semvec/pypi-packages huggingface dataset:
latest-chromedriver 2024.4.1
A small package to find the correct chromedriver version in windows machines
License: MIT
Requires Python: >=3
Keywords: chromedriver windows

# Latest ChromeDriver

The `latest_chromedriver` is a module for python scripts in order to find the correct version of the ChromeDriver depending on Google Chrome version.

## Purpose

The purpose of this module is not to be deployed to live system like webservers or similar.

This module is intended for use in scripts that are running on different client machines, where Google Chrome could be in different versions or auto updated.

## Getting Started

These instructions will get you a copy of the project up and running on your local. Currently there are three systems supported:

* [Windows](https://en.wikipedia.org/wiki/Microsoft_Windows)
* [Linux](https://en.wikipedia.org/wiki/Linux)
* [Darwin](https://en.wikipedia.org/wiki/Darwin_(operating_system))

### Example Usage

```python
import latest_chromedriver

latest_chromedriver.safely_set_chromedriver_path()
```

If `Google Chrome` is installed in a different location:

```python
import latest_chromedriver

latest_chromedriver.safely_set_chromedriver_path(chrome_path='/adhoc/locatation/googe-chrome')
```

If `chromedriver` needs to be installed in a different location:

```python
import latest_chromedriver

latest_chromedriver.safely_set_chromedriver_path(chromedriver_folder='/tmp')
```

### Prerequisites

In order to install this module you need to have:

* Google Chrome
* Python >= 3

### Installation

Install `latest-chromedriver` with [pip](https://pip.pypa.io)

```shell
python -m pip install latest-chromedriver
```

## Built With

* [logzero](https://logzero.readthedocs.io/en/latest/) - A simple library for easy logging (could change it to standard logging module)
* [requests](https://requests.readthedocs.io/en/latest/) - Requests is an elegant and simple HTTP library for Python, built for human beings
* [ubelt](https://ubelt.readthedocs.io/en/latest/ubelt.html) - A “utility belt” of commonly needed utility and helper functions
* [py-cpuinfo](https://github.com/workhorsy/py-cpuinfo) - Gets CPU info with pure Python

## Contributing

Please read [CONTRIBUTING.md](https://github.com/hargikas/latest-chromedriver/blob/main/CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us.

## Authors

* **Charalampos Gkikas** - *Initial work* - [hargikas](https://github.com/hargikas)

## License

This project is licensed under the MIT License - see the [LICENSE](https://github.com/hargikas/latest-chromedriver/blob/main/LICENSE) file for details
fishersapi 1.0
fishersapi: An API for applying a fast Fisher's Exact Test to variable pairs in pandas DataFrames
License: MIT

## fishersapi
[![Build Status](https://travis-ci.com/agartland/fishersapi.svg?branch=master)](https://travis-ci.com/agartland/fishersapi)

A package for applying a fast implementation of Fisher's exact test to observations in a pandas DataFrame.

Contingency tables are computed based on all pairs of columns in cols and all pairs of unique values within the columns.
The results are tested against scipy.stats.fishers_exact and fallback on scipy if numba is not avilable.

## Installation
The package is compatible with Python 3 and can be installed from PyPI or cloned and installed directly.

```bash
pip install fishersapi
```

## Example
```python
import fishersapi

a = np.random.randint(1, 50, size=n)
b = np.random.randint(1, 50, size=n)
c = np.random.randint(1, 100, size=n)
d = np.random.randint(1, 100, size=n)
    
ORs, pvalues = fishersapi.fishers_vec(a, b, c, d, alternative='two-sided')

n = 50
df = pd.DataFrame({'VA':np.random.choice(['TRAV14', 'TRAV12', 'TRAV3', 'TRAV23', 'TRAV11', 'TRAV6'], n),
                   'JA':np.random.choice(['TRAJ4', 'TRAJ2', 'TRAJ3','TRAJ5', 'TRAJ21', 'TRAJ13'], n),
                   'VB':np.random.choice(['TRBV14', 'TRBV12', 'TRBV3', 'TRBV23', 'TRBV11', 'TRBV6'], n),
                   'JB':np.random.choice(['TRBJ4', 'TRBJ2', 'TRBJ3','TRBJ5', 'TRBJ21', 'TRBJ13'], n)})
df = df.assign(Count=1)
df.loc[:10, 'Count'] = 15

res = fishersapi.fishers_frame(df, ['VA', 'JA', 'VB', 'JB'], count_col=None, alternative='two-sided')
```