【Python】列表推导，词频和根据单词长度对list进行排序

# 用list comprehension的方法推导出一个长度小于6个字母的单词列表word_list_six_minus，并根据单词的长度进行倒序排序
    word_list_six_minus = [word for word in unique_word_list if 0 < len(word) < 6]
    print(sorted(word_list_six_minus, key=len, reverse=True))
    或者
    word_list_six_minus.sort(key=len)
    
    #用pandas模块的value_count()方法计算出词频
    # df = pd.DataFrame(word_list)
    series = pd.Series(word_list)
    word_frequency = series.value_counts()
    print(word_frequency)

# 列表推导式
squares = []
for i in range(10):
    squares.append(i * i)
print(squares)

def square(i):
    return i * i

nums = [1, 2, 3, 4, 5, 6, 7, 8, 9]
print(list(map(square, nums)))

# 从可迭代对象中取一个(满足条件的)元素，把它传入表达式计算后，放在列表里
squares = [i * i for i in range(10) if i % 2 == 0]
print(squares)

list1 = ['apple', 'orange', 'banana', 'grape']
list2 = ['grapefruit', 'apple', 'grape', 'pear']

common_items = [i for i in list1 if i in list2]
print(common_items)

s = [136, 95, -44, -3, -9, 128]
s2 = [i if i>0 else 0 for i in s]
print(s2)

mlist = ['tensor', 'ADa', 'Python', 'China']
l1 = [x.upper() if len(x) > 3 else x.lower() for x in mlist]
print(l1)

# 带有For嵌套的推导
l2 = [x + y for x in '*' for y in ('Google', 'Tencent', 'Apple')]
print(l2)

# 多个if语句的嵌套
l3 = [i for i in range(1, 101) if i % 2 == 0 if i % 5 ==0]
print(l3)

l4 = [i for i in range(1, 101) if i % 10 == 0]
print(l4)

# 展开嵌套列表
l5 = [[1,2,3], [4,5,6],[7,8,9]]
l6 = [i for j in l5 for i in j]
print(l6)