data = [1, 2, 3, 4, -5]
 
# 使用列表推导式
result = [i for i in data if i >= 0]
 
# 使用 fliter 过滤函数
result = filter(lambda x: x >= 0, data)

from random import randint
 
d = {x: randint(50, 100) for x in range(1, 21)}
r = {k: v for k, v in d.items() if v > 80}

from random import randint, sample
 
s1 = {x: randint(1, 4) for x in sample("abfcdrg", randint(1, 5))}
d = {k: v for k, v in zip(s1.values(), s1.keys())}

# 可以使用字典来统计，以列表中的数据为键，以出现的次数为值
from random import randint
 
# 构造随机序列
data = [randint(0, 20) for _ in range(30)]
 
# 列表中出现数字出现的次数
d = dict.fromkeys(data, 0)
 
for v in d:
  d[v] += 1

# 直接使用 collections 模块下面的 Counter 对象
from collections import Counter
from random import randint
 
data = [randint(0, 20) for _ in range(30)]
 
c2 = Counter(data)
 
# 查询元素出现次数
c2[14]
 
# 统计频度出现最高的3个数
c2.most_common(3)

import re
from collections import Counter
 
# 统计某个文章中英文单词的词频
with open("test.txt", "r", encoding="utf-8") as f:
  d = f.read()
 
# 所有的单词列表
total = re.split("\W+", d)
result = Counter(total)
print(result.most_common(10))

# 利用 zip 将字典转化为元组，再用 sorted 进行排序
from random import randint
 
data = {x: randint(60, 100) for x in "xyzfafs"}
sorted(data)
data = sorted(zip(data.values(), data.keys()))

# 利用 sorted 函数的 key 参数
from random import randint
 
data = {x: randint(60, 100) for x in "xyzfafs"}
data.items()
sorted(data.items(), key=lambda x: x[1])

from random import randint, sample
from functools import reduce
 
# 模拟随机的进球球员和进球数
s1 = {x: randint(1, 4) for x in sample("abfcdrg", randint(1, 5))}
s2 = {x: randint(1, 4) for x in sample("abfcdrg", randint(1, 5))}
s3 = {x: randint(1, 4) for x in sample("abfcdrg", randint(1, 5))}
 
# 首先获取字典的 keys，然后取每轮比赛 key 的交集。由于比赛轮次数是不定的，所以使用 map 来批量操作
# map(dict.keys, [s1, s2, s3])
 
# 然后一直累积取其交集，使用 reduce 函数
reduce(lambda x, y: x & y, map(dict.keys, [s1, s2, s3]))