本文实例讲述了Python pickle模块用法。分享给大家供大家参考。具体分析如下:
pickle提供了一个简单的持久化功能。可以将对象以文件的形式存放在磁盘上。
pickle.dump(obj, file[, protocol])
序列化对象,并将结果数据流写入到文件对象中。参数protocol是序列化模式,默认值为0,表示以文本的形式序列化。protocol的值还可以是1或2,表示以二进制的形式序列化。
pickle.load(file)
反序列化对象。将文件中的数据解析为一个Python对象。
其中要注意的是,在load(file)的时候,要让python能够找到类的定义,否则会报错:
比如下面的例子
import pickle class Person: def __init__(self,n,a): self.name=n self.age=a def show(self): print self.name+"_"+str(self.age) aa = Person("JGood", 2) aa.show() f=open('d:\\p.txt','w') pickle.dump(aa,f,0) f.close() #del Person f=open('d:\\p.txt','r') bb=pickle.load(f) f.close() bb.show()
如果不注释掉del Person的话,那么会报错如下:
>>> JGood_2 Traceback (most recent call last): File "C:/py/test.py", line 15, in <module> bb=pickle.load(f) File "C:\Python27\lib\pickle.py", line 1378, in load return Unpickler(file).load() File "C:\Python27\lib\pickle.py", line 858, in load dispatch[key](self) File "C:\Python27\lib\pickle.py", line 1069, in load_inst klass = self.find_class(module, name) File "C:\Python27\lib\pickle.py", line 1126, in find_class klass = getattr(mod, name) AttributeError: 'module' object has no attribute 'Person'
意思就是当前模块找不到类的定义了。
clear_memo()
清空pickler的“备忘”。使用Pickler实例在序列化对象的时候,它会“记住”已经被序列化的对象引用,所以对同一对象多次调用dump(obj),pickler不会“傻傻”的去多次序列化。
看下面的例子:
import StringIO import pickle class Person: def __init__(self,n,a): self.name=n self.age=a def show(self): print self.name+"_"+str(self.age) aa = Person("JGood", 2) aa.show() fle = StringIO.StringIO() pick = pickle.Pickler(fle) pick.dump(aa) val1=fle.getvalue() print len(val1) pick.clear_memo() pick.dump(aa) val2=fle.getvalue() print len(val2) fle.close()
上面的代码运行如下:
>>> JGood_2 66 132 >>>
此时再注释掉pick.clear_memo()后,运行结果如下:
>>> JGood_2 66 70 >>>
主要是因为,python的pickle如果不clear_memo,则不会多次去序列化对象。
希望本文所述对大家的Python程序设计有所帮助。

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

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Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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