semvec/pypi-packages huggingface dataset:
xminds 0.7.0
Crossing Minds data science python library and API client
Requires Python: >=3.6

See our [readthedocs](https://xminds.readthedocs.io/en/latest/) for the Python Data Science library documentation.

See the [API Documentation](https://docs.api.crossingminds.com/) for the Crossing Minds universal recommendation API documentation.

Our tools are licensed under the MIT License.
streamlit_pandera 0.0.2
Streamlit Data Validator Tool helps you validate your data with dataframe-type objects
Requires Python: <4.0,>=3.10

# Welcome to Data Validator!

[Go to App](https://pandera.streamlit.app)

[![Release](https://github.com/resilientinfrastructure/streamlit-pandera/actions/workflows/publish_pypi.yml/badge.svg)](https://github.com/resilientinfrastructure/streamlit-pandera/actions/workflows/publish_pypi.yml)

Data Validator is your go-to tool for clean data ingestion and validation, seamlessly integrating Streamlit and Panderas to make your data processing tasks a breeze.

## Features

- **Easy Data Ingestion:** Select your file format (CSV or JSON) and provide a URL to a Panderas schema.
- **Streamlined Validation:** With just a few clicks, submit your selections and validate your data effortlessly.


## streamlit-pandera

## Installation instructions

```sh
pip install streamlit-pandera
```

## Usage instructions

```python
import streamlit as st

from  import run_validate_file


def validate():
    standards = {
        "store_schema": ("https://raw.githubusercontent.com/resilientinfrastructure/standards/main/panderas_schema.yml")
    }
    st.set_page_config(
        page_title="Data Validator Home",
        page_icon="📊",
    )
    validated_df = run_validate_file(standards=standards)
    # TODO: DO SOMETHING ELSE WITH VALIDATED_DF
```


## Instructions

### Step 1: Select File Format
Choose the file format you want to ingest. Whether it's CSV or JSON, streamlit-pandera has got you covered.

### Step 2: Provide Panderas Schema URL
Enter the URL to the Panderas schema you want to use for validation. Don't worry, we support various schema sources to suit your needs.

### Step 3: Submit and Validate
Hit that submit button and let streamlit-pandera work its magic! Your data will be validated against the specified schema in no time.



[Go to Demo](https://youtu.be/9Ry_A9LgrbQ)

[![Video](http://img.youtube.com/vi/9Ry_A9LgrbQ/0.jpg)](https://youtu.be/9Ry_A9LgrbQ)


## Run Tests
To ensure everything is functioning smoothly, run the tests using the following commands:

```bash
poetry install
poetry run playwright install
poetry run pytest e2e
```

## Run the App
Ready to see streamlit-pandera in action? Simply run the following command:

```bash
poetry run streamlit run streamlit_pandera/Home.py
```

## Contributions
We welcome contributions from the community! If you have any ideas, bug fixes, or enhancements, feel free to submit a pull request.

## Feedback
We'd love to hear your feedback! Whether you have suggestions for improvement or just want to share your experience using streamlit-pandera, don't hesitate to reach out.

Happy data ingesting with streamlit-pandera!

Feel free to customize it further to better suit your project's tone and style!
CherwellAPIClient 1.0.0
Zenoss API client module
Requires Python: >=3.6
Keywords: zenoss, monitoring, api

# Cherwell API Client

![Tests badge](https://github.com/dan-smalley/CherwellAPIClient/actions/workflows/test_and_lint.yml/badge.svg) 
[![Documentation Status](https://readthedocs.org/projects/cherwellapiclient/badge/?version=latest)](https://cherwellapiclient.readthedocs.io/en/latest/?badge=latest) 
![PyPI - Version](https://img.shields.io/pypi/v/CherwellAPIClient?logo=pypi&label=PyPI)
![GitHub Release](https://img.shields.io/github/v/release/dan-smalley/CherwellAPIClient?include_prereleases&logo=github&label=Release)
![TestPyPI - Version](https://img.shields.io/pypi/v/CherwellAPIClient?pypiBaseUrl=https%3A%2F%2Ftest.pypi.org&logo=pypi&label=TestPyPI) 

> NOTE: This module is inspired by the excellent [ZenossAPIClient](https://github.com/Zuora-TechOps/ZenossAPIClient) by Zuora-TechOps and the [fork](https://github.com/boblickj/ZenossAPIClient) contributed by boblickj.
> 
> My own fork of that project is available via [PyPI as zenAPIClient](https://pypi.org/project/zenAPIClient/) 
> 
>-dan-smalley

Python module for interacting with the Cherwell API in an object-oriented way.
Tested with API version 10.1.0, no guarantees for earlier versions...

The philosophy here is to use objects to work with everything in the Cherwell API, and to try to normalize the various calls to the different routers.
Thus `get` methods will always return an object, `list` methods will return data.
All methods to add or create start with `add`, all remove or delete start with `delete`.
As much as possible the methods try to hide the idiosyncrasies of the JSON API, and to do the work for you, for example by letting you use a device name instead of having to provide the full device UID for every call.

## Installing

```
pip install CherwellAPIClient
```

## Using

```
In [1]: from cherwellapi import apiclient as capi

In [2]: cherwell_client = capi.Client(host=CHERWELL_URL, client_id=CLIENT_ID, username=USERNAME, passwrod=PASSWORD)

```
fastsupabased-acl 0.1.4
FastAPI Dependency for Supabase-based ACL using Supabase Roles
Requires Python: <4.0,>=3.11

# FastSupabased ACL

This is a simple ACL (Access Control List) implementation for Supabase. It is based on the [FastAPI](https://fastapi.tiangolo.com/) framework.

## Installation

```bash
pip install fastsupabased-acl
```

## Usage

Set the environment variables `SUPABASE_URL` and `SUPABASE_KEY` to your Supabase URL and key.

```bash
export SUPABASE_URL=https://your-supabase-url.com
export SUPABASE_KEY=your-supabase-key
```

Then, on your source code:

```python
from fastapi import FastAPI, Depends
from fastsupabased_acl import FastSupabasedACL

app = FastAPI()

authenticated_acl = FastSupabasedACL(role=["authenticated"])

@app.get("/test", dependencies=[Depends(authenticated_acl)]
def simple_authenticated_route():
    return {"message": "Hello, authenticated user!"}
```