semvec/pypi-packages huggingface dataset:
Propylean 0.0.7
The open-source analytics package for chemical process industries.
License: MIT
Keywords: chemical,manufacturing,hydraulics,steady-state,process-flow-diagram,line-sizing,control-valves,pump-sizing,vessel-sizing

# Propylean
#### The open-source analytics package for chemical process industries

## What is Propylean
Propylean name is derived from words Process/Production + Python + lean programming/manufacturing.
Propylean is a Python library which will help engineers simulate production plants focusing on chemical and related process industries using only Python environments. 
By coupling this library with available data analysis/machine learning frameworks, engineers will be able to enhace operations of their plants. Ultimate aspiration of this project is to enable process industries transit to zero carbon operation.

## Objective
To increase the utilization of open-source software and analytics tools in the chemical and related industries.

## Vision
Vision of Propylean is to:
- Enable chemical and related industries to use analytics and ML frameworks
- Improve operational agility and efficiency of the plant
- Be a cheap simulation-software alternative for small manufacturers
- Help the industry transition to Net-Zero emissions
- Inculcate coding/programing skills in manufacturing engineers

## Installation and Requirements
When published in PyPi, user will be able to download using pip or directly from PyPi website. 

Pip command:
`pip install propylean`
fshof 0.2.0
Higher-order functions for the filesystem
License: MIT

# fshof

FsHof (**F**ile**s**ystem **H**igher-**o**rder **f**unctions) provides a useful
functional interface for filesystem interactions.

Here is how you could get the total number of lines of all txt files in the
current directory:

```python
import fshof as fs

fs.Dir(".").files.filter_ext("txt").map_lc().sum()
```

The library is very flexible, allowing you to write both short and long forms for
most properties:

```python
import fshof as fs

# Short form (for when you quickly need to prototype something)
fs.Dir(".").files.filter_ext("txt").map_lc().sum()

# Long form (for when you need to use the library in a codebase)
fs.Dir(".").files.filter_extension("txt").map_line_count().sum()
```

You can also pass your own functions to the higher-order functions:

```python
import fshof as fs

# Short form (for when you quickly need to prototype something)
fs.Dir(".").files.filter(lambda f: f.ext == "txt").map(lambda f: f.t().lc).sum()

# Long form (for when you need to use the library in a codebase)
fs.Dir(".").files.filter(lambda f: f.extension == "txt").map(lambda f: f.text_file().line_count).sum()
```

## Requirements

You need Python >= 3.9 for this library.

## Installation

You can install fshof using the pip package manager:

```shell
pip install fshof
```
shap-selection 0.1.6
Selecting features using SHAP values
License: MIT

.. -*- mode: rst -*-

|pypi_version|_ |pypi_downloads|_

.. |pypi_version| image:: https://img.shields.io/pypi/v/shap-selection.svg
.. _pypi_version: https://pypi.python.org/pypi/shap-selection/

.. |pypi_downloads| image:: https://pepy.tech/badge/shap-selection/month
.. _pypi_downloads: https://pepy.tech/project/shap-selection

=====
SHAP-Selection: Selecting feature using SHAP values
=====

Due to the increasing concerns about machine learning interpretability, we believe that interpretation could be added to pre-processing steps. Using this library, you will be able to select the most important features from a multidimensional dataset while explaining your decisions!

To use SHAP-Selection, you will need:
  * `SHAP <https://github.com/slundberg/shap>`_

-----------
Instalation
-----------

.. code:: python
 
       pip install shap-selection


-----------
Citation
-----------

.. code:: bibtex

       @INPROCEEDINGS{MarcilioJr2020shapselection,  
         author={W. E. {MarcÃ­lio} and D. M. {Eler}}, 
         booktitle={2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)},   
         title={From explanations to feature selection: assessing SHAP values as feature selection mechanism},   
         year={2020},  
         pages={340-347},  
         doi={10.1109/SIBGRAPI51738.2020.00053}
       }


-----------
Usage 
-----------

To use SHAP-Selection, you must have a trained model. It works both for classification and regression purposes!

