semvec/pypi-packages huggingface dataset:
collaborative-filtering 0.0.7
A package for collaborative filtering recommendation
Requires Python: >=3.6

# Collaborative Filtering

A package for collaborative filtering recommendation.


## Requirements

- Linux-based OS
- Python 3.6+


## Get started

### Install the package

Install from <https://pypi.org/>:

```bash
pip install collaborative-filtering
```

Or install it manually:

```bash
git clone https://github.com/yusanshi/collaborative-filtering.git
cd collaborative-filtering
pip install .
```

### Prepare the dataset

TODO

### Run

TODO

## TODO

TODO
aioflask 0.4.0
Flask running on asyncio.
Requires Python: >=3.6

# aioflask

![Build status](https://github.com/miguelgrinberg/aioflask/workflows/build/badge.svg) [![codecov](https://codecov.io/gh/miguelgrinberg/aioflask/branch/main/graph/badge.svg?token=CDMKF3L0ID)](https://codecov.io/gh/miguelgrinberg/aioflask)

Flask 2.x running on asyncio!

Is there a purpose for this, now that Flask 2.0 is out with support for async
views? Yes! Flask's own support for async handlers is very limited, as the
application still runs inside a WSGI web server, which severely limits
scalability. With aioflask you get a true ASGI application, running in a 100%
async environment.

WARNING: This is an experiment at this point. Not at all production ready!

## Quick start

To use async view functions and other handlers, use the `aioflask` package
instead of `flask`.

The `aioflask.Flask` class is a subclass of `flask.Flask` that changes a few
minor things to help the application run properly under the asyncio loop. In
particular, it overrides the following aspects of the application instance:

- The `route`, `before_request`, `before_first_request`, `after_request`, 
  `teardown_request`, `teardown_appcontext`, `errorhandler` and `cli.command`
  decorators accept coroutines as well as regular functions. The handlers all
  run inside an asyncio loop, so when using regular functions, care must be
  taken to not block.
- The WSGI callable entry point is replaced with an ASGI equivalent.
- The `run()` method uses uvicorn as web server.

There are also changes outside of the `Flask` class:

- The `flask aiorun` command starts an ASGI application using the uvicorn web
  server.
- The `render_template()` and `render_template_string()` functions are
  asynchronous and must be awaited.
- The context managers for the Flask application and request contexts are
  async.
- The test client and test CLI runner use coroutines.

## Example

```python
import asyncio
from aioflask import Flask, render_template

app = Flask(__name__)

@app.route('/')
async def index():
    await asyncio.sleep(1)
    return await render_template('index.html')
```
robotframework-excelutil 9.12
Robotframework library for excel xlsx file format

# Robotframework-excelutil library

This Robotframework Excel Util library provides Robot keywords to Open, Read, Write and Save XLSX files. 
This library uses python to read and write datas in xlsx files.

## Pre-requisities
- Python - 3.x (Not tested with Python 2.x)
- Robotframework - 4.x (Not tested with other versions)
- Openpyxl

## Installation
```pip install robotframework-excelutil```

## Settings
| Library    ExcelUtil |

## To open Excel
| Open Excel |  C:\\Data\\ExcelTest.xlsx  |

## To open Excel using relative path. I.e., relative to project folder
| Open Excel From Relative Path     |  ../../Data/data.xlsx  |

## To open Excel sitting in project folder
| Open Excel From Project Directory     |  data.xlsx  |

## To get sheet names of the workbook
- | Open Excel      |  C:\\Data\\ExcelTest.xlsx  |
- | Get sheet names      |

## To get the column count of the given sheet
- | Open Excel      |  C:\\Data\\ExcelTest.xlsx  |
- | Get Column count     |  Sheet1 |

## To get the row count of the given sheet
- | Open Excel      |  C:\\Data\\ExcelTest.xlsx  |
- | Get Row count     |  Sheet1 |

## To get the sheet count of the given sheet
- | Open Excel      |  C:\\Data\\ExcelTest.xlsx  |
- | Get Sheet count     |

## To get sheetnames of the workbook
- | Open Excel |  C:\\Data\\ExcelTest.xlsx  |
- | Get sheet names      |

## To get the value of a cell by giving the sheetname, row value & column value
- | Read Cell Data By Coordinates     |  SheetName | Row Number |  Column Number  |
- | Read Cell Data By Coordinates     |  Sheet1 |  1  |  1  |
- To pass integer arguments
- | Read Cell Data By Coordinates     |  Sheet1 |  ${1}  |  ${3}  |

## To get the values of all cells in all rows for given sheetname
- | Get All Rows Data     |  SheetName |
- | Get All Rows Data     |  Sheet1 |

## To Write the value to a call using its co-ordinates
- | Write Data By Coordinates    |  SheetName  | Row Number | Column Number |  Data  |
- | Write Data By Coordinates    | Sheet1 | 1 | 1 |  TestData  |

## To Save the excel file after writing the data.
- Update existing file:
-   | Openexcel File       |  C:\\Data\\ExcelTest.xlsx  |
-   | Save Excelfile       |  C:\\Data\\ExcelTest.xlsx  |

## Save in new file:
- | Openexcel File       |  C:\\Data\\ExcelTest.xlsx  |
- | Save Excelfile       |  D:\\Data\\ExcelRobotNewFile.xlsx  |

## To add a new sheet
| Add new sheet        | SheetName

## License

Â© 2021 Nagesh B Nagaraja Rao ( Email: nagesh.nagaraja@gmail.com )

This repository is licensed under the MIT license. See LICENSE for details.
easy-tfrecord 0.3
TF Records for image classification.
Requires Python: >=3.6

**It provides**:
- an easy way to convert images to TFRecords with it's labels.
- conversion from TFRecords to tf.data.

**How to Use**:

Here is the [link](https://www.kaggle.com/wangdorjeesherpa/tfrecords-vs-tfdata "Open Kaggle Notebook") to kaggle notebook which show's its usage.