semvec/pypi-packages huggingface dataset:
japt 1.0.1
A python package for better update and manage your debian packages.
Requires Python: >=3.6
Keywords: python-apt,apt,package-manager,debian,japt

# japt
A python package for better update and manage your debian packages.

[![PyPI version](https://badge.fury.io/py/japt.svg)](https://badge.fury.io/py/japt)
[![Downloads](https://pepy.tech/badge/japt/month)](https://pepy.tech/project/japt)
[![Downloads](https://static.pepy.tech/personalized-badge/japt?period=total&units=international_system&left_color=green&right_color=blue&left_text=Total%20Downloads)](https://pepy.tech/project/japt)
![GitHub commit activity](https://img.shields.io/github/commit-activity/m/jakbin/japt)
![GitHub last commit](https://img.shields.io/github/last-commit/jakbin/japt)

## Introduction

When you run "apt list --upgradable" in your terminal, you find around 1000 upgradable packages (if you are using parrot os or kali linux like distibution). It hard to find your important package or security releases.  

For this solution, here is "japt".

## Compatability
Python 3.6+ is required.

## Installation

```sh
pip install japt
```
or 

```sh
pip3 install japt
```

## Then run it with:

```sh
japt -h
```

## Todo-List 

- [x] Pretty Output 
- [x] Update and upgrade packages from Here
- [x] Add or Remove packages form list
- [x] Use sqlite
temppythonsample 0.3.1
First Python Library
Requires Python: ~=3.6
Keywords: Makana Python Library

# Python-Sample-Library
This is custom library
    
# we first need to install build.
    pip install build twine

# To create a Python source package (.tar.gz) and the binary package (.whl) in the dist/ directory, do:
    python -m build

# Publish the python library on Pypi
    twine upload dist/*
wunger-cdk 0.0.2
CDK python package


# cdk pip packages
  
this project shows how to package cdk constructs as pip modules and import and use them in our projects  
  
## install required tools  
pip install setuptools  
pip install twine  
pip install wheel  
  
## .pypirc
you must have a file named .pypirc in your user folder   
content must be some like this :   

[distutils]   
index-servers=pypi  
  
[pypi]   
repository = https://upload.pypi.org/legacy/   
username=change2yourusername  
password=change2yourpassword  
    
  
## project structure  
cdk_pip_modules  
â”œâ”€â”€ LICENSE    
â”œâ”€â”€ wunger_cdk_constructs ( folder with the constructs, content will be packaged as pip module)   
â”‚   â”œâ”€â”€ __init__.py    
â”‚   â”œâ”€â”€ cdk classes    
â”‚   â”œâ”€â”€ ...  
â”œâ”€â”€ stacks (wrapper stacks to test the constructs before creating the pip module)   
â”œâ”€â”€ usage ( stacks that will import and use the final pip modules)  
â”œâ”€â”€ README.md    
â”œâ”€â”€ app.py ( main python file, entry point )    
â””â”€â”€ setup.py    

-please create your constructs in the folder wunger_cdk_constructs or your own folder ( my_cdk_constructs)  
-adapt setup.py   
-adapt the app.py   
-build the module and install locally  
-upload to remote repo  
-use the module by cdk stacks in usage folder, see example  
  

## check
python setup.py check
## create package
python setup.py bdist_wheel


python -m pip install dist/wunger_cdk-0.0.2-py3-none-any.whl
install same version new
python -m pip install dist/wunger_cdk-0.0.2-py3-none-any.whl --force


## upload

twine upload -r pypi dist/wunger_cdk-0.0.2-py3-none-any.whl

## use in client project
setup requirements.txt: 
wunger-cdk==0.0.1

pip install -r requirements.txt 
or explicit :
pip install wunger-cdk

import class:
from wunger_cdk.ecr_repo import WUngerEcrConstruct



https://betterscientificsoftware.github.io/python-for-hpc/tutorials/python-pypi-packaging/

https://packaging.python.org/

https://dzone.com/articles/executable-package-pip-install
airflowPlugin 1.0.0
DARTS airflow plugins containing custom operators, sensors, callbacks and hooks
Requires Python: >=3.7
Keywords: airflow,scheduler

**##### Airflow DARTS Plugin Library**

##### **Table of Contents**

Overview
Setup
Usage

Overview
Dynamic dag builder is a library for dynamically generating Airflow DAG from YAML configuration file.  

