semvec/pypi-packages huggingface dataset:
askit 0.1.5
Ask OpenAI your question right inside your terminal
Requires Python: >=3.10,<4.0

# AskAI

Ask OpenAI your question from the command line and get a response. Super janky right now.

# Installation

    pip install askit

# Add API key first time use

    ask api your-openai-key-goes-here

# Usage

    ask it "Is this a well formulated question for openAI?"

# Notes
You need to have your question in a string format, and you must end your question with a question mark or it won't work.

"Questions must look like this?"

This is super janky. Feedback welcomed.
plotter 1.3.20
Plotting library with descriptive approach

# Plotter

## Generate this README
- in html: `make html-readme` then open `README.html` in web browser, `firefox README.html` for instance.
- in pdf: `make pdf-readme`

## Description
Plotter is a plotting library based on `matplotlib`. It intends to simplify the plotting of data in Python by creating a descriptive approach for plots. The plots can be static or dynamic.

## Source code location
[https://github.com/T-TROUCHKINE/plotter](https://github.com/T-TROUCHKINE/plotter)

## Prerequisities
### Linux packages
- tk :
  - ArchLinux : `pacman -S tk`
  - Debian : `apt install tk`

### Python modules
- matplotlib : `pip install matplotlib`
- tikzplotlib : `pip install tikzplotlib`
- numpy : `pip install numpy`

**GTK3**  
For using GTK3 modules: see PyGObject documentation:
[https://pygobject.readthedocs.io/en/latest/index.html](https://pygobject.readthedocs.io/en/latest/index.html)

## Installation
### From PIP
`pip install plotter`

### From source
`python3 setup.py install`

## Examples
### Simplest example:
The code:
```python
import numpy as np
from plotter import Plotter

x = np.linspace(-np.pi, np.pi, 201)

to_plot = [{
    "title": "Example",
    "type": "plot",
    "data": [x, np.sin(x)]
}]

pl = Plotter(to_plot)
pl.show()
```
Gives:

![First example](img/ex1.png)

### Multi-plot:
The code:
```python
import numpy as np
from plotter import Plotter

x = np.linspace(-np.pi, np.pi, 201)

to_plot = [{
    "title": "Example 1",
    "type": "plot",
    "data": [x, np.sin(x)]
},
{
    "title": "Example 2",
    "type": "matrix",
    "data": np.random.random((100,100))
}]

pl = Plotter(to_plot, figsuptitle="Multi-plot")
pl.show()
```
Gives:

![Second example](img/ex2.png)

## Documentation
Online documentation at [https://plotter-doc.xyz](https://plotter-doc.xyz)

### Build documentation locally

#### Requirements
- jekyll ([https://jekyllrb.com/](https://jekyllrb.com/))
- bundler ([https://bundler.io/](https://bundler.io/))

#### Command
```sh
cd doc
bundle exec jekyll build
```

Then the documentation is accessible in the `doc/_site/` directory. Usable using
a web browser for instance: `firefox doc/_site/index.html`
flake8-airflow 0.1.2
Flake8 extension for Apache Airflow static analysis rules.
Requires Python: ~=3.9
Keywords: airflow,flake8,static-analysis

# `flake8-airflow`

[![CI](https://github.com/bradleybonitatibus/flake8-airflow/actions/workflows/ci.yaml/badge.svg)](https://github.com/bradleybonitatibus/flake8-airflow/actions/workflows/ci.yaml)
[![PyPI version](https://badge.fury.io/py/flake8-airflow.svg)](https://badge.fury.io/py/flake8-airflow)

An opinioned `flake8` plugin with Apache Airflow rules.

## Installing

You can install this extension from `pypi` using
```
pip install flake8-airflow
```

## Rules

The following is a table of rules, what they mean, and why they exist.

| Rule Name | Description | Purpose |
| --------- | ----------- | ------- |
| `AA101`   | Use of `SubDagOperator` | Airflow has deprecated `SubDagOperator` since 2.0 and should not be used |
| `AA102`   | Use of `BashOperator` | Airflow does not escape strings sent to the `bash_command` <sup>[1](https://registry.astronomer.io/providers/apache-airflow/modules/bashoperator)</sup> and is a potential security risk |
| `AA103`   | Missing `retries` default argument in `DAG` constructor | Retries improve DAG resiliency  |
auto-feature-engineering 0.0.2
A package for automatically generating features
Requires Python: >=3.8

# Intro

This is a package for automatically feature engineering. 

Suitable for tabular numerical data and the classification modelling.



## Install

```shell
pip install auto-feature-engineering
```



## Usage

```python
from autofe import autofe

x_train, x_test, y_train, y_test, feature_cols = load_data()

afe = autofe.AutoFE('primary_key', feature_cols)

afe.fit(x_train, y_train)

x_train = afe.transform(x_train)
x_test = afe.transform(x_test)

finaldf = afe.generator(x_train, x_test, y_train, y_test)
```



## Examples

To see how the demo for running the package, see the [demo](tests/demo.py).



## Contributing

[@XinlinWang](https://github.com/LeylaWong)



## License

[MIT](LICENSE) Â© Xinlin Wang