(本文仅作为学习记录使用) 感谢太白老师和王sir的谆谆教导.
01 昨日内容回顾
02 作业讲解
03 字典
04 字典的嵌套
05 集合(了解)
01 昨日内容回顾
注意顾头不顾腚的原则.
list的切片.
list[1]
list[-2]
list[1:3]
list[-2:1:-1]
list[-2::-2]
list[::]
list[::-1]
列表常用操作:
列表增有3种:
append:追加.一次只能追加1个.不能连续追加.
insert:插入,其中l1.insert(索引,字符串)
extend:迭代添加.假如是'abc',分别添加则为['a','b','c']
列表删有3种:(del单独列)
pop:按索引删除. l1.pop(索引)
remove:按元素删除. l1.remove(元素)
clear:清空list. l1.clear
del有3种用法:
按字符删除. del l1[1]
按切片删除. del l1[1:3]. 所有的这种按切片删或者添或者改的,只要步长发生变化,就一定要一一对应.
删除list. del l1 运算结果会报错(如果输出l1的话,因为l1被删除)
列表改只有1种:
直接改:
按索引:l1[1] ='a'
按切片:l1[1:3] = 'adad'
注意,当用切片时候,连在一起时候可以任意添加(会续下去),如果步长不是正负1的话,只能一一对应.
列表查:
索引查.
切片查.
for循环查看.
其他操作:
len:输出list长度.
sort:对list进行排序.
注意,有sort(reverse = True)为翻转排序.
reverse:对list进行翻转.
count:对元素进行计数.
index:寻求元素的索引.
列表的嵌套的话主要是通过赋值或者一个一个找下去直接赋值解决的.(def的话日后再说吧)
元组:tuple,不能改,里面的如list等的容量型数据可以改.
range的话主要是用在限制for循环的(个人理解).正好用在有限循环.
链接:https://www.cnblogs.com/smithpath/articles/10480185.html
02 作业讲解
# 1. 写代码,有如下列表,按照要求实现每⼀个功能li = ["alex", "WuSir", "ritian", "barry", "wenzhou"] # li = ["alex", "WuSir", "ritian", "barry", "wenzhou"] # a.计算列表的⻓度并输出 # print(len(li)) # b.列表中追加元素"seven", 并输出添加后的列表 # li.append('seven') # print(li) # c.请在列表的第1个位置插入元素"Tony", 并输出添加后的列表 # li.insert(0,'Tony') # print(li) # d.请修改列表第2个位置的元素为"Kelly", 并输出修改后的列表 # li[1]='Kelly' # print(li) # e.请将列表l2 = [1, "a", 3, 4, "heart"]的每⼀个元素添加到列表li中,⼀行代码实现,不允许循环添加。 # l2 = [1, "a", 3, 4, "heart"] # li.extend(l2) # print(li) # f.请将字符串s = "qwert"的每⼀个元素添加到列表li中,⼀行代码实现,不允许循环添加。 # s = "qwert" # li.extend(s) # print(li) # g.请删除列表中的元素"ritian", 并输出添加后的列表 # li.remove("ritian") # print(li) # h.请删除列表中的第2个元素,并输出删除的元素和删除元素后的列表 # print(li.pop(1)) # print(li) # i.请删除列表中的第2至4个元素,并输出删除元素后的列表 # del li[1:4] # print(li) # j.请将列表所有得元素反转,并输出反转后的列表 # 法一 # l1 = li[::-1] # print(l1) # 法二 # li.reverse() # print(li) # k.请计算出"alex"元素在列表li中出现的次数,并输出该次数。 # print(li.count('alex')) # 2.写代码,有如下列表,利用切片实现每⼀个功能 # li = [1, 3, 2, "a", 4, "b", 5, "c"] # a.通过对li列表的切片形成新的列表l1, l1 = [1, 3, 2] # l1 = li[:3] # print(l1) # b.通过对li列表的切片形成新的列表l2, l2 = ["a", 4, "b"] # l2 =li[3:6] # print(l2) # c.