一些Python 5行代码的神奇操作汇总

def count(n):
    if (1 == n or 2 == n):
        return 1
    elif (n >= 2):
        return count(n - 2) + count(n - 1)
print(count(36) * 2)
 

num1 = input("第一个数：")
num2 = input("第二个数：")
new_num1 = int(num1)
new_num2 = int(num2)
print(new_num1 + new_num2)

while(True):
    question = input()
    answer = question.replace('吗', '呢')
    answer = answer.replace('？', '！')
    print(answer)

from Http import server
from http.server import SimpleHTTPRequestHandler
server_address = ('127.0.0.1', 8888)
httpd = server.httpserver(server_address, SimpleHTTPRequestHandler)
httpd.serve_forever()

for i in range(1, 10):
    for j in range(1, i+1):
        print('{}x{}={}\t'.fORMat(j, i, i*j), end='')
    print()

for i in range(1, 10):
    for j in range(i, 10):
        print(f'{i}x{j}={i*j}',end='\t')
    print(" ")
print("\n")

def nixu(n):
    l = str(n)
    l_str = l[::-1]
    print("逆序:%s" % ( l_str))
nixu(2020)

from Wordcloud import WordCloud
import PIL.Image as image

with open('wordcloud.txt') as fp:
    text = fp.read()
    wordcloud = WordCloud().generate(text)
    img = wordcloud.to_image()
    img.show()

from MyQR import myqr
myqr.run(
    words='https://www.baidu.com/',
    colorized=True,
    save_name='baidu_code.png')

import os, paddlehub as hub
huseg = hub.Module(name='deeplabv3p_xception65_humanseg') # 加载模型
path = './imgs/' # 文件目录
files = [path + i for i in os.listdir(path)] # 获取文件列表
results = huseg.segmentation(data={'image': files}) # 抠图