semvec/pypi-packages huggingface dataset:
telefly 0.1.3
Create Telegram bots in Python.

# Telefly
Create Telegram bots in Python.


### Important
> This package was only created for [OYSA](https://github.com/ztpnk/oysa). It will not be further developed or maintained but stays online for understanding the OYSA-Code. Look at [pythin-telegram-bot](https://github.com/python-telegram-bot/python-telegram-bot/) for a python telegram bot wrapper.


## Features
- Simple to use
- Very lightweight

## Usage

```bash
$ pip install telefly
```

```python
from telefly import telefly


tf = telefly(TOKEN)

tf.sendMessage(MESSAGE, chatID)

```
featureselect 0.0.5
An elegant and effectice solution to get best set of features from a numerical dataset!
Keywords: featureselect

=========================
Feature Select PyPackage
=========================

`Feature Select`_ is a simple yet effective solution to select features from a numeric dataset, which yields the best results, given a Machine Learning algorithm.

- GitHub repo: https://github.com/himanshu-dutta/featureselect/
- Free software: MIT license

Features
--------

- Multiple optimization algorithms to work with.
- Works with most class based Machine Learning models over a range of libraries.
- Compatible with all platforms.

.. _`Feature Select` : https://github.com/himanshu-dutta/featureselect/

Quickstart
----------

Install the latest Feature Select with ::

    pip install featureselect


Usage
-----

.. code:: python

   from featureselect import DEOptimizer, SAOptimizer, GAOptimizer, PSOptimizer
   from sklearn.tree import DecisionTreeClassifier
   import pandas as pd
   
   # loading a dataset
   dataset = pd.read_csv("dataset.csv", header=None)
   dataset[34] = dataset[34].apply(lambda x: 1 if x == "g" else 0)
   dataset = dataset.dropna()
   X, y = dataset.iloc[:, :-1].to_numpy(), dataset.iloc[:, -1].to_numpy()

   # best_accuracy, index_of_best_features = GAOptimizer((X, y), DecisionTreeClassifier, epochs = 10, threshold=0.6, verbose=1, max_depth=3)
   # best_accuracy, index_of_best_features = SAOptimizer((X, y), DecisionTreeClassifier, epochs = 10, threshold=0.6, verbose=True, max_depth=3)
   # best_accuracy, index_of_best_features = PSOptimizer((X, y), DecisionTreeClassifier, epochs = 10, verbose=1, max_depth=3)


   best_accuracy, index_of_best_features = DEOptimizer((X, y), DecisionTreeClassifier, epochs = 10, threshold=0.6, verbose=1, max_depth=3)

   #############
   #   Output
   #############
   Initial Accuracy: 0.887.
   ----------------------------------
   *  Epoch:  1 | Accuracy: 0.958.
   ----------------------------------
   *  Epoch:  2 | Accuracy: 0.958.
   ----------------------------------
   *  Epoch:  3 | Accuracy: 0.958.
   ----------------------------------
   *  Epoch:  4 | Accuracy: 0.958.
   ----------------------------------
   *  Epoch:  5 | Accuracy: 0.972.
   ----------------------------------
   *  Epoch:  6 | Accuracy: 0.972.
   ----------------------------------
   *  Epoch:  7 | Accuracy: 0.972.
   ----------------------------------
   *  Epoch:  8 | Accuracy: 0.972.
   ----------------------------------
   *  Epoch:  9 | Accuracy: 0.986.
   ----------------------------------
   *  Epoch: 10 | Accuracy: 0.986.
   ----------------------------------
   (0.9859154929577465, array([ 2,  4,  5,  6,  9, 11, 12, 13, 14, 17, 19, 20, 21, 24, 26, 29, 32]))




Note
----

The project is still in developement phase and will be expanded and made better over time. Any contribution to it is welcomed. Stable release would be made available soon.
aioHelloWorld 0.3a0.post0
This is a asynchronous package for printing hello world

### Asynchronous module for printing your dream programme `Hello World` in Python.

<br>

## Installing
```
pip install aioHelloWorld
```

## Example
```
import aioHelloWorld
import asyncio


async def main():
    await aioHelloWorld.say()

if __name__ == "__main__":
    asyncio.get_event_loop().run_until_complete(main())
```

## Github
You can view the source code [here](https://github.com/Exainz/aioHelloWorld)

## Support
You can find me on [discord](https://discord.com/users/761976845200785408)

### Thank You :)
icon-etl 0.1.3
Tools for exporting ICON blockchain data to CSV or JSON
Requires Python: >=3.6.0,<3.8.0

# ICON ETL

[![Build Status](https://travis-ci.org/insight-icon/icon-etl.svg?branch=master)](https://travis-ci.org/github/insight-icon/icon-etl)
[![Pypi Version](https://img.shields.io/pypi/v/icon-etl)](https://pypi.org/project/icon-etl/)
[![Pypi Version](https://img.shields.io/pypi/l/icon-etl)](https://pypi.org/project/icon-etl/)

ICON ETL lets you convert ICON blockchain data into convenient formats like CSVs and relational databases.

