Python Learning

Python List Exercises Guide

Python Web Scraping This learning path you’ll learn the core Python technologies and skills you need to build your own web scraper. Web scraping is about downloading structured data from the web and processing selected data. 👉 You should already be comfortable writing Python scripts 🔗 Learn Here

📦 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 Understand this and imports become much less confusing.

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📂 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.

❌ 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.

✅ 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.

🔥 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 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