2023/03/25NCRE二级Python综合题：jieba库的使用与文件读取写入

Posted 2023-03-26 阿白啥也不会

题目描述（主要内容复现）：

在data.txt中有一段文字（全部是中文），本文选用的例子是国家新一代人工智能治理专业委员会2021年9月25日发布的《新一代人工智能伦理规范》

1.请用jieba库提取其中的字符长度大于等于3的关键词（不要求顺序）将其按行写入out1.txt。

2.将关键词和对应的次数写入out2.txt中（频次从高到低，同频次不分顺序）

1.jieba库

①pip install

考试的时候作者看到是懵逼的——真没听说过，不过不急，我们可以利用help现场学习。由于考试环境已为我们配置好，已安装jieba库，为复现，先讲一下怎么安装

jieba库的安装

②help

会使用help就相当于会作弊（懂得都懂）,从题目要求大概知道要使用jieba库的函数，我们直接利用help查看jieba的构成，着重看function部分可知cut和cut_for_search都可以用来解决问题，作者选的是后一个。其解释为 method of Tokenizer instance，Finer segmentation for search engines.（Tokenizer实例的方法搜索引擎的细分更精细。）

>>> import jieba as j
>>> help(j)
Help on package jieba:

NAME
    jieba

PACKAGE CONTENTS
    __main__
    _compat
    analyse (package)
    finalseg (package)
    lac_small (package)
    posseg (package)

CLASSES
    builtins.object
        Tokenizer
    
    class Tokenizer(builtins.object)
     |  Tokenizer(dictionary=None)
     |  
     |  Methods defined here:
     |  
     |  __init__(self, dictionary=None)
     |      Initialize self.  See help(type(self)) for accurate signature.
     |  
     |  __repr__(self)
     |      Return repr(self).
     |  
     |  add_word(self, word, freq=None, tag=None)
     |      Add a word to dictionary.
     |      
     |      freq and tag can be omitted, freq defaults to be a calculated value
     |      that ensures the word can be cut out.
     |  
     |  calc(self, sentence, DAG, route)
     |  
     |  check_initialized(self)
     |  
     |  cut(self, sentence, cut_all=False, HMM=True, use_paddle=False)
     |      The main function that segments an entire sentence that contains
     |      Chinese characters into separated words.
     |      
     |      Parameter:
     |          - sentence: The str(unicode) to be segmented.
     |          - cut_all: Model type. True for full pattern, False for accurate pattern.
     |          - HMM: Whether to use the Hidden Markov Model.
     |  
     |  cut_for_search(self, sentence, HMM=True)
     |      Finer segmentation for search engines.
     |  
     |  del_word(self, word)
     |      Convenient function for deleting a word.
     |  
     |  get_DAG(self, sentence)
     |  
     |  get_dict_file(self)
     |  
     |  initialize(self, dictionary=None)
     |  
     |  lcut(self, *args, **kwargs)
     |  
     |  lcut_for_search(self, *args, **kwargs)
     |  
     |  load_userdict(self, f)
     |      Load personalized dict to improve detect rate.
     |      
     |      Parameter:
     |          - f : A plain text file contains words and their ocurrences.
     |                Can be a file-like object, or the path of the dictionary file,
     |                whose encoding must be utf-8.
     |      
     |      Structure of dict file:
     |      word1 freq1 word_type1
     |      word2 freq2 word_type2
     |      ...
     |      Word type may be ignored
     |  
     |  set_dictionary(self, dictionary_path)
     |  
     |  suggest_freq(self, segment, tune=False)
     |      Suggest word frequency to force the characters in a word to be
     |      joined or splitted.
     |      
     |      Parameter:
     |          - segment : The segments that the word is expected to be cut into,
     |                      If the word should be treated as a whole, use a str.
     |          - tune : If True, tune the word frequency.
     |      
     |      Note that HMM may affect the final result. If the result doesn't change,
     |      set HMM=False.
     |  
     |  tokenize(self, unicode_sentence, mode='default', HMM=True)
     |      Tokenize a sentence and yields tuples of (word, start, end)
     |      
     |      Parameter:
     |          - sentence: the str(unicode) to be segmented.
     |          - mode: "default" or "search", "search" is for finer segmentation.
     |          - HMM: whether to use the Hidden Markov Model.
     |  
     |  ----------------------------------------------------------------------
     |  Static methods defined here:
     |  
     |  gen_pfdict(f)
     |  
     |  ----------------------------------------------------------------------
     |  Data descriptors defined here:
     |  
     |  __dict__
     |      dictionary for instance variables (if defined)
     |  
     |  __weakref__
     |      list of weak references to the object (if defined)

FUNCTIONS
    add_word(word, freq=None, tag=None) method of Tokenizer instance
        Add a word to dictionary.
        
        freq and tag can be omitted, freq defaults to be a calculated value
        that ensures the word can be cut out.
    
    calc(sentence, DAG, route) method of Tokenizer instance
    
    cut(sentence, cut_all=False, HMM=True, use_paddle=False) method of Tokenizer instance
        The main function that segments an entire sentence that contains
        Chinese characters into separated words.
        