[Full documentation available here.](https://icon-etl.readthedocs.io/)

## Quickstart

Install ICON ETL:

```bash
pip3 install icon-etl
```

Export blocks and transactions:

```bash
> iconetl export_blocks_and_transactions --start-block 0 --end-block 500000 \
--blocks-output blocks.csv --transactions-output transactions.csv
```

Export logs and receipts:

```bash
> iconetl export_receipts_and_logs --transaction-hashes hashes.txt \
--receipts-output receipts.csv --logs-output logs.csv
```

## Running Tests

```bash
> pip3 install -e .[dev]
> export ICON_ETL_RUN_SLOW_TESTS=True
> pytest -vv
```

### Running Tox Tests

```bash
> pip3 install tox
> tox
```
long_file 0.2.5
asdfasdf

# asfasdf

```python
import long_file
json_file = 'test.json'
data_dict = {"aa": 11, "baa": 22}
long_file.write_json(json_file, data_dict, save_path='static')
```

# asdf

```python
import long_file
json_file = 'static/test.json'
data_dict = long_file.read_json(json_file)
print(data_dict)
```
BoydCut 1.0.0
Thai Sentence Segmenter
Requires Python: >=3.6
Keywords: NLP,THAI,SENTENCE

# BoydCut: Thai Sentence Segmenter
> Bidirectional LSTM-CNN Model for Thai Sentence Segmenter


# Development Status
This project is the part of my Thesis in Master's degree at Big Data Engineering, CITE, Dhurakij Pundij University
https://cite.dpu.ac.th/bigdata/


**My Advisor**
- Asst. Prof. Dr. Duangjai Jitkongchuen
- Asst. Prof. Dr. Peerasak Intarapaiboon


# Requirements
- Tensorflow 2.0+
- Python 3.6.x
- pip install -r requirements
> if pip install -r requirements not work please follow Installation steps

## Installation steps
- pip install numpy pandas tensorflow
- pip install deepcut
- pip install pythainlp

## How to use and Examples
- pip install BoydCut
- Version 1.0.0
- [Notebook Example](https://colab.research.google.com/drive/1uMssc6RQQTeDhsja_aOWwLRl9Bjk31Qc?usp=sharing)
```
boydcut = BoydCut()
sent_ls = boydcut.sentenize("ประเทศฝรั่งเศสแผ่นดินใหญ่ทอดตัวตั้งแต่ทะเลเมดิเตอร์\
                                        เรเนียนจนถึงช่องแคบอังกฤษและทะเลเหนือ")
for sent in sent_ls:
    print(sent)

> <B-CLS>ประเทศฝรั่งเศส|แผ่นดิน|ใหญ่|ทอด|ตัว|ตั้งแต่|ทะเลเมดิเตอร์เรเนียน|จนถึง|ช่อง|แคบ<E-CLS>
> <B-CLS>อังกฤษ|และ|ทะเล|เหนือ<E-CLS>


boydcut = BoydCut()
sent_ls = boydcut.sentenize(['ประเทศฝรั่งเศส','แผ่นดิน','ใหญ่','ทอดตัว','ตั้งแต่',
                            'ทะเลเมดิเตอร์เรเนียน','จนถึง','ช่อง','แคบ',
                            'อังกฤษ','และ','ทะเล','เหนือ'], _tokenize=False)
for sent in sent_ls:
    print(sent)

> <B-CLS>ประเทศฝรั่งเศส|แผ่นดิน|ใหญ่|ทอด|ตัว|ตั้งแต่|ทะเลเมดิเตอร์เรเนียน|จนถึง|ช่อง|แคบ<E-CLS>
> <B-CLS>อังกฤษ|และ|ทะเล|เหนือ<E-CLS>
```

## Limitation 
- Document feeding is not available yet !
- Max Word for feeding: 200 words / paragraph
- Please use "\n" for decrease size of paragraph
- Results: [sentence1, sentence2, sentence3,..., sentenceN]

## Dependency
- POS apply pythainlp.tag.pos_tag(_sentence_ls, corpus="orchid") 
- Tokenization apply pythainlp.tokenize.word_tokenize(_text_ls, engine="deepcut")



# Contributor
Sorratat Sirirattanajakarin (Boyd)

- Youtube: https://youtube.com/c/BigDataRPG
- Fanpage: https://www.facebook.com/bigdatarpg/
- Medium: https://www.medium.com/bigdataeng
- Github: https://www.github.com/BigDataRPG
- Kaggle: https://www.kaggle.com/boydbigdatarpg
- Linkedin: https://www.linkedin.com/in/boyd-sorratat
- Twitter: https://twitter.com/BoydSorratat
- GoogleScholar: https://scholar.google.com/citations?user=9cIeYAgAAAAJ&hl=en

# License and reference
Please make sure to cite the paper if you use BoydCut for your research ^^:
>**BoydCut: Bidirectional LSTM-CNN Model for Thai Sentence Segmenter** 
*S. Sirirattanajakarin, D. Jitkongchuen, P. Intarapaiboon* 2020 1st International Conference on Big Data Analytics and Practices (IBDAP)