semvec/pypi-packages huggingface dataset:
sakatsuku04 1.0.4
Opensource game and save editor for J.League Pro Soccer Club o Tsukurou! 04 (PS2).
Requires Python: >=3.13

# Sakatsuku04 save editor

![](data/cover.jpg)

Opensource game and save editor for J.League Pro Soccer Club o Tsukurou! 04 (PS2).

## Quick Start

The recommended way to run `sakatsuku04` is by using [`uv`](https://github.com/astral-sh/uv) to create and manage a virtual environment:

```bash
uv venv --python python3.13
uv pip install sakatsuku04
uv run sakatsuku04
```

Alternatively, you can download the latest prebuilt releases from GitHub:
👉 [https://github.com/caol64/sakatsuku04/releases](https://github.com/caol64/sakatsuku04/releases)

## Features

Currently supported viewing features:

- Club information
  - Club name
  - Funds
  - Game year, month, date
  - Manager name
  - Game difficulty
- My Team information
  - Player basic information (name, age, birthplace, rank, growth types, cooperation types, tone types, play styles)
  - Player abilities
- Other team information
  - Teams (name, friendliness) and players (name, age, rank, growth types, cooperation types, tone types)
- Scout
  - My Scout abilities

Currently supported editing features:

- Club information
  - Funds
  - Game year
  - Game difficulty
- My Team information
  - Player basic information (age, birthplace, growth types, cooperation types, tone types, play styles)
  - Player abilities
- Other team information
  - Teams (friendliness)

## Screenshots

![](data/1.png)

![](data/2.png)

![](data/3.png)

![](data/4.png)

## Hacker notes

[Save File Format Analysis](docs/save_file_format_analysis.md)

## Acknowledgements

The data used in this project was obtained from [サカつく04緑本追補] and [サカつく04データ置き場]. I am grateful for their contribution to making this information publicly available.

## References

- [サカつく04緑本追補](https://sites.google.com/view/sakatsuku04/home)
- [サカつく04データ置き場](https://www.potato.ne.jp/ando/sakatuku/index.html)
KB-Bank-Transaction-Lookup 0.0.5
KB Kookmin Bank library for easy inquiry (Selenium not used)
Requires Python: >=3.6

# example

from KB_Bank_Transaction_Lookup import get_transactions

transaction_list = get_transactions(
        bank_num='74670101827583',
        birthday='900101',
        password='0000',
        # days=30 # Optional: default is 30
    )

for trs in transaction_list:
    print(trs['date'], trs['amount'], trs['transaction_by'])
pdf-password-remover 0.3.0
A Python based CLI tool to remove passwords from PDF files.
Requires Python: >=3.9

# PDF Password Remover

A Python-based CLI tool to remove passwords from PDF files. Supports both single files and batch processing of directories. Includes an optional GUI file picker for convenience.

[![PyPI version](https://badge.fury.io/py/pdf-password-remover.svg)](https://badge.fury.io/py/pdf-password-remover)
[![Builds and Tests](https://github.com/thecasualdev17/pdf-password-remover/actions/workflows/build-test.yml/badge.svg)](https://github.com/thecasualdev17/pdf-password-remover/actions/workflows/build-test.yml)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

## Features
- ✅Remove passwords from individual PDF files or all PDFs in a directory
- ✅Optional GUI file/directory picker
- ✅Output unlocked PDFs to a specified directory
- ✅Simple command-line interface

## Installation

### From PyPI

Install the latest release from PyPI:
```sh
pip install pdf-password-remover
```

### From Source

1. Clone the repository:
   ```sh
   git clone <repo-url>
   cd pdf-password-remover
   ```
2. Install dependencies using Poetry:
   ```sh
   poetry install
   ```

## Usage

Run the CLI tool using Poetry:

```sh
poetry run pdf-password-remover remove-password [INPUT_PATH] [PASSWORD] [--output OUTPUT_DIR]
```

Or, if installed via pip:

```sh
pdf-password-remover remove-password [INPUT_PATH] [PASSWORD] [--output OUTPUT_DIR]
```

- `INPUT_PATH`: Path to the password-protected PDF file or directory (optional, will prompt if omitted)
- `PASSWORD`: Password for the PDF file(s) (optional, will prompt if omitted)
- `--output`/`-o`: Directory to save the unlocked PDF file(s) (optional)

### Example

Remove password from a single file:
```sh
pdf-password-remover remove-password myfile.pdf mypassword
```

Batch process a directory and specify output directory:
```sh
pdf-password-remover remove-password ./pdfs mypassword --output ./unlocked
```

If you omit arguments, the tool will prompt you interactively and offer a GUI picker.

