Python容器

2019-02-13  本文已影响0人  davidic

列表[]

列表是可变的,这是它区别于字符串和元组的最重要的特点,一句话概括即:列表可以修改,而字符串和元组不能。

创建

直接创建

list1 = ['a','b']
list2 = [1,2]

list函数创建

list3 = list("hello")
print list3

输出

[‘h’, ‘e’, ‘l’, ‘l’, ‘o’]

lista = [0] * 6

过滤

 [elem for elem in li if len(elem) > 1]
 
 # 用filter过滤
 list(filter(lambda x: x[1]<0, exp.local_exp[1]))

划分

a=[1,2,3,4]
#不包括1
a[:1]
#输出[1]
#包括2
a[2:]
#输出[3、4]

判断数组为空

if not nums:
    return None

遍历索引和元素

#遍历列表, 打印索引和元素
names = ['Tom', 'Jerry', 'Marry']
for index, name in enumerate(names):
    print('names[{}] = {}'.format(index, name))
 
 
打印结果:
names[0] = Tom
names[1] = Jerry
names[2] = Marry

把列表中某个值划分出去

if featureVec[axis] == value:
    reducedFeatVec = featureVec[:axis]
    reducedFeatVec.extend(featureVec[axis+1:])

二维列表

dataSet = [[1, 1, 'yes'],
               [1, 1, 'yes'],
               [1, 0, 'no'],
               [0, 1, 'no'],
               [0, 1, 'no']]

定义一个5×4的都是0的二维数组

c=[[0 for i in range(4)] for j in range(5)]

合并

circle_file = glob.glob(os.path.join(self.resource_dir, 'circle/*.png'))
table_file  = glob.glob(os.path.join(self.resource_dir, 'table/*.png'))
 # 直接相加
 self.jump_file = [cv2.imread(name, 0) for name in circle_file + table_file] 

generator转list

import jieba
# jieba的cut返回的是一个generator
a = jieba.cut('我喜欢吃土豆')
b = list(a)

列表扩展的两种方式

a=[1,2,3]
b=[4,5,6]
a.append(b)

[1,2,3,[4,5,6]]


a.extend(b)
[1,2,3,4,5,6]

保存为csv

元组()

元组与列表一样,也是一种序列,唯一不同的是元组不能被修改(字符串其实也有这种特点)。

创建

t1=1,2,3
t2="jeffreyzhao","cnblogs"
t3=(1,2,3,4)
t4=()
t5=(1,)
print t1,t2,t3,t4,t5

输出:

(1, 2, 3) (‘jeffreyzhao’, ‘cnblogs’) (1, 2, 3, 4) () (1,)

从上面我们可以分析得出:

a、用逗号分隔一些值,元组自动创建完成

b、元组大部分时候是通过圆括号括起来的;

c、空元组可以用没有包含内容的圆括号来表示;

d、只含一个值的元组,必须加个逗号(,);

list转元组

tuple函数和序列的list函数几乎一样:以一个序列作为参数并把它转换为元组。如果参数就是元组,那么该参数就会原样返回

t1=tuple([1,2,3])
t2=tuple("jeff")
t3=tuple((1,2,3))
print t1
print t2
print t3
t4=tuple(123)
print t4

输出:

(1, 2, 3)
(‘j’, ‘e’, ‘f’, ‘f’)
(1, 2, 3)

t4=tuple(123)
TypeError: ‘int’ object is not iterable

词典{}

prices = {
    'A':123,
    'B':450.1,
    'C':12,
    'E':444,
}

prices['A']

创建词典

>>>dict()                        # 创建空字典
{}
>>> dict(a='a', b='b', t='t')     # 传入关键字
{'a': 'a', 'b': 'b', 't': 't'}
>>> dict(zip(['one', 'two', 'three'], [1, 2, 3]))   # 映射函数方式来构造字典
{'three': 3, 'two': 2, 'one': 1} 
>>> dict([('one', 1), ('two', 2), ('three', 3)])    # 可迭代对象方式来构造字典
{'three': 3, 'two': 2, 'one': 1}
>>>

读取文件创建词典

#读取代码
fr = open('dic.txt','r')
dic = {}
keys = [] #用来存储读取的顺序
for line in fr:
    v = line.strip().split(':')
    dic[v[0]] = v[1]
    keys.append(v[0])
fr.close()
print(dic)
#写入文件代码 通过keys的顺序写入
fw = open('wdic.txt','w')
for k in keys:
    fw.write(k+':'+dic[k]+'\n')
 
fw.close()

转list

li = dict.items()

结果类似于

[(u'11', 50808340), (u'1101', 9842378)]

排序

转为list后再排序


判断key是否存在

#生成一个字典
d = {'name':{},'age':{},'sex':{}}
#打印返回值
print d.has_key('name')
#结果返回True

判断词典是否包含某个元素

labelCount={}
for feature in dataSet:
    label = feature[-1]
    if label not in labelCount[label]: labelCount[label] = 0 

词典的遍历

iteritems

sentences = ["我喜欢吃土豆","土豆是个百搭的东西","我不喜欢今天雾霾的北京"]

words = []
for doc in sentences:
    words.append(list(jieba.cut(doc)))

dic = corpora.Dictionary(words)

for word,index in dic.token2id.iteritems():
    print word + ', index: ' + str(index)

在3.x 里 用 items()替换iteritems()

增加元素

#比如有个词典
action = {
  "_index": elastic_urls_index,
  "_type": doc_type_name,
  "_id": data[0],
  "_source": {
  "iclick_id": data[0],
  "onsite_id": data[1],
  "create_time": self.today_2
  }
}

#要增加元素
data['_soupyrce']['age'] = 'aa'

提取文本的高频词

documents = ["Human machine interface for lab abc computer applications",
             "A survey of user opinion of computer system response time"]


stoplist = set('for in and'.split())
texts = [ [word for word in document.lower().split() if word not in stoplist ] for document in documents]

from collections import defaultdict
frequency = defaultdict(int)
for text in texts:
    for word in text:
        frequency[word]+=1

texts = [ [word for word in text if frequency[word]>1] for text in texts  ]

映射mapping

集合set

定义

aaa = set()

增加

aaa.add(1)

判断是否在集合

if 1 in aaa:

数组转集合

a = [11,22,33,44,11,22]  
b = set(a)

通过set去除停用词

documents = ["Human machine interface for lab abc computer applications",
             "A survey of user opinion of computer system response time"]


stoplist = set('for in and'.split())
texts = [ [word for word in document.lower().split() if word not in stoplist ] for document in documents]

set增加数据

vocabSet = set([])
for document in dataSet:
        vocabSet = vocabSet | set(document) 
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