Advanced Python Mastery (course by @dabeaz). Contribute to Tengas/python-mastery development by creating an account on GitHub.
GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.
5. Atom Atom 是 Github 的开源代码编辑器,支持 Python 开发 Atom 类似于 Sublime Text 并提供几乎相同的功能,强调速度和可用性 价格:免费 Atom 最显着的特性包括: 支持大量插件 智能自动完成 支持用户与编辑器交互的自定义命令 支持跨平台开发 6. Jupyter Jupyter广泛应用于数据科学领域 它易于使用、交互并允许实...
六、python机器学习 AI Learning 机器学习实战 中文（GitHub 15000星）Kaggle 机器学习基础学习 动手学深度...
books = ('Atomic habits', 'Ego is the enemy', 'Outliers', 'Mastery') print(books.index('Mastery')) # 3 ▍13、将字符串转换为字符串列表 假设你在函数中获得输出,原本应该是一个列表,但实际上却是一个字符串。 input = "[1,2,3]" 你可能第一时间会想到使用索引或者正则表达式。实际上,使用ast...
Dive into the world of programming with our comprehensive course “Python Mastery with Generative AI: Coding to AI Integration”. This course is meticulously designed for both beginners and experienced developers who aspire to master Python while integrating cutting-edge Generative AI technologies. Starti...
Due to their mastery of the common Python features that make development particularly quick and intuitive, a Python expert can use the language efficiently. These tools include data structures (e.g., collections), list manipulation functions (e.g., generators, itertools, and list comprehensions),...
mini project – SQLAlchemy, Github Action, Docker Requirements: A Little Bit of Programming Knowledge is Beneficial, But Not Mandatory! Description: This course title encapsulates the breadth of your tutorial, emphasizing the progression to advanced topics like Object-Oriented Programming (OOP), Design...
I have been working with Python three for a couple of years now in data analysis, but lacked complete mastery in a software sense. This book is the perfect solution . It has helped me learn what I needed to know grow my career! Solid foundation of object oriented programming that is simp...
df=pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv')# CalculateACFandPACFupto50lags# acf_50=acf(df.value,nlags=50)# pacf_50=pacf(df.value,nlags=50)# Draw Plot fig,axes=plt.subplots(1,2,figsize=(16,3),dpi=100)plot_acf(df.value.tolist(),lags=50...