semvec/pypi-packages huggingface dataset:
clouddl 0.5.0
Python library that allows you to parse Google Drive & Dropbox links and download files
Requires Python: >=3.7

# clouddl

Downloader for Google Drive & DropBox

- Python 3 
- Works on all operating systems
- No API keys / credentials needed
- Auto extracts .zip files
- Auto extracts .rar files (requires 7zip)
- Auto deletes compressed files after extraction

## Installation 
```console
pip install clouddl
```

## Usage
```python3
from clouddl import grab

# grab(url, folder_path)
grab('https://drive.google.com/file/d/.../view?usp=sharing', './Downloads/')
```

## Verbose
```python3
from clouddl import grab
#grab(url, folder_path, quiet=False)
grab('https://drive.google.com/file/d/.../view?usp=sharing', './Downloads/', quiet=False)
```

## Bulk Usage
```python3
from clouddl import grab

download_list = ['URL1', 'URL2', 'URL3']

for url in download_list:
 grab(url, './')
```

## Variable Access
```python3
from clouddl import DROPBOX_URL, GDRIVE_URL
```
## Supported URLs
Google Drive
```txt
https://drive.google.com/drive/folders/...?usp=sharing
https://drive.google.com/file/d/.../view?usp=sharing
```
Dropbox
```txt
https://www.dropbox.com/s/.../...?dl=0
https://www.dropbox.com/s/.../...?dl=1
https://www.dropbox.com/sh/.../...?dl=0
https://www.dropbox.com/sh/.../...?dl=1
```
 
## Acknowledgements 
 
Thank you to the authors of the following repos:
- "gdrivedl" by matthuisman - https://github.com/matthuisman/gdrivedl
- "lootdl" by jesusyanez - https://github.com/jesusyanez/lootdl
ordinalgbt 0.1.2
A library to build Gradient boosted trees for ordinal labels

[![Python application](https://github.com/adamingas/ordinalgbt/actions/workflows/python-app.yml/badge.svg)](https://github.com/adamingas/ordinalgbt/actions/workflows/python-app.yml)
# OrdinalGBT
## Introduction
OrdinalGBT, which stands for Ordinal gradient boosted trees, is a Python package that implements an ordinal regression loss function using the lightGBM framework. Ordinal regression is a type of regression analysis used for predicting an ordinal variable, i.e. a variable that can be sorted in some order. LightGBM is a gradient boosting framework that uses tree-based learning algorithms and is designed to be distributed and efficient.

## Installation
You can install OrdinalGBT using pip:

```shell
pip install ordinalgbt
```

## Usage 

Here are a few examples on how to use the `LGBMOrdinal` class:

1. **Fitting the model**

```python
from ordinalgbt.lgb import LGBMOrdinal
import numpy as np

# Create the model
model = LGBMOrdinal()

# Generate some data
X = np.random.rand(100, 10)
y = np.random.randint(0, 3, 100)

# Fit the model
model.fit(X, y)
```

2. **Predicting with the model**

After fitting the model, you can use it to make predictions:

```python
# Generate some new data
X_new = np.random.rand(10, 10)

# Use the model to make predictions
# the .predict method returns the class prediction rather than raw score or
# probabilities
y_pred = model.predict(X_new)

print(y_pred)
```

3. **Predicting probabilities with the model**

The `predict_proba` method can be used to get the probabilities of each class:

```python
# Use the model to predict probabilities
y_proba = model.predict_proba(X_new)

print(y_proba)
```
demin 0.0.2
Useful Data Science functions for Geometallurgical Analysis.
Requires Python: >=3.10.12

# Demin
Demin presents helpful functions for data analysis in the field of Mining Engineering. It leverages the resources of widely recognized data science libraries, such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn.

## Instalation (Google Colaboratory):
To integrate Demin into your Google Colaboratory environment, use the following pip command:
```
!pip install demin
```

To ensure you have the latest version:
```
!pip install --upgrade demin
```

## How to import (Google Colaboratory):
Once installed, importing Demin is straightforward. Use the following Python code in your Google Colaboratory notebook:
```
import demin as dm
```

## History
Demin emerged from an idea during my internship in Data Science at the Department of Mining Engineering (DEMIN) at the Federal University of Minas Gerais (UFMG). As data science is still an underexplored resource in this field, Demin proposes to bring functions that are easy to understand and can assist in common tasks related to geometallurgical data analysis. In this early stage of development, many functions are yet to be added and tested. Consequently, comprehensive documentation will not be available until version 1.0.0. 

## About the Developer
For feedback, questions, or collaboration, feel free to reach out:

- **LinkedIn:** [Gustavo Ortiz](https://www.linkedin.com/in/gstvortiz/)
googledrivedl 0.1.0
Python library that allows you to download Google Drive files and folders.
Requires Python: >=3.7

# googledrivedl

Google Drive Download Script

-   Python 2 / 3 compatible
-   No API keys / credentials required
-   Supports all operating systems
-   No external dependencies
-   Works with shared files or folders
-   Works with large files
-   Files / folders must have been shared via link

## Installation
``` pip install googledrivedl ```
## Usage

```bash
python gdrivedl.py <URL>
```
-   URL is a shared Google Drive URL

Multiple urls can be used by seperating them with a space. eg. ```python gdrivedl.py <URL1> <URL2> <URL3>```<br>
On some systems you may need to enclose the url within quotes. eg ```python gdrivedl.py "<URL>"```

### Options
- `-P` `--directory-prefix` Output directory (default is current directory)
- `-O` `--output-document` Download to a particular filename (defaults to the
  GDrive filename). Only valid when downloading a single file.
- `-q` `--quiet` Disable console output
- `-d` `--debug` Show more vebose debug console output
- `-m` `--mtimes` Try use modified times to check for changed files
- `-f` `--urlfile` Text file containing Google Drive URLS to download (one per line)
pineapple-spark 1.4.2
Pineapple is an extension of Apache Sedona for processing large-scale complex spatial queries
Requires Python: >=3.6

# Pyneapple

This library is the Python wrapper for Pyneapple project.

Pyneapple is an extension of Apache Sedona supporting complex spatial operators
including spatial group-by aggregate counting and a parallel doubly-connected edge list.