Python Learning

📘Python Data Science Handbook ✍️ Author: Jake VanderPlas 🔗 Read Online #Python #DataScience ──────────────────── 👉 @free_programming_books_bds 👈

📊 Essential Python Libraries to build your career in Data Science 1. NumPy: - Efficient numerical operations and array manipulation. 2. Pandas: - Data manipulation and analysis with powerful data structures (DataFrame, Series). 3. Matplotlib: - 2D plotting library for creating visualizations. 4. Seaborn: - Statistical data visualization built on top of Matplotlib. 5. Scikit-learn: - Machine learning toolkit for classification, regression, clustering, etc. 6. TensorFlow: - Open-source machine learning framework for building and deploying ML models. 7. PyTorch: - Deep learning library, particularly popular for neural network research. 8. SciPy: - Library for scientific and technical computing. 9. Statsmodels: - Statistical modeling and econometrics in Python. 10. NLTK (Natural Language Toolkit): - Tools for working with human language data (text). 11. Gensim: - Topic modeling and document similarity analysis. 12. Keras: - High-level neural networks API, running on top of TensorFlow. 13. Plotly: - Interactive graphing library for making interactive plots. 14. Beautiful Soup: - Web scraping library for pulling data out of HTML and XML files. 15. OpenCV: - Library for computer vision tasks. As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch.

Python List Methods

mypy Mypy is a static type checker for Python. Python is a dynamic language, so usually you'll only see errors in your code when you attempt to run it. Mypy is a static checker, so it finds bugs in your programs without even running them. Creator: python Stars ⭐️: 20,507 Forked by: 3,225 Github Repo: https://github.com/python/mypy ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @github_repositories_bds for more cool repositories. This channel belongs to @bigdataspecialist group

🚀 Essential Python snippets to explore data:   1.   .head() - Review top rows 2.   .tail() - Review bottom rows 3.   .info() - Summary of DataFrame 4.   .shape - Shape of DataFrame 5.   .describe() - Descriptive stats 6.   .isnull().sum() - Check missing values 7.   .dtypes - Data types of columns 8.   .unique() - Unique values in a column 9.   .nunique() - Count unique values 10.  .value_counts() - Value counts in a column 11.  .corr() - Correlation matrix

Python Syllabus

super() is linear. Your brain is not. You have class A, B, C. Multiple inheritance. You call super().method() inside B. Which method runs? Not necessarily the parent of B. It depends on the Method Resolution Order of the instance. Most developers learn MRO once, forget it, then get confused when super() jumps sideways instead of up. Take this:

class A:
    def f(self): print("A")

class B(A):
    def f(self): print("B"); super().f()

class C(A):
    def f(self): print("C"); super().f()

class D(B, C):
    def f(self): print("D"); super().f()

D().f() prints D, B, C, A. Not B then A. Because super() in B calls next in MRO which is C, not A. This is not a bug. It's cooperative multiple inheritance. It allows mixins and dependency injection. But if you don't understand it, you will spend hours wondering why super().f() skipped a generation. ✔️ The rule: super() follows the MRO, not the parent hierarchy. Print ClassName.__mro__ before you debug.

⚠️ __pycache__ is not your enemy, but it will lie to you You delete a module. The import still works. You rename a class. Old bytecode still runs. You spend an hour asking “why is this line still executing?” 👉 Python caches compiled bytecode in __pycache__. That’s great for speed. But when you delete a .py file, the .pyc stays forever. Python finds it and imports it like nothing happened. No warning. No error. ✅ The idea: clear __pycache__ before you debug import issues. Or set PYTHONDONTWRITEBYTECODE=1 in development. Or just accept that Python will gaslight you once a month and move on.

Python Assignment Operators

🐍 How to Learn Python Fast (Even If You've Never Coded Before) Python is everywhere. Web dev, data science, automation, AI… But where should YOU start if you're a beginner? Don’t worry. Here’s a 6-step roadmap to master Python the smart way (no fluff, just action)👇 🔹 𝗦𝘁𝗲𝗽 𝟭: Learn the Basics (Don’t Skip This!) ✅ Variables, data types (int, float, string, bool) ✅ Loops (for, while), conditionals (if/else) ✅ Functions and user input Start with: Python.org Docs YouTube: Programming with Mosh / CodeWithHarry Platforms: W3Schools / SoloLearn / FreeCodeCamp Spend a week here. Practice > Theory. 🔹 𝗦𝘁𝗲𝗽 𝟮: Automate Boring Stuff (It’s Fun + Useful!) ✅ Rename files in bulk ✅ Auto-fill forms ✅ Web scraping with BeautifulSoup or Selenium Read: “Automate the Boring Stuff with Python” It’s beginner-friendly and practical! 🔹 𝗦𝘁𝗲𝗽 𝟯: Build Mini Projects (Your Confidence Booster) ✅ Calculator app ✅ Dice roll simulator ✅ Password generator ✅ Number guessing game These small projects teach logic, problem-solving, and syntax in action. 🔹 𝗦𝘁𝗲𝗽 𝟰: Dive Into Libraries (Python’s Superpower) ✅ Pandas and NumPy - for data ✅ Matplotlib - for visualizations ✅ Requests - for APIs ✅ Tkinter - for GUI apps ✅ Flask - for web apps Libraries are what make Python powerful. Learn one at a time with a mini project. 🔹 𝗦𝘁𝗲𝗽 𝟱: Use Git + GitHub (Be a Real Dev) ✅ Track your code with Git ✅ Upload projects to GitHub ✅ Write clear README files ✅ Contribute to open source repos Your GitHub profile = Your online CV. Keep it active! 🔹 𝗦𝘁𝗲𝗽 𝟲: Build a Capstone Project (Level-Up!) ✅ A weather dashboard (API + Flask) ✅ A personal expense tracker ✅ A web scraper that sends email alerts ✅ A basic portfolio website in Python + Flask