运行方法:
1. 打开python2 IDLE;
2. 输入 from craps import *
3. 按提示输入运行命令。例如,玩游戏就输入play();查看余额就输入check_bankroll();
自动玩看胜率就输入auto()
craps.py
import random point_set = False bet = 10 bankroll = 1000 sim_win = 0 sim_lose = 0 print """ Welcome to the 'Seven Star' casino! You are playing craps now, your started bankroll is '$1000', the started bet is '$10', command: play(): "Rolling the dices" check_bankroll(): "Checking your current balance" all_in(): Showing "hand" set_bet(): "Setting a new bet" game(): "Check your game status" auto(): "It can be played automatically for you until reach a specific bankroll" """ def roll(): d1 = random.randrange(1,7) d2 = random.randrange(1,7) print "You rolled", d1, "+", d2, "=", d1+d2 return d1 + d2 def play(): global point_set, bankroll, point global sim_win, sim_lose if bankroll < bet: print "Sorry, you can't play since you don't have enough money!" print """Do you wanna get more money? 1: Yes 2: No """ choice = raw_input(">>") if choice == str(1): money = raw_input("How much do you wanna get?") bankroll += int(money) print "Your current bankroll is: ", bankroll if choice == str(2): print "Thanks for playing! See you next time!" else: if not point_set: print print "New game. Your bet is: ", bet # for the first roll r = roll() if not point_set: if r in (7, 11): bankroll += bet sim_win += 1 print "Congratz! You Won! Your bankroll is: ", bankroll elif r in (2, 3, 12): bankroll -= bet sim_lose += 1 print "Oops! You lost! Your bankroll is: ", bankroll else: point = r point_set = True print "Your point is", "[", point, "]" # for subsequence rolls elif r == 7: bankroll -= bet sim_lose += 1 point_set = False print "You crapped out! Your bankroll is: ", bankroll elif r == point: bankroll += bet sim_win += 1 point_set = False print "You made your point! Your bankroll is: ", bankroll def set_bet(inp): global bet, bankroll, point_set print if point_set: print "WARNING!" print "The game has started, you will lose half of your bet if resetting your bet!" prompt = raw_input(""" 1: Yes, I am wanna reset my bet! 2: No, I don't wanna reset my bet! """) if prompt == "1": point_set = False bankroll -= bet/2 print "Forfeiting current bet. Your bankroll is: ", bankroll else: pass bet = int(inp) print "New bet size is: ", bet def all_in(): set_bet(bankroll) def check_bankroll(): global bet print "Your current balance is: ", bankroll def game(): total = sim_win + sim_lose percent = float(sim_win)/total * 100 print "So far, the games that you have been playing are: ", total print "Won ", sim_win print "Lost ", sim_lose print "Overall, you have %d%% to win!" %percent def auto(): game_status = True purpose = raw_input("How much are you gonna reach? ") while game_status: play() if bankroll == int(purpose) or bankroll == 0: game_status = False game()
以上所述就是本文的全部内容了,希望能够对大家学习Python有所帮助。

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


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