Day 25：Python 模块 collections 3 个常用类
NamedTuple
数据分析或机器学习领域，用好 NamedTuples 会写出可读性更强、更易于维护的代码。
做特征工程的时候，如果把特征扔到一个list当中，以便日后取用，但是取用的时候，难免出现整数索引，代码可读性差，所以使用NamedTuple，避免出现整数索引，希望是直接按属性索引：
from collections import namedtuple #创建一个带有 14 个属性，名字为 Person 的 NamedTuple 实例 Person Person = namedtuple('Person',['id','age','height','name','address','province','city','town','country','birth_address','father_name', 'monther_name','telephone','emergency_telephone']) # 调用实例 Person，创建一个 id=10086 的 Person 对象 a = ['']*11 Person(10086,19,'xiaoming',*a) output: Person(id=10086, age=19, height='xiaoming', name='', address='', province='', city='', town='', country='', birth_address='', father_name='', monther_name='', telephone='', emergency_telephone='')
假设有个任务，再有老数据的情况下，有了一份新数据，现在要比较，哪些人的居住地址（对应字段 address）、联系电话（对应字段 telephone）、出生地信息（对应字段 birth address）发生了变化，统计出这些人。
使用NamedTuple方法：
def update_persons_info(old_data,new_data): changed_list = [] for line in new_data: new_props = line.split() new_person = Person(new_props) # new_props 与 Person 参数卡对好 for old in old_data: old_props = old.split() old_person = Person(old_props) if old_person.id != new_person.id: changed_list.append(old_person.id) elif old_person.address != new_person.address: changed_list.append(old_person.address) elif old_person.birth_address != new_person.birth_address: changed_list.append(old_person.birth_address) return changed_list
但是在带来这样的遍历同时，也带来一个问题，NamedTuple 创建后，它的属性取值不允许被修改，也就是属性只能是可读的，就看怎么用了。
from collections import namedtuple #创建一个带有 14 个属性，名字为 Person 的 NamedTuple 实例 Person Person = namedtuple('Person',['id','age','height','name','address','province','city','town','country','birth_address','father_name', 'monther_name','telephone','emergency_telephone']) # 调用实例 Person，创建一个 id=10086 的 Person 对象 a = ['']*11 xiaoming = Person(10086,19,'xiaoming',*a) print(type(xiaoming)) xiaoming.age = 20 output: <class '__main__.Person'> --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-5-dabf7d8e12b1> in <module>() 7 xiaoming = Person(10086,19,'xiaoming',*a) 8 print(type(xiaoming)) ----> 9 xiaoming.age = 20 AttributeError: can't set attribute
Counter
主要用于统计中的计数，使用 Counter，期待能写出更加简化的代码
from collections import Counter # 统计出现次数 freq = [3, 8, 3, 10, 3, 3, 1, 3, 7, 6, 1, 2, 7, 0, 7, 9, 1, 5, 1, 0] Counter(freq).most_common() output: [(3, 5), (1, 4), (7, 3), (0, 2), (8, 1), (10, 1), (6, 1), (2, 1), (9, 1), (5, 1)]
并且，是按照频数由高到低排序的，牛的
使用 Counter 能快速统计，一句话中单词出现次数，一个单词中字符出现次数。如下所示：
text = """ def update_persons_info(old_data,new_data): changed_list = [] for line in new_data: new_props = line.split() new_person = Person(new_props) # new_props 与 Person 参数卡对好 for old in old_data: old_props = old.split() old_person = Person(old_props) if old_person.id != new_person.id: changed_list.append(old_person.id) elif old_person.address != new_person.address: changed_list.append(old_person.address) elif old_person.birth_address != new_person.birth_address: changed_list.append(old_person.birth_address) return changed_list""" Counter(text).most_common() output: [(3, 5), (1, 4), (7, 3), (0, 2), (8, 1), (10, 1), (6, 1), (2, 1), (9, 1), (5, 1)] text = """ def update_persons_info(old_data,new_data): changed_list = [] for line in new_data: new_props = line.split() new_person = Person(new_props) # new_props 与 Person 参数卡对好 for old in old_data: old_props = old.split() old_person = Person(old_props) if old_person.id != new_person.id: changed_list.append(old_person.id) elif old_person.address != new_person.address: changed_list.append(old_person.address) elif old_person.birth_address != new_person.birth_address: changed_list.append(old_person.birth_address) return changed_list""" Counter(text).most_common() text = """ def update_persons_info(old_data,new_data): changed_list = [] for line in new_data: new_props = line.split() new_person = Person(new_props) # new_props 与 Person 参数卡对好 for old in old_data: old_props = old.split() old_person = Person(old_props) if old_person.id != new_person.id: changed_list.append(old_person.id) elif old_person.address != new_person.address: changed_list.append(old_person.address) elif old_person.birth_address != new_person.birth_address: changed_list.append(old_person.birth_address) return changed_list""" Counter(text).most_common() [(' ', 182), ('e', 45), ('d', 42), ('s', 40), ('n', 38), ('o', 36), ('r', 33), ('p', 31), ('_', 30), ('l', 24), ('a', 23), ('i', 21), ('t', 16), ('\n', 15), ('.', 14), ('w', 9), ('(', 8), (')', 8), ('h', 8), ('=', 8), ('f', 7), (':', 6), ('c', 5), ('g', 5), ('P', 3), ('!', 3), ('b', 3), ('u', 2), (',', 1), ('[', 1), (']', 1), ('#', 1), ('与', 1), ('参', 1), ('数', 1), ('卡', 1), ('对', 1), ('好', 1)]
DefaultDict
DefaultDict 能自动创建一个被初始化的字典，也就是每个键都已经被访问过一次。
如何创建默认初始化某类型的字典值
from collections import defaultdict # 创建一个字典值类型为 int 的默认字典： dict_1 = defaultdict(int) # 创建一个字典值类型为 list 的默认字典： dict_2 = defaultdict(list) dict_2 output: defaultdict(list, {}) s = 'from collections import defaultdict' for index,i in enumerate(s): dict_2[i].append(index) print(dict_2) output: defaultdict(<class 'list'>, {'f': [0, 26], 'r': [1, 21], 'o': [2, 6, 13, 20], 'm': [3, 18], ' ': [4, 16, 23], 'c': [5, 10, 33], 'l': [7, 8, 29], 'e': [9, 25], 't': [11, 22, 30, 34], 'i': [12, 17, 32], 'n': [14], 's': [15], 'p': [19], 'd': [24, 31], 'a': [27], 'u': [28]})
