数据处理：pandas、numpy 数据建模：scipy、scikit-learn、statesmodel、keras 数据可视化：matplotlib、seabor...
self.value = value self.fall = False # 如果匹配到的case语句中没有break,则fall为true。 defiter(self): yield self.match # 调用match方法 返回一个生成器 raise StopIteration # StopIteration 异常来判断for循环是否结束 def match(self, *args): # 模拟case子句的方法 if self.fall or not args: # ...
其中SF(t) 是包含词t tt的句子频率,Sentenceall 表示所有句子数量。最后计算每个term的分值公式如下:S(t)=TRel∗TPositionTcase+TFnormTRel+TSentenceTRel S(t)表示的是单词 t 的分值情况,其中 s(t) 分值越小,表示的单词 t 越重要。 安装和使用 代码语言:javascript 代码运行次数:0 运行 AI代码解释 pip...
[1, 2, 2, 3, 4, 4, 4, 5] unique_numbers = {x for x in numbers} print(unique_numbers) # 输出:{1, 2, 3, 4, 5} #获取一个句子中所有不重复的单词 sentence = "apple banana apple cherry banana" unique_words = {word for word in sentence.split()} print(unique_words) # 输出:...
In particular, the expression to the right of the assignment operator can include the same variable that’s on the left of the operand. That last sentence may sound confusing, so here’s an example that clarifies the point: Python >>> total = 10 >>> total = total + 5 >>> total ...
sentence = "这是一个句子。" paragraph = """这是一个段落。 包含了多个语句""" 1. 2. 3. 4. Python注释 单行注释: # 开头追加说明文字 注意:尽量在文字前面打个空格 在# 号前面需要追加两个空格。例如 print() # 注释 #!/usr/bin/python ...
nlp = spacy.load('en') new_norm=[] print("Lemmatizing Sentences") for sentence in tqdm(sentence_list): new_norm.append(_lemmatize_text(sentence, nlp).strip()) return new_norm def _lemmatize_text(sentence, nlp): sent = "" doc = nlp(sentence) for token in...
自定义switch case类: # This class provides the functionality we want. You only need to look at # this if you want to know how this works. It only needs to be defined # once, no need to muck around with its internals. class switch(object): ...
defupper(s): return s.upper()mylist =list(map(upper, ['sentence', 'fragment']))print(mylist)# ['SENTENCE', 'FRAGMENT']# Convert a string representation of# a number into a list of ints.list_of_ints =list(map(int, "1234567")))print(list_of_ints)# [1, 2, 3,...
from stanfordcorenlp import StanfordCoreNLP# Other human languages support, e.g. Chinesesentence = '清华大学位于北京。'with StanfordCoreNLP(r'install_packages/stanford-corenlp-full-2016-10-31', lang='zh') as nlp:print(nlp.word_tokenize(sentence))print(nlp.pos_tag(sentence))print(nlp.ner(se...