semvec/pypi-packages huggingface dataset:
json-configparser 0.4.2
A module that parses and validates JSON configuration files.
Requires Python: >=3.6, <3.9
Keywords: configuration options

# JSON-Configparser
[![Build status](https://dev.azure.com/guiilunga/JSON-Configparser/_apis/build/status/GIlunga.JSON-Configparser)](https://dev.azure.com/guiilunga/JSON-Configparser/_build?definitionId=-1)
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[![MIT license](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/GIlunga/JSON-Configparser/blob/master/LICENSE)
[![PyPI pyversions](https://img.shields.io/pypi/pyversions/json-configparser.svg)](https://pypi.org/project/json-configparser/)
[![Documentation Status](https://readthedocs.org/projects/json-configparser/badge/?version=latest)](https://json-configparser.readthedocs.io/en/latest/?badge=latest)


This python package enables the usage of JSON files as configuration files that can be properly validated.

The examples folder contains examples of using this package.

## Main Features
- Parse several different datatypes from a JSON configuration file:
    - ints
    - floats
    - strings
    - booleans
    - lists
    - dictionaries
    - lists/dictionaries of all other types
- Define options, including their types, bounds, extra validations, and defaults
- Parse a configuration file, returning an object with attributes or a dictionary
- All types, bounds, and defaults are validated by the package

## List of TODOs
- Implement compatibility with 3.7 dataclasses
- Improve tests
- Improve function names
- Improve the creation of Bounds
- Allow command line interaction, e.g., when running a script pass the path to the json and new values for some arguments
physoce 0.0.2
Python tools for Physical Oceanography

# physoce

Python tools for physical oceanography. Includes functions for plotting, time series analysis, statistics and surface wave dynamics.

Documentation can be found at https://physoce.github.io/physoce-py

To install using pip, run the following the terminal/command prompt:

`pip install git+https://github.com/physoce/physoce-py`

Feedback and ideas for future work are welcome. Please feel free to submit an issue.
exceldirreader 1.1
Reads multiple excel files from a directory

# exceldirreader

It reads multiple excel files in a directory and returns a dataframe with all tables appended

## Getting Started

After installing it, import the package:

```
import exceldirreader
```

Instantiate an object:

```
reader = exceldirreader.Reader("your_directory_path")
```

Read the files by calling the function read_files and passing a dictionary where the keys are the file names and the values are the worksheet names:
```
my_dict = {
    "file1.xlsx":"SheetX",
    "file2.xlsx":"SheetY",
    "file3.xlsx":"SheetZ"
}
final_dataframe = reader.read_files(my_dict)
```

### Installing

In your virtual environment, in the command line:

```
pipenv install excelsdirectoryreader
```

## Built With

* [Pandas](https://pandas.pydata.org/pandas-docs/stable/)
* [xlrd](https://xlrd.readthedocs.io/en/latest/)

## Author

* **Afonso Schulz Albrecht** - *Initial work* - [schulzalbrecht](https://github.com/schulzalbrecht)
topsis-101703191 0.0.1
TOPSIS method

#TOPSIS-Python

Project 1 : UCS633


Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, numerical data, interval data, positive ideal solution, negative ideal solution. Introduction. Multi-criteria decision making (MCDM) refers to making choice of the best alternative from among a finite set of decision alternatives in terms of multiple, usually conflicting criteria.


Run from Command line

python <your-file-name> <your-dataset-name.csv>  <weight array>("w1,w2,w3,w4,...") <impact array>("i1,i2,i3,i4,...")