Python中可以使用 pickle 模块将对象转化为文件保存在磁盘上,在需要的时候再读取并还原。具体用法如下:
pickle是Python库中常用的序列化工具,可以将内存对象以文本或二进制格式导出为字符串,或者写入文档。后续可以从字符或文档中还原为内存对象。新版本的Python中用c重新实现了一遍,叫cPickle,性能更高。 下面的代码演示了pickle库的常用接口用法,非常简单:
import cPickle as pickle # dumps and loads # 将内存对象dump为字符串,或者将字符串load为内存对象 def test_dumps_and_loads(): t = {'name': ['v1', 'v2']} print t o = pickle.dumps(t) print o print 'len o: ', len(o) p = pickle.loads(o) print p # 关于HIGHEST_PROTOCOL参数,pickle 支持3种protocol,0、1、2: # http://stackoverflow.com/questions/23582489/python-pickle-protocol-choice # 0:ASCII protocol,兼容旧版本的Python # 1:binary format,兼容旧版本的Python # 2:binary format,Python2.3 之后才有,更好的支持new-sytle class def test_dumps_and_loads_HIGHEST_PROTOCOL(): print 'HIGHEST_PROTOCOL: ', pickle.HIGHEST_PROTOCOL t = {'name': ['v1', 'v2']} print t o = pickle.dumps(t, pickle.HIGHEST_PROTOCOL) print 'len o: ', len(o) p = pickle.loads(o) print p # new-style class def test_new_sytle_class(): class TT(object): def __init__(self, arg, **kwargs): super(TT, self).__init__() self.arg = arg self.kwargs = kwargs def test(self): print self.arg print self.kwargs # ASCII protocol t = TT('test', a=1, b=2) o1 = pickle.dumps(t) print o1 print 'o1 len: ', len(o1) p = pickle.loads(o1) p.test() # HIGHEST_PROTOCOL对new-style class支持更好,性能更高 o2 = pickle.dumps(t, pickle.HIGHEST_PROTOCOL) print 'o2 len: ', len(o2) p = pickle.loads(o2) p.test() # dump and load # 将内存对象序列化后直接dump到文件或支持文件接口的对象中 # 对于dump,需要支持write接口,接受一个字符串作为输入参数,比如:StringIO # 对于load,需要支持read接口,接受int输入参数,同时支持readline接口,无输入参数,比如StringIO # 使用文件,ASCII编码 def test_dump_and_load_with_file(): t = {'name': ['v1', 'v2']} # ASCII format with open('test.txt', 'w') as fp: pickle.dump(t, fp) with open('test.txt', 'r') as fp: p = pickle.load(fp) print p # 使用文件,二进制编码 def test_dump_and_load_with_file_HIGHEST_PROTOCOL(): t = {'name': ['v1', 'v2']} with open('test.bin', 'wb') as fp: pickle.dump(t, fp, pickle.HIGHEST_PROTOCOL) with open('test.bin', 'rb') as fp: p = pickle.load(fp) print p # 使用StringIO,二进制编码 def test_dump_and_load_with_StringIO(): import StringIO t = {'name': ['v1', 'v2']} fp = StringIO.StringIO() pickle.dump(t, fp, pickle.HIGHEST_PROTOCOL) fp.seek(0) p = pickle.load(fp) print p fp.close() # 使用自定义类 # 这里演示用户自定义类,只要实现了write、read、readline接口, # 就可以用作dump、load的file参数 def test_dump_and_load_with_user_def_class(): import StringIO class FF(object): def __init__(self): self.buf = StringIO.StringIO() def write(self, s): self.buf.write(s) print 'len: ', len(s) def read(self, n): return self.buf.read(n) def readline(self): return self.buf.readline() def seek(self, pos, mod=0): return self.buf.seek(pos, mod) def close(self): self.buf.close() fp = FF() t = {'name': ['v1', 'v2']} pickle.dump(t, fp, pickle.HIGHEST_PROTOCOL) fp.seek(0) p = pickle.load(fp) print p fp.close() # Pickler/Unpickler # Pickler(file, protocol).dump(obj) 等价于 pickle.dump(obj, file[, protocol]) # Unpickler(file).load() 等价于 pickle.load(file) # Pickler/Unpickler 封装性更好,可以很方便的替换file def test_pickler_unpickler(): t = {'name': ['v1', 'v2']} f = file('test.bin', 'wb') pick = pickle.Pickler(f, pickle.HIGHEST_PROTOCOL) pick.dump(t) f.close() f = file('test.bin', 'rb') unpick = pickle.Unpickler(f) p = unpick.load() print p f.close()