通过对li列表的切片形成新的列表l3, l3 = ["1,2,4,5] # l3 = li[::2] # print(l3) # d.通过对li列表的切片形成新的列表l4, l4 = [3, "a", "b"] # l4 = li[1:6:2] # print(l4) # e.通过对li列表的切片形成新的列表l5, l5 = ["c"] # 法一: # l5 = [li[-1]] # print(l5) # 法二: # print(li[-1:]) #加个冒号即可. # f.通过对li列表的切片形成新的列表l6, l6 = ["b", "a", 3] # l6 =li[5::-2] # print(l6) # 3.写代码,有如下列表,按照要求实现每⼀个功能。 # lis = [2, 3, "k", ["qwe", 20, ["k1", ["tt", 3, "1"]], 89], "ab", "adv"] # a.将列表lis中的"tt"变成大写(用两种方式)。 # 法一: # lis[3][2][1][0] = lis[3][2][1][0].upper() # print(lis) # 法二: # a1 = lis[3] # a2 = a1[2] # a3 = a2[1] # a3[0] = a3[0].upper() # print(lis) # b.将列表中的数字3变成字符串"100"(用两种方式)。 # 法一: # lis[1] = '100' # lis[3][2][1][1] ='100' # print(lis) # 法二: # lis.remove(3) # lis[2][2][1].remove(3) # lis.insert(1,'100') # lis[3][2][1].insert(1,'100') # print(lis) # 法三:(NB!!!) # def list_for(lis): # l1 = [] # for i in lis : # if type(i) is not list : # l1.append(str(i).lower()) # else : # l1.extend(list_for(i)) # return l1 # for i in list_for(lis): # print(i) # c.将列表中的字符串"1"变成数字101(用两种方式)。 # 法一: # lis[3][2][1][2] =101 # print(lis) # 法二: # lis[3][2][1].remove('1') # lis[3][2][1].insert(2,101) # print(lis) # 4.请用代码实现:li = ["alex", "wusir", "taibai"] # 利用下划线将列表的每⼀个元素拼接成字符串"alex_wusir_taibai" # li = ["alex", "wusir", "taibai"] # print('_'.join(li)) # 5.利用for循环和range打印出下面列表的索引。 # li = ["alex", "WuSir", "ritian", "barry", "wenzhou"] # for i in range(0,len(li)) : # print(i) # 6.利用for循环和range找出100以内所有的偶数并将这些偶数插入到⼀个新列表中。 # 法一: # l1 = [] # for i in range(0,101) : # if i % 2==0 : # l1.append(i) # print(l1) # 法二: # l1 = [] # for i in range(0, 101): # if i % 2 == 0: # l1.extend([i]) # print(l1) # 7.利用for循环和range 找出50以内能被3整除的数,并将这些数插入到⼀个新列表中。 # 法一: # l1 = [] # for i in range(0, 50): # if i % 3 == 0: # l1.append(i) # print(l1) # 法二: # l1 = [] # for i in range(0, 50): # if i % 3 == 0: # l1.extend([i]) # print(l1) # 8.利用for循环和range从100~1,倒序打印。 # for i in range(100,0,-1) : # print(i) # 9.利用for循环和range从100~10,倒序将所有的偶数添加到⼀个新列表中,然后对列表的元素进⾏筛选, # 将能被4整除的数留下来。 # #如果有如[1,2,2,2,3,4,4]这样的话会漏值,所以最好从后往前删. # l1 = [] # for i in range(100, 9, -1): # if i % 2 == 0: # l1.append(i) # for i in l1: # if i % 4 != 0: # l1.remove(i) # print(l1)#其中append和remove可以用extend,pop,del等替代(可能吧...) # 10.