Statistical Data Processor

A small Python program for processing and analyzing numeric datasets. It can take input either from the command line or from CSV/JSON files. The operations are organized around a simple state machine.

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Project Structure

├── main.py # Entry point
├── work/
│ ├── ops.py # Statistic operation
│ ├── io_utils.py # Input da CLI / CSV / JSON and output
│ └── init.py
├── user_interface/
│ ├── menu.py # Menu control (CLI)
│ └── init.py
├── tests/
│ ├── test_ops.py # Operation test
│ └── test_io_utils.py # Test I/O
└── pytest.ini

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How to Run

python3 main.py

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Main Components

Datablock (work/ops.py)

Stores data and provides statistical operations. Uses a function dictionary to avoid repeated calls. Implements:

• Count, Sum, Min, Max, Mean
• Median, Mode
• Variance, Standard deviation
• Quantile, Interquartile range
• z-Scores
• Covariance and Correlation (for paired datasets)

I/O utilities (work/io_utils.py)

• Manual numeric input
• Load from CSV or JSON (with automatic header handling)
• Save results to file (JSON or CSV)

CLI Interface (user_interface/menu.py)

• Text-based interaction
• Controlled by a simple state machine in main.py

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Tests

Unit tests implemented with pytest.
Run from the project root:

pytest -q

All current tests (9 total) validate both the mathematical correctness and file-handling robustness.

Design Notes

The point of this project was mainly to practice clean module separation and state-based control. Whit This layout allows adding new features or replacing components without modifying the entire codebase.

Known Limitations

• JSON loader returns full Datablock objects; no partial loading yet.
• Numeric input validation is minimal.
• UI is CLI-only (no GUI, no API).

Author’s Note

This code was developed to practice a clean modular workflow and will serve as a base for future projects.