        Parameter:
            - sentence: The str(unicode) to be segmented.
            - cut_all: Model type. True for full pattern, False for accurate pattern.
            - HMM: Whether to use the Hidden Markov Model.
    
    cut_for_search(sentence, HMM=True) method of Tokenizer instance
        Finer segmentation for search engines.
    
    del_word(word) method of Tokenizer instance
        Convenient function for deleting a word.
    
    disable_parallel()
    
    enable_parallel(processnum=None)
        Change the module's `cut` and `cut_for_search` functions to the
        parallel version.
        
        Note that this only works using dt, custom Tokenizer
        instances are not supported.
    
    get_DAG(sentence) method of Tokenizer instance
    
    get_FREQ lambda k, d=None
    
    get_dict_file() method of Tokenizer instance
    
    initialize(dictionary=None) method of Tokenizer instance
    
    lcut(*args, **kwargs) method of Tokenizer instance
    
    lcut_for_search(*args, **kwargs) method of Tokenizer instance
    
    load_userdict(f) method of Tokenizer instance
        Load personalized dict to improve detect rate.
        
        Parameter:
            - f : A plain text file contains words and their ocurrences.
                  Can be a file-like object, or the path of the dictionary file,
                  whose encoding must be utf-8.
        
        Structure of dict file:
        word1 freq1 word_type1
        word2 freq2 word_type2
        ...
        Word type may be ignored
    
    log(...)
        log(x, [base=math.e])
        Return the logarithm of x to the given base.
        
        If the base not specified, returns the natural logarithm (base e) of x.
    
    md5 = openssl_md5(...)
        Returns a md5 hash object; optionally initialized with a string
    
    setLogLevel(log_level)
    
    set_dictionary(dictionary_path) method of Tokenizer instance
    
    suggest_freq(segment, tune=False) method of Tokenizer instance
        Suggest word frequency to force the characters in a word to be
        joined or splitted.
        
        Parameter:
            - segment : The segments that the word is expected to be cut into,
                        If the word should be treated as a whole, use a str.
            - tune : If True, tune the word frequency.
        
        Note that HMM may affect the final result. If the result doesn't change,
        set HMM=False.
    
    tokenize(unicode_sentence, mode='default', HMM=True) method of Tokenizer instance
        Tokenize a sentence and yields tuples of (word, start, end)
        
        Parameter:
            - sentence: the str(unicode) to be segmented.
            - mode: "default" or "search", "search" is for finer segmentation.
            - HMM: whether to use the Hidden Markov Model.

DATA
    DEFAULT_DICT = None
    DEFAULT_DICT_NAME = 'dict.txt'
    DICT_WRITING = 
    PY2 = False
    __license__ = 'MIT'
    absolute_import = _Feature((2, 5, 0, 'alpha', 1), (3, 0, 0, 'alpha', 0...
    check_paddle_install = 'is_paddle_installed': False
    default_encoding = 'utf-8'
    default_logger = <Logger jieba (DEBUG)>
    dt = <Tokenizer dictionary=None>
    log_console = <StreamHandler <stderr> (NOTSET)>
    pool = None
    re_eng = re.compile('[a-zA-Z0-9]')
    re_han_default = re.compile('([一-\\u9fd5a-zA-Z0-9+#&\\\\._%\\\\-]+)')
    re_skip_default = re.compile('(\\r\\n|\\\\s)')
    re_userdict = re.compile('^(.+?)( [0-9]+)?( [a-z]+)?$')
    string_types = (<class 'str'>,)
    unicode_literals = _Feature((2, 6, 0, 'alpha', 2), (3, 0, 0, 'alpha', ...
    user_word_tag_tab = 

VERSION
    0.42.1

FILE
    d:\\anaconda\\lib\\site-packages\\jieba\\__init__.py

    cut(sentence, cut_all=False, HMM=True, use_paddle=False) method of Tokenizer instance
        The main function that segments an entire sentence that contains
        Chinese characters into separated words.
        
        Parameter:
            - sentence: The str(unicode) to be segmented.
            - cut_all: Model type. True for full pattern, False for accurate pattern.
            - HMM: Whether to use the Hidden Markov Model.
    
    cut_for_search(sentence, HMM=True) method of Tokenizer instance
        Finer segmentation for search engines.

 2.题解

思路比较简单，不赘述，代码里面有注释。执行过程在下面。

import jieba as j
f=open(r"C:\\Users\\hqh\\Desktop\\data.txt",'r',encoding='utf-8').readlines()
g='' #先将文本内容作为长字符串读入
for i in f:
    g+=i.strip('\\n')
c=list(j.cut_for_search(g))

#1
out1=open(r"C:\\Users\\hqh\\Desktop\\out1.txt",'w')
s=set() #利用集合储存关键词
for i in c:
    if len(i)>=3:
        s.add(i)
for i in s:
    out1.write(i)
    out1.write('\\n')
out1.close()

#2
d=
for i in s:
    d[i]=c.count(i)
v=sorted(d.items(),key=lambda x:x[1],reverse=True) #按value排序，作为tuple储存在列表中
out2=open(r"C:\\Users\\hqh\\Desktop\\out2.txt",'w')
for i in v:
    out2.write('%s:%d\\n'%(i[0],i[1]))
out2.close()

>>> 
============= RESTART: C:/Users/hqh/Desktop/python自学/94jieba.py =============
Building prefix dict from the default dictionary ...
Loading model from cache C:\\Users\\hqh\\AppData\\Local\\Temp\\jieba.cache
Loading model cost 0.532 seconds.
Prefix dict has been built successfully.