https://github.com/ibensusan/mean-variance-standard-deviation-calculator

https://github.com/ibensusan/mean-variance-standard-deviation-calculator

Last synced: 25 days ago
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• Host: GitHub
• URL: https://github.com/ibensusan/mean-variance-standard-deviation-calculator
• Owner: iBensusan
• Created: 2024-09-25T09:09:42.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-27T11:01:22.000Z (3 months ago)
• Last Synced: 2024-10-18T07:02:30.577Z (3 months ago)
• Language: Python
• Size: 3.91 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Project: Matrix Calculations with NumPy

This project demonstrates how to perform various statistical calculations on a list of numbers after converting it into a 3x3 matrix using NumPy.

## Objectives:

1. **Input Validation**:

   - Ensure that the input list contains exactly 9 numbers.

   - Raise a `ValueError` if the input does not meet the required conditions.

2. **Matrix Reshaping**:

   - Convert the input list into a 3x3 matrix using NumPy.

3. **Statistical Calculations**:

   - Calculate the following statistics for the matrix:

     - **Mean**: The average of the values across the columns, rows, and the entire matrix.

     - **Variance**: The measure of how spread out the numbers are, calculated along the columns, rows, and the whole matrix.

     - **Standard Deviation**: The square root of variance, computed for both axes and the whole matrix.

     - **Maximum and Minimum Values**: The largest and smallest values in each axis and the entire matrix.

     - **Sum**: The total sum of the values along both axes and the whole matrix.

4. **Result Structure**:

   - Return the calculated statistics in a dictionary where each key corresponds to a specific calculation (e.g., mean, variance), and each value is a list containing:

     - Column-wise result

     - Row-wise result

     - Overall matrix result