利用for循环和range,将1 - 30的数字⼀次添加到⼀个列表中, # 并循环这个列表,将能被3整除的数改成 *。 # l1 = [] # for i in range(1,31): # l1.append(i) # if int(i) % 3 == 0: # l1[i-1] = '*' # print(l1) # 11.查找列表li中的元素,移除每个元素的空格,并找出以"A"或者"a"开头,并以"c"结尾 # 的所有元素,并添加到⼀个新列表中, 最后循环打印这个新列表。 # 法一: # li = ["TaiBai ", "alexC", "AbC ", "egon", " riTiAn", "WuSir", " aqc"] # l1 = [] # for i in li : # x = i.strip() # if (x.startswith('a') or x.startswith('A')) and x.endswith('c') : # l1.append(x) # for i in l1 : # print(i) # 法二: # li = ["TaiBai ", "alexC", "AbC ", "egon", " riTiAn", "WuSir", " aqc"] # l1 = [] # for i in li: # x = i.strip() # if (x[0].lower() == 'a') and x.endswith('c'): # l1.append(x) # for i in l1: # print(i) # 12.开发敏感词语过滤程序,提示用户输入评论内容,如果用户输入的内容中包含特殊的字符: # 敏感词列表li = ["苍老师", "东京热", "武藤兰", "波多野结衣"] # 则将用户输入的内容中的敏感词汇替换成等⻓度的 *(苍老师就替换 ** *),并添加到⼀个列表中; # 如果用户输入的内容没有敏感词汇,则直接添加到上述的列表中。 # li = ["苍老师", "东京热", "武藤兰", "波多野结衣"] # msg = input('请输入内容:') # l1 = [] # for i in li : # # if i in msg : #该语句可以不写 # msg = (msg.replace(i,'*'*len(i))) # l1.append(msg) # print(l1) # 13.有如下列表li = [1, 3, 4, "alex", [3, 7, 8, "TaiBai"], 5, "RiTiAn"] # 循环打印列表中的每个元素,遇到列表则再循环打印出它里面的元素。 # 我想要的结果是: # 1 # 3 # 4 # "alex" # 3 # 7, # 8 # "taibai" # 5 # ritian #法一:(麻烦了,只需要打印即可) # li = [1, 3, 4, "alex", [3, 7, 8, "TaiBai"], 5, "RiTiAn"] # l1 = [] # for i in li : # if type(i) == list : # for i1 in li[li.index(i)] : # l1.append(i1) # else : # l1.append(i) # for i in l1 : # if str(i).isalpha() : # l1[l1.index(i)] = l1[l1.index(i)].lower() # for i in l1 : # print(i) #法二: # li = [1, 3, 4, "alex", [3, 7, 8, "TaiBai"], 5, "RiTiAn"] # for i in li : # if type(i) == list : # for i1 in i : # print(str(i1).lower()) # else : # print(str(i).lower())
03 字典
首先看字典的增删改查,
为什么要用字典?
1.list存储的数据多的话,相对来说查询较慢;
2.list存储的的数据相互之间没关联.
字典是Python基础数据类型之一. 是Python中唯一的映射类数据. 花括号{}括起来. 以键值对形式存储. 每一对键值对之间以冒号相连,不同键值对以逗号隔开.
PS:
数据类型的划分:
容器与非容器划分:
容器类型数据:list,tuple,dict,list.
非容器类型数据:int , str ,bool .
可变与不可变的划分:
可变(不可hash)的数据类型: list ,dic ,set.
不可变(可hash)的数据类型: str , bool ,int ,tuple .
