semvec/pypi-packages huggingface dataset:
stocksymbol 0.0.5
Provides a list of stock symbols from all major stock exchanges across various regions and markets.
Requires Python: >=3.7

# Introduction

[Stocksymbol](https://pypi.org/project/stocksymbol/) connects to the [StockSymbol API](https://stock-symbol.herokuapp.com/) that I created to provide a list of stock symbols from all major stock exchanges across various regions and markets.

# Quick Start

## Install package

```python
pip install stocksymbol
```

## Get an API key

You can register for a free API key [here](https://stock-symbol.herokuapp.com/).

## Symbol list

```python
from stocksymbol import StockSymbol

api_key = '<your API key here>'
ss = StockSymbol(api_key)

# get symbol list based on market
symbol_list_us = ss.get_symbol_list(market="US") # "us" or "america" will also work

# get symbol list based on index
symbol_list_spx = ss.get_symbol_list(index="SPX")

# show a list of available market
market_list = ss.market_list

# show a list of available index
index_list = ss.index_list
```

# Further reads
Check out this [blog post](https://yonghongtan.medium.com/download-list-of-all-stock-symbols-using-this-python-package-12937073b25) (free link [here](https://yonghongtan.medium.com/download-list-of-all-stock-symbols-using-this-python-package-12937073b25?source=friends_link&sk=0d0305ab61ea7a71b47e7c8cec01c74e)) for a detailed tutorial with output examples and the motivation behind stocksymbol.
hi-lasso 1.0.6
High-Demensional LASSO
Requires Python: >=3.6

# Hi-LASSO
Hi-LASSO(High-Dimensional LASSO) can theoretically improves a LASSO model providing better performance of both prediction and feature selection on extremely 
high-dimensional data.  Hi-LASSO alleviates bias introduced from bootstrapping, refines importance scores, improves the performance taking advantage of 
global oracle property, provides a statistical strategy to determine the number of bootstrapping, and allows tests of significance for feature selection with 
appropriate distribution.  In Hi-LASSO will be applied to Use the pool of the python library to process parallel multiprocessing to reduce the time required for 
the model.

## Installation
**Hi-LASSO** support Python 3.6+, Additionally, you will need ``numpy``, ``scipy``, ``tqdm`` and ``glmnet``. 
However, these packages should be installed automatically when installing this codebase. 

``Hi-LASSO`` is available through PyPI and can easily be installed with a
pip install::

```
pip install hi_lasso
```

## Documentation
Read the documentation on [readthedocs](https://hi_lasso.readthedocs.io/en/latest/)

## Quick Start
```python
#Data load
import pandas as pd
X = pd.read_csv('https://raw.githubusercontent.com/datax-lab/Hi-LASSO/master/simulation_data/X.csv')
y = pd.read_csv('https://raw.githubusercontent.com/datax-lab/Hi-LASSO/master/simulation_data/y.csv')

#General Usage
from hi_lasso.hi_lasso import HiLasso

# Create a HiLasso model
hilasso = HiLasso(q1='auto', q2='auto', L=30, alpha=0.05, logistic=False, random_state=None, parallel=False, n_jobs=None)

# Fit the model
hilasso.fit(X, y, sample_weight=None)

# Show the coefficients
hilasso.coef_
```
track-changes 0.8
Project Short Description
Requires Python: >=3.6
Keywords: track_changes

# Generate History/Changelog

It should work on MacOS, Linux and Windows with git >= 2.0 installed.

## Usage
```
pip install track_changes
cd <project_root>
track_changes > HISTORY.md
```

# 🎁 Release notes (0.7)

## Changes
```
fix: fixed tempalted not being included
ref: fixed pypi readme markdown
ref: updated history md
fix: format
fix: github somehow ignored the newline
ref: updated changes log
fix: fixed errors on macos
ref: renamed
ref: updated doc; changelog
feat: usable
fix: fixed entrypoint; fixed version style
ref: rm redundant code
```

## Metadata
```
This version -------- 0.7
Previous version ---- 0.1
Total commits ------- 12
```