Setup
first create virtual env in your local setup - 
Note - make sure to create virtual env ending with _venv so the at the time of pushing code to git it must be ignored.
`python3 -m venv <virtual_env_name_venv>`
`source <virtual_env_name_venv>/bin/activate`
Change the interpreter setting in your IDE to point it to correct virtual env

Right now, the library is not hosted on PyPi so you will need to do a local install on your system. Below command is only be using once we will upload it on PyPi.
`pip3 install airflow-dag-builder` - As of now do not use this command

First, clone this repo to your local system. After you clone the repo, make sure to run the setup.py file, so you can install any dependencies you may need. To run the setup.py file, run the following command in your terminal.

Run requirement.txt in your virtual env. This will install all the dependencies listed in the requirements.txt file
`pip install -r requirements.txt`

Below command will create a build for the same.
`python3 setup.py install`

Below command will create wheel file.
`python setup.py bdist_wheel`


Usage
After installing airflow-dag-builder in your Airflow environment, there are two steps to creating DAGs. First, we need to create a YAML configuration file. For example:

`env: prod
dag_global_default_args:
    {
      owner: 'clm',
      depends_on_past: False,
      email: ['airflow@example.com'],
      email_on_failure: False,
      email_on_retry: False,
      retries: 2,
      #retry_delay: timedelta(minutes=5),
      queue: 'bash_queue'
    }
dag:
  dag1:
    dag_local_default_args: {owner: 'airflow', depends_on_past: True, email: ['airflow@example.com'], email_on_failure: False,
                             email_on_retry: False, retries: 1}
    dag_id: "dag1"
    description: 'A simple tutorial DAG'
    schedule_interval: timedelta(days=1)
    start_date: 2022-3-15   # pendulum.datetime(2016, 1, 1, tz="UTC")
    end_date: 2022-3-29
    catchup: False
    tags: 'dynamic'

    tasks:
      task1:
        operator: bash_operator
        operator_conf: {task_id: "print_date", bash_command: 'date', depends_on_past: False, retries: 1}
        dependencies: []

      task2:
        operator: bash_operator
        operator_conf: {task_id: 'sleep', bash_command: 'sleep 5', depends_on_past: False, retries: 3}
        dependencies: [print_date]`

Then in your client use below code - 
 `obj_app_config = AppConfig(conf_file_path)
 obj_app_config.from_yaml()
 DAGController.create_dag_file()`

This will create a DAG file inside ./docker/dags/ and airflow scheduler will automatically pick it from there
clioexlsx 2.1
Compare worksheet or workbook, then highlight the different rows or cells on book 2 and then generate the output file. This module uses Pandas and XLWings
Keywords: xlsx,compare excel

This module is to compare excel worksheet or workbook, then highlight either the different rows or cells on the second book and then generate the output file.

- Cheny Lioe -

Read the documentation after you install the package:
$ python -m pydoc -p 8000
Server ready at http://localhost:8000/
Server commands: [b]rowser, [q]uit
server> b
Then browse to clioecsv(package) to read documentation

Package contents: clioexlsx
compare_ws >highlight_diff_cells_comments, highlight_diff_rows
compare_wb > highlight_diff_cells_comments, highlight_diff_rows
flasktts 0.0.9
flasktts
Requires Python: >=3.5

# flasktts
![python_version](https://img.shields.io/static/v1?label=Python&message=3.5%20|%203.6%20|%203.7&color=blue) [![PyPI downloads/month](https://img.shields.io/pypi/dm/flasktts?logo=pypi&logoColor=white)](https://pypi.python.org/pypi/flasktts)

## Description


## Install
~~~~bash
pip install flasktts
# or
pip3 install flasktts
~~~~