Metadata-Version: 2.4
Name: dataset_holder
Version: 0.6.0
Summary: A central repository to hold, manage, monitor, scan, and ingest Python datasets.
Author-email: Your Name <your.email@example.com>
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: openpyxl
Requires-Dist: pyarrow

# Dataset Holder (dataset_holder)

A lightweight, central dataset repository, live SQL analytics workspace, and cryptographic vault for Python.

`dataset_holder` acts as an invisible historical memory engine for your variables, objects, and DataFrames. It automatically handles background serialization into a local SQLite database, flags files lagging your computer performance load, and lets you query your stored variables using raw SQL command matrices or export them directly to dark-mode HTML web dashboards.

## Key Features

- **Automatic SQLite History Persistence:** Datasets are instantly stored safely into a long-term local database file.
- **Invisible Password Safe (Cryptography Vault):** Securely scrambles sensitive API tokens or passwords using salt-shifted encryption routines to protect them from leaks.
- **Live SQL Sandbox Engine:** Run raw SQL commands (`SELECT`, `WHERE`, `ORDER BY`) straight against your stored dictionary records or Pandas DataFrames directly in the terminal interface.
- **Beautiful HTML Dashboard Exporter:** Convert your stored matrices into clean, beautifully styled standalone web reports with a single command string.
- **Computer Performance Diagnostics & Compression:** Automatically detect heavy "troublemaker" datasets and throttle them behind password-protected GZIP compression blocks.
- **Automated Directory Scanner & Batch Loader:** Programmatically sweep and ingest an entire directory of discovered datasets into memory at once.

## Installation

```bash
pip install dataset_holder
```

## Quick Start Guide

### 1. Store and Query Secure Keys via the Cryptography Vault
Protect your sensitive strings, GitHub keys, or private passwords right in your local storage database layout:
```python
from dataset_holder import DatasetHolder
holder = DatasetHolder()

# Encrypt and lock a private API token safely into the vault
holder.save_secure_credential("Github_Key", "secret_token_abc_123!")

# Retrieve and decrypt it back into plain text string format instantly
decrypted_token = holder.get_secure_credential("Github_Key")
print(decrypted_token)
```

### 2. Query Your Stored Datasets with Raw SQL Syntax
Transform your variables into fully searchable in-memory tables straight from the command line:
```python
from dataset_holder import DatasetHolder
holder = DatasetHolder()

gradebook = [
    {"name": "Alex", "class": "Software Eng", "grade": "95"},
    {"name": "Blake", "class": "Web Design", "grade": "78"},
    {"name": "Charlie", "class": "Software Eng", "grade": "92"}
]
holder.add("student_grades", gradebook)

# Run standard database queries across your active data records!
query_text = "SELECT name, grade FROM student_grades WHERE class = 'Software Eng'"
holder.query(query_text, "student_grades")
```

### 3. Export Datasets to Standalone HTML Web Reports
Generate a dark-mode website overview dashboard for your files instantly:
```python
from dataset_holder import DatasetHolder
holder = DatasetHolder()

# Compiles a stunning standalone web view grid report dashboard file
holder.export_to_html("student_grades")
```

### 4. Run Performance Diagnostics and Password-Locked Compression
```python
from dataset_holder import DatasetHolder
holder = DatasetHolder()

# Scan for files crossing a size threshold and isolate them behind a security password check
holder.optimize_system_load(size_threshold_kb=10.0)
```

## License
Distributed under the MIT License. Open-source contribution assets are free for personal and commercial applications.