字典是以键值对形式存储的,
键:不重复的,唯一的,键的数据类型必须是不可变的数据类型。
值:任意数据类型,对象。
字典可以存储大量的键值对数据,
python3.5包括3.5之前:字典是无序的。
python3.6之后,字典变成有序的。
字典的优点:
1,字典存储大量的关系型数据。
2,字典的查询速度非常快。
字典的增:
demo1: # 增:2种方法 # 1.直接赋值修改 # 2.setdefault改 # 1.有则修改,无则添加 # dic['hight'] = 176 # dic['lover'] = 'zhaomin' # print(dic) # 2.有则不变,无则添加 # dic.setdefault('father') # print(dic) # dic.setdefault('father','zhang cuishan ') # print(dic)
字典的删:
demo2:
# 删:3种方法 # 1.pop删键 # 2.clear清空字典 # 3.del删除 # pop:按照键删除 # ret = dic.pop('name') # print(ret) #按照键删除键值对 # print(dic) # ret1 = dic.pop('age1') # print(ret1) # print(dic)#报错 # ret1 = dic.pop('age1','无此键') # print(ret1) # print(dic) #后面定义了之后不报错 # clear:清空 # dic.clear() # print(dic) # popitem: 3.6版本之后就是删除最后一个键值对 # ret = dic.popitem() # print(ret) # print(dic) # del # del dic['name'] #按照键删除 # print(dic) # del dic['name1'] # print(dic) #报错 # del dic # print(dic) #删除字典
字典的改:
# 改: # dic['age'] = 30 # print(dic) # update # dic.update(kongfu = 'jiuyang',xinjing = 'jijinjing') # print(dic) # dic2 ={'name':'yangguo','lover':'xiaolongnv','country':'Song'} # dic.update(dic2) # print(dic) #将dic里面的键值对覆盖到dic2中
字典的查:
demo3: # 查 # 1.直接查 # print(dic['age']) # print(dic['age1']) #报错,因为没age1 # 2.get # print(dic.get('age')) # print(dic.get('age1'))#找不到默认返回none # print(dic.get('age1','666'))#可以自己设置返回值
字典的其他操作:
demo4: # 其他操作:3个类似list的类型. # dic.keys 类似list的类型 # dic.values 类似list的类型 # dic.items 可以生成元组. # dic.keys() 这是类似list的容器类类型 # dic = { # 'name': 'zhangwuji', # 'age': 23, # 'hobby': 'study kongfu' # } # ret = dic.keys() # print(ret,type(ret)) # dic.values() # print(dic.values()) # 转换成list # print(list(dic.values())) # for v in dic.values() : # print(v) # print(dic.items()) # print(type(dic.items())) # for i in dic.items() : # print(i)#输出的生成为元组. # 分别赋值 # a, b = (1, 2) # print(a, b) # a, b, c = (1, 2, 3) # print (a,b,c) # a,b = 100 ,1000 # print(a,b) # a,b=[100,1000] # print(a,b) # 相互赋值:(不用像c语言那样拿个中间值换碗) # a,b = 1,2 # b,a = a,b # print(a,b) # a, b = {'name': 'zhangwuji', 'age': '23'} # print(a, b) # 只能输出key值 # print(dic.items()) # for k,v in dic.items() : # ''' # k,v = ('movie',''HarryPoter') # k,v = ('money',666) # k,v = ('sky','land') # '''#诸如此类 # print('这是键',k) # print('这是值:',v)
04 字典的嵌套:
demo5:(引用taibai上课举的例子) # 字典嵌套: # dic = { # 'name_list': ['博哥', '菊哥', 'b哥', 'alex'], # 'barry': { # 'name': '太白金星', # 'age': 18, # 'hobby': 'wife', # } # } # 1,给这个列表['博哥', '菊哥', 'b哥', 'alex'] 追加一个元素 '老男孩'。 # l1 = dic['name_list'] # l1.append('老男孩') # print(dic) # 简写: # dic['name_list'].append('老男孩') # print(dic) # 2,将这个列表['博哥', '菊哥', 'b哥', 'alex']中的alex变成首字母大写。 # dic['name_list'][-1]= dic['name_list'][-1].capitalize() # print(dic) # 3,将这个键值对 'name': '太白金星' 的 '太白金星' 改成男神。 # dic['barry']['name'] = '男神' # print(dic) # 4,给barry对应的小字典增加一个键值对: weight: 160 # 法一: # dic['barry'].setdefault('weigth',160) # print(dic) # 法二: # dic['barry']['weight'] = 160 # print(dic)
05 set
set的话并没有讲. 简单了解一下即可.
set的输出:
注意,set中的元素不可重复.
