2.TestCase类的属性如下: ['__call__', '__class__', '__delattr__', '__dict__', '__doc__', '__eq__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_e
Out[122]: {0: 4, 1: 3, 2: 2, 3: 1, 4: 0} 后面会谈到dict comprehensions,另一种构建字典的优雅方式。 默认值 下面的逻辑很常见: if key in some_dict: value = some_dict[key] else: value = default_value 因此,dict的方法get和pop可以取默认值进行返回,上面的if-else语句可以简写成下面: ...
x_dict={'a':666,'b':888,'c':'NB'}# create a dict object. x_set={1,2,'c'}# crate a set object. 1. 2. 3. 4. 5. 6. 7. print(x_list[1])# Using the subscripts to access element of a specified locationg. 1. 2 1. print(x_tuple[0])# Tuples also support the use...
# 下载数据集 from sklearn.datasets import fetch_20newsgroups from sklearn.model_selection import train_test_split def get_data(): """ subset:选择要加载的数据集:“ train”(训练集),“ test”(测试集),“ all”(两者),并按随机顺序排序。 shuffle:是否对数据进行随机化:对于假设样本是独立且均匀分...
scripts脚本 self自身 search查找 salary薪水 switch判断语句 string字符串类型 successful成功 search查询 square平方 system系统 step步长 sep/separate分隔 seq/sequence序列 swap互换 subset子集 sub附属 superset父集/超集 symmetric对称 set集合 settings设置 sort排序 strip去除 syntax语法 start开始 ...
set2 = {1,5}# 判断 set2 是否是 set1 的子集is_subset = set2.issubset(set1)print(is_subset)# 输出: True [7]判断 set1 = {1,2,3,4,5} set2 = {5,6}# 判断两个集合是否相等is_equal = set1 == set2print(is_equal)# 输出: False ...
df.head().style.format(format_dict).bar(color='red', subset=['data science', 'deep learning'])结果如下:此外,我们还可以结合以上功能并生成更复杂的可视化效果。 df.head(10).style.format(format_dict).background_gradient(subset = ['data science','machine learning'],cmap ='BuGn')。highlight...
ret1=set1.issubset(set2)print(ret1)#=> True, set1 is the subset of set2#issuperset() is the oppositeret1 =set1.issuperset(set2) ret2=set2.issuperset(set1)print(ret1)#Falseprint(ret2)#True#pop() remove and return an arbitrary set element.#raises keyError if the set is emptyset...
subset_attributes= ['residual sugar','total sulfur dioxide','sulphates','alcohol','volatile acidity','quality']rs = round(red_wine[subset_attributes].describe(),2)ws = round(white_wine[subset_attributes].describe(),2) pd.concat([rs, ws]...
print('list_1 is subset list-2',list_1.issubset(list_2)) # False print('list_1 is superset list_2',list_1.issuperset(list_2)) # False list_3 = set([2,7]) print(list_3.issubset(list_1)) # True print(list_1.issuperset(list_3)) # True ...