ncert-learn 4.9.0

A Python Module for NCERT Class 12 Computer Science - Learn Python and MySQL with ease!

ncert_learn

ncert_learn is a comprehensive Python module designed to support NCERT Class 12 Computer Science students. It offers a wide range of utility functions across topics like Python programming, MySQL database interactions, mathematical operations, data structures, system utilities, and much more.

Key Features

Mathematical Functions

• Prime number check, Fibonacci series, even/odd checks
• Advanced mathematical computations: GCD, LCM, prime factorization, modular exponentiation
• Newly added functions in version 4.9.0:
  • trigonometric_sine, trigonometric_cosine, trigonometric_tangent
  • quadratic_roots, power, logarithm, factorial
  • derivative, definite_integral, series_sum
  • Geometric computations like area/volume calculations

Data Structures

• Stack operations: push, pop, peek, and display
• Sorting algorithms: Bubble Sort, Insertion Sort

MySQL Operations

• Database and table management
• Optimized features like mysql_execute_advanced_mode

File Handling

• Text, binary, and CSV file management
• ZIP file operations: compress, extract, list contents

System Utilities

• Fetch system and environment details
• Manage services like XAMPP MySQL/Apache

Advanced Numerical Functions

• numerical_add, numerical_subtract, numerical_mean, numerical_std
• Matrix operations: inversion, determinant, singular value decomposition

Cryptographic Functions

• Encode/decode methods: Base64, Hex, Caesar cipher, and more

Geometric Calculations

• Area and volume calculations for 2D and 3D shapes:
  • Circles, triangles, spheres, cylinders, cones

API Functions

• Perform CRUD operations:
  • api_create_item, api_read_item, api_update_item, api_delete_item
• User management and file handling:
  • api_create_user, api_upload_file

Machine Learning Functions

• Dataset preprocessing:
  • handle_missing_values, normalize_data, standardize_data
• Models:
  • Linear regression, logistic regression, decision trees, random forests
• Evaluation metrics:
  • mean_squared_error, accuracy_score
• Visualization:
  • Feature importance plots, decision boundaries

Code Quality Tools

• Format and lint Python code:
  • format_code, lint_code, check_code_quality

Search Algorithms

• Binary, linear, jump, and interpolation search methods

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Changelog

All notable changes to this project are documented in the Changelog.

Recommendation: Upgrade to Version 4.9.0

We recommend downloading version 4.9.0, as it includes important bug fixes and new features that enhance performance, usability, and stability. Upgrade today for an improved experience.

Disclaimer

This module is intended for educational purposes only. Using this module for any illegal activities is strictly prohibited. The authors and contributors are not responsible for any misuse of the module.

Installation

To install ncert_learn, use pip:

pip install ncert_learn