if x%2 == 0:
			if x%3 == 0:
				print('被2和3整除')
			else:
				print('只被2整除')
		elif x%3 == 0:
			print('只被3整除')

		x = 3
		ans = 0
		itersLeft = x
		while (itersLeft != 0):
			ans = ans + x
			itersLeft = itersLeft – 1
		print(str(x) + '*' + str(x) + ' = ' + str(ans))

		for  in :
			 

		range(n) = [0, 1, 2, 3, …, n-1]
		range(m,n) = [m, m+1, …, n-1]
		xrange(n)

		def  ():
			 

		def max(x, y):
			if x > y:
				return x
			else:
				return y

		>>>z = max(3, 4)

		pi = 3.14159

		def area(radius):
			return pi*(radius**2)

		def circumference(radius):
			return 2*pi*radius

		import cicle as crl
		#OR
		from  circle import area
		#OR
		from circle import *

		def recurMul(a, b):
			if b == 1:
				return a
			else:
				return a + recurMul(a, b-1)

		try:
			f = open(‘grades.txt’)
			# … code to read and process grades
		except:
			raise Exception('Can’t open grades file')

		def divide(x, y):
			try:
				result = x / y
			except ZeroDivisionError, e:
				print "division by zero! " + str(e)
			else:
				print "result is", result
			finally:
				print "executing finally clause"

			安装：pip install pygame

			key_pressed = pygame.key.get_pressed()  
			# 获得用户所有的键盘输入序列
			if key_pressed[K_w] or key_pressed[K_UP]:  
				# 如果用户通过键盘发出“向上”的指令
				me.move_up()
		

			if not delay % 3:
				witch_image = not switch_image
			if switch_image:
				screen.blit(me.image1, me.rect)  
				# 绘制我方飞机的两种不同的形式
			else:
				screen.blit(me.image2, me.rect)
		

		XML/JSON/CSV内容解析、文本解析、正则表达式、XPath
		

		BeautifulSoup、htmlParser
		

		 Scrapy 、 Charlotte 
		

		from PIL import Image
		import zbarlight
		file_path = './tests/fixtures/one_qr_code_and_one_ean.png'
		with open(file_path, 'rb') as image_file:
			image = Image.open(image_file)
			image.load()
		codes = zbarlight.scan_codes('qrcode', image)
		print('QR codes: %s' % codes)
		/pre> 

		Vue + Django Rest Framework 前后端分离技术
		Sentry 错误日志的监控和告警
		第三方登录和支付宝支付的集成
		

		vue前端项目
		django rest framework 实现API
		xadmin后台管理系统
		富文本DjangoUeditor
		

				Numpy、Pandas（dataframes）、Matplotlib、SciPy
		

				Pytorch、Orange3、Caffe、scikit-learn、TensorFlow
		

				基础算法：K-均值聚类、决策树、线性回归、logistic 回归
				高阶算法: 支持向量机、随机森林、降维算法、神经网络
		

		Initialize replay memory D to size N
		Initialize action-value function Q with random weights
		for episode = 1, M do
			Initialize state s_1
			for t = 1, T do
				With probability ϵ select random action a_t
				otherwise select a_t=max_a  Q($s_t$,a; $θ_i$)
				Execute action a_t in emulator and observe r_t and s_(t+1)
				Store transition (s_t,a_t,r_t,s_(t+1)) in D
				Sample a minibatch of transitions (s_j,a_j,r_j,s_(j+1)) from D
				Set y_j:=
					r_j for terminal s_(j+1)
					r_j+γ*max_(a^' )  Q(s_(j+1),a'; θ_i) for non-terminal s_(j+1)
				Perform a gradient step on (y_j-Q(s_j,a_j; θ_i))^2 with respect to θ
			end for
		end for