如何使用泡菜来保存字典？

Question

我查看了Python文档提供的信息,但我仍然有点困惑.有人可以发布编写新文件的示例代码,然后使用pickle将字典转储到其中吗？

Answer 1

试试这个:

import pickle

a = {'hello': 'world'}

with open('filename.pickle', 'wb') as handle:
    pickle.dump(a, handle, protocol=pickle.HIGHEST_PROTOCOL)

with open('filename.pickle', 'rb') as handle:
    b = pickle.load(handle)

print a == b

Answer 2

import pickle

your_data = {'foo': 'bar'}

# Store data (serialize)
with open('filename.pickle', 'wb') as handle:
    pickle.dump(your_data, handle, protocol=pickle.HIGHEST_PROTOCOL)

# Load data (deserialize)
with open('filename.pickle', 'rb') as handle:
    unserialized_data = pickle.load(handle)

print(your_data == unserialized_data)

优点HIGHEST_PROTOCOL是文件变小.这使得unpickling有时更快.

重要提示:pickle的最大文件大小约为2GB.

替代方式

import mpu
your_data = {'foo': 'bar'}
mpu.io.write('filename.pickle', data)
unserialized_data = mpu.io.read('filename.pickle')

替代格式

• CSV:超简单格式(读写)
• JSON:很适合编写人类可读的数据; 非常常用(读写)
• YAML:YAML是JSON的超集,但更容易阅读(读写,JSON和YAML的比较)
• pickle:Python序列化格式(读写)
• MessagePack(Python包):更紧凑的表示(读写)
• HDF5(Python包):很适合矩阵(读写)
• XML:存在太多*叹*(读与写)

对于您的应用程序,以下可能很重要:

• 其他编程语言的支持
• 读/写性能
• 紧凑性(文件大小)

另请参见:数据序列化格式的比较

如果您正在寻找一种制作配置文件的方法,您可能希望阅读我的简短文章Python中的配置文件

Answer 3

# Save a dictionary into a pickle file.
import pickle

favorite_color = {"lion": "yellow", "kitty": "red"}  # create a dictionary
pickle.dump(favorite_color, open("save.p", "wb"))  # save it into a file named save.p

# -------------------------------------------------------------
# Load the dictionary back from the pickle file.
import pickle

favorite_color = pickle.load(open("save.p", "rb"))
# favorite_color is now {"lion": "yellow", "kitty": "red"}

Answer 4

通常,dict除非你只有简单的对象,如字符串和整数,否则pickle 将失败.

Python 2.7.9 (default, Dec 11 2014, 01:21:43) 
[GCC 4.2.1 Compatible Apple Clang 4.1 ((tags/Apple/clang-421.11.66))] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from numpy import *
>>> type(globals())     
<type 'dict'>
>>> import pickle
>>> pik = pickle.dumps(globals())
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 1374, in dumps
    Pickler(file, protocol).dump(obj)
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 224, in dump
    self.save(obj)
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 286, in save
    f(self, obj) # Call unbound method with explicit self
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 649, in save_dict
    self._batch_setitems(obj.iteritems())
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 663, in _batch_setitems
    save(v)
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 306, in save
    rv = reduce(self.proto)
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/copy_reg.py", line 70, in _reduce_ex
    raise TypeError, "can't pickle %s objects" % base.__name__
TypeError: can't pickle module objects
>>> 

即使是非常简单dict也常常会失败.它只取决于内容.

>>> d = {'x': lambda x:x}
>>> pik = pickle.dumps(d)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 1374, in dumps
    Pickler(file, protocol).dump(obj)
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 224, in dump
    self.save(obj)
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 286, in save
    f(self, obj) # Call unbound method with explicit self
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 649, in save_dict
    self._batch_setitems(obj.iteritems())
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 663, in _batch_setitems
    save(v)
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 286, in save
    f(self, obj) # Call unbound method with explicit self
  File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.py", line 748, in save_global
    (obj, module, name))
pickle.PicklingError: Can't pickle <function <lambda> at 0x102178668>: it's not found as __main__.<lambda>

但是,如果您使用更好的序列化程序,dill或者cloudpickle,那么大多数词典都可以被腌制:

>>> import dill
>>> pik = dill.dumps(d)

或者如果你想保存dict到一个文件......

>>> with open('save.pik', 'w') as f:
...   dill.dump(globals(), f)
... 

后一个例子与这里发布的任何其他好的答案相同(除了忽略dict好的内容的可挑选性之外).

Answer 5

如果您只想将 dict 存储在单个文件中，请pickle像这样使用

import pickle

a = {'hello': 'world'}

with open('filename.pickle', 'wb') as handle:
    pickle.dump(a, handle)

with open('filename.pickle', 'rb') as handle:
    b = pickle.load(handle)

如果要在多个文件中保存和恢复多个字典以缓存和存储更复杂的数据，请使用anycache。它可以完成您需要的所有其他工作pickle

from anycache import anycache

@anycache(cachedir='path/to/files')
def myfunc(hello):
    return {'hello', hello}

Anycachemyfunc根据不同文件的参数存储不同的结果cachedir并重新加载它们。

有关更多详细信息，请参阅文档。

Answer 6

>>> import pickle
>>> with open("/tmp/picklefile", "wb") as f:
...     pickle.dump({}, f)
... 

通常,最好使用cPickle实现

>>> import cPickle as pickle
>>> help(pickle.dump)
Help on built-in function dump in module cPickle:

dump(...)
    dump(obj, file, protocol=0) -- Write an object in pickle format to the given file.

    See the Pickler docstring for the meaning of optional argument proto.

Answer 7

将Python数据（例如字典）转储到pickle文件的简单方法。

import pickle

your_dictionary = {}

pickle.dump(your_dictionary, open('pickle_file_name.p', 'wb'))