half = len(a_list)//2 return a_list[:half], a_list[half:] A = [1,2,3,4,5,6] B, C = split_list(A) 您需要在Python 3中强制进行int除法。//是必需的。 好的解决方案,谢谢。它还适用于Python3 B = A[:(len(A) 10) * 8] C = A[(len(A) 10) * 8:]中的分数80/20 一些...
示例2:按固定大小进行分割 defsplit_list(data,chunk_size):"""按指定大小分割列表"""return[data[i:i+chunk_size]foriinrange(0,len(data),chunk_size)]# 示例数据data=[1,2,3,4,5,6,7,8,9,10]# 分割result=split_list(data,3)print("分割结果:",result) 1. 2. 3. 4. 5. 6. 7. 8....
**kwargs)...next(c)...returnc...returnwrapper...>>>@coroutine...defcomplain_about2(substring):...print('Please talk to me!')...whileTrue:...text = (yield)...ifsubstringintext:...print('Oh no: I found a %s again!'...% (substring))...>>>c = complain_about2('JavaScript...
In the above code, we were dealing with a string containing an even number of characters. Thelen()function here is used to return the length of the string. We split the string into one half containing the first half of the characters and the second substring containing the other half. ...
>>>clothes=['skirt','red sock']>>>forclothinginclothes:# Iterate over the list...if'sock'inclothing:# Find stringswith'sock'...clothes.append(clothing)# Add the sock's pair...print('Added a sock:',clothing)# Inform the user...Added a sock:red sock Added ...
IPython7.31.1--An enhanced Interactive Python.Type'?'forhelp.In[1]:a=5In[2]:a Out[2]:5 再尝试一个复杂点的对象,使用NumPy生成一组随机数字: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 In[5]:importnumpyasnp In[6]:data=[np.random.standard_normal()foriinrange(7)]In[7]:data...
>>>clothes = ['skirt','red sock']>>>forclothinginclothes:# Iterate over the list...if'sock'inclothing:# Find strings with 'sock'...clothes.append(clothing)# Add the sock's pair...print('Added a sock:', clothing)# Inform the user... Added ...
dataset = pd.get_dummies(df, columns = ['sex', 'cp','fbs','restecg','exang', 'slope','ca', 'thal'])from sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerstandardScaler = StandardScaler()columns_to_scale = ['age', 'trestbps', 'chol', ...
body X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, stratify=y) 我们继续从训练集中学习词汇,并使用默认设置的CountVectorizer转换两个数据集,以获得近 26,000 个特征: vectorizer = CountVectorizer() X_train_dtm = vectorizer.fit_transform(X_train) X_test_dtm = ...
>>> for clothing in clothes: # Iterate over the list. ... if 'sock' in clothing: # Find strings with 'sock'. ... clothes.append(clothing) # Add the sock's pair. ... print('Added a sock:', clothing) # Inform the user. ...