semvec/pypi-packages huggingface dataset:
pycensuskr 0.2.7
A Python package for Korean census data processing
License: MIT
Requires Python: >=3.8

# pycensuskr

A Python package template for Korean census data processing.

## Installation

### From source

```bash
git clone https://github.com/sigmafelix/pycensuskr.git
cd pycensuskr
pip install -e .

# python setup.py install
```

### For development

```bash
git clone https://github.com/sigmafelix/pycensuskr.git
cd pycensuskr
pip install -e ".[dev]"
```

## Usage

```python
from pycensuskr import __version__
from pycensuskr import CensusKR
from matplotlib import pyplot as plt
import geopandas as gpd

# Print version
print(__version__)

# Create a CensusData instance
census = CensusKR()

# load specific year data
data_2020 = census.load_data(2020)
print(data_2020)

# load district boundaries for a specific year
districts_2020 = census.load_districts(2020)
districts_2020["adm2_re"] = districts_2020["adm2_code"].astype(str).str.slice(0,4)
# aggregate geometries by adm2_re
districts_2020 = districts_2020.dissolve(by="adm2_re", as_index=False)
districts_2020["adm2_code"] = districts_2020["adm2_re"] + "0"
districts_2020["adm2_code"] = districts_2020["adm2_code"].astype(int)

# cleaned data with variable types
df_tax_2020 = census.anycensus(year = 2020, type = "tax", aggregator = "sum")

districts_tax_2020 = districts_2020.merge(df_tax_2020, on="adm2_code")
print(districts_tax_2020)

districts_tax_2020.plot("income_labor_mil")
plt.show()
```

## Notes on data updates
Our data cleaning and processing pipeline is based on the original R package `tidycensuskr`. We periodically synchronize the bundled datasets used in this package with those in R `tidycensuskr` and `tidycensussfkr` to ensure accuracy and relevance. Please refer to the [tidycensuskr webpage](https://github.com/sigmafelix/tidycensuskr) for details on the latest data updates and changes.


## Development

### Running tests

```bash
pytest
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.
shieldcommit 1.0.0
Lightweight pre-commit secret scanner CLI with EKS/RDS version warnings
License: MIT
Requires Python: >=3.8

🛡️ ShieldCommit v1.0.0

Lightweight pre-commit secret scanner CLI with comprehensive infrastructure version warnings across AWS, Azure, and Google Cloud.

## Features

✅ **Secret Detection** (v0.1)
- 40+ regex patterns for cloud/API keys, tokens, private keys
- Real-time scanning of staged files
- Support for multiple secret types (AWS, GCP, Azure, GitHub, etc.)

✨ **Infrastructure Version Warnings** (v0.2-v0.3)

### Kubernetes/Container Orchestration
- **AWS EKS** - Kubernetes version detection (deprecated/extended support)
- **Azure AKS** - Kubernetes version detection with EOL tracking
- **Google Cloud GKE** - Kubernetes versions + release channel recommendations (RAPID/REGULAR/STABLE)

### Database Engines
- **AWS RDS** - PostgreSQL, MySQL, MariaDB version detection
- **Azure Database** - SQL Server, MySQL, PostgreSQL version detection
- **Google Cloud SQL** - MySQL, PostgreSQL, SQL Server version detection

### Additional Features
- Non-blocking warnings (safe for beta)
- Zero cloud API calls required
- End-of-life (EOL) date tracking
- Extended support period detection
- Actionable upgrade guidance
- Consistent warning format across all platforms
- Works with Terraform, CloudFormation, and IaC files

SHIELDCOMMIT 1.0.0 TRANSFORMATION COMPLETE
                    Pattern-Based → Intelligent Detection System
════════════════════════════════════════════════════════════════════════════════

✅ INTELLIGENT DETECTION SYSTEM FEATURES:

1. ENTROPY ANALYSIS
   - Shannon entropy calculation for high-entropy secret detection
   - Dynamically adjusts confidence based on entropy levels (0-5.7 bits)
   - Minimum 12+ character length with character variety requirement
   - Detects unknown secret formats without predefined patterns

2. SEMANTIC ANALYSIS
   - Recognizes 60+ secret-related keywords in variable names
   - Keywords: password, secret, token, api_key, credential, etc.
   - Analyzes variable naming conventions (snake_case, camelCase, etc.)
   - Works with ANY naming convention, not predefined ones

3. FORMAT DETECTION
   - 15+ known secret format patterns (AWS, Stripe, GitHub, etc.)
   - AWS Access Keys (AKIA, ASIA prefixes)
   - API keys: Google, OpenAI, Stripe, SendGrid, Mailgun
   - Tokens: GitHub, GitLab, Slack, Telegram, Discord
   - OAuth, JWT, Bearer tokens, Private keys (RSA, DSA, EC, OpenSSH)
   - Database URIs: MongoDB, PostgreSQL, MySQL

4. CONTEXT ANALYSIS
   - 20+ context keywords for secret detection
   - Analyzes surrounding code for secret indicators
   - Understands assignment patterns and declarations
   - Recognizes authentication, authorization patterns




## Installation

```bash
pip install shieldcommit==1.0.0