semvec/pypi-packages huggingface dataset:
rickrollprinter 0.1.3
A Python package that prints Rickroll lyrics and plays the song.
Requires Python: >=3.6

# RickrollPrinter

RickrollPrinter is a Python package that prints Rickroll lyrics as ASCII art and plays the song "Never Gonna Give You Up" by Rick Astley. This project demonstrates how to create and organize a Python package with dependencies.

## Installation

You can install this package using pip:

```bash
pip install rickrollprinter==0.1.0
```

# Project Setup
To create the project structure, use the following commands:

```bash
mkdir same_Name_As_The_Project_Folder
cd same_Name_As_The_Project_Folder
mkdir same_Name_As_The_Project_Folder tests
cd same_Name_As_The_Project_Folder
echo > __init__.py
cd ..
echo > LICENSE
echo > README.md
echo > setup.py
```

# Usage
Here is an example of how to use the package:
```bash
from rickrollprinter import rickroll_time

rickroll_time()
```


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# Upload for future packages

```bash
pip install --upgrade setuptools wheel
python setup.py sdist bdist_wheel
pip install twine
twine upload dist/*
```
velib-spot-predictor 0.4.1a1
Predicting spots at Velib stations
Requires Python: !=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*,>=3.9

# Velib Spot Predictor

This project aims at developing an end-to-end project using Paris Open Data on velib stations in order to predict the available Velib spots.

## Getting started

## Usage

### Launching the API
```bash
uvicorn velib_spot_predictor.api:app --reload
```

## Deployment

### Docker
Image is published in clementcome/velib repository.

### PyPI
Package is published under velib-spot-predictor package.

## Development

### Installation

To develop locally, clone the repo and in a virtual environment with python 3.9 and poetry installed, run the command `poetry install`.
qolagg 1.5
This library code execute aggregate analysis for the Quality of Life Exporer variables
Keywords: urban institute, python, Mecklenburg County, quality of life explorer, aggregate, NPA


# Aggregate Data Analysis with ArcPy

This package provides a Python class `aggregate` tailored for analyzing and aggregating spatial data using ArcPy. The class is designed for Urban Institute's Quality of Life (QOL) variables, offering methods for merging, spatial joining, and exporting data to CSV files.

## Installation

Install `aggregate` from PyPI using pip:


pip install aggqol

# Usage

import aggqol as ag

A = ag.aggregate('Banks')

A.withinNPA('NPA','BanksNPA')

A = ag.aggregate('CreditUnion')

A.withNPAID('NPA', 'CreditNPAID')

## Methods

Methods
`__init__(self, InFeatureClass)`

Initialize the aggregate class with the input feature class.

`merge(self, *FeatureClassesToBeMerged)`

Combine feature classes from multiple sources into one feature class for analysis.

`withinNPA(self, NPA, OutputName)`

Aggregate all points feature classes that are completely contained by an NPA polygon.

`withNPAID(self, NPA, OutputName)`
Assign NPA ID to all point feature classes that are completely within an NPA.

`exportcsv(self, OutputDirectory, PopulationFile, PopulationColumn, FileName)`

Export the results to a CSV file, joining population data and calculating summary statistics.

## Additonal Information

- The aggregate class is initialized with one argument ( Point feature classes ).This class has two methods:
    - The *withinNPA* method: This method aggregates point features classes that are completely contained by each NPA 
      The withinNPA method takes two arguments: 
       - NPA feature class
       - Name of output feature class
        
    - The *withNPAID* method: This method assign NPA IDs to point feature classes that are completely within each NPA
       - NPA feature class 
       - Name of output feature class

License
This project is licensed under the MIT License - see the LICENSE file for details.