Professional Summary: File Handling in Python

Python provides built-in functions for seamless file operations. The core function is open(), which returns a file object.

Key Modes:
* 'r': Read (default)
* 'w': Write (overwrites existing file)
* 'a': Append
* 'x': Exclusive creation (fails if file exists)
* 'b': Binary mode (e.g., 'rb' or 'wb')
* 't': Text mode (default)
* '+': Updating (reading and writing, e.g., 'r+')

Best Practice: Use Context Manager
The with statement automatically handles file closing, even if an error occurs.

with open('filename.txt', 'r') as file:
    data = file.read()

Essential Methods:
* Reading:
* .read(): Reads the entire file content.
* .readline(): Reads a single line.
* .readlines(): Returns a list of all lines.
* Writing:
* .write(string): Writes a string to the file.
* .writelines(list): Writes a list of strings to the file.
* Positioning:
* .seek(offset): Changes the file pointer's position.
* .tell(): Returns the current file pointer's position.

Handling Different Data:
* Text Files: Use default text mode.
* Structured Data (CSV/JSON): Use specialized modules (csv, json).
* Binary Files (Images): Use binary mode ('rb', 'wb').


By: t.iss.one/DataScienceQ 🚀

Professional Summary: File Handling in Python (Part 2 - Examples)

1. Reading an Entire File:

with open('data.txt', 'r') as f:
    content = f.read()
    print(content)

2. Reading Line by Line:

with open('data.txt', 'r') as f:
    for line in f:
        print(line.strip()) # strip() removes newline characters

3. Writing to a File (Overwrites):

with open('output.txt', 'w') as f:
    f.write('Hello, World!\n')
    f.write('This is a new line.')

4. Appending to a File:

with open('log.txt', 'a') as f:
    f.write('New log entry\n')

5. Reading and Writing with `r+` mode:

with open('data.txt', 'r+') as f:
    content = f.read()
    f.seek(0) # Move pointer to beginning
    f.write('New content at start\n' + content)

6. Handling JSON Files:

import json
# Writing JSON
data = {"name": "Alice", "age": 30}
with open('data.json', 'w') as f:
    json.dump(data, f)

# Reading JSON
with open('data.json', 'r') as f:
    loaded_data = json.load(f)
    print(loaded_data['name']) # Output: Alice

7. Handling CSV Files:

import csv
# Writing CSV
with open('data.csv', 'w', newline='') as f:
    writer = csv.writer(f)
    writer.writerow(['Name', 'Age'])
    writer.writerow(['Bob', 25])

# Reading CSV
with open('data.csv', 'r') as f:
    reader = csv.reader(f)
    for row in reader:
        print(row) # Output: ['Name', 'Age'], then ['Bob', '25']

By: t.iss.one/DataScienceQ 🚀

30-Day Intensive Python Roadmap (4 Hours/Day)

Week 1: Core Fundamentals (Days 1-7)
* Day 1-2 (8h): Basic Syntax, Variables, Data Types, Operators.
* Day 3-4 (8h): Data Structures (Lists, Tuples, Sets, Dictionaries).
* Day 5 (4h): Control Flow (If, For, While loops).
* Day 6 (4h): Functions (def, lambda, *args, **kwargs).
* **Day 7 (4h):** Practice & Mini-Project (CLI Calculator, To-Do List).
* Checkpoint: You can solve basic algorithmic problems and build simple scripts.

Week 2: Intermediate Concepts (Days 8-14)
* Day 8-9 (8h): File I/O (Reading/Writing files, with statement).
* Day 10 (4h): Error Handling (Try/Except/Else/Finally).
* Day 11-12 (8h): Object-Oriented Programming (Classes, Objects, Inheritance).
* Day 13 (4h): Modules and Packages (import, pip, Virtual Environments).
* Day 14 (4h): Practice & Mini-Project (File Sorter, Basic OOP program).
* Checkpoint: You can structure code using OOP and handle external data files.