**Load a dataset**

.. code:: python

       iris_data = load_iris()

       X, y = iris_data.data, iris_data.target
       feature_names = np.array(iris_data.feature_names)

       X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)


**Fit a model**

.. code:: python

       model = cb.CatBoostClassifier(verbose=False)    
       model.fit(X_train, y_train)

**Use SHAP-Selection**

.. code:: python

       from shap_selection import feature_selection

       # please, use agnostic = True to use with any model...
       # agnostic = False will only work with tree-based models
       feature_order = feature_selection.shap_select(model, X_train, X_test, feature_names, agnostic=False)


-----------
Support 
-----------

Please, if you have any questions feel free to contact me at wilson_jr@outlook.com
thwAPITabs 1.1.0
Keeping Tabs With The Hashnode Writeathon (THW)
License: MIT
Keywords: Hashnode Writeathon Challenges Blogging Tech

# THW Info Tabs (Updater)
The Hashnode Writeathon Python Module &amp; API CONNECTOR

This is a simple THW updater that I created for learning purposes during the challenge;

## Usage

Use one of the following APIs to test the different Hashtags

Web 3: https://hashnode.com/api/feed/tag/thw-web3

Web Apps: https://hashnode.com/api/feed/tag/thw-web-apps

Mobile Apps: https://hashnode.com/api/feed/tag/thw-mobile-apps 

Cloud Computing: https://hashnode.com/api/feed/tag/thw-cloud-computing

```
API_URLs = ['https://hashnode.com/api/feed/tag/thw-cloud-computing',
            'https://hashnode.com/api/feed/tag/thw-mobile-apps',
            'https://hashnode.com/api/feed/tag/thw-web3',
            'https://hashnode.com/api/feed/tag/thw-web-apps'
            
            ]
```

## ```code```

``` pip install thwInfoTabs```

```
import 

URL = 'https://hashnode.com/api/feed/tag/thw-web3'
web_three = THW(URL).count()

print(web_three)
```

Expected output;

```
......
There are currently 95 posts written!
....

```

The output depends at a time of running this;

Refer to Tests File for more examples;

Read Blog-Series here; https://blog.octachart.com/series/tabs-with-thw

Star this repo if you learnt something from my mini-series
thwInfoTabs 1.1.0
Keeping Tabs With The Hashnode Writeathon (THW)
License: MIT
Keywords: Hashnode Writeathon Challenges Blogging Tech

# THW Info Tabs (Updater)
The Hashnode Writeathon Python Module &amp; API CONNECTOR

This is a simple THW updater that I created for learning purposes during the challenge;

## Usage

Use one of the following APIs to test the different Hashtags

Web 3: https://hashnode.com/api/feed/tag/thw-web3

Web Apps: https://hashnode.com/api/feed/tag/thw-web-apps

Mobile Apps: https://hashnode.com/api/feed/tag/thw-mobile-apps 

Cloud Computing: https://hashnode.com/api/feed/tag/thw-cloud-computing

```
API_URLs = ['https://hashnode.com/api/feed/tag/thw-cloud-computing',
            'https://hashnode.com/api/feed/tag/thw-mobile-apps',
            'https://hashnode.com/api/feed/tag/thw-web3',
            'https://hashnode.com/api/feed/tag/thw-web-apps'
            
            ]
```

## ```code```

``` pip install thwInfoTabs```

```
import 

URL = 'https://hashnode.com/api/feed/tag/thw-web3'
web_three = THW(URL).count()

print(web_three)
```

Expected output;

```
......
There are currently 95 posts written!
....

```

The output depends at a time of running this;

Refer to Tests File for more examples;

Read Blog-Series here; https://blog.octachart.com/series/tabs-with-thw

Star this repo if you learnt something from my mini-series