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.

Python Syllabus

🚀 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

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

📘Python Machine Learning Projects

✍️ Authors: Lisa Tagliaferri, Michelle Morales, Ellie Birkbeck, Alvin Wan

🗓 Year: 2019

📄 Pages: 135

🧠 This book will set you up with a Python programming environment if you don't have one already, then provide you with a conceptual understanding of machine learning in the chapter "An Introduction to Machine Learning." What follows next are three Python machine learning projects. They will help you create a machine learning classifier, build a neural network to recognize handwritten digits, and give you a background in deep reinforcement learning through building a bot for Atari.

#Python #MachineLearning
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Python List Methods

📊 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 Data Science Handbook

✍️ Author: Jake VanderPlas

🔗 Read Online

#Python #DataScience
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🔥 12 Python Tricks That Make Your Code Cleaner

Here are some Python tricks every developer should know.

1. Swap variables without a temporary variable.

a, b = b, a

2. Reverse a list.

nums[::-1]

3. Chain comparisons.

10 < age < 30

4. Multiple assignment.

x = y = z = 0

5. Unpack lists.

first, *middle, last = nums

6. Use underscores for ignored values.

name, _, age = data

7. Format strings with f-strings.

print(f"Hello {name}")

8. Merge dictionaries.

new = dict1 | dict2

9. Remove duplicates.

unique = list(set(nums))

10. Check membership using sets.

if color in {"red", "green", "blue"}:

11. Readable large numbers.

salary = 1_000_000

12. Use with for files.

with open("data.txt") as f:
    data = f.read()

✅ Python Scenario-Based Interview Question – List Comprehension 🐍

Scenario:
You are given a list of numbers:

numbers = [1, 2, 3, 4, 5, 6]

Question:
Write Python code to create a new list that contains:
1. Only the even numbers from the original list.
2. Each even number multiplied by 2.

Expected Output:


Answer:

even_doubled = [num * 2 for num in numbers if num % 2 == 0]
print(even_doubled)

Explanation:
⦁ The list comprehension iterates over each num in numbers.
⦁ The if num % 2 == 0 condition filters to only even numbers (remainder 0 when divided by 2).
⦁ For those, num * 2 doubles them, building the new list concisely.

❌ Five mistakes almost every Python developer makes once


1️⃣ Giving a function a default value that's a list or dictionary. This one is sneaky because it works in your first few tests and then quietly breaks the moment the function gets called more than once because that default gets created a single time, not fresh on every call, and it silently keeps growing in the background.

2️⃣ Creating a bunch of small functions inside a loop that each reference the loop variable
People expect each one to remember its "own" value from when it was created. They don't. They all end up referencing whatever the loop variable became by the time the loop finished, which is almost never what you wanted.

3️⃣ Comparing decimal numbers with a plain equals sign
Computers don't store decimal math with perfect precision, so two numbers that should obviously be equal sometimes aren't, according to the computer. There's a proper "close enough" comparison built for exactly this.

4️⃣ Confusing a quick copy with a real copy
A fast, shallow copy of something with nested lists or dictionaries inside still shares those inner pieces with the original change one, and you accidentally change both. A true independent copy needs a different approach entirely.

5️⃣ Catching every possible error with one generic catch-all
It feels protective in the moment, but it also hides real bugs behind the same wall as the error you actually expected, and you lose the ability to tell them apart.

None of these mean you're bad at this. Almost everyone hits each one exactly once, and then never forgets it.

📂 Understanding Python File Modes

When opening a file, the mode determines what you're allowed to do.

Mode Meaning
r 👉 Read only
w 👉 Write (overwrites existing file)
a 👉 Append to the end
x 👉 Create a new file
rb 👉 Read binary files
wb 👉 Write binary files
r+ 👉 Read and write

👉 Using the wrong mode is one of the easiest ways to accidentally erase a file.

📦 The Difference Between a Package and a Module

These terms get mixed up a lot.

📗A module is a single Python file.

math.py

📚A package is a folder containing multiple modules.

utils/
    helpers.py
    parser.py
    formatter.py

Think of it like this:
📖 Module = One book
📚 Package = An entire bookshelf