## Development

- Source code is in `src/pdf_password_remover/`
- Main CLI entry point: `main.py`
- PDF utilities: `pdf_utils.py`

## License
MIT
Mouni2212 0.1
A Python package for data analysis and visualization using Pandas, NumPy, Matplotlib, and Seaborn.
Requires Python: >=3.6

# Mouni2212 📊

*Mouni2212* is a lightweight and easy-to-use Python library built to accelerate Exploratory Data Analysis (EDA) and preliminary dataset inspection for data science projects. It wraps essential Pandas, NumPy, Seaborn, and Matplotlib utilities to help you quickly understand the structure, quality, and relationships within your dataset.

---

## ✨ Features

- ✅ Quick DataFrame summary: shape, datatypes, missing values, and statistics  
- ✅ Visualize correlation matrix with heatmaps  
- ✅ Detect outliers using the IQR method  
- ✅ Plot numeric feature distributions  
- ✅ Report missing values as proportions  
- ✅ Get value counts for all categorical columns  

---

## 📦 Installation


bash
pip install Mouni2212


Since this is a custom library, clone or copy the source code into your project directory or package it using setuptools if needed.

bash
# Clone this repository or include the file directly
git clone https://github.com/yourusername/Mouni2212.git




In your Python project:




from Mouni2212 import df_summary, missing_report, correlation_matrix, outlier_summary, plot_distributions, value_counts_all




🧪 How to Use


1. df_summary(df)
Provides an overall summary of the DataFrame including shape, data types, missing values, and descriptive statistics.



import pandas as pd
from Mouni2212 import df_summary

df = pd.read_csv("your_data.csv")
df_summary(df)


2. missing_report(df)
Returns a Series with the proportion of missing values in each column (sorted descending).


from Mouni2212 import missing_report

missing_report(df)


3. correlation_matrix(df)
Plots a heatmap of correlation coefficients for all numeric features in the DataFrame.


from Mouni2212 import correlation_matrix

correlation_matrix(df)



4. outlier_summary(df)
Returns a count of outliers per numeric column using the IQR method.

from Mouni2212 import outlier_summary

outlier_summary(df)


5. plot_distributions(df)
Displays histograms of all numeric columns in the DataFrame.


from Mouni2212 import plot_distributions

plot_distributions(df)


6. value_counts_all(df)
Returns a dictionary with value counts for all object (categorical) columns.


from Mouni2212 import value_counts_all

value_counts_all(df)



🧑‍💻 Developer
Pavan Aditya Kumar Gorrela
Artificial Intelligence & Data Science | ML & Web Dev Enthusiast

📜 License
This project is licensed under the MIT License. Feel free to use and modify!

🙌 Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you'd like to change.

🧠 Future Features
Advanced outlier detection (Z-score, Isolation Forest)

Interactive plots using Plotly

Automatic report generation using pandas-profiling or sweetviz


Let me know if you'd like help setting this up as a proper Python package
blankit 0.2.1
Detection and redaction of PII using GLiNER
Requires Python: >=3.10

# blankit: PII detection and removal in python

A lightweight package for detecting and removing Personally Identifiable Information (PII). 

blankit currently uses [GLiNER](https://github.com/urchade/GLiNER) as a backend to remove different types of PII

## PII types
Due to the versatility of GLiNER, blankit can essentially detect any PII type, though by default it supports the following:
- Name
- Location
- Ethnicity
- Gender
- Business Name
- Email address
- Phone number

However, you can specify your own PII types for more bespoke applications if needed.

## Getting started

### Installation
Install the package either from this repo or via your favourite package manager, such as `conda` or `uv`

`uv add blankit`

### Detecting PII

Detect and remove PII as follows:

```
from blankit.scanner import Scanner

text = "My name's gary and i live in fairfield with bruz and mugsy"

scanner = Scanner(pii_types=['Name', 'Location'])

# extract all the pii
entities = scanner.find_pii(text)

# remove pii from the text
text_redacted = scanner.redact(text)
```