semvec/pypi-packages huggingface dataset:
Topsis-UpkarKaur-102497017 1.0.1
A Python package to implement TOPSIS method.
Requires Python: >=3.6

# Topsis-UpkarKaur-102497017

Python implementation of TOPSIS method.

## Installation

pip install Topsis-UpkarKaur-102497017

## Usage

topsis data.csv 1,1,1,2 +,+,-,+ result.csv

## Requirements
- Python >= 3.6
- pandas
- numpy
brownllama 0.1.40
Standard PIP Package for GCP integration apps.
Requires Python: >=3.12

# brownLlama Pip Package

## Overview

`brownllama-pip` (or `brownllama` when imported in Python) is a utility library designed to provide common functionalities and standardized components for VNP's Python projects. It aims to reduce code duplication and promote best practices across different projects.

## Modules

The package currently includes the following modules:

- **bigquery.py**:
  - Provides the `BigQueryController` class for simplified interaction with Google BigQuery.
  - Supports creating tables, checking table existence, loading data from JSON, loading data from Google Cloud Storage (GCS), and executing queries.
  - Offers methods for schema inference from JSON data and efficient data loading via GCS staging.

- **logger.py**:
  - Provides the `get_logger` function for obtaining configured logger instances.
  - Standardizes logging format and setup across projects.
  - Configures both root logger and common third-party library loggers to ensure consistent logging behavior.

- **secret_manager.py**
  - Provides the `get_secret` function for retrieving secrets from Google Secret Manager.
  - Enables to get, create, delete and lists the secrets.

- **storage.py**:
  - Provides the `StorageManager` class for managing Google Cloud Storage operations.
  - Supports uploading data (dictionaries, lists of dictionaries, Pandas DataFrames) to GCS as JSON files.
  - Includes functionality for deleting files from GCS.

## Building and Publishing

To build and publish the package, follow these steps:

1.  Install build dependencies:

    ```bash
    uv add build twine
    ```

2.  Change the version number in `pyproject.toml` file

    ```toml
    version = "0.1.XXX"
    ```

3.  Build the package:

    ```bash
    uv build
    ```

4.  Publish the package in PyPI

    ```bash
    uvx twine upload --verbose dist/*
    ```

    NOTE: PyPI is public and should not be used for sensitive information. Also, you need to setup `~/.pypirc` with your PyPI credentials.

### Versioning Error:

If there is some error on versioning, then you simply delete `dist` directory and run `uv build` again.

## Usage

After installation, you can import and use the modules and classes provided by `brownllama` in your Python projects.

**Example (using BigQueryController):**

```python
from brownllama.bigquery import BigQueryController

bq_controller = BigQueryController(bigquery_payload=bigquery_payload, key_path=key_path)

# Example: Export JSON data to BigQuery via GCS
gcs_uri = bq_controller.export_to_bq_via_gcs(json_data=data)
print(f"Data loaded to BigQuery via GCS: {gcs_uri}")
```

Refer to the individual module files (`bigquery.py`, `logger.py`, `storage.py`) for detailed class and function documentation and usage examples.
stackit-authorization 0.7.0
STACKIT Membership API
Requires Python: <4.0,>=3.9

# stackit.authorization
The Membership API is used to manage memberships, roles and permissions of STACKIT resources, like projects, folders, organizations and other resources.


This package is part of the STACKIT Python SDK. For additional information, please visit the [GitHub repository](https://github.com/stackitcloud/stackit-sdk-python) of the SDK.


## Installation & Usage
### pip install

```sh
pip install stackit-authorization
```

Then import the package:
```python
import stackit.authorization
```

## Getting Started

[Examples](https://github.com/stackitcloud/stackit-sdk-python/tree/main/examples) for the usage of the package can be found in the [GitHub repository](https://github.com/stackitcloud/stackit-sdk-python) of the SDK.
cua-agent 0.7.26
Cua (Computer Use) Agent for AI-driven computer interaction
Requires Python: <3.14,>=3.12

# Cua Agent

Computer-Use framework with liteLLM integration for running agentic workflows on macOS, Windows, and Linux sandboxes.

**[Documentation](https://cua.ai/docs/cua/reference/agent-sdk)** - Installation, guides, and configuration.