semvec/pypi-packages huggingface dataset:
workpathapi 1.0.0
Package to interact with workpath API
Requires Python: >=2.7

# workpathapi

This package allows you to interact with the [Workpath](https://www.workpath.com/) [API](https://developer.workpath.com/).

## Prerequisites

There is only one mandatory Python package that needs to be installed to use `workpath` i. e. `requests`. One can install it with the following command.

```
pip install requests
```

If you want the API to return a pandas DataFrame you additionally need to install pandas.

```
pip install pandas
```

## Installation

If all prerequisites are met the installation is as simple as executing the command
```
pip install workpath
```

## Usage

To get data from the Workpath API you only need three steps.

### 1. Import module

Import the module, for example like this.

```
from workpathapi import WorkpathAPI
```

### 2. Instanciate WorkpathAPI

Create an instance of the WorkpathAPI.

```
api = WorkpathAPI(company='dataatelier', api_token='YOUR_WORKPATH_TOKEN')
```

You need to pass two arguments. Your company and the API token. 
Note: If your API token is set as an environment variable as `WORKPATH_API_TOKEN = "YOUR_WORKPATH_TOKEN"` then you don't have to pass the argument `api_token`.

### 3. Get goals from the API

If you want to get all goals, simply execute

```
goals = api.get_goals()
```

The output corresponds to the output of API endpoint `goals`, described [here](https://developer.workpath.com/#get-goals)
If you want the result as a pandas DataFrame you can pass the argument `output_format='DataFrame'`


If you want information about a specific goal, one can execute

```
goal = api.get_goal(goal_id=1001)
```

See API [documentation](https://developer.workpath.com/#get-goal-details) for further information.
erexplain 0.1.2
library to explain models for Entity Resolution
Requires Python: >=3.6

# ExplainER

[![Build Status](https://travis-ci.org/joemccann/dillinger.svg?branch=master)](https://travis-ci.org/joemccann/dillinger)

ERExplain is a package to explain any Entity Resolution model, using a Black-Box approach


# Key Features
The explanations can be provided into two different ways:
  - **Attribute-Level**: the system shows to the user a histogram reporting the importance of the attributes with respect to the prediction. The importance is a number in [0,1]
  - **Value-level**: the system shows to the user, for each chosen attribute set/single attribute A, the most influential values for A, with respect to the prediction
### Tech

Dillinger uses some well-know libraries:

* [Pandas](https://pandas.pydata.org/): To create perturbation
* [Numpy](https://numpy.org/)
* [MLXtend](http://rasbt.github.io/mlxtend/): To create explanations at value level

### Installation

To install the package simply run

```sh
$ pip install erexplain
```
cloudendure 0.3.5
Python wrapper and CLI for CloudEndure
Requires Python: >=3.7,<4.0

# cloudendure-python

Python wrapper and CLI for [CloudEndure](https://www.cloudendure.com/)

[![PyPI](https://img.shields.io/pypi/v/cloudendure) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/cloudendure)](https://pypi.org/project/cloudendure/) [![PyPi Publish](https://github.com/2ndWatch/cloudendure-python/workflows/PyPi%20Publish/badge.svg)](https://2ndwatch.github.io/cloudendure-python/) [![Documenation](https://github.com/2ndWatch/cloudendure-python/workflows/Github%20Pages/badge.svg)](https://2ndwatch.github.io/cloudendure-python/)

## Requirements

[Python 3.7+](https://www.python.org/downloads/)

## Installation & Usage

### Basic Installation / pip

```sh
pip install cloudendure
cloudendure version
```

### Local Development with Poetry

```sh
brew install poetry # if not installed
poetry install
```

### Local Development with Docker

```sh
docker run --rm -it cloudendurepy/cloudendure bash
```

### Usage

Then import the package:

```python
import cloudendure
```

## Getting Started
CloudEndure Pipeline Flow
![CloudEndure Pipeline Flow](images/ce_migration_pipeline.png)

CloudEndure Data Flow
![CloudEndure Data Flow](images/ce_dataflow.png)

Pipeline Flow (as seen in AWS Console)
![Pipeline_Flow](images/stepfunctions_graph.svg)

### Logging in via CLI using environment variables

Please note: `cloudendure` and `ce` can be used interchangeably

```sh
export CLOUDENDURE_USERNAME=<your_ce_user>
export CLOUDENDURE_PASSWORD=<your_ce_password>
export CLOUDENDURE_DESTINATION_ACCOUNT=<destination_aws_account_id>

cloudendure api login
```

or

```sh
export CLOUDENDURE_USER_API_TOKEN=<your_ce_user_api_token>
export CLOUDENDURE_DESTINATION_ACCOUNT=<destination_aws_account_id>

ce api login
```

### Logging in via CLI inline

Please note: `cloudendure` and `ce` can be used interchangeably

```sh
cloudendure api login --user=<your_ce_user> --password=<your_ce_password>
```

or

```sh
ce api login --token=<your_ce_user_api_token>
```

Logging in for the first time will generate the `~/.cloudendure.yml` file.

## Coming Soon

This project is currently a work in progress and will actively change. This client has not yet been finalized and is entirely subject to change.

## Changelog

Check out the [CHANGELOG](CHANGELOG.md)