Python-advanced-methods and working with data structures and file handling

Includes advanced in-built functions for various datatypes in Python and more.

Advanced Python Methods — advpy.ipynb

This Jupyter notebook (advpy.ipynb) is a hands-on collection of examples demonstrating intermediate-to-advanced Python features and standard library modules. It is intended as a learning / reference file covering string operations, collections, file and OS interaction, datetime, regular expressions, and other commonly used utilities.

Repository notebook permalink: https://github.com/devcodes2108/Python-advanced-methods/blob/cd4e6116d003726f3132c50aaa79c41d91648855/advpy.ipynb

Contents (high level)

• String properties and methods (immutability, slicing, case conversions, formatting)
• Number formatting and conversions (hex, binary, rounding, absolute)
• Collections: lists, sets, dictionaries — advanced operations and methods
• Advanced modules:
  • collections (Counter, defaultdict, namedtuple)
  • os and shutil (file operations, walking directories)
  • datetime (time, date, timedelta arithmetic)
  • re (regular expressions examples and searching)
• Examples that show typical usage patterns and small demonstrations (printing outputs)
• A few interactive cells that accept user input (via input())

Notable examples / cells

• Demonstration of string immutability (attempt to assign to string index raises TypeError).
• Various string methods: .upper(), .lower(), .title(), .split(), .count(), .find(), .center()
• String formatting using .format() and f-strings; numeric formatting with precision specifiers.
• Converting integers to hex and binary using hex() / bin() (cells use input()).
• Rounding and absolute value demonstrations using round() and abs() (cells use input()).
• Set operations: add, discard, union, intersection, issubset, issuperset, isdisjoint, difference, symmetric_difference.
• Dictionary comprehensions and methods: .items(), .keys(), .values().
• List methods: append, extend, insert, pop, remove, sort, reverse, sorted().
• collections module:
  • Counter for counting items and words (Counter.most_common()).
  • defaultdict usage for default values.
  • namedtuple usage.
• File and OS operations:
  • Basic file read/write using open().
  • os.getcwd(), os.listdir(), os.walk() examples.
  • Notes about safely deleting files (send2trash suggestion in the notebook).
• datetime:
  • datetime.time, datetime.date, datetime.datetime, timedelta arithmetic and .total_seconds().
• Regular expressions:
  • re.search, re.findall, re.finditer, and examples for phone-number pattern matching.

Requirements

• Python 3.x
• Jupyter Notebook (or JupyterLab)
• The notebook uses only Python standard library modules (collections, os, shutil, datetime, re). No external dependencies are required for the demonstrated examples.
• If you want to try the "move" or secure-delete suggestions, you may optionally install send2trash (not required): pip install send2trash

How to run

1. Clone the repository: git clone https://github.com/devcodes2108/Python-advanced-methods.git
2. Change to the repository directory: cd Python-advanced-methods
3. Start Jupyter: jupyter notebook
4. Open advpy.ipynb in your browser and run the cells interactively.

Note: Some cells use input() and will prompt for user input. Running the entire notebook non-interactively may hang on those cells; either provide the expected input or skip those cells.

Known issues / notes

• One cell intentionally demonstrates that strings are immutable by attempting item assignment which raises: TypeError: 'str' object does not support item assignment This is expected behavior and is shown for teaching purposes.
• The notebook was authored and executed on a specific machine (paths like C:\Users\devan\Documents appear in outputs). Outputs and file listings will differ per environment.
• A few cells are examples and may write files into the current working directory (e.g., practice.txt). Remove or move these files if not needed.

Suggestions for improvement

• Convert interactive input() cells to parameterized examples or Notebook widgets for smoother automated runs.
• Add unit-test style example scripts for core demonstrations to make them runnable in CI.
• Split large topics into separate notebooks (strings.ipynb, collections.ipynb, datetime_re.ipynb) for modular learning.

License & Author

• Author: devcodes2108
• Check the repository for the project license (if present). If none exists, assume all code is provided for educational purposes and confirm intended license before reusing.

If you'd like, I can:

• Create a cleaned, non-interactive version of this notebook (replace input() with example values).
• Split topics into separate notebooks or add a requirements.txt and execution script.