pickle.dump(obj, file[, protocol])
这是将对象持久化的方法,参数的含义分别为:
对象被持久化后怎么还原呢?pickle 模块也提供了相应的方法,如下:
pickle.load(file)
只有一个参数 file ,对应于上面 dump 方法中的 file 参数。这个 file 必须是一个拥有一个能接收一个整数为参数的 read() 方法以及一个不接收任何参数的 readline() 方法,并且这两个方法的返回值都应该是字符串。这可以是一个打开为读的文件对象、StringIO 对象或其他任何满足条件的对象。
下面是一个基本的用例:
# -*- coding: utf-8 -*- import pickle # 也可以这样: # import cPickle as pickle obj = {"a": 1, "b": 2, "c": 3} # 将 obj 持久化保存到文件 tmp.txt 中 pickle.dump(obj, open("tmp.txt", "w")) # do something else ... # 从 tmp.txt 中读取并恢复 obj 对象 obj2 = pickle.load(open("tmp.txt", "r")) print obj2 # -*- coding: utf-8 -*- import pickle # 也可以这样: # import cPickle as pickle obj = {"a": 1, "b": 2, "c": 3} # 将 obj 持久化保存到文件 tmp.txt 中 pickle.dump(obj, open("tmp.txt", "w")) # do something else ... # 从 tmp.txt 中读取并恢复 obj 对象 obj2 = pickle.load(open("tmp.txt", "r")) print obj2
不过实际应用中,我们可能还会有一些改进,比如用 cPickle 来代替 pickle ,前者是后者的一个 C 语言实现版本,拥有更快的速度,另外,有时在 dump 时也会将第三个参数设为 True 以提高压缩比。再来看下面的例子:
# -*- coding: utf-8 -*- import cPickle as pickle import random import os import time LENGTH = 1024 * 10240 def main(): d = {} a = [] for i in range(LENGTH): a.append(random.randint(0, 255)) d["a"] = a print "dumping..." t1 = time.time() pickle.dump(d, open("tmp1.dat", "wb"), True) print "dump1: %.3fs" % (time.time() - t1) t1 = time.time() pickle.dump(d, open("tmp2.dat", "w")) print "dump2: %.3fs" % (time.time() - t1) s1 = os.stat("tmp1.dat").st_size s2 = os.stat("tmp2.dat").st_size print "%d, %d, %.2f%%" % (s1, s2, 100.0 * s1 / s2) print "loading..." t1 = time.time() obj1 = pickle.load(open("tmp1.dat", "rb")) print "load1: %.3fs" % (time.time() - t1) t1 = time.time() obj2 = pickle.load(open("tmp2.dat", "r")) print "load2: %.3fs" % (time.time() - t1) if __name__ == "__main__": main() # -*- coding: utf-8 -*- import cPickle as pickle import random import os import time LENGTH = 1024 * 10240 def main(): d = {} a = [] for i in range(LENGTH): a.append(random.randint(0, 255)) d["a"] = a print "dumping..." t1 = time.time() pickle.dump(d, open("tmp1.dat", "wb"), True) print "dump1: %.3fs" % (time.time() - t1) t1 = time.time() pickle.dump(d, open("tmp2.dat", "w")) print "dump2: %.3fs" % (time.time() - t1) s1 = os.stat("tmp1.dat").st_size s2 = os.stat("tmp2.dat").st_size print "%d, %d, %.2f%%" % (s1, s2, 100.0 * s1 / s2) print "loading..." t1 = time.time() obj1 = pickle.load(open("tmp1.dat", "rb")) print "load1: %.3fs" % (time.time() - t1) t1 = time.time() obj2 = pickle.load(open("tmp2.dat", "r")) print "load2: %.3fs" % (time.time() - t1) if __name__ == "__main__": main()
在我的电脑上执行结果为:
dumping… dump1: 1.297s dump2: 4.750s 20992503, 68894198, 30.47% loading… load1: 2.797s load2: 10.125s
可以看到,dump 时如果指定了 protocol 为 True,压缩过后的文件的大小只有原来的文件的 30% ,同时无论在 dump 时还是 load 时所耗费的时间都比原来少。因此,一般来说,可以建议把这个值设为 True 。
另外,pickle 模块还提供 dumps 和 loads 两个方法,用法与上面的 dump 和 load 方法类似,只是不需要输入 file 参数,输入及输出都是字符串对象,有些场景中使用这两个方法可能更为方便。