Metadata-Version: 2.4
Name: data_advisor
Version: 0.2.0
Summary: A production-grade diagnostic and code generation advisor for timed data analysis exams and rapid exploratory data science.
Author: Principal Data Science Architect
License-Expression: MIT
Keywords: data-analysis,eda,data-science,hypothesis-testing,code-generation,machine-learning,automated-eda,pandas,scipy,scikit-learn
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Code Generators
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: pandas>=2.0.0
Requires-Dist: numpy>=1.24.0
Requires-Dist: scipy>=1.10.0
Requires-Dist: scikit-learn>=1.2.0
Requires-Dist: matplotlib>=3.7.0
Requires-Dist: seaborn>=0.12.0
Provides-Extra: dev
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: pytest-cov>=4.0.0; extra == "dev"

# DataAdvisor (`data-advisor`)

Quick guide on how to upload this package to PyPI and how to use it on any other system.

---

## 🚀 Part 1: How to Upload to PyPI (From This System)

Run these commands in PowerShell inside this directory:

### 1. Build the Package
```powershell
python -m build
```
*(This bundles all 52 DAUP questions and 17 IoT lab solutions directly inside the package).*

### 2. Upload to PyPI
```powershell
python -m twine upload dist/*
```
- **Username**: `__token__`
- **Password**: `<paste your PyPI API token starting with pypi-...>`

> If you don't have a token yet: Register at [pypi.org](https://pypi.org), enable 2FA in Account Settings, and create an API Token.

---

## 💻 Part 2: What to Do on Other Systems

Once uploaded, on **any other computer, VM, or lab machine** with Python installed:

### 1. Install via `pip`
```bash
pip install data-advisor
```

---

### 2. How to Use on the Other System

#### A. View / Retrieve Complete Code Files
Directly from terminal:
```bash
# View complete code with header & line count:
data-advisor --get q1
data-advisor --get q24 --line-numbers
data-advisor --get q52

# View IoT sketches and scripts:
data-advisor --get sketch.ino
data-advisor --get weather_analysis.py

# List all available 52 questions & 17 IoT projects:
data-advisor --list
```

Or inside Python / Jupyter Notebook:
```python
from data_advisor import get_file, read_file

# Print complete code directly:
get_file("q1")
get_file(24, line_numbers=True)

# Or get the code as a string to copy / save:
code = read_file("q24")
```

---

#### B. Run Any File Directly
Execute any question or project script directly:
```bash
data-advisor --run q24
data-advisor --run 10
data-advisor --run iss
```

Or inside Python:
```python
from data_advisor import run_file
run_file("q24")
```

---

#### C. Search by Keyword
```bash
data-advisor --search "linear regression"
data-advisor --search "dht22"
data-advisor --search "kmeans"
```

Or inside Python:
```python
from data_advisor import search
search("linear regression")
```

---

#### D. Audit Any Exam Dataset & Generate Python Code
When given an exam dataset (e.g. `exam_dataset.csv`):
```bash
# Print diagnostic report + complete executable Python solution:
data-advisor exam_dataset.csv --target churn --code

# Save generated Python script directly to a file:
data-advisor exam_dataset.csv --target churn --output solution.py
```

Or inside Python:
```python
from data_advisor import data_advisor
data_advisor("exam_dataset.csv", target="churn")
```
