举例简单讲解Python中的数据存储模块shelve的用法
shelve类似于一个key-value数据库，可以很方便的用来保存Python的内存对象，其内部使用pickle来序列化数据，简单来说，使用者可以将一个列表、字典、或者用户自定义的类实例保存到shelve中，下次需要用的时候直接取出来，就是一个Python内存对象，不需要像传统数据库一样，先取出数据，然后用这些数据重新构造一遍所需要的对象。下面是简单示例：
import shelve
def test_shelve():
# open 返回一个Shelf类的实例
#
# 参数flag的取值范围：
# 'r'：只读打开
# 'w'：读写访问
# 'c'：读写访问，如果不存在则创建
# 'n'：读写访问，总是创建新的、空的数据库文件
#
# protocol：与pickle库一致
# writeback：为True时，当数据发生变化会回写，不过会导致内存开销比较大
d = shelve.open('shelve.db', flag='c', protocol=2, writeback=False)
assert isinstance(d, shelve.Shelf)
# 在数据库中插入一条记录
d['abc'] = {'name': ['a', 'b']}
d.sync()
print d['abc']
# writeback是False，因此对value进行修改是不起作用的
d['abc']['x'] = 'x'
print d['abc'] # 还是打印 {'name': ['a', 'b']}
# 当然，直接替换key的value还是起作用的
d['abc'] = 'xxx'
print d['abc']
# 还原abc的内容，为下面的测试代码做准备
d['abc'] = {'name': ['a', 'b']}
d.close()
# writeback 为 True 时，对字段内容的修改会writeback到数据库中。
d = shelve.open('shelve.db', writeback=True)
# 上面我们已经保存了abc的内容为{'name': ['a', 'b']}，打印一下看看对不对
print d['abc']
# 修改abc的value的部分内容
d['abc']['xx'] = 'xxx'
print d['abc']
d.close()
# 重新打开数据库，看看abc的内容是否正确writeback
d = shelve.open('shelve.db')
print d['abc']
d.close()
这个有一个潜在的小问题，如下：
>>> import shelve
>>> s = shelve.open('test.dat')
>>> s['x'] = ['a', 'b', 'c']
>>> s['x'].append('d')
>>> s['x']
['a', 'b', 'c']
存储的d到哪里去了呢？其实很简单，d没有写回，你把['a', 'b', 'c']存到了x，当你再次读取s['x']的时候，s['x']只是一个拷贝，而你没有将拷贝写回，所以当你再次读取s['x']的时候，它又从源中读取了一个拷贝，所以，你新修改的内容并不会出现在拷贝中，解决的办法就是，第一个是利用一个缓存的变量，如下所示
>>> temp = s['x']
>>> temp.append('d')
>>> s['x'] = temp
>>> s['x']
['a', 'b', 'c', 'd']
在python2.4以后有了另外的方法，就是把open方法的writeback参数的值赋为True，这样的话，你open后所有的内容都将在cache中，当你close的时候，将全部一次性写到硬盘里面。如果数据量不是很大的时候，建议这么做。
下面是一个基于shelve的简单数据库的代码
#database.py
import sys, shelve
def store_person(db):
"""
Query user for data and store it in the shelf object
"""
pid = raw_input('Enter unique ID number: ')
person = {}
person['name'] = raw_input('Enter name: ')
person['age'] = raw_input('Enter age: ')
person['phone'] = raw_input('Enter phone number: ')
db[pid] = person
def lookup_person(db):
"""
Query user for ID and desired field, and fetch the corresponding data from
the shelf object
"""
pid = raw_input('Enter ID number: ')
field = raw_input('What would you like to know? (name, age, phone) ')
field = field.strip().lower()
print field.capitalize() + ':', \
db[pid][field]
def print_help():
print 'The available commons are: '
print 'store :Stores information about a person'
print 'lookup :Looks up a person from ID number'
print 'quit :Save changes and exit'
print '? :Print this message'
def enter_command():
cmd = raw_input('Enter command (? for help): ')
cmd = cmd.strip().lower()
return cmd
def main():
database = shelve.open('database.dat')
try:
while True:
cmd = enter_command()
if cmd == 'store':
store_person(database)
elif cmd == 'lookup':
lookup_person(database)
elif cmd == '?':
print_help()
elif cmd == 'quit':
return
finally:
database.close()
if __name__ == '__main__': main()
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
AI Hentai Generator
Generate AI Hentai for free.