demo6: set1 = set({1, 2, 'zhangwuji'}) set2 = {1, 2, 'zhangwuji'} print(set1, set2)
set的增加(2种方法):
demo7: set1 = {'guojing','huangrong','yangguo','xiaolongnv','guoxiang'} set1.add('zhangwuji')#随机添加进入 print(set1) update:迭代着增加 set1.update('weiwuxian') print(set1) set1.update('lanwangji') print(set1) set1.update([1,2,3]) print(set1)#添加list会保持在一起 set1.update(['weiwuxian']) set1.update(['lanwangji']) print(set1)
set的删除(4种操作):
demo8: set1 = {'guojing','huangrong','yangguo','xiaolongnv','guoxiang'} set1.remove('guojing') # 删除一个元素 print(set1) set1.pop() # 随机删除一个元素 print(set1) set1.clear() # 清空集合 print(set1) del set1 # 删除集合 print(set1)
set的其他操作:
demo*(直接复制老师的,理解即可) 4.1 交集。(& 或者 intersection) set1 = {1,2,3,4,5} set2 = {4,5,6,7,8} print(set1 & set2) # {4, 5} print(set1.intersection(set2)) # {4, 5} 4.2 并集。(| 或者 union) set1 = {1,2,3,4,5} set2 = {4,5,6,7,8} print(set1 | set2) # {1, 2, 3, 4, 5, 6, 7,8} print(set2.union(set1)) # {1, 2, 3, 4, 5, 6, 7,8} 4.3 差集。(- 或者 difference) set1 = {1,2,3,4,5} set2 = {4,5,6,7,8} print(set1 - set2) # {1, 2, 3} print(set1.difference(set2)) # {1, 2, 3} 4.4反交集。 (^ 或者 symmetric_difference) set1 = {1,2,3,4,5} set2 = {4,5,6,7,8} print(set1 ^ set2) # {1, 2, 3, 6, 7, 8} print(set1.symmetric_difference(set2)) # {1, 2, 3, 6, 7, 8} 4.5子集与超集 set1 = {1,2,3} set2 = {1,2,3,4,5,6} print(set1 < set2) print(set1.issubset(set2)) # 这两个相同,都是说明set1是set2子集。 print(set2 > set1) print(set2.issuperset(set1)) # 这两个相同,都是说明set2是set1超集。 5,frozenset不可变集合,让集合变成不可变类型。 s = frozenset('barry') print(s,type(s)) # frozenset({'a', 'y', 'b', 'r'}) <class 'frozenset'>
PS:
1.养成习惯,input后面带个strip.(区分strip和split)
2.面试可能会问:
Python3.5(包括3.5之前):字典是无序的.
Python3.6之后:字典变成有序的.(按首次创建字典的顺序排列)
3.dict是选key输出value. key相当于索引.
4.分别赋值的概念很重要.
5.update后面的key不要加''. 值不能是数字.
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6.keys, values, items. 类似list.
7.有返回值才能print.(注意观察)
8.others:(不想再分了)
有返回值才能print.(注意观察)
extend:把参数中的每个元素拆分添加到当前列表当中.
元组(上图)
[]:索引操作符
(list , tuple ,str 都可以用)
多层列表的话推荐如下写法.![]()
![](data:image/png;base64,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)
Python 文档解释:(了解item和iteritems) dict.items(): Return a copy of the dictionary’s list of (key, value) pairs. dict.iteritems(): Return an iterator over the dictionary’s (key, value) pairs. dict.items()返回的是一个完整的列表,而dict.iteritems()返回的是一个生成器(迭代器)。 dict.items()返回列表list的所有列表项,形如这样的二元组list:[(key,value),(key,value),...],dict.iteritems()是generator, yield 2-tuple。相对来说,前者需要花费更多内存空间和时间,但访问某一项的时间较快(KEY)。后者花费很少的空间,通过next()不断取下一个值,但是将花费稍微多的时间来生成下一item。 --------------------- 转载自作者:iidol 来源:CSDN 原文:https://blog.csdn.net/u012542422/article/details/52052877