有时候你会发现Django数据库API带给你的也只有这么多,那你可以为你的数据库写一些自定义SQL查询。 你可以通过导入django.db.connection对像来轻松实现,它代表当前数据库连接。 要使用它,需要通过connection.cursor()得到一个游标对像。 然后,使用cursor.execute(sql, [params])来执行SQL语句,使用cursor.fetchone()或者cursor.fetchall()来返回记录集。 例如:
>>> from django.db import connection >>> cursor = connection.cursor() >>> cursor.execute(""" ... SELECT DISTINCT first_name ... FROM people_person ... WHERE last_name = %s""", ['Lennon']) >>> row = cursor.fetchone() >>> print row ['John']
connection和cursor几乎实现了标准Python DB-API,你可以访问` http://www.python.org/peps/pep-0249.html
不要把你的视图代码和django.db.connection语句混杂在一起,把它们放在自定义模型或者自定义manager方法中是个不错的主意。 比如,上面的例子可以被整合成一个自定义manager方法,就像这样:
from django.db import connection, models class PersonManager(models.Manager): def first_names(self, last_name): cursor = connection.cursor() cursor.execute(""" SELECT DISTINCT first_name FROM people_person WHERE last_name = %s""", [last_name]) return [row[0] for row in cursor.fetchone()] class Person(models.Model): first_name = models.CharField(max_length=50) last_name = models.CharField(max_length=50) objects = PersonManager()
然后这样使用:
>>> Person.objects.first_names('Lennon') ['John', 'Cynthia']

Python's flexibility is reflected in multi-paradigm support and dynamic type systems, while ease of use comes from a simple syntax and rich standard library. 1. Flexibility: Supports object-oriented, functional and procedural programming, and dynamic type systems improve development efficiency. 2. Ease of use: The grammar is close to natural language, the standard library covers a wide range of functions, and simplifies the development process.

Python is highly favored for its simplicity and power, suitable for all needs from beginners to advanced developers. Its versatility is reflected in: 1) Easy to learn and use, simple syntax; 2) Rich libraries and frameworks, such as NumPy, Pandas, etc.; 3) Cross-platform support, which can be run on a variety of operating systems; 4) Suitable for scripting and automation tasks to improve work efficiency.

Yes, learn Python in two hours a day. 1. Develop a reasonable study plan, 2. Select the right learning resources, 3. Consolidate the knowledge learned through practice. These steps can help you master Python in a short time.

Python is suitable for rapid development and data processing, while C is suitable for high performance and underlying control. 1) Python is easy to use, with concise syntax, and is suitable for data science and web development. 2) C has high performance and accurate control, and is often used in gaming and system programming.

The time required to learn Python varies from person to person, mainly influenced by previous programming experience, learning motivation, learning resources and methods, and learning rhythm. Set realistic learning goals and learn best through practical projects.

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.


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