如何在泡菜中保存字典

Question

我正在尝试使用Pickle将字典保存在文件中。 保存字典的代码运行没有任何问题，但是当我尝试从Python Shell中的文件中检索字典时，出现EOF错误：

>>> import pprint
>>> pkl_file = open('data.pkl', 'rb')
>>> data1 = pickle.load(pkl_file)
 Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
     File "/usr/lib/python2.7/pickle.py", line 1378, in load
     return Unpickler(file).load()
     File "/usr/lib/python2.7/pickle.py", line 858, in load
      dispatch[key](self)
      File "/usr/lib/python2.7/pickle.py", line 880, in load_eof
      raise EOFError
      EOFError

我的代码如下。

它计算每个单词的频率和数据的日期（日期是文件名。），然后将单词保存为字典的键，将（freq，date）的元组保存为每个键的值。 现在，我想将此字典用作工作另一部分的输入：

def pathFilesList():
    source='StemmedDataset'
    retList = []
    for r,d,f in os.walk(source):
        for files in f:
            retList.append(os.path.join(r, files))
    return retList

def parsing():
    fileList = pathFilesList()
    for f in fileList:
        print "Processing file: " + str(f)
        fileWordList = []
        fileWordSet = set()
        fw=codecs.open(f,'r', encoding='utf-8')
        fLines = fw.readlines()
        for line in fLines:
            sWord = line.strip()
            fileWordList.append(sWord)
            if sWord not in fileWordSet:
                fileWordSet.add(sWord)
        for stemWord in fileWordSet:
            stemFreq = fileWordList.count(stemWord)
            if stemWord not in wordDict:
                wordDict[stemWord] = [(f[15:-4], stemFreq)]
            else:
                wordDict[stemWord].append((f[15:-4], stemFreq))
        fw.close()

if __name__ == "__main__":
    parsing()
    output = open('data.pkl', 'wb')
    pickle.dump(wordDict, output)
    output.close()

您认为问题是什么？

Answer 1

由于这是Python2，因此您通常必须更加明确地说明源代码的编码方式。所引用的PEP-0263对此进行了详细说明。 我的建议是，您尝试将以下内容添加到unpickle.py前两行

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# The rest of your code....

顺便说一句，如果您要使用非ASCII字符进行大量工作，最好改用Python3。

Answer 2

# Added some code and comments.  To make the code more complete.
# Using collections.Counter to count words.

import os.path
import codecs
import pickle
from collections import Counter

wordDict = {}

def pathFilesList():
    source='StemmedDataset'
    retList = []
    for r, d, f in os.walk(source):
        for files in f:
            retList.append(os.path.join(r, files))
    return retList

# Starts to parse a corpus, it counts the frequency of each word and
# the date of the data (the date is the file name.) then saves words
# as keys of dictionary and the tuple of (freq,date) as values of each
# key.
def parsing():
    fileList = pathFilesList()
    for f in fileList:
        date_stamp = f[15:-4]
        print "Processing file: " + str(f)
        fileWordList = []
        fileWordSet = set()
        # One word per line, strip space. No empty lines.
        fw = codecs.open(f, mode = 'r' , encoding='utf-8')
        fileWords = Counter(w for w in fw.read().split())
        # For each unique word, count occurance and store in dict.
        for stemWord, stemFreq in fileWords.items():
            if stemWord not in wordDict:
                wordDict[stemWord] = [(date_stamp, stemFreq)]
            else:
                wordDict[stemWord].append((date_stamp, stemFreq))
        # Close file and do next.
        fw.close()


if __name__ == "__main__":
    # Parse all files and store in wordDict.
    parsing()

    output = open('data.pkl', 'wb')

    # Assume wordDict is global.
    print "Dumping wordDict of size {0}".format(len(wordDict))
    pickle.dump(wordDict, output)

    output.close()

Answer 3

如果您正在寻找可以将大量数据字典保存到磁盘或数据库中并且可以利用酸洗和编码（编解码器和哈希图）的工具，那么您可能想要看看klepto 。

klepto提供了字典抽象，用于写入数据库，包括将文件系统视为数据库（即将整个字典写入单个文件，或将每个条目写入其自己的文件）。 对于大数据，我经常选择将字典表示为文件系统上的目录，并将每个条目都作为一个文件。 klepto还提供了缓存算法，因此，如果您在字典中使用文件系统后端，则可以通过使用内存缓存来避免某些速度损失。

>>> from klepto.archives import dir_archive
>>> d = {'a':1, 'b':2, 'c':map, 'd':None}
>>> # map a dict to a filesystem directory
>>> demo = dir_archive('demo', d, serialized=True) 
>>> demo['a']
1
>>> demo['c']
<built-in function map>
>>> demo          
dir_archive('demo', {'a': 1, 'c': <built-in function map>, 'b': 2, 'd': None}, cached=True)
>>> # is set to cache to memory, so use 'dump' to dump to the filesystem 
>>> demo.dump()
>>> del demo
>>> 
>>> demo = dir_archive('demo', {}, serialized=True)
>>> demo
dir_archive('demo', {}, cached=True)
>>> # demo is empty, load from disk
>>> demo.load()
>>> demo
dir_archive('demo', {'a': 1, 'c': <built-in function map>, 'b': 2, 'd': None}, cached=True)
>>> demo['c']
<built-in function map>
>>> 

klepto还具有如其它标志compression和memmode可用于定制数据是如何被存储（例如压缩级别，存储器映射模式，等等）。 使用（MySQL等）数据库作为后端而不是文件系统同样容易（完全相同的接口）。 您还可以关闭内存缓存，因此每次读取/写入都直接通过设置cached=False直接进入存档。

klepto通过构建自定义keymap来提供对自定义编码的访问。

>>> from klepto.keymaps import *
>>> 
>>> s = stringmap(encoding='hex_codec')
>>> x = [1,2,'3',min]
>>> s(x)
'285b312c20322c202733272c203c6275696c742d696e2066756e6374696f6e206d696e3e5d2c29'
>>> p = picklemap(serializer='dill')
>>> p(x)
'\x80\x02]q\x00(K\x01K\x02U\x013q\x01c__builtin__\nmin\nq\x02e\x85q\x03.'
>>> sp = s+p
>>> sp(x)
'\x80\x02UT28285b312c20322c202733272c203c6275696c742d696e2066756e6374696f6e206d696e3e5d2c292c29q\x00.' 

在此处获取klepto ： https : //github.com/uqfoundation