Week 3: Advanced Topics & Specialization (Days 15-23)
* Day 15 (4h): Decorators and Generators.
* Day 16 (4h): Iterators, __iter__, __next__.
* Day 17-18 (8h): Choose one:
* Web: Flask/Django basics (Routes, Templates).
* Data: NumPy & Pandas basics.
* Automation: Working with OS module, APIs (requests library).
* Day 19-20 (8h): Dive deeper into your chosen specialization.
* Day 21 (4h): Testing (Introduction to unittest or pytest).
* Day 22-23 (8h): Work on a larger project in your chosen track.
* Checkpoint: You can build a functional application in your chosen domain.

Week 4: Polishing & Deployment (Days 24-30)
* Day 24 (4h): Version Control with Git (Basics: add, commit, push).
* Day 25 (4h): Code Readability (PEP 8, writing clean code).
* Day 26-28 (12h): Final Project. Build something that uses all your skills.
* Day 29 (4h): Debugging techniques and logging.
* Day 30 (4h): Deploy your project (e.g., on GitHub, Heroku, PythonAnywhere).
* Final Checkpoint: You have a complete portfolio project and are ready for entry-level tasks.


By: t.iss.one/DataScienceQ 🚀

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==================================
🧠 By: https://t.iss.one/DataScienceM

Advanced Python Test

1. What is the output of the following code?

def func(x, l=[]):
    for i in range(x):
        l.append(i * i)
    return l

print(func(2))
print(func(3, []))
print(func(3))

A) [0, 1] [0, 1, 4] [0, 1, 4]
B) [0, 1] [0, 1, 4] [0, 1, 4, 0, 1, 4]
C) [0, 1] [0, 1, 4] [0, 1, 4, 0, 1, 4, 0, 1, 4]
D) [0, 1] [0, 1, 4] [0, 1, 4, 0, 1, 4, 0, 1, 4, 0, 1, 4]

2. Which statement about metaclasses in Python is TRUE?
A) A metaclass is used to create class instances
B) The __call__ method of a metaclass controls instance creation
C) All classes must explicitly specify a metaclass
D) Metaclasses cannot inherit from other metaclasses

3. What does this decorator do?

from functools import wraps

def debug(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__}")
        return func(*args, **kwargs)
    return wrapper

A) Measures function execution time
B) Logs function calls with arguments
C) Prints the function name when called
D) Prevents function execution in debug mode

4. What is the purpose of context managers?
A) To manage class inheritance hierarchies
B) To handle resource allocation and cleanup
C) To create thread-safe operations
D) To optimize memory usage in loops

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By: t.iss.one/DataScienceQ 🚀

Here are links to the most important free Python courses with a brief description of their value.

1. Coursera: Python for Everybody
Link: https://www.coursera.org/specializations/python
Importance: A perfect starting point for absolute beginners. Covers Python fundamentals and basic data structures, leading to web scraping and database access.

2. freeCodeCamp: Scientific Computing with Python
Link: https://www.freecodecamp.org/learn/scientific-computing-with-python/
Importance: Project-based certification. You build applications like a budget app or a time calculator, reinforcing learning through practical, portfolio-worthy projects.

3. Harvard's CS50P: CS50's Introduction to Programming with Python
Link: https://cs50.harvard.edu/python/2022/
Importance: A rigorous university-level course. Teaches core concepts and problem-solving skills with exceptional depth and clarity, preparing you for complex programming challenges.

4. Real Python Tutorials
Link: https://realpython.com/
Importance: An extensive resource for all levels. Offers in-depth articles, tutorials, and code examples on nearly every Python topic, from basics to advanced specialized libraries.

5. W3Schools Python Tutorial
Link: https://www.w3schools.com/python/
Importance: Excellent for quick reference and interactive learning. Allows you to read a concept and test code directly in the browser, ideal for fast learning and checking syntax.

6. Google's Python Class
Link: https://developers.google.com/edu/python
Importance: A concise, fast-paced course for those with some programming experience. Includes lecture videos and well-designed exercises to quickly get up to speed.

#Python #LearnPython #PythonProgramming #Coding #FreeCourses #PythonForBeginners #Developer #Programming


By: t.iss.one/DataScienceQ 🚀

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