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
1384
52
PHP and Python: Code Examples and Comparison
Apr 15, 2025 am 12:07 AM
PHP and Python have their own advantages and disadvantages, and the choice depends on project needs and personal preferences. 1.PHP is suitable for rapid development and maintenance of large-scale web applications. 2. Python dominates the field of data science and machine learning.
Python vs. JavaScript: Community, Libraries, and Resources
Apr 15, 2025 am 12:16 AM
Python and JavaScript have their own advantages and disadvantages in terms of community, libraries and resources. 1) The Python community is friendly and suitable for beginners, but the front-end development resources are not as rich as JavaScript. 2) Python is powerful in data science and machine learning libraries, while JavaScript is better in front-end development libraries and frameworks. 3) Both have rich learning resources, but Python is suitable for starting with official documents, while JavaScript is better with MDNWebDocs. The choice should be based on project needs and personal interests.
How is the GPU support for PyTorch on CentOS
Apr 14, 2025 pm 06:48 PM
Enable PyTorch GPU acceleration on CentOS system requires the installation of CUDA, cuDNN and GPU versions of PyTorch. The following steps will guide you through the process: CUDA and cuDNN installation determine CUDA version compatibility: Use the nvidia-smi command to view the CUDA version supported by your NVIDIA graphics card. For example, your MX450 graphics card may support CUDA11.1 or higher. Download and install CUDAToolkit: Visit the official website of NVIDIACUDAToolkit and download and install the corresponding version according to the highest CUDA version supported by your graphics card. Install cuDNN library:
Detailed explanation of docker principle
Apr 14, 2025 pm 11:57 PM
Docker uses Linux kernel features to provide an efficient and isolated application running environment. Its working principle is as follows: 1. The mirror is used as a read-only template, which contains everything you need to run the application; 2. The Union File System (UnionFS) stacks multiple file systems, only storing the differences, saving space and speeding up; 3. The daemon manages the mirrors and containers, and the client uses them for interaction; 4. Namespaces and cgroups implement container isolation and resource limitations; 5. Multiple network modes support container interconnection. Only by understanding these core concepts can you better utilize Docker.
MiniOpen Centos compatibility
Apr 14, 2025 pm 05:45 PM
MinIO Object Storage: High-performance deployment under CentOS system MinIO is a high-performance, distributed object storage system developed based on the Go language, compatible with AmazonS3. It supports a variety of client languages, including Java, Python, JavaScript, and Go. This article will briefly introduce the installation and compatibility of MinIO on CentOS systems. CentOS version compatibility MinIO has been verified on multiple CentOS versions, including but not limited to: CentOS7.9: Provides a complete installation guide covering cluster configuration, environment preparation, configuration file settings, disk partitioning, and MinI
How to operate distributed training of PyTorch on CentOS
Apr 14, 2025 pm 06:36 PM
PyTorch distributed training on CentOS system requires the following steps: PyTorch installation: The premise is that Python and pip are installed in CentOS system. Depending on your CUDA version, get the appropriate installation command from the PyTorch official website. For CPU-only training, you can use the following command: pipinstalltorchtorchvisiontorchaudio If you need GPU support, make sure that the corresponding version of CUDA and cuDNN are installed and use the corresponding PyTorch version for installation. Distributed environment configuration: Distributed training usually requires multiple machines or single-machine multiple GPUs. Place
How to choose the PyTorch version on CentOS
Apr 14, 2025 pm 06:51 PM
When installing PyTorch on CentOS system, you need to carefully select the appropriate version and consider the following key factors: 1. System environment compatibility: Operating system: It is recommended to use CentOS7 or higher. CUDA and cuDNN:PyTorch version and CUDA version are closely related. For example, PyTorch1.9.0 requires CUDA11.1, while PyTorch2.0.1 requires CUDA11.3. The cuDNN version must also match the CUDA version. Before selecting the PyTorch version, be sure to confirm that compatible CUDA and cuDNN versions have been installed. Python version: PyTorch official branch
How to update PyTorch to the latest version on CentOS
Apr 14, 2025 pm 06:15 PM
Updating PyTorch to the latest version on CentOS can follow the following steps: Method 1: Updating pip with pip: First make sure your pip is the latest version, because older versions of pip may not be able to properly install the latest version of PyTorch. pipinstall--upgradepip uninstalls old version of PyTorch (if installed): pipuninstalltorchtorchvisiontorchaudio installation latest
