is most common to use datetime instances from the date
+time module, or possibly system timestamps, as used in the time module. For numerical
+data, especially financial data involving decimals, numbers may be represented as Dec
+imal instances from the decimal module. Unfortunately, the exact mapping varies by
+database backend so you’ll have to read the associated documentation.
+
+
+Another extremely critical complication concerns the formation of SQL statement
+strings. You should never use Python string formatting operators (e.g., %) or the .for
+mat() method to create such strings. If the values provided to such formatting operators
+are derived from user input, this opens up your program to an SQL-injection attack (see
+http://xkcd.com/327). The special ? wildcard in queries instructs the database backend
+to use its own string substitution mechanism, which (hopefully) will do it safely.
+
+
+Sadly, there is some inconsistency across database backends with respect to the wildcard.
+Many modules use ? or %s, while others may use a different symbol, such as :0 or :1,
+to refer to parameters. Again, you’ll have to consult the documentation for the database
+module you’re using. The paramstyle attribute of a database module also contains information
+about the quoting style.
+
+
+For simply pulling data in and out of a database table, using the database API is usually
+simple enough. If you’re doing something more complicated, it may make sense to use
+a higher-level interface, such as that provided by an object-relational mapper. Libraries
+such as SQLAlchemy allow database tables to be described as Python classes and for
+database operations to be carried out while hiding most of the